U.S. patent application number 16/779229 was filed with the patent office on 2020-10-01 for predicting suicidality using a combined genomic and clinical risk assessment.
The applicant listed for this patent is Indiana University Research and Technology Corporation, United States Government as Represented by the Department of Veterans Affairs. Invention is credited to Alexander Bogdan Niculescu.
Application Number | 20200312425 16/779229 |
Document ID | / |
Family ID | 1000004896758 |
Filed Date | 2020-10-01 |
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United States Patent
Application |
20200312425 |
Kind Code |
A1 |
Niculescu; Alexander
Bogdan |
October 1, 2020 |
PREDICTING SUICIDALITY USING A COMBINED GENOMIC AND CLINICAL RISK
ASSESSMENT
Abstract
Biomarkers and methods for screening expression levels of the
biomarkers for predicting suicidality (referred herein to suicidal
ideation and actions, future hospitalizations and suicide
completion) are disclosed. Also disclosed are quantitative
questionnaires and mobile applications for assessing affective
state and for assessing socio-demographic and psychological suicide
risk factors, and their use to compute scores that can predict
suicidality. Finally, an algorithm that combines biomarkers and
computer apps for identifying subjects who are at risk for
committing suicide is disclosed, as well as methods to mitigate and
prevent suicidality based on the biomarkers and computer apps.
Inventors: |
Niculescu; Alexander Bogdan;
(Indianapolis, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Indiana University Research and Technology Corporation
United States Government as Represented by the Department of
Veterans Affairs |
Indianapolis
Washington |
IN
DC |
US
US |
|
|
Family ID: |
1000004896758 |
Appl. No.: |
16/779229 |
Filed: |
January 31, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15735304 |
Dec 11, 2017 |
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PCT/US2016/036985 |
Jun 10, 2016 |
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16779229 |
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62278707 |
Jan 14, 2016 |
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62174880 |
Jun 12, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 1/6883 20130101;
Y02A 90/10 20180101; G01N 2800/304 20130101; G01N 2800/50 20130101;
G16B 25/00 20190201; G16H 50/20 20180101; G16H 50/30 20180101; C12Q
1/6876 20130101; G16B 20/00 20190201; C12Q 2600/158 20130101; G01N
33/48 20130101; C12Q 1/68 20130101; G16H 10/20 20180101; G01N
33/6893 20130101; C12Q 2600/118 20130101 |
International
Class: |
G16B 20/00 20060101
G16B020/00; G16B 25/00 20060101 G16B025/00; G01N 33/48 20060101
G01N033/48; C12Q 1/68 20060101 C12Q001/68; C12Q 1/6883 20060101
C12Q001/6883; G16H 50/20 20060101 G16H050/20; G16H 10/20 20060101
G16H010/20; G16H 50/30 20060101 G16H050/30; C12Q 1/6876 20060101
C12Q001/6876; G01N 33/68 20060101 G01N033/68 |
Goverment Interests
STATEMENT OF GOVERNMENT SUPPORT
[0002] This invention was made with government support under
OD007363 awarded by National Institutes of Health and 2I01CX000139
merit award by the Veterans Administration. The Government has
certain rights in the invention.
Claims
1-20. (canceled)
21. A method for assessing and mitigating suicidality in a subject
in need thereof, comprising: determining an expression level of a
panel of biomarkers in a biological sample from the subject,
computing a score for the panel, based on the gene expression data
for the biomarkers in the panel, which is z-scored for each of the
biomarkers in the biomarker panel with a reference database,
multiplying each biomarker z-scored value by a weight coefficient
related to their functional evidence of involvement in suicidality
to obtain a second score for each biomarker of the biomarker panel,
with the resulting values for the increased in expression (risk)
biomarkers being added, and the resulting values for the decreased
in expression (protective) biomarkers being subtracted, wherein
when the subject is male; the panel of biomarkers comprises: (i)
solute carrier family 4 (sodium bicarbonate cotransporter), member
4 (SLC4A4), cell adhesion molecule 1 CADM1, dystrobrevin, alpha
(DTNA), spermidine/spermine Nl-acetyl transferase 1 (SAT1),
interleukin 6 (IL-6), RAS-like family 11 member B (RASL11B),
glutamate receptor, Ionotropic, kainate 2 (GRIK2), histone cluster
1, H2bo (HIST1H2BO), jun proto--oncogene (JUN), and GRB2-associated
binding protein 1 (GAB1), wherein the expression level of the
biomarker(s) in the sample is increased relative to a reference
expression level, denoting increased suicidality; or (ii) spindle
and kinetochore associated complex subunit 2 (SKA2), CAP-GLY domain
containing linker protein family, member 4 (CLIP4), kinesin family
member 2C (KIF2C), kelch domain containing 3 (KLHDC3), chemokine
(C-C motif) ligand 28 (CCL28), v-ets avian erythroblastosis virus
E26 oncogene homolog (ERG), adenylate kinase 2 (AK2), myelin basic
protein (MBP), and fatty acid desaturase 1 (FADS1), wherein the
expression level of the biomarker(s) in the sample is decreased
relative to a reference expression level, denoting increased
suicidality; or wherein when the subject is female, and the panel
of biomarkers comprises: (i) erythrocyte membrane protein band 4.1
like 5 (EPB41L5), HtrA serine peptidase 1 (HTRA1), deleted in
primary ciliary dyskinesia homolog (DPCD), general transcription
factor IIIC (GTF3C3), period circadian clock 1 (PERI),
pyridoxal-dependent decarboxylase domain containing 1 (PDXDC1),
kelch-like family member 28 (KLHL28), ubiquitin interaction motif
containing 1 (UIMC1), sorting nexin family member 27 (SNX27),
glutamate receptor ionotropic kainate 2 (GRIK2), wherein the
expression level of the biomarker(s) in the sample is increased
relative to a reference expression level, denoting increased
suicidality; or (ii) phosphatidylinositol 3-kinase, catalytic
subunit type 3 (PIK3C3), aldehyde dehydrogenase 3 family member A2
(ALDH3A2), ARP3 actin-related protein 3 homolog (yeast) (ACTR3),
B-cell CLL (BCL2), MOB kinase activator 3B (MOB3B), casein kinase 1
alpha 1 (CSNK1A1), La ribonucleoprotein domain family member 4
(LARP4), zinc finger protein 548 (ZNF548), prolylcarboxypeptidase
(angiotensinase C) (PRCP), and solute carrier family 35 (adenosine
3'-phospho 5'-phosphosulfate transporter) member B3 (SLC35B3),
wherein the expression level of the biomarker(s) in the sample is
decreased relative to a reference expression level, denoting
increased suicidality; determining a reference score for the panel,
obtained in a clinically relevant population identifying a
difference between the score of the panel of biomarker(s) in the
sample and the reference score of the panel of biomarker(s); and
identifying the subject having suicidality based on the difference
between the biomarker panel score of the subject relative to the
biomarker panel score of reference; and administering to the
subject identified as having suicidality a specific therapeutic
drug(s) to treat suicidality, based on the specific biomarkers that
are changed in the subject wherein the therapeutic drug (s) is
selected from: (i) a group of psychiatric treatments: ketamine and
other dissociants, lithium and other mood stabilizers, clozapine,
chlorpromazine, prochlorperazine, and other antipsychotics,
selegeline, fluoxetine, trimipramine, and other antidepressants,
docosahexaenoic acid and other omega-3 fatty acids, and
combinations thereof; or (ii) a group of new method of
use/repurposed drugs consisting of: tocilizumab, tenoxicam,
ramifenazone, and other anti-inflammatories; betulin, dl-alpha
tocopherol, hesperidin, calcium folinate, harpagoside, rilmenidine,
harman, homatropine, diphenhydramine, pirenperone, asiaticoside,
adiphenine, metformin, chlorogenic acid, verapamil, metaraminol,
yohimbine, trimethadione, and combinations thereof.
22. The method of claim 21, wherein before the step of generating
the biomarker panel score, each biomarker is given a weighted
coefficient, wherein the weighted coefficient is related to the
importance of said each biomarker in assessing and predicting
suicide risk.
23. The method of claim 21, wherein the biological sample is a
peripheral tissue sample or a fluid.
24. The method of claim 21, wherein biomarker expression level
measures RNA or protein of the biomarker in the biological
sample.
25. The method of claim 21, wherein the subject is male, and the
drug is selected based on the specific biomarkers that are changed
in expression in the subject, and is selected from the group
consisting of: thiamine, homatropine, vitexin, ergocalciferol,
tropicamide, (-)-atenolol, haloperidol, spaglumic acid, and
combinations thereof.
26. The method of claim 21, wherein the subject is female, and the
drug is selected based on the specific biomarkers that are changed
in expression in the subject, and is selected from the drug group
consisting of: mifepristone, lansoprazole, nafcillin, betulin, and
combinations thereof.
27. The method of claim 21, wherein the subject has a psychiatric
disorder selected from the group consisting of: bipolar disorder,
major depressive disorder, schizophrenia, schizoaffective disorder,
anxiety disorders, post-traumatic stress disorder, and combinations
thereof.
28. A method of assessing and mitigating suicidality in a subject
in need thereof, comprising: calculating an Up-Suicide Scorebased
on the equation: (Biomarker Panel Score)+(Suicidality Risk
Score)+(Mood Score)+(Anxiety Score)=Up-Suicide Score; wherein the
Biomarker Panel Score is obtained as per the method of claim 21;
wherein the Suicidality Risk Score is calculated by (i) summing the
binary results of the individual items in the CFI-S scale; wherein
a yes/present answer generates a score of 1 and a no/absent answer
generates a score of zero; and (ii) dividing the summed score by
the number of items answered and multiplying by 100; wherein the
individual items in the CFI-S scale are: lack of coping skills
(cracks under pressure); dissatisfaction with present life; lack of
hope for the future; current substance abuse; acute stresses:
losses, grief; chronic stress: lack of positive relationships,
social isolation; acute stress: rejection, history of excessive
extroversion and impulsive behaviors (including rage, anger,
physical fights, seeking revenge); acute/severe medical illness,
pain; lack of children; Gender: Male; Personally knowing somebody
who committed suicide; Psychiatric illness diagnosed and treated;
past history of suicidal acts/gestures; Age: Older>60 or
Younger<25; History of abuse: physical, sexual, emotional,
neglect; History of command hallucinations of self-directed
violence; Family history of suicide in blood relatives; With poor
treatment compliance; Lack of religious beliefs; History of
excessive introversion, conscientiousness; Chronic stress:
perceived uselessness, not feeling needed, burden to extended kin;
wherein the Mood Score is calculated by using a mood-rating scale;
wherein the Anxiety Score is calculated by using an anxiety-rating
scale; assessing the level of suicidality of the subject by
comparing the subject's Up-Suicide Score to a reference Up-Suicide
Score; administering a treatment for suicidality to the subject
when the subject's Up-Suicide Score is greater than a reference
Up-Suicide Score; and monitoring the subject's response to a
treatment for suicidality by determining changes in the Up-Suicide
Score after initiating a treatment.
29. The method of claim 28, wherein the method further comprises
receiving, in a computer system, Biomarker Panel Score, Suicidality
Risk Score, Mood Score, Anxiety Score, and/or Up-Suicide Score for
the subject, the computer system comprising a database, wherein the
database comprises a plurality of suicidality treatment
profiles.
30. The method of claim 29, wherein the method further comprises a
step of outputting from the computer system the identity of the
suicidality treatment for administering to the subject.
31. The method of claim 28, wherein a user enters the Biomarker
Panel Score, Suicidality Risk Score, Mood Score, Anxiety Score,
and/or Up-Suicide Score of the subject in the computer system.
32. The method of claim 28, wherein the Biomarker Panel Score,
Suicidality Risk Score, Mood Score, Anxiety Score, and/or
Up-Suicide Score of the subject is received directly from equipment
used in determining the subject's suicidality blood biomarker
score.
33. The method of claim 28, wherein the Biomarker Panel Score,
Suicidality Risk Score, Mood Score, and Anxiety Score of the
subject are z-scored prior to the calculation of the Up-Suicide
Score.
34. The method of claim 28, wherein the subject is male, the panel
of biomarkers is (i) solute carrier family 4 (sodium bicarbonate
cotransporter) member 4 (SLC4A4), cell adhesion molecule 1 CADM1,
dystrobrevin alpha (DTNA), spermidine/spermine Nl-acetyl
transferase 1 (SAT1), interleukin 6 (IL-6), RAS-like family 11
member B (RASL11B), glutamate receptor ionotropic kainate 2
(GRIK2), histone cluster 1 H2bo (HIST1H2BO), jun proto--oncogene
(JUN), and GRB2-associated binding protein 1 (GAB1), wherein the
expression level of the biomarker(s) in the sample is increased
relative to a reference expression level, denoting increased
suicidality; or (ii) spindle and kinetochore associated complex
subunit 2 (SKA2), CAP-GLY domain containing linker protein family,
member 4 (CLIP4), kinesin family member 2C (KIF2C), kelch domain
containing 3 (KLHDC3), chemokine (C--C motif) ligand 28 (CCL28),
v-ets avian erythroblastosis virus E26 oncogene homolog (ERG),
adenylate kinase 2 (AK2), myelin basic protein (MBP), and fatty
acid desaturase 1 (FADS1), wherein the expression level of the
biomarker(s) in the sample is decreased relative to a reference
expression level, denoting increased suicidality; or wherein when
the subject is female, the panel of biomarkers is (i) erythrocyte
membrane protein band 4.1 like 5 (EPB41L5), HtrA serine peptidase 1
(HTRA1), deleted in primary ciliary dyskinesia homolog (DPCD),
general transcription factor IIIC polypeptide 3 (GTF3C3), period
circadian clock 1 (PERI), pyridoxal-dependent decarboxylase domain
containing 1 (PDXDC1), kelch-like family member 28 (KLHL28),
ubiquitin interaction motif containing 1 (UIMC1), sorting nexin
family member 27 (SNX27), glutamate receptor ionotropic kainate 2
(GRIK2), wherein the expression level of the biomarker(s) in the
sample is increased relative to a reference expression level,
denoting increased suicidality; or (ii) phosphatidylinositol
3-kinase, catalytic subunit type 3 (PIK3C3), aldehyde dehydrogenase
3 family member A2 (ALDH3A2), ARP3 actin-related protein 3 homolog
(yeast) (ACTR3), B-cell CLL (BCL2), MOB kinase activator 3B
(MOB3B), casein kinase 1 alpha 1 (CSNK1A1), La ribonucleoprotein
domain family member 4 (LARP4), zinc finger protein 548 (ZNF548),
prolylcarboxypeptidase (PRCP), solute carrier family 35 member B3
(SLC35B3), wherein the expression level of the biomarker(s) in the
sample is decreased relative to a reference expression level,
denoting increased suicidality.
35. A method of assessing and mitigating suicidality in a subject
in need thereof, comprising: calculating a Suicidality Risk Score
by adding the score of the individual items in the CFI-S scale,
wherein a yes/present answer generates a score of 1 and a no/absent
answer generates a score of zero, and dividing the summed score by
the number of items answered and multiplying by 100, wherein the
individual items in the CFI-S scale are: lack of coping skills
(cracks under pressure); dissatisfaction with present life; lack of
hope for the future; current substance abuse; acute stresses:
losses, grief; chronic stress: lack of positive relationships,
social isolation; acute stress: rejection, history of excessive
extroversion and impulsive behaviors (including rage, anger,
physical fights, seeking revenge); acute/severe medical illness,
pain; lack of children; Gender: Male; Personally knowing somebody
who committed suicide; Psychiatric illness diagnosed and treated;
past history of suicidal acts/gestures; Age: Older>60 or
Younger<25; History of abuse: physical, sexual, emotional,
neglect; History of command hallucinations of self-directed
violence; Family history of suicide in blood relatives; With poor
treatment compliance; Lack of religious beliefs; History of
excessive introversion, conscientiousness; Chronic stress:
perceived uselessness, not feeling needed, burden to extended kin;
assessing the level of suicidality of the subject by comparing the
subject's Suicidality Risk Score to a reference Suicidality Risk
Score; administering a treatment for suicidality to the subject
when the subject's Suicidality Risk Score is greater than a
reference Suicidality Risk Score; monitoring the response to a
treatment of the subject by determining changes in the Suicidality
Risk Score after initiation of a treatment; and decreasing the
Suicidality Risk Score by targeting with psycho-social
interventions and other treatments specific items of the CFI-S that
are scored as yes/present in a particular subject.
36. The method of claim 35, wherein the method further comprises
receiving, in a computer system, the scores of the individual items
in the CFI-S scale or the subject's Suicidality Risk Score, the
computer system comprising a database, wherein the database
comprises a plurality of suicidality treatment profiles.
37. The method of claim 35, wherein the method further comprises a
step of outputting from the computer system the identity of the
targeted suicidality psycho-social intervention and other
treatments for administering to the subject.
38. The method of claim 36, wherein a user enters the scores of the
individual items in the CFI-S scale or subject's Suicidality Risk
Score in the computer system.
39. The method of claim 36, wherein the scores of the individual
items in the CFI-S scale or the subject's Suicidality Risk Score is
received directly from equipment used in determining the subject's
suicidality blood biomarker score.
40. The method of claim 35, wherein the subject's Suicidality Risk
Score is z-scored along the Suicidality Risk Scores of subjects
with similar Suicidality Risk Scores.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a Continuation Application and claims
priority to U.S. application Ser. No. 15/735,304 filed Dec. 11,
2017, which is the U.S. National Stage Application of International
Patent Application PCT/US2016/036985, filed Jun. 10, 2016, which
claims priority to and the benefit of U.S. Provisional Provisional
Application No. 62/278,707, filed Jan. 14, 2016, and U.S.
Provisional Application No. 62/174,880, filed on Jun. 12, 2015, the
disclosures of which are hereby incorporated by reference in their
entireties.
BACKGROUND OF THE DISCLOSURE
[0003] The present disclosure relates generally to biomarkers and
their use for predicting a subject's risk of suicidality (e.g.,
suicide ideation and actions, future hospitalization due to
suicidality, and suicide completion). More particularly, the
present disclosure relates to gene expression biomarkers, and to
methods of screening for biomarkers, for identifying subjects who
are at risk of committing suicide, as well as for preventing and
treating subjects for suicidality. The present disclosure further
relates to quantitative clinical information assessments through
questionnaires and mobile applications (referred to herein as
"apps") for assessing affective state (mood and anxiety), for
assessing socio-demographic and psychological suicide risk factors,
and for identifying subjects who are at risk of committing suicide.
Finally, the present disclosure relates to an algorithm for
combining biomarkers and apps for identifying subjects who are at
risk for committing suicide.
[0004] Suicide is a leading cause of death in psychiatric patients,
and in society at large. Particularly, suicide accounts for one
million deaths worldwide each year. Worldwide, one person dies
every 40 seconds through suicide, a potentially preventable cause
of death. Further, although women have a lower rate of suicide
completion as compared to men, due in part to the less-violent
methods used, women have a higher rate of suicide attempts. A
limiting step in the ability to intervene is the lack of objective,
reliable predictors. One cannot just ask individuals if they are
suicidal, as the desire to not be stopped or future impulsive
changes of mind may make their self-report of feelings, thoughts
and plans unreliable.
[0005] There are currently no objective tools to assess and track
changes in suicidal risk without asking the subjects directly. Such
tools, however, could prove substantially advantageous as the
subjects at risk often choose not to share their suicidal ideation
or intent with others, for fear of stigma, hospitalization, or that
their plans will be thwarted. The ability to assess and track
changes in suicidal risk without asking a subject directly would
further allow for intervening prior to suicide attempt and suicide
completion by the subject.
[0006] Conventionally, a convergence of methods assessing the
subject's internal subjective feelings and thoughts, along with
external, more objective, ratings of actions and behaviors, are
used de facto in clinical practice, albeit not in a formalized and
systematic way. Accordingly, there exists a need to develop more
quantitative and objective ways for predicting and tracking
suicidal states. More particularly, it would be advantageous if
objective tools and screening methods could be developed for
determining expression levels of biomarkers to allow for
determining suicidal risk and other psychotic depressed mood
states, as well as monitoring a subject's response to treatments
for lessening suicidal risk. The ability to assess and track
changes in suicidal risk without asking a subject directly would
further allow for intervening prior to suicide attempt and suicide
completion by the subject.
BRIEF DESCRIPTION OF THE DISCLOSURE
[0007] The present disclosure is generally related to predicting
state (suicidal ideation) and trait--future psychiatric
hospitalizations for suicidality. The methods described herein
increase the predictive accuracy for specifically identifying
subjects who are at risk for committing suicide and for predicting
future hospitalization due to suicidality. In one particular
aspect, the methods described herein increase the predictive
accuracy for specifically identifying subjects who are at risk for
committing suicide and for predicting future hospitalization due to
suicidality.
[0008] In one aspect, the present disclosure is directed to a
method for predicting suicidality in a subject. The method
comprises: obtaining an expression level of a blood biomarker in a
sample obtained from the subject; obtaining a reference expression
level of a blood biomarker; and identifying a difference between
the expression level of the blood biomarker in a sample obtained
from the subject and the reference expression level of a blood
biomarker, wherein the difference in the expression level of the
blood biomarker in the sample obtained from the subject and the
reference expression level of the blood biomarker indicates a risk
for suicide.
[0009] In another aspect, the present disclosure is directed to a
method for mitigating suicidality in a subject in need thereof. The
method comprises: obtaining an expression level of a blood
biomarker in a sample obtained from the subject; obtaining a
reference expression level of the blood biomarker; identifying a
difference in the expression level of the blood biomarker in the
sample and the reference expression level of the blood biomarker;
and administering a treatment, wherein the treatment reduces the
difference between the expression level of the blood biomarker in
the sample and the reference expression level of the blood
biomarker to mitigate suicidality in the subject.
[0010] In another aspect, the present disclosure is directed to a
computer-implemented method for assessing mood, anxiety, and
combinations thereof in the subject using a computer-implemented
method for assessing mood, anxiety, and combinations thereof, the
method implemented using a first computer device coupled to a
memory device, the method comprising: receiving mood information,
anxiety information, and combinations thereof into the first
computer device; storing, by the first computer device, the mood
information, anxiety information, and combinations thereof in the
memory device; presenting, by the first computer device, in visual
form the mood information, anxiety information, and combinations
thereof to a second computer device; receiving a request from the
second computer device for access to the mood information, anxiety
information, and combinations thereof; and transmitting, by the
first computer device, the mood information, anxiety information,
and combinations thereof to the second computer device to assess
mood, anxiety, and combinations thereof in the subject.
[0011] In another aspect, the present disclosure is directed to a
computer-implemented method for assessing
socio-demographic/psychological suicidal risk factors in the
subject using a computer-implemented method for assessing
socio-demographic/psychological suicidal risk factors in the
subject, the method implemented using a first computer device
coupled to a memory device, the method comprising: receiving
socio-demographic/psychological suicidal risk factor information
into the first computer device; storing, by the first computer
device, the socio-demographic/psychological suicidal risk factor
information in the memory device; presenting, by the first computer
device, in visual form the socio-demographic/psychological suicidal
risk factor information to a second computer device; receiving a
request from the second computer device for access to
socio-demographic/psychological suicidal risk factor information;
and transmitting, by the first computer device, the
socio-demographic/psychological suicidal risk factor information to
the second computer device to assess the
socio-demographic/psychological suicidal risk factors in the
subject.
[0012] In one aspect, the present disclosure is directed to a
method for predicting suicidality in a subject. The method
comprises: identifying a difference in the expression level of a
blood biomarker in a sample obtained from a subject and a reference
expression level of the blood biomarker by obtaining the expression
level of the blood biomarker in a sample obtained from a subject;
obtaining a reference expression level of a blood biomarker;
analyzing the blood biomarker in the sample obtained from the
subject and the reference expression level of the blood biomarker
to detect the difference between the blood biomarker in the sample
and the reference expression level of the blood biomarker;
assessing mood, anxiety, and combinations thereof in the subject,
using a first computer device coupled to a memory device, wherein
the first computer device receives mood information, anxiety
information, and combinations thereof into the first computer
device; storing, by the first computer device, the mood
information, anxiety information, and combinations thereof in the
memory device; computing, by the first computer device, of the mood
information, anxiety information, and combinations thereof, a score
that can be used to predict suicidality; presenting, by the first
computer device, in visual form the mood information, anxiety
information, and combinations thereof to a second computer device;
receiving a request from the second computer device for access to
the mood information, anxiety information, and combinations
thereof; and transmitting, by the first computer device, the mood
information, anxiety information, and combinations thereof to the
second computer device to assess mood, anxiety, and combinations
thereof in the subject; assessing socio-demographic/psychological
suicidal risk factors in the subject using the first computer
device coupled to a memory device, wherein the first computer
device receives socio-demographic/psychological suicidal risk
factor information into the first computer device; storing, by the
first computer device, the socio-demographic/psychological suicidal
risk factor information in the memory device; computing, by the
first computer device, of the socio-demographic/psychological
suicidal risk factor information, a score that can be used to
predict suicidality; presenting, by the first computer device, in
visual form the socio-demographic/psychological suicidal risk
factor information to the second computer device; receiving a
request from the second computer device for access to
socio-demographic/psychological suicidal risk factor information;
and transmitting, by the first computer device, the
socio-demographic/psychological suicidal risk factor information to
the second computer device to assess the
socio-demographic/psychological suicidal risk factors in the
subject; and predicting suicidality in the subject by the
combination of the difference between the expression level of the
biomarker in the subject and the reference expression level of the
blood biomarker; the assessment of mood, anxiety, and combinations
thereof; and the assessment of socio-demographic/psychological
suicidal risk factor information.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The disclosure will be better understood, and features,
aspects and advantages other than those set forth above will become
apparent when consideration is given to the following detailed
description thereof. Such detailed description makes reference to
the following drawings, wherein:
[0014] FIGS. 1A-1C depict the Discovery cohort of Example 1:
longitudinal within subject analysis. Phchp### is the study ID for
each participant. V# denotes visit number (1, 2, 3, 4, 5, or 6).
FIG. 1A depicts suicidal ideation (SI) scoring. FIG. 1B depicts
subjects and visits. FIG. 1C depicts PhenoChipping: two-way
unsupervised hierarchical clustering of all participant visits in
the discovery cohort vs. 18 quantitative phenotypes measuring
affective state and suicidality. SASS--Simplified Affective State
Scale. A--Anxiety items (Anxiety, Uncertainty, Fear, Anger,
Average). M--Mood items (Mood, Motivation, Movement, Thinking,
Self-esteem, Interest, Appetite, Average). STAI-STATE is State
Trait Anxiety Inventory, State Subscale. YMRS is Young Mania Rating
Scale.
[0015] FIGS. 2A-2C depict the Biomarker Discovery, Prioritization
and Validation of Example 1. FIG. 2A depicts Discovery--number of
probe sets carried forward from the AP and DE analyses, with an
internal score of 1 and above. Underline-increased in expression in
High SI, bold-decreased in expression in High SI. FIG. 2B depicts
Prioritization--CFG integration of multiple lines of evidence to
prioritize suicide-relevant genes from the discovery step. FIG. 2C
depicts Validation--Top CFG genes validated in the cohort of
suicide completers, with a total score of 4 and above. All the
genes shown were significantly changed in ANOVA from No SI to High
SI to Suicide Completers. *survived Bonferroni correction. SAT1
(.times.3) had three different probe sets with the same total score
of 8.
[0016] FIGS. 3A-3C depict the Convergent Functional Information for
Suicide (CFI-S) Scale as analyzed in Example 1. FIG. 3A depicts
Validation of scale. CFI-S levels in the Discovery Cohort and
Suicide Completers. FIG. 3B depicts Validation of items. CFI-S was
developed independently of any data from this Example by compiling
known socio-demographic and clinical risk factors for suicide. It
is composed of 22 items that assess the influence of mental health
factors, as well as of life satisfaction, physical health,
environmental stress, addictions, cultural factors known to
influence suicidal behavior, and two demographic factors, age and
gender. These 22 items are shown here validated in the discovery
cohort and suicide completers in a manner similar to that for
biomarkers. Additionally, a student's t-test was used to evaluate
items that were increased in suicide completers when compared to
living participants with high suicidal ideation. FIG. 3C depicts
CFI-S predictions for suicidal ideation in the independent test
cohort and predicting future hospitalizations due to
suicidality.
[0017] FIGS. 4A & 4B depict the testing of Universal Predictor
for Suicide (UP-Suicide). UP-Suicide is a combination of the best
genomic data (top increased and decreased biomarkers from discovery
and prioritization by CFG, and validation in suicide completers),
and phenomic data (CFI-S and SASS). The graph in FIG. 4A depicts
Area Under the Curve (AUC) for the UP-Suicide predicting suicidal
ideation and hospitalizations within the first year in all
participants, as well as separately in bipolar (BP), major
depressive disorder (MDD), schizophrenia (SZ), and schizoaffective
(SZA) participants. Two asterisks indicate the comparison survived
Bonferroni correction for multiple comparisons. A single asterisk
indicates nominal significance of p<0.05. Bold outline indicates
that the UP-Suicide was synergistic to its components, i.e.
performed better than the gene expression or phenomic markers
individually. The table in FIG. 4B summarizes descriptive
statistics for all participants together, as well as separately in
BP, MDD, SZ, and SZA. Bold indicates the measure survived
Bonferroni correction for 200 comparisons (20 genomic and phenomic
markers/combinations.times.2 testing cohorts for SI and future
hospitalizations in the first year.times.5 diagnostic
categories--all, BP, MDD, SZA, SZ). Pearson correlation data in the
suicidal ideation test cohort is shown for HAMD-SI vs. UP-Suicide,
as well as Pearson correlation data in the hospitalization test
cohort for frequency of hospitalizations for suicidality in the
first year, and for frequency of hospitalizations for suicidality
in all future available follow-up intervals (that varies among
subjects, from 1 year to 8.5 years).
[0018] FIGS. 5A-5C depict prediction of Suicidal Ideation by
UP-Suicide. The graph in FIG. 5A (top left) depicts Receiver
operating curve identifying participants with suicidal ideation
against participants with No SI or intermediate SI. The graph in
FIG. 5A (top right) depicts suicidal ideation prediction. The Y
axis contains the average UP-suicide scores with standard error for
no SI, intermediate SI, and high SI. The graph in FIG. 5A (bottom
right) is a Scatter plot depicting HAMD-SI score on the Y-axis and
UP-Suicide score on the X axis with linear trendline. The table in
FIG. 5B summarizes the descriptive statistics. ANOVA was performed
between groups with no SI, intermediate SI, and high SI. FIG. 5C
depicts the number of subjects correctly identified in the test
cohort by categories based on thresholds in the discovery cohort.
Category 1 means within 1 standard deviation above the average of
High SI subjects in the discovery cohort, Category 2 means between
1 and 2 standard deviations above, and so on. Category -1 means
within 1 standard deviation below the average of the No SI subjects
in the discovery cohort, Category -2 means between 1 and 2 standard
deviations below, and so on.
[0019] FIG. 6 depicts the Simplified Affective State Scale (SASS)
questionnaire for measuring mood and anxiety.
[0020] FIGS. 7A & 7B depict a screen image of the SASS mobile
app (FIG. 7A) and CFI-S mobile app (FIG. 7B).
[0021] FIGS. 8A & 8B summarize biological pathways and diseases
as analyzed in Example 1.
[0022] FIG. 9 is a table summarizing the top biomarkers for all
diagnoses, the top biomarkers for bipolar disorder, the top
biomarkers for depression, the top biomarkers for schizoaffective
disorder, and the top biomarkers for schizophrenia as analyzed in
Example 1.
[0023] FIGS. 10A-10C depict biomarker discovery as analyzed in
Example 2. Discovery cohort: longitudinal within-participant
analysis. Phchp### is study ID for each participant. V# denotes
visit number (1, 2, 3, 4, 5, or 6). FIG. 10A depicts suicidal
ideation (SI) scoring. FIG. 10B depicts participants and visits.
FIG. 10C depicts PhenoChipping: two-way unsupervised hierarchical
clustering of all participant visits in the discovery cohort vs. 18
quantitative phenotypes measuring affective state and suicidality.
SASS--Simplified Affective State Scale. A--Anxiety items (Anxiety,
Uncertainty, Fear, Anger, Average). M--Mood items--Mood,
Motivation, Movement, Thinking, Self-esteem, Interest, Appetite,
Average). STAI-STATE is State Trait Anxiety Inventory, State
Subscale. YMRS is Young Mania Rating Scale.
[0024] FIGS. 11A-11C depict biomarker prioritization and validation
as analyzed in Example 2. FIG. 11A depicts Discovery--number of
probesets carried forward from the AP and DE analyses, with an
internal score of 1 and above. Underline-increased in expression in
High SI, bold--decreased in expression in High SI. FIG. 11B depicts
the Prioritization--CFG integration of multiple lines of evidence
to prioritize suicide--relevant genes from the discovery step. FIG.
11C depicts Validation--Top CFG genes, with a total score of 4 and
above, validated in the cohort of suicide completers. All the genes
shown were significantly changed and survived Bonferroni correction
in ANOVA from No SI to High SI to Suicide Completers. Some genes
with (x n) after the symbol had multiple different probesets with
the same total score.
[0025] FIGS. 12A & 12B depict Convergent Functional Information
for Suicide (CFI-S) Scale as analyzed in Example 2. CFI-S was
developed independently of any data from this Example, by compiling
known socio-demographic and clinical risk factors for suicide. It
is composed of 22 items that assess the influence of mental health
factors, as well as of life satisfaction, physical health,
environmental stress, addictions, cultural factors known to
influence suicidal behavior, and two demographic factors, age and
gender. FIG. 12A depicts testing of scale in females. Prediction of
high suicidal ideation in females in a larger cohort that combines
the discovery and test cohorts used for biomarker work. The table
depicts individual items and their ability to differentiate between
No SI and High SI. FIG. 12B depicts testing of the scale in males,
in a larger cohort that combines the discovery and test cohorts
used for the biomarker work in Example 1. The table depicts
individual items and their ability to differentiate between No SI
and High SI.
[0026] FIGS. 13A & 13B depict UP-Suicide predictions of
suicidal ideation in the independent test cohort, and predicting
future hospitalizations due to suicidality as analyzed in Example
2. FIG. 13A (Top left) depicts receiver operating curve identifying
participants with suicidal ideation against participants with No SI
or intermediate SI; (Top right): Y axis contains the average
UP-Suicide scores with standard error of mean for no SI,
intermediate SI, and high SI; (Bottom right): Scatter plot
depicting HAMD-SI score on the Y-axis and UP-Suicide score on the X
axis with linear trend line; and (Bottom Table) summarizes
descriptive statistics. FIG. 13B (Top left) depicts receiver
operating curve identifying participants with future
hospitalizations due to suicidality against participants without
future hospitalizations due to suicidality; (Top right): Y axis
contains the average UP-Suicide scores with standard error of mean
for no future hospitalizations due to suicidality and participants
with future hospitalizations due to suicidality; (Bottom right):
Scatter plot depicting frequency of future hospitalizations due to
suicidality on the Y-axis and UP-Suicide score on the X axis with
linear trend line; and (Bottom Table) summarizes descriptive
statistics.
[0027] FIG. 14 is a table depicting the cohorts used in Example
2.
[0028] FIG. 15 is a table depicting biological pathways and
diseases as analyzed in Example 2.
[0029] FIG. 16 is a table depicting UP-suicide predictions as
analyzed in Example 2. UP-Suicide is composed of 50 validated
biomarkers (18 increased in expression, 32 decreased in
expression), along with clinical measures app scores (CFI-S, SASS).
SASS is composed of Mood scale and Anxiety scale.
[0030] FIG. 17 depicts convergent functional information for
suicide (CFI-S) App testing across genders. Prediction of high
suicidal ideation in men and women in a larger cohort that combines
the cohorts used in Examples 1 and 2 by gender. CFI-S was developed
independently of any data from this disclosure, by compiling known
socio-demographic and clinical risk factors for suicide. It is
composed of 22 items that assess the influence of mental health
factors, as well as of life satisfaction, physical health,
environmental stress, addictions, cultural factors known to
influence suicidal behavior, and two demographic factors, age and
gender. The table depicts individual items and their ability to
differentiate between No Suicidal Ideation and High Suicidal
Ideation. These items provide clinical predictors and targets for
psycho-therapeutic intervention.
[0031] FIG. 18 depicts convergent functional information for future
hospitalization for suicide (CFI-S) App testing across genders.
Particularly, prediction of future hospitalizations for suicidality
in men and women in a larger cohort that combines the cohorts used
in our studies by gender.
[0032] While the disclosure is susceptible to various modifications
and alternative forms, specific embodiments thereof have been shown
by way of example in the drawings and are herein described below in
detail. It should be understood, however, that the description of
specific embodiments is not intended to limit the disclosure to
cover all modifications, equivalents and alternatives falling
within the spirit and scope of the disclosure as defined by the
appended claims.
DETAILED DESCRIPTION
[0033] Unless defined otherwise, all technical and scientific terms
used herein have the same meaning as commonly understood by one of
ordinary skill in the art to which the disclosure belongs. Although
any methods and materials similar to or equivalent to those
described herein can be used in the practice or testing of the
present disclosure, the preferred methods and materials are
described below.
[0034] New data for discovery, prioritization, validation and
testing of next generation broader-spectrum blood biomarkers for
suicidal ideation and behavior, across psychiatric diagnoses are
disclosed. Also disclosed are two clinical information
questionnaires in the form of apps, one for affective state
(Simplified Affective Scale, SASS) and one for suicide risk factors
(Convergent Functional Information for Suicide, CFI-S), that are
useful in predicting suicidality. Both of these instruments do not
directly ask about suicidal ideation. Also disclosed is a
comprehensive universal predictor for suicide (UP-Suicide),
composed of the combination of top biomarkers (from discovery,
prioritization and validation), along with CFI-S, and SASS, which
predicts in independent test cohorts suicidal ideation and future
psychiatric hospitalizations for suicidality.
[0035] As disclosed herein, "patient psychiatric information" may
include mood information, anxiety information, and other
psychiatric symptom information and combinations thereof
[0036] As used herein, "predicting suicidality in a subject" is
used herein to indicate in advance that a subject will attempt
suicide and/or complete suicide.
[0037] As known by those skilled in the art, "suicidal ideation"
refers to thoughts, feelings, intent, external actions and
behaviors about completing suicide. Suicidal ideation can vary from
fleeting thoughts to unsuccessful attempts. In some embodiments,
the reference expression level of a biomarker can be obtained for a
subject who has no suicidal ideation at the time the sample is
obtained from the subject, but who later exhibits suicide ideation.
As used herein, "suicidality" includes both suicide ideation and
suicidal acts.
[0038] As used herein, "a reference expression level of a
biomarker" refers to the expression level of a biomarker
established for a subject with no suicidal ideation, expression
level of a biomarker in a normal/healthy subject with no suicidal
ideation as determined by one skilled in the art using established
methods as described herein, and/or a known expression level of a
biomarker obtained from literature. The reference expression level
of the biomarker can further refer to the expression level of the
biomarker established for a high suicide risk subject, including a
population of high suicide risk subjects. The reference expression
level of the biomarker can also refer to the expression level of
the biomarker established for a low suicide risk subject, including
a population of low suicide risk subjects. The reference expression
level of the biomarker can also refer to the expression level of
the biomarker established for any combination of subjects such as a
subject with no suicidal ideation, expression level of the
biomarker in a normal/healthy subject with no suicidal ideation,
expression level of the biomarker for a subject who has no suicidal
ideation at the time the sample is obtained from the subject, but
who later exhibits suicide ideation, expression level of the
biomarker as established for a high suicide risk subject, including
a population of high suicide risk subjects, and expression level of
the biomarker can also refer to the expression level of the
biomarker established for a low suicide risk subject, including a
population of low suicide risk subjects. The reference expression
level of the biomarker can also refer to the expression level of
the biomarker obtained from the subject to which the method is
applied. As such, the change within a subject from visit to visit
can indicate an increased or decreased risk for suicide. For
example, a plurality of expression levels of a biomarker can be
obtained from a plurality of samples obtained from the same subject
and used to identify differences between the plurality of
expression levels in each sample. Thus, in some embodiments, two or
more samples obtained from the same subject can provide an
expression level(s) of a blood biomarker and a reference expression
level(s) of the blood biomarker.
[0039] As used herein, "expression level of a biomarker" refers to
the process by which a gene product is synthesized from a gene
encoding the biomarker as known by those skilled in the art. The
gene product can be, for example, RNA (ribonucleic acid) and
protein. Expression level can be quantitatively measured by methods
known by those skilled in the art such as, for example, northern
blotting, amplification, polymerase chain reaction, microarray
analysis, tag-based technologies (e.g., serial analysis of gene
expression and next generation sequencing such as whole
transcriptome shotgun sequencing or RNA-Seq), Western blotting,
enzyme linked immunosorbent assay (ELISA), and combinations
thereof.
[0040] As used herein, a "difference" in the expression level of
the biomarker refers to an increase or a decrease in the expression
of a blood biomarker when analyzed against a reference expression
level of the biomarker. In some embodiments, the "difference"
refers to an increase or a decrease by about 1.2-fold or greater in
the expression level of the biomarker as identified between a
sample obtained from the subject and the reference expression level
of the biomarker. In one embodiment, the difference in expression
level is an increase or decrease by about 1.2 fold. As used herein
"a risk for suicide" can refer to an increased (greater) risk that
a subject will attempt to commit suicide and/or complete suicide
For example, depending on the biomarker(s) selected, the difference
in the expression level of the biomarker(s) can indicate an
increased (greater) risk that a subject will attempt to commit
suicide and/or complete suicide. Conversely, depending on the
biomarker(s) selected, the difference in the expression level of
the biomarker(s) can indicate a decreased (lower) risk that a
subject will attempt to commit suicide and/or complete suicide.
[0041] In accordance with the present disclosure, biomarkers useful
for objectively predicting, mitigating, and/or preventing
suicidality in subjects have been discovered. In one aspect, the
present disclosure is directed to a method for predicting
suicidality in a subject. The method includes obtaining a reference
expression level of a blood biomarker; and determining an
expression level of the blood biomarker in a sample obtained from
the subject. A change in the expression level of the blood
biomarker in the sample obtained from the subject as compared to
the reference expression level indicates suicidality. In some
embodiments, the methods further include obtaining clinical risk
factor information and clinical scale data such as for anxiety,
mood and/or psychosis from the subject in addition to obtaining
blood biomarker expression level in a sample obtained from the
subject.
[0042] In one embodiment, the expression level of the blood
biomarker in the sample obtained from the subject is increased as
compared to the reference expression level of the biomarker. It has
been found that an increase in the expression level of particular
blood biomarkers in the sample obtained from the subject as
compared to the reference expression level of the biomarker
indicates a risk for suicide. Suitable biomarkers that indicate a
risk for suicide when the expression level increases can be, for
example, one or more biomarkers as listed in Table 1 and
combinations thereof.
TABLE-US-00001 TABLE 1 Top Candidate Biomarker Genes - increase in
expression Gene Gene Name Symbol interleukin 6 (interferon, beta 2)
IL6 spermidine/spermine N1-acetyltransferase 1 SAT1 solute carrier
family 4 (sodium bicarbonate cotrans- SLC4A4 porter), member 4
monoamine oxidase B MAOB Glutamate Receptor, Ionotropic, Kainate 2
GRIK2 Rho GTPase activating protein 26 ARHGAP26 B-cell CLL/lymphoma
2 BCL2 cadherin 4, type 1, R-cadherin (retinal) CDH4 chemokine
(C-X-C motif) ligand 11 CXCL11 EMI domain containing 1 EMID1 family
with sequence similarity 49, member B FAM49B GRB2-Associated
Binding Protein 1 GAB1 GRINL1A complex locus 1 GCOM1
hippocalcin-like 1 HPCAL1 mitogen-activated protein kinase 9 MAPK9
nuclear paraspeckle assembly transcript 1 (non-protein NEAT1
coding) protein tyrosine kinase 2 PTK2 RAS-like, family 11, member
B RASL11B small nucleolar RNA, H/ACA box 68 SNORA68 superoxide
dismutase 2, mitochondrial SOD2 transcription factor 7-like 2
(T-cell specific, HMG- TCF7L2 box) v-raf murine sarcoma viral
oncogene homolog B BRAF chromosome 1 open reading frame 61 C1orf61
Calreticulin CALR calcium/calmodulin-dependent protein kinase II
beta CAMK2B caveolin 1, caveolae protein, 22 kDa CAV1 chromodomain
helicase DNA binding protein 2 CHD2 clathrin, light chain A CLTA
cAMP responsive element modulator CREM Cortactin CTTN dishevelled
associated activator of morphogenesis 2 DAAM2 Dab,
mitogen-responsive phosphoprotein, homolog 2 DAB2 (Drosophila)
GABA(A) receptor-associated protein like 1 GABARAPL1 GABA(A)
glutamate-ammonia ligase GLUL helicase with zinc finger HELZ
immunoglobulin heavy constant gamma 1 (G1m IGHG1 marker)
interleukin 1, beta IL1B jun proto-oncogene JUN jun B
proto-oncogene JUNB lipoma HMGIC fusion partner LHFP myristoylated
alanine-rich protein kinase C substrate MARCKS metallothionein 1E
MT1E metallothionein 1H MT1H metallothionein 2A MT2A N-myc
downstream regulated 1 NDRG1 nucleobindin 2 NUCB2 PHD finger
protein 20-like 1 PHF20L1 phosphatase and tensin homolog PTEN
reversion-inducing-cysteine-rich protein with kazal RECK motifs
shisa family member 2 SHISA2 transmembrane 4 L six family member 1
TM4SF1 trophoblast glycoprotein TPBG tumor protein D52-like 1
TPD52L1 TSC22 domain family, member 3 TSC22D3 vacuole membrane
protein 1 VMP1 ZFP36 ring finger protein ZFP36 zinc fingers and
homeoboxes 2 ZHX2 UDP-Gal:betaGlcNAc beta 1,4-
galactosyltransferase, B4GALT1 polypeptide 1 BTB (POZ) domain
containing 3 BTBD3 cell adhesion molecule 1 CADM1 chitobiase,
di-N-acetyl- CTBS DEP domain containing 5 DEPDC5 dystrobrevin,
alpha DTNA egf-like module containing, mucin-like, hormone EMR2
receptor-like 2 endogenous retrovirus group 3, member 2 ERV3-2
family with sequence similarity 183, FAM183CP member C, pseudogene
histone cluster 1, H2bo HIST1H2BO potassium channel tetramerization
domain containing KCTD21 21 Keratocan KERA laminin, beta 1 LAMB1
uncharacterized LOC100289061 LOC100129917 uncharacterized LOC285500
LOC285500 RAB36, member RAS oncogene family RAB36 uncharacterized
LOC283352 RP11-66N7.2 transcription factor Dp-1 TFDP1 TMLHE
antisense RNA 1 TMLHE-AS1 superoxide dismutase 2, mitochondrial
SOD2 period circadian clock 1 PER1 Ras association (RalGDS) RAPH1
spondin 1, extracellular matrix protein SPON1 forkhead box P1 FOXP1
hepatitis A virus cellular receptor 2 HAVCR2 Rho GTPase activating
protein 15 ARHGAP15 gap junction protein, alpha 1, 43 kDa GJA1 hes
family bHLH transcription factor 1 HES1 HtrA serine peptidase 1
HTRA1 TIMP metallopeptidase inhibitor 1 TIMP1 erythrocyte membrane
protein band 4.1 like 5 EPB41IL5 interleukin 1 receptor, type I
IL1R1 intelectin 1 (galactofuranose binding) ITLN1 killer cell
immunoglobulin-like receptor, two KIR2DL4 domains, long cytoplasmic
tail, 4 nudix (nucleoside diphosphate linked moiety X)-type NUDT10
motif 10 pyridoxal-dependent decarboxylase domain containing PDXDC1
1 family with sequence similarity 214, member A FAM214A heat shock
60 kDa protein 1 (chaperonin) HSPD1 zinc finger, MYND-type
containing 8 ZMYND8 adenylate kinase 2 AK2 AF4/FMR2 family, member
3 AFF3 mitochondrial ribosomal protein S5 MRPS5 v-akt murine
thymoma viral oncogene homolog 3 AKT3 aspartate beta-hydroxylase
ASPH ataxin 1 ATXN1 Brain and reproductive organ-expressed
(TNFRSF1A BRE modulator) ClpB caseinolytic peptidase B homolog (E.
coli) CLPB deleted in primary ciliary dyskinesia homolog (mouse)
DPCD ECSIT signalling integrator ECSIT ectonucleoside triphosphate
diphosphohydrolase 1 ENTPD1 EPH receptor B4 EPHB4 Fanconi anemia,
complementation group I DANCI general transcription factor IIIC,
polypeptide 3, 102 GTF3C3 kDa inter-alpha-trypsin inhibitor heavy
chain family, ITIH5 member 5 kelch-like family member 28 KLHL28
major histocompatibility complex, class I-related MR1 protein
inhibitor of activated STAT, 1 PIAS1 periphilin 1 PPHLN1 retinol
dehydrogenase 13 (all-trans/9-cis) RDH13 strawberry notch homolog 1
(Drosophila) SBN01 sorting nexin family member 27 SNX27
single-stranded DNA binding protein 2 SSBP2 striatin, calmodulin
binding protein STRN tetratricopeptide repeat domain 7A TTC7A
ubiquitin interaction motif containing 1 UIMC1 Z-DNA binding
protein 1 ZBP1 zinc finger protein 596 ZNF596 adaptor-related
protein complex 3, sigma 2 subunit AP3S2
[0043] In one particularly suitable embodiment, the subject is a
male and the blood biomarker that increases in expression level as
compared to the reference expression level is selected from solute
carrier family 4 (sodium bicarbonate cotransporter), member 4
(SLC4A4), cell adhesion molecule 1 CADM1, dystrobrevin, alpha
(DTNA), spermidine/spermine N1-acetyltransferase 1 (SAT1),
interleukin 6 (interferon, beta 2) (IL6) and combinations thereof.
In another embodiment, the subject is a female and the blood
biomarker that increases in expression level as compared to the
reference expression level is selected from erythrocyte membrane
protein band 4.1 like 5 (EPB41L5), HtrA serine peptidase 1 (HTRA1),
deleted in primary ciliary dyskinesia homolog (DPCD), general
transcription factor IIIC, polypeptide 3, 102 kDa (GTF3C3), period
circadian clock 1 (PER1), pyridoxal-dependent decarboxylase domain
containing 1 (PDXDC1), kelch-like family member 28 (KLHL28),
ubiquitin interaction motif containing 1 (UIMC1), sorting nexin
family member 27 (SNX27) and combinations thereof.
[0044] In another embodiment, the expression level of the blood
biomarker in the sample obtained from the subject is decreased as
compared to the reference expression level of the biomarker.
Suitable biomarkers that indicate a risk for suicide when the
expression level decreases as compared to the reference expression
level have been found to include, for example, one or more
biomarkers as listed in Table 2 and combinations thereof.
TABLE-US-00002 TABLE 2 Top Candidate Biomarker Genes - decrease in
expression Gene Name Gene Symbol spindle and kinetochore associated
SKA2 complex subunit 2 coiled-coil domain containing 136 CCDC136
CD44 molecule (Indian blood group) CD44 fatty acid desaturase 1
FADS1 FK506 binding protein 5 FKBP5 forkhead box N3 FOXN3
hydroxyacyl-CoA dehydrogenase/3- HADHA ketoacyl-CoA
thiolase/enoyl-CoA hydratase (trifunctional protein), alpha subunit
adenosylhomocysteinase-like 1 AHCYL1 AKT1 substrate 1
(proline-rich) AKT1S1 aldehyde dehydrogenase 3 family, ALDH3 A2
member A2 B-cell CLL/lymphoma 2 BCL2 C20orf27 calpain, small
subunit 1 CAPNS1 CDC42 effector protein (Rho GTPase CDC42EP4
binding) 4 EH domain binding protein 1 EHBP1 eukaryotic translation
initiation factor 5A EIF5A fumarate hydratase FH glycoprotein M6B
GPM6B homeobox and leucine zipper encoding HOMEZ inhibitor of kappa
light polypeptide gene IKBKB enhancer in B-cells, kinase beta
integrin, beta 4 ITGB4 low density lipoprotein receptor adaptor
LDLRAP1 protein 1 uncharacterized LOC728543 LOC728543
mitogen-activated protein kinase kinase 5 MAP2K5 neuromedin B NMB
platelet-activating factor acetylhydrolase PAFAH1B2 1b, catalytic
subunit 2 (30 kDa) pterin-4 alpha-carbinolamine PCBD2
dehydratase/dimerization cofactor of hepatocyte nuclear factor 1
alpha (TCF1) 2 phosphatidylinositol-4-phosphate 3- PIK3C2A kinase,
catalytic subunit type 2 alpha plakophilin 4 PKP4 solute carrier
family 5 (sodium/ SLC5A3 myoinositol cotransporter), member 3
spectrin repeat containing, nuclear SYNE2 envelope 2 trans-golgi
network protein 2 TGOLN2 trafficking protein, kinesin binding 2
TRAK2 adrenergic, beta, receptor kinase 1 ADRBK1
adenosylhomocysteinase-like 2 AHCYL2 aminoacyl tRNA synthetase
complex- AIMP1 interacting multifunctional protein 1 ATPase, H+
transporting, lysosomal ATP6V0E1 9 kDa, V0 subunit e1
BRCA1/BRCA2-containing complex, BRCC3 subunit 3 2',3'-cyclic
nucleotide 3' CNP phosphodiesterase collagen, type IX, alpha 2
COL9A2 cleavage and polyadenylation specific CPSF2 factor 2, 100
kDa cullin 4B CUL4B delta-like 1 (Drosophila) DLL1 dynein,
axonemal, heavy chain 2 DNAH2 dipeptidyl-peptidase 4 DPP4
G2/M-phase specific E3 ubiquitin protein G2E3 ligase guanylate
kinase 1 GUK1 Janus kinase 3 JAK3 lysosomal protein transmembrane 4
beta LAPTM4B lysophosphatidic acid receptor 1 LPAR1 membrane
associated guanylate kinase, MAGI3 WW and PDZ domain containing 3
myelin basic protein MBP microspherule protein 1 MCRS1 myocyte
enhancer factor 2C MEF2C opioid growth factor receptor OGFR
protocadherin 9 PCDH9 pleckstrin homology domain containing,
PLEKHB1 family B (evectins) member 1 polymerase (RNA) II (DNA
directed) POLR2D polypeptide D protein kinase, cAMP-dependent,
PRKACA catalytic, alpha protein kinase C, beta PRKCB proteasome
(prosome, macropain) PSMB4 subunit, beta type, 4 RAB35, member RAS
oncogene family RAB35 RNA binding motif protein, X-linked RBMX
ribonuclease L (2',5'-oligoisoadenylate RNASEL
synthetase-dependent) selenium binding protein 1 SELENBP1 solute
carrier family 35, member E1 SLC35E1 synaptosomal-associated
protein, 23 kDa SNAP23 transmembrane protein 254 TMEM254
transmembrane protein 259 TMEM259 tensin 1 TNS1 tripartite motif
containing 23 TRIM23 tetraspanin 33 TSPAN33 pre-B lymphocyte 3
VPREB3 zinc finger, FYVE domain containing 21 ZFYVE21 zinc finger
protein 519 ZNF519 cation channel, sperm associated 3 CATSPER3
chemokine (C-C motif) ligand 28 CCL28 CAP-GLY domain containing
linker CLIP4 protein family, member 4 chromosome Y open reading
frame 17 CYorf17 DDB1 and CUL4 associated factor 15 DCAF15 EPH
receptor A10 EPHA10 v-ets avian erythroblastosis virus E26 ERG
oncogene homolog heparan sulfate (glucosamine) 3-O- HS3ST3B1
sulfotransferase 3B1 IQ motif containing H IQCH kinesin family
member 2C KIF2C kelch domain containing 3 KLHDC3 uncharacterized
LOC100129917 LOC100129917 uncharacterized LOC100996345 LOC100996345
mediator complex subunit 21 MED21 PDX1 C-terminal inhibiting factor
1 PCIF1 plectin PLEC RAD23 homolog A (S. cerevisiae) RAD23A
Rh-associated glycoprotein RHAG roundabout, axon guidance receptor,
ROBO4 homolog 4 (Drosophila) ribosomal protein L6 pseudogene 17
RPL6P17 SET domain containing (lysine SETD8 methyltransferase) 8
SH3-domain GRB2-like endophilin B2 SH3GLB2 ST6
(alpha-N-acetyl-neuraminyl-2,3- ST6GALNAC4 beta-galactosyl-1,3)-N-
acetylgalactosaminide alpha-2,6- sialyltransferase 4 testis
expressed 10 TEX10 testis expressed 261 TEX261 thymosin beta 15B
TMSB15B tubulin, gamma complex associated TUBGCP3 protein 3
thioredoxin reductase 2 TXNRD2 ubiquitin specific peptidase 12
USP12 vascular endothelial growth factor B VEGFB zinc finger and
BTB domain containing ZBTB7A 7A glycogen synthase kinase 3 beta
GSK3B adaptor-related protein complex 1, sigma AP1S2 2 subunit
catalase CAT chromosome 18 open reading frame 54 C19orf54 long
intergenic non-protein coding RNA LINC00342 342 MOB kinase
activator 3B MOB3B phosphatidylinositol-4-phosphate 5- PIP5K1B
kinase, type I, beta prolylcarboxypeptidase (angiotensinase PRCP C)
CD200 receptor 1 CD200R1 CD84 molecule CD84 centrosomal protein 44
kDa CEP44 carnitine O-octanoyltransferase CROT DDB1 and CUL4
associated factor 5 DCAF5 DTW domain containing 2 DTWD2 endoplasmic
reticulum protein 27 ERP27 family with sequence similarity 173,
FAM173B member B glucosidase, alpha; neutral C GANC general
transcription factor IIIC, GTF3C2 polypeptide 2, beta 110 kDa INO80
complex subunit D INO80D inositol polyphosphate-4-phosphatase,
INPP4A type I, 107 kDa Jrk homolog (mouse) JRK potassium channel
tetramerization KCTD5 domain containing 5 methyltransferase like 15
METTL15 phosphatidylinositol 3-kinase, catalytic PIK3C3 subunit
type 3 RNA binding motif protein 48 RBM48 SWI/SNF Related, Matrix
Associated, SMARCA2 Actin Dependent Regulator Of Chromatin,
Subfamily A, Member 2 ubiquitin carboxyl-terminal hydrolase L5
UCHL5 vacuolar protein sorting 53 homolog VPS53 (S. cerevisiae)
zinc finger protein 302 ZNF302 capping protein (actin filament)
muscle CAPZA2 Z-line, alpha 2 leucine rich repeat containing 8
family, LRRC8B member B protein phosphatase, Mg2+ PPM1B ARP3
actin-related protein 3 homolog ACTR3 (yeast) SH2 domain containing
1A SH2D1A ALG13, UDP-N- ALG13 acetylglucosaminyltransferase subunit
Rho GTPase activating protein 35 ARHGAP35 AT rich interactive
domain 4B (RBP1- ARID4B like) charged multivesicular body protein
2B CHMP2B casein kinase 1, alpha 1 CSNK1A1 ethanolamine kinase 1
ETNK1 F-box and leucine-rich repeat protein 3 FBXL3 HECT and RLD
domain containing E3 HERC4 ubiquitin protein ligase 4 jumonji
domain containing 1C JMJD1C La ribonucleoprotein domain family,
LARP4 member 4 muscleblind-like splicing regulator 1 MBNL1 mex-3
RNA binding family member C MEX3C nudix (nucleoside diphosphate
linked NUDT6 moiety X)-type motif 6 polyhomeotic homolog 3
(Drosophila) PHC3 peroxiredoxin 3 PRDX3 Pvt1 oncogene (non-protein
coding) PVT1 RAB22A, member RAS oncogene family RAB22A solute
carrier family 35 (adenosine 3'- SLC35B3 phospho 5'-phosphosulfate
transporter), member B3 small nuclear ribonucleoprotein 27 kDa
SNRNP27 (U4 USP6 N-terminal like USP6NL WW domain containing
adaptor with WAC coiled-coil wings apart-like homolog (Drosophila)
WAPAL zinc finger, AN1-type domain 5 ZFAND5 zinc finger protein 117
ZNF117 zinc finger protein 141 ZNF141 zinc finger protein 548
ZNF548 signal sequence receptor, alpha SSR1
[0045] In one particularly suitable embodiment, the subject is a
male and the blood biomarker that decreases in expression level as
compared to the reference expression level is spindle and
kinetochore associated complex subunit 2 (SKA2), CAP-GLY domain
containing linker protein family, member 4 (CLIP4), kinesin family
member 2C (KIF2C), kelch domain containing 3 (KLHDC3) and
combinations thereof. In another embodiment, the subject is a
female and the blood biomarker that decreases in expression level
as compared to the reference expression level is selected from
phosphatidylinositol 3-kinase, catalytic subunit type 3 (PIK3C3),
aldehyde dehydrogenase 3 family, member A2 (ALDH3A2), ARP3
actin-related protein 3 homolog (yeast) (ACTR3), B-cell CLL (BCL2),
MOB kinase activator 3B (MOB3B), casein kinase 1, alpha 1
(CSNK1A1), La ribonucleoprotein domain family, member 4 (LARP4),
zinc finger protein 548 (ZNF548) and combinations thereof.
[0046] Table 3 further discloses the top biomarkers across gender
having expression levels that increase or decrease (as indicated)
as compared to the reference expression levels to predict
suicidality.
TABLE-US-00003 TABLE 3 Top Universal Biomarkers for Suicide Across
Genders Discovery in Significant Prediction Blood Validation of
Suicidal Ideation (Direction of in Blood Across All and Gene Symbol
Affymetrix Change)/ ANOVA p- Best In a Diagnostic Gene Name
Probesets Score value/Score Group ROC AUC/p-value BCL2 203685_at
(D)/1 5.98E-11/4 All B-cell 0.609/0.005 CLL/ Male SZ/SZA lymphoma 2
0.68/0.011 CD164 208654_s_at (D)/2 3.01E-08/4 All CD164 0.589/0.017
molecule, Male BP sialomucin 0.68/0.020 CD47 211075_s_at (D)/2
1.62E-17/4 All CD47 0.598/0.010 molecule Male SZ/SZA 0.67/0.016
DLG1 202514_at (D)/1 0.0000844 All discs, large 0.58/0.036 homolog
1 Male SZ/SZA (Drosophila) 0.65/0.030 DLG1 202516_s_at (D)/1
0.0000000000016/4 .sup. All discs, large 0.58/0.029 homolog 1
(Drosophila) DYRK2 202969_at (D)/1 0.00000000000017/4 All
dual-specificity 0.58/0.034 tyrosine-(Y)- Male SZ/SZA phosphory-
0.68/0.010 lation regulated kinase 2 ITGB1BP1 203336_s_at (D)/1
0.000000025/4 .sup. All integrin beta 1 0.57/0.042 binding protein
1 APOE 203382_s_at (I)/1 3.44E-09/4 All apolipo- 0.59/0.021 protein
E Male BP 0.71/0.0091 MRPS14 203800_s_at (D)/1 0.00000000039/4
.sup. Male SZ/SZA mitochondrial 0.69/0.0080 ribosomal protein S14
MRPS14 203801_at (D)/1 2.45E-17/4 All mitochondrial 0.60/0.0069
ribosomal protein Male SZ/SZA S14 0.68/0.011 IL6 205207_at (I)/1
1.82E-15/4 All interleukin 6 0.58/0.038 AKAP13 209534_x_at (I)/1
.sup. 0.000021/4 Male PTSD A kinase (PRKA) 0.78/0.0083 anchor
protein 13 SECISBP2L 212450_at (D)/1 .sup. 0.000063/4 All SECIS
binding 0.59/0.021 protein 2-like Male BP 0.71/0.0076 SOD2
215078_at (I)/2 2.27E-34/4 superoxide dismutase 2, mitochondrial
LHFP 218656_s_at (I)/1 0.00000000040/4 .sup. All lipoma HMGIC
0.57/0.05 fusion partner Male MDD 0.69/0.034 SKA2 225686_at (D)/1
4.55E-03/2 All spindle and 0.62/0.003 kinetochore Male SZ/SZA
associated 0.75/0.00063 complex subunit 2 GSK3B 226183_at (D)/1
2.19E-36/4 glycogen synthase kinase 3 beta ITPKB 232526_at AP
0.0000000045/4 All inositol- (I)/1 0.62/0.0019 trisphosphate 3-
Male BP kinase B 0.76/0.0013 MTERF4 1557966_x_at (D)/2 6.72E-06/4
All mitochondrial 0.61/0.005 transcription Male SZ/SZA termination
factor 0.72/0.0019 4 GDI2 200008_s_at (D)/2 1.52E-11/4 All GDP
dissociation 0.59/0.013 inhibitor 2 Male BP 0.67/0.024 PRKAR1A
200605_s_at (D)/2 2.47E-06/4 Male BP protein kinase, 0.72/0.0059
cAMP- dependent, regulator, type I, alpha NR3C1 201866_s_at (D)/1
1.64E-03/2 Male BP nuclear receptor 0.67/0.029 subfamily 3, group
C, member 1 (glucocorticoid receptor) ADK 204119_s_at DE
0.000000020/4 .sup. All adenosine kinase (D)/4 0.62/0.0026 Male
SZ/SZA 0.66/0.019 PGK1 217383_at (D)/2 4.07E-07/4 Male SZ/SZA
phosphoglycerate 0.63/0.046 kinase 1 ZFYVE21 219929_s_at (D)/2
5.96E-06/4 All zinc finger, 0.58/0.026 FYVE domain containing 21
RBM3 222026_at (D)/2 1.73E-05/4 RNA binding motif (RNP1, RRM)
protein 3 FAM107B 223058_at (D)/2 2.36E-02/2 All family with
0.58/0.024 sequence Male BP similarity 107, 0.71/0.0079 member B
ECHDC1 223087_at (D)/2 3.35E-09/4 All enoyl CoA 0.60/0.009
hydratase domain Male containing 1 SZ/SZA 0.66/0.019 TBL1XR1
235890_at AP 0.000000023/4 .sup. Male BP transducin (beta)- (D)/2
0.66/0.034 1 ike 1 X-linked receptor 1 LONRF2 235977_at (I)/1
1.48E-03/2 Male BP LON peptidase 0.73/0.0040 N-terminal domain and
ring finger 2 QKI 211938_at (I)/2 1.88E-03/2 Male QKI, KH domain
PTSD containing, RNA 0.77/0.011 binding YWHAH 242325_at (I)/2
6.65E-11/4 All tyrosine 3- 0.571/0.047 monooxygenase/ Male BP
tryptophan 5- 0.66/0.033 monooxygenase activation protein, eta
SLC4A4 210739_x_at (I)/1 7.74E-05/4 All solute carrier 0.64/0.00038
family 4 (sodium Male BP bicarbonate 0.77/0.00094 cotransporter),
member 4 GDI2 200009_at (D)/1 .sup. 0.000015/4 All GDP dissociation
0.64/0.0006 inhibitor 2 Male SZ/SZA 0.72/0.0028 UQCRC2 200883_at
(D)/1 .sup. 0.012/2 All ubiquinol- 0.61/0.0035 cytochrome c Male
SZ/SZA reductase core 0.67/0.013 protein II CTNNB1 201533_at (D)/1
.sup. 0.0023/2 All catenin 0.59/0.018 (cadherin- Male BP associated
0.74/0.0037 protein), beta 1, 88 kDa PSMB4 202243_s_at (D)/1
6.55E-14/4 All proteasome 0.6/0.011 (prosome, Male SZ/SZA
macropain) 0.68/0.010 subunit, beta type, 4 PRKACB 202742_s_at
(D)/1 .sup. 0.00042/2 All protein kinase, 0.58/0.028 cAMP-
dependent, catalytic, beta LPAR1 204036_at (D)/1 1.35003E-234 Male
BP lysophosphatidic 0.68/0.022 acid receptor 1 HTR2C 207307_at
(I)/1 4.30E-02/2 All 5-hydroxy- 0.583/0.025 tryptamine Male MDD
(serotonin) 0.69/0.035 receptor 2C, G protein-coupled CTTN
214782_at DE 1.042E-19/4 Male BP cortactin (I)/1 0.76/0.0016 PDCL3
219043_s_at (D)/2 1.37E-02/2 All phosducin-like 3 0.6/0.009 Male
SZ/SZA 0.65/0.030 SNX6 222410_s_at DE 0.0000068/4 All sorting nexin
6 (D)/1 0.62/0.0025 Male SZ/SZA 0.65/0.024 PIK3CA 231854_at DE
2.41E-37/4 All phosphatidyl- (D)/1 0.57/0.042 inositol-4,5- Male BP
bisphosphate 3- 0.65/0.047 kinase, catalytic subunit alpha MBP
225408_at (D)/2 8.34E-07/4 myelin basic protein CCDC136 226972_s_at
(D)/4 3.13E-03/2 coiled-coil domain containing 136 AIMP1 227605_at
(D)/2 1.02E-05/4 All aminoacyl tRNA 0.60/0.007 synthetase Male
SZ/SZA complex- 0.66/0.018 interacting multifunctional protein 1
PITHD1 229856_s_at (D)/4 0.000000067/4 .sup. Female BP PITH
(C-terminal 0.83/0.031 proteasome- interacting domain of
thioredoxin-like) domain containing 1 PCDH9 238919_at (D)/2
6.61E-05/4 protocadherin 9 CAPZA2 201238_s_at (D)/1 .sup. 0.00029/2
All capping protein 0.6/0.0086 (actin filament) Male BP muscle
Z-line, 0.65/0.047 alpha 2 PSME4 237180_at (I)/1 2.64E-36/4 All
Proteasome 0.6/0.011 Activator Subunit Male PTSD 4 0.79/0.0062
GABRB1 1557256_a_at (I)/1 .sup. 0.012/2 Male BP gamma- 0.74/0.0034
aminobutyric acid (GABA) A receptor, beta 1 CNP 1557943_at (D)/1
.sup. 0.019/2 2',3'-cyclic nucleotide 3'
phosphodiesterase RAP1A 202362_at (D)/1 .sup. 0.035/2 All RAP1A,
member 0.6/0.011 of RAS oncogene Male BP family 0.71/0.0082 NGFR
205858_at (I)/1 2.24E-15/4 All nerve growth 0.59/0.018 factor
receptor Male SZ/SZA 0.72/0.0020 CAMK2B 209956_s_at DE .sup.
0.00078/2 All calcium/calmodulin- (I)/1 0.62/0.0017 dependent Male
BP protein kinase II 0.74/0.0029 beta CLN5 214252_s_at DE
1.79E-15/4 All ceroid- (D)/1 0.65/0.0002 lipofuscinosis, Male
SZ/SZA neuronal 5 0.68/0.010 CLTA 216295_s_at DE 1.74E-15/4 All
clathrin, light (D)/1 0.64/0.0006 chain A Male BP 0.73/0.0049 DOCK8
232843_s_at DE .sup. 0.0022/2 All dedicator of (D)/1 0.6/0.0079
cytokinesis 8 Male BP 0.78/0.00078 RARS2 232902_s_at DE .sup.
0.022/2 All arginyl-tRNA (D)/1 0.63/0.0014 synthetase 2, Male
SZ/SZA mitochondrial 0.70/0.0043 PTK2 241453_at DE 2.87E-32/4 All
protein tyrosine (I)/1 0.61/0.0045 kinase 2 Male MDD 0.69/0.033
PLCL1 241859_at (D)/1 .sup. 0.040/2 Male PTSD phospholipase
0.78/0.0083 C-like 1 LPAR1 204038_s_at (D)/2 1.66E-04/2
lysophosphatidic acid receptor 1 AK2 205996_s_at (D)/2
0.00000011/4.sup. All adenylate kinase 0.64/0.0005 2 Male SZ/SZA
0.74/0.0012 APLP2 208703_s_at (D)/2 3.65E-02/2 amyloid beta (A4)
precursor- like protein 2 BACE1 224335_s_at (D/1 .sup. 0.00037/2
All beta-site APP- 0.58/0.032 cleaving enzyme Male BP 1 0.67/0.024
ELOVL5 214153_at (I)/1 .sup. 0.0028/2 Male PTSD ELOVL fatty
0.76/0.012 acid elongase 5 KIF2C 211519_s_at (D)/4 .sup. 0.014/2
kinesin family member 2C Significant Prediction of Future
Hospitalizations Drugs that for Suicidality Convergent Modulate the
Across All and Genetic and Brain Other Psychiatric Biomarker in
Best in a Diagnostic Evidence For and Related Opposite Gene Symbol
Group ROC AUC/ Involvement Disorders Direction Gene Name p-value in
Suicide Evidence to Suicide BCL2 Male 5 Aging Omega-3 B-cell PTSD
Alcoholism Lithium CLL/ 0.83/0.013 Anxiety BP lymphoma 2 Mood
Disorders PTSD SZ CD164 Male 4 BP Clozapine CD164 PTSD Cocaine
molecule, 0.96/0.0004 Dependence sialomucin Stress CD47 Male 4 MDD
Clozapine CD47 PTSD Stress Omega-3 molecule 0.87/0.0048 SZ DLG1
Male 4 Alcoholism Omega-3 discs, large PTSD BP homolog 1 309/0.0023
MDD (Drosophila) SZ DLG1 Male 4 Alcoholism Omega-3 discs, large
PTSD BP homolog 1 0.79/0.028 MDD (Drosophila) SZ DYRK2 Male 4 Aging
Clozapine dual-specificity PTSD BP tyrosine-(Y)- 0.93/0.001 MDD
phosphory- Sleep Disorders lation regulated kinase 2 ITGB1BP1 Male
4 Alzheimer's Disease Lithium integrin beta 1 PTSD BP binding
0.83/0.013 Mood Disorders protein 1 SZ APOE 6 Aggression Omega-3
apolipo- Aging protein E Alcoholism Alzheimer's Disease Autism
Dementia Depression-related Longevity MDD Psychosis PTSD SZ MRPS14
Male 4 SZ Omega-3 mitochondrial PTSD ribosomal protein 0.84/0.0093
S14 MRPS14 Male 4 SZ Omega-3 mitochondrial PTSD ribosomal protein
0.77/0.035 S14 IL6 Female 6 Aggression interleukin 6 PTSD Anxiety
1/0.028 BP Cognition Dementia Depression Longevity MDD Mood
Disorders Panic Psychosis PTSD Sleep Disorders Stress SZ AKAP13 All
4 Cocaine Clozapine A kinase (PRKA) 0.57/0.047 Dependence anchor
protein 13 Male PTSD Panic 0.80/0.022 Stress SECISBP2L Male 4
Cocaine Clozapine SECIS binding PTSD Dependence protein 2-like
0.89/0.0034 MDD SZ SOD2 Male 5 Longevity Clozapine superoxide PTSD
MDD dismutase 2, 0.85/0.010 Methamphetamine mitochondrial Abuse
Mood Disorders SZ LHFP Male 4 SZ Omega-3 lipoma HMGIC MDD fusion
partner 0.79/0.004 SKA2 Male 8 PTSD spindle and PTSD Stress
kinetochore 0.84/0.0093 associated complex subunit 2 GSK3B Male 6
Aging Lithium glycogen PTSD Alcoholism synthase kinase 3
0.84/0.0093 BP beta Dementia Depression Mood Stabilizers response
Lithium response MDD SZ ITPKB Male 4 Aging Omega-3 inositol- PTSD
Alcoholism trisphosphate 3- 0.87/0.0048 Alzheimer's Disease kinase
B Autism BP MDD Multiple Sclerosis Stress SZ SZA MTERF4 Male 4
Stress mitochondrial PTSD transcription 0.94/0.0006 termination
factor 4 GDI2 4 BP Clozapine GDP dissociation MDD inhibitor 2 Mood
Disorders SZ PRKAR1A Male 4 Alcoholism protein kinase, PTSD BP
cAMP- 0.90/0.0023 Epilepsy dependent, Mood Disorders regulator,
type I, Stress alpha SZ NR3C1 Male 5 Alcoholism Clozapine nuclear
receptor PTSD Anxiety subfamily 3, 0.91/0.0015 BP group C, member
Depression 1 (glucocorticoid Longevity receptor) MDD PTSD Response
to escitalopram (SSRI) Response to Nortriptyline (TCA) Stress SZ
ADK Male 0 Depression Omega-3 adenosine kinase PTSD 0.84/0.0093
PGK1 4 Alcoholism Clozapine phosphoglycerate BP kinase 1 MDD SZ SZA
ZFYVE21 All 4 SZ zinc finger, 0.58/0.030 FYVE domain Male MDD
containing 21 0.78/0.0044 RBM3 Female 4 Epilepsy Omega-3 RNA
binding PTSD Response to Lithium Lithium motif (RNP1, 1/0.028 SZ
RRM) protein 3 FAM107B Male 4 BP Lithium family with PTSD MDD
sequence 0.93/0.001 Psychosis similarity 107, Response to Lithium
member B Sleep Disorder SZ ECHDC1 Male 4 Addictions enoyl CoA PTSD
BP hydratase domain 0.94/0.0006 PTSD containing 1 TBL1XR1 Female 2
Alcoholism Clozapine transducin (beta)- PTSD BP 1 ike 1 X-linked
1/0.028 Longevity receptor 1 LONRF2 Male 5 Stress Omega-3 LON
peptidase PTSD BP N-terminal 0.77/0.039 domain and ring finger 2
QKI All 4 BP Omega-3 QKI, KH domain 0.58/0.031 Longevity
containing, RNA MDD binding PTSD Stress SZ YWHAH 4 Alcoholism
Omega-3 tyrosine 3- BP monooxygenase/ Longevity tryptophan 5- MDD
monooxygenase SZ
activation protein, eta SLC4A4 6 Circadian solute carrier
abnormalities family 4 (sodium Longevity bicarbonate MDD
cotransporter), SZ member 4 GDI2 4 BP Clozapine GDP dissociation
MDD inhibitor 2 Mood Disorders SZ UQCRC2 Male 4 ADHD Omega-3
ubiquinol- PTSD Alcohol cytochrome c 0.81/0.017 BP reductase core
MDD protein II Multiple Sclerosis SZ CTNNB1 Male 4 MDD Clozapine
catenin PTSD PTSD (cadherin- 0.80/0.022 Stress associated SZ
protein), beta 1, 88 kDa PSMB4 Male 4 BP proteasome PTSD MDD
(prosome, 0.80/0.022 SZ macropain) SZA subunit, beta type, 4 PRKACB
Male 4 Alcohol Clozapine protein kinase, PTSD Alzheimer's Disease
cAMP- 0.96/0.0004 BP dependent, Chronic Fatigue catalytic, beta
Syndrome LPAR1 4 Aging Clozapine lysophosphatidic BP Omega-3 acid
receptor 1 Longevity MDD Mood PTSD SZ HTR2C 6 Affective Disorder
Clozapine 5-hydroxy- Alcohol tryptamine Antipsychotics (serotonin)
BP receptor 2C, G MDD protein-coupled Mood Disorders Panic Disorder
SZ CTTN 4 BP Clozapine cortactin Effect of valproate Omega-3 MDD
Stress PDCL3 Male 5 Sleep Disorders phosducin-like 3 PTSD
0.80/0.022 SNX6 Male 4 Panic 0 sorting nexin 6 PTSD 0.86/0.0068
PIK3CA 4 Longevity Lithium phosphatidyl- MDD inositol-4,5- Stress
bisphosphate 3- SZ kinase, catalytic subunit alpha MBP 4 Alcohol
Clozapine myelin basic Alzheimer's Disease Omega-3 protein BP
Lithium MDD Mood Disorders SZ CCDC136 4 Psychosis Clozapine
coiled-coil domain containing 136 AIMP1 Male 4 aminoacyl tRNA PTSD
synthetase 0.93/0.001 complex- interacting multifunctional protein
1 PITHD1 Male BP PITH (C-terminal PTSD Psychosis proteasome-
0.87/0.0048 SZ interacting domain of thioredoxin-like) domain
containing 1 PCDH9 4 Aging Clozapine protocadherin 9 MDD Omega-3
Psychosis SZ CAPZA2 Male 4 BP capping protein PTSD MDD (actin
filament) 0.93/0.001 PTSD muscle Z-line, SZ alpha 2 PSME4 4 Autism
Proteasome Activator Subunit 4 GABRB1 4 Alcohol gamma- Autism
aminobutyric Mood Stabilizers acid (GABA) A BP receptor, beta 1 MDD
SZ SZA CNP Female 4 Alcohol Clozapine 2',3'-cyclic SZ/SZA Epilepsy
Omega-3 nucleotide 3' 1/0.029 MDD phosphodiesterase Multiple
Sclerosis Sleep Disorders SZ RAP1A Male 4 Longevity RAP1A, member
PTSD SZ of RAS oncogene 0.83/0.013 SZA family NGFR 4 MDD nerve
growth OCD factor receptor Panic Disorder SZ CAMK2B 4 Addictions
Clozapine calcium/calmodulin- BP dependent SZ protein kinase II
beta CLN5 Male 4 ceroid- PTSD lipofuscinosis, 0.84/0.0093 neuronal
5 CLTA 4 Alzheimer's Disease clathrin, light BP chain A MDD DOCK8
Male 4 ADHD dedicator of PTSD Longevity cytokinesis 8 0.76/0.044
RARS2 Male 4 PTSD arginyl-tRNA PTSD BP synthetase 2, 0.86/0.0068
mitochondrial PTK2 4 Alcohol 0 protein tyrosine Autism kinase 2 BP
Circadian abnormalities MDD Psychosis Stress SZ PLCL1 4 Alcohol
Clozapine phospholipase Psychosis C-like 1 SZ LPAR1 4 Aging
Clozapine lysophosphatidic BP Omega-3 acid receptor 1 Longevity MDD
Mood Disorders PTSD SZ AK2 2 BP adenylate kinase SZ 2 APLP2 4 BP
Lithium amyloid beta Depression Omega-3 (A4) precursor- Effect of
valproate like protein 2 Chronic Fatigue Syndrome BACE1 4
Alzheimer's Disease beta-site APP- Cocaine cleaving enzyme
Dependence 1 MDD Psychosis ELOVL5 3 Alcohol ELOVL fatty Autism acid
elongase 5 BP Circadian abnormalities Cocaine Dependence MDD Mood
Disorders KIF2C kinesin family member 2C
[0047] Particularly suitable subjects are humans. Suitable subjects
can also be experimental animals such as, for example, monkeys and
rodents, that display a behavioral phenotype associated with
suicide, for example, a mood disorder or psychosis. In one
particular aspect, the subject is a female human. In another
particular aspect, the subject is a male human.
[0048] In another aspect, the subject can further be diagnosed with
a psychiatric disorder as known in the art. In particular aspects,
the psychiatric disorder can be bipolar disorder, major depressive
disorder, schizophrenia, and schizoaffective disorder,
post-traumatic stress disorder, and combinations thereof.
[0049] In one embodiment, the subject can be diagnosed as having or
as suspected of having bipolar disorder (BP) and the biomarker can
be selected from DTNA; HS3ST3B1; CADM1; Unknown gene; KSR1; CD44;
DAPP1; OPRM1; SPTBN1; AKT1S1; SAT1; C20orf27; and combinations
thereof. As summarized in FIG. 17, the biomarker expression level
can increase above a reference expression level of the biomarker or
decrease below a reference expression level of the biomarker.
[0050] In another embodiment, the subject can be diagnosed as
having or as suspected of having depression (MDD) and the biomarker
can be selected from PHF20; EIF1B-AS1; TLN1; NUCKS1; DLK1; BBIP1;
BDNF; SKA2; IL10; GATM; PRPF40A; and combinations thereof. As
summarized in FIG. 17, the biomarker expression level can increase
above a reference expression level of the biomarker or decrease
below a reference expression level of the biomarker.
[0051] In another embodiment, the subject can be diagnosed as
having or as suspected of having schizoaffective disorder (SZA) and
the biomarker can be selected from USP48; NPRL3; TSPYL1; TMSB15B;
IL6; TNS1; TNF; S100B; JUN; BATF2; ANXA11; and combinations
thereof. As summarized in FIG. 17, the biomarker expression level
can increase above a reference expression level of the biomarker or
decrease below a reference expression level of the biomarker.
[0052] In another embodiment, the subject can be diagnosed as
having or as suspected of having schizophrenia (SZ) and the
biomarker can be selected from RP11-389C8.2; CYB561; LOC100128288;
CDDC163P; C1orf61; SKA2; BDNF; HTR2A; SLC5A3; ATP6V0E1; JUN;
LOC100131662; and combinations thereof. As summarized in FIG. 17,
the biomarker expression level can increase above a reference
expression level of the biomarker or decrease below a reference
expression level of the biomarker.
[0053] A particularly suitable sample for which the expression
level of a biomarker is determined can be, for example, blood,
including whole blood, serum, plasma, leukocytes, and
megakaryocytes.
[0054] The method can further include assessing mood, anxiety, and
other like psychiatric symptoms, and combinations thereof in the
subject using questionnaires and/or a computer-implemented method
for assessing mood, anxiety, other like psychiatric symptoms, and
combinations thereof. In one aspect, the method is implemented
using a first computer device coupled to a memory device, the
method comprising: receiving mood information, anxiety information,
and combinations thereof into the first computer device; storing,
by the first computer device, the mood information, anxiety
information, and combinations thereof in the memory device;
computing, by the first computer device, of the mood information,
anxiety information, and combinations thereof, a score that can be
used to predict suicidality; presenting, by the first computer
device, in visual form the mood information, anxiety information,
and combinations thereof to a second computer device; receiving a
request from the second computer device for access to the mood
information, anxiety information, and combinations thereof and
transmitting, by the first computer device, the mood information,
anxiety information, and combinations thereof to the second
computer device to assess mood, anxiety, and combinations thereof
in the subject. Suitable mood and anxiety information is described
herein in more detail below.
[0055] The method can further include assessing
socio-demographic/psychological suicidal risk factors in the
subject using a computer-implemented method for assessing
socio-demographic/psychological suicidal risk factors in the
subject, the method implemented using a first computer device
coupled to a memory device, the method comprising: receiving
socio-demographic/psychological suicidal risk factor information
into the first computer device; storing, by the first computer
device, the socio-demographic/psychological suicidal risk factor
information in the memory device; presenting, by the first computer
device, in visual form the socio-demographic/psychological suicidal
risk factor information to a second computer device; receiving a
request from the second computer device for access to
socio-demographic/psychological suicidal risk factor information;
and transmitting, by the first computer device, the
socio-demographic/psychological suicidal risk factor information to
the second computer device to assess the
socio-demographic/psychological suicidal risk factors in the
subject. Suitable socio-demographic/psychological suicidal risk
factors are described herein in more detail below.
[0056] In accordance with the present disclosure, biomarkers useful
for objectively predicting future hospitalization due to
suicidality in subjects have been discovered. In one aspect, the
present disclosure is directed to a method for future
hospitalization due to suicidality in a subject. The method
includes obtaining a first expression level of a blood biomarker in
an initial sample obtained from the subject; and determining a
second expression level of the blood biomarker in a subsequent
sample obtained from the subject, wherein an increase in the
expression level of the blood biomarker in the subsequent sample
obtained from the subject as compared to the expression level of
the initial sample indicates a higher risk of future
hospitalizations due to suicidality. In some embodiments, the
methods further include obtaining clinical risk factor information
and clinical scale data such as for anxiety, mood and/or psychosis
from the subject in addition to obtaining blood biomarker
expression level in a sample obtained from the subject.
[0057] Suitable biomarkers for predicting future hospitalization
due to suicidality in a subject wherein an increase in the
expression level of the blood biomarker occurs can be, for example,
the blood biomarker(s) set forth in Table 1.
[0058] In another embodiment, the expression level of the blood
biomarker in the sample obtained from the subject is increased as
compared to the reference expression level of the biomarker.
Suitable biomarkers that indicate a risk for future hospitalization
due to suicidality when the expression level increases in males as
compared to the reference expression level have been found to
include, for example, solute carrier family 4 (sodium bicarbonate
cotransporter), member 4 (SLC4A4), cell adhesion molecule 1 CADM1,
dystrobrevin, alpha (DTNA), spermidine/spermine
N1-acetyltransferase 1 (SAT1), interleukin 6 (interferon, beta 2)
(IL6) and combinations thereof. Suitable biomarkers that indicate a
risk for future hospitalization due to suicidality when the
expression level increases in females as compared to the reference
expression level have been found to include, for example,
erythrocyte membrane protein band 4.1 like 5 (EPB41L5), HtrA serine
peptidase 1 (HTRA1), deleted in primary ciliary dyskinesia homolog
(DPCD), general transcription factor IIIC, polypeptide 3, 102 kDa
(GTF3C3), period circadian clock 1 (PER1), pyridoxal-dependent
decarboxylase domain containing 1 (PDXDC1), kelch-like family
member 28 (KLHL28), ubiquitin interaction motif containing 1
(UIMC1), sorting nexin family member 27 (SNX27) and combinations
thereof.
[0059] In another embodiment, the expression level of the blood
biomarker in the sample obtained from the subject is decreased as
compared to the reference expression level of the biomarker.
Suitable biomarkers that indicate a risk for future hospitalization
due to suicidality when the expression level decreases in males as
compared to the reference expression level have been found to
include, for example, spindle and kinetochore associated complex
subunit 2 (SKA2), CAP-GLY domain containing linker protein family,
member 4 (CLIP4), kinesin family member 2C (KIF2C), kelch domain
containing 3 (KLHDC3) and combinations thereof. Suitable biomarkers
that indicate a risk for future hospitalization due to suicidality
when the expression level decreases in females as compared to the
reference expression level have been found to include, for example,
phosphatidylinositol 3-kinase, catalytic subunit type 3 (PIK3C3),
aldehyde dehydrogenase 3 family, member A2 (ALDH3A2), ARP3
actin-related protein 3 homolog (yeast) (ACTR3), B-cell CLL (BCL2),
MOB kinase activator 3B (MOB3B), casein kinase 1, alpha 1
(CSNK1A1), La ribonucleoprotein domain family, member 4 (LARP4),
zinc finger protein 548 (ZNF548) and combinations thereof.
[0060] Particularly suitable subjects are humans. Suitable subjects
can also be experimental animals such as, for example, monkeys and
rodents, that display a behavioral phenotype associated with
suicide, for example, a mood disorder or psychosis. In one
particular embodiment, the subject is a female human. In another
particular aspect, the subject is a male human.
[0061] In another aspect, the subject can further be diagnosed with
a psychiatric disorder. The psychiatric disorder can be bipolar
disorder, major depressive disorder, schizophrenia, and
schizoaffective disorder, post-traumatic stress disorder and
combinations thereof.
[0062] A particularly suitable sample for which the expression
level of a biomarker is determined can be, for example, blood,
including whole blood, serum, plasma, leukocytes, and
megakaryocytes.
[0063] Suitable biomarkers found to have a difference in expression
level include, for example, spermidine/spermine
N1-acetyltransferase 1 (SAT1), interleukin 6 (interferon beta 2)
(IL6), solute carrier family 4 (sodium bicarbonate cotransporter),
member 4 (SLC4A4), spindle and kinetochore associated complex
subunit 2 (SKA2), jun proto-oncogen (JUN), cell adhesion molecule 1
(CADM1), dystrobrevin alpha (DTNA), monoamine oxidase B (MAOB),
myristoylated alanine-rich protein kinase C substrate (MARCKS),
phosphatase and tensin homolog (PTEN), fatty acid desaturase 1
(FADS1), Rho GTPase activating protein 26 (ARHGAP26), B-cell
CLL/lymphoma 2 (BCL2), cadherin 4 type 1 R cadherin (retinal)
(CDH4), chemokine (C-X-C motif) ligand 11 (CXCL11), EMI domain
containing 1 (EMID1), family with sequence similarity 49 member B
(FAM49B), GRINUA complex locus (GCOM1), hippocalcin-like 1
(HPCAL1), mitogen-activated protein kinase 9 (MAPK9), nuclear
paraspeckle assembly transcript 1 (NEAT1), protein tyrosine kinase
2 (PTK2), RAS-like family 11 member B (RASL11B), small nucleolar
RNA H/ACA box 68 (SNORA68), superoxide dismutase 2 mitochondrial
(SOD2), transcription factor 7-like 2 (T-cell specific HMG-box)
(TCF7L2), v-raf murine sarcoma viral oncogene homolog (BRAF),
Chromosome 1 Open Reading Frame 61 (C1orf61), calreticulin (CALR),
calcium/calmodulin-dependent protein kinase II beta (CAMK2B),
caveolin 1 caveolae proein 22 kDa (CAV1), chromodomain helicase DNA
binding protein 2 (CHD2), cAMP responsive element modulators
(CREM), cortactin (CTTN), disheveled associated activator of
morphogenesis 2 (DAAM2), Dab mitogen responsive phosphoprotein
homolog 2 (DAB2), GABA(A) receptor associated protein like 1
(GABARAPL1), glutamate-ammonia ligase (GLUL), helicase with zinc
finger (HELZ), immunoglobulin heavy chain constant gamma 1 (IGHG1),
interleukin 1 beta (IL1B), jun B proto-oncogen (JUNB), lipoma HMGIC
fusion partner (LHFP), metallothionein 1 E (MT1E), metallothionein
1 H (MT1H), metallothionein 2 (MT2A), N-myc downstream regulated 1
(NDRG1), nucleobindin 2 (NUCB2), PHD finger protein 20-like 1
(PHF20L1), cysteine-rich protein with kazal motifs (RECK), shisa
family member 2 (SHISA2), transmembrane 4 L six family member 1
(TM4SF1), trophoblast glycoprotein (TPBG), tumor protein D52-like 1
(TPD52L1), TSC22 domain family member 3 (TSC22D3), vacuole membrane
protein 1 (VMP1), ZFP 36 ring finger protein (ZFP36), zink finger
FYVE domain containing 21 (ZHX2), histone cluster 1 H2bo
(HIST1H2BO), keratocan (KERA), transcription factor Dp-1 (TFDP1),
Single-Stranded DNA Binding Protein 2 (SSBP2), Transcription Factor
EC (TFEC), Diphosphoinositol Pentakisphosphate Kinase 1 (PPIP5K1),
Fibroblast Growth Factor Receptor 1 Oncogene Partner 2 (FGFR1OP2),
Zinc Finger MYND-Type Containing 8 (ZMYND8), Interferon Gamma
(IFNG), Brain-Derived Neurotrophic Factor (BDNF), cAMP Responsive
Element Binding Protein 1 (CREB1), Hes Family BHLH Transcription
Factor 1 (HES1), Ankyrin Repeat And MYND Domain Containing 1
(ANKMY1), Aldehyde Dehydrogenase 3 Family Member A2 (ALDH3A2),
Heparan Sulfate (Glucosamine) 3-O-Sulfotransferase 3B1 (HS3ST3B1),
Kinase Suppressor Of Ras 1 (KSR1), Dual Adaptor Of Phosphotyrosine
And 3-Phosphoinositides (DAPP1), Opioid Receptor Mu 1 (OPRM1),
Spectrin Beta Non-Erythrocytic 1 (SPTBN1), PHD Finger Protein 20
(PHF20), EIF1B Antisense RNA 1 (EIF1B-AS1), Talin 1 (TLN1), Nuclear
Casein Kinase And Cyclin-Dependent Kinase Substrate 1 (NUCKS1),
Delta-Like 1 Homolog (DLK1), BBSome Interacting Protein 1 (BBIP1),
Interleukin 10 (IL10), Glycine Amidinotransferase (GATM), PRP40
Pre-MRNA Processing Factor 40 Homolog A (PRPF40A), Ubiquitin
Specific Peptidase 48 (USP48), Nitrogen Permease Regulator-Like 3
(NPRL3), Testis-Specific Y-Encoded-Like Protein-Like 1 (TSPYL1),
thymosin beta 15B (TMSB15B), Minichromosome Maintenance Complex
Component 8 (MCM8), tensin 1 (TNS1), Tumor Necrosis Factor (TNF),
S100 Calcium Binding Protein B (S100B), Basic Leucine Zipper
Transcription Factor ATF-Like 2 (BATF2), Annexin A11 (ANX11),
RP11-389C8.2, Cytochrome B561 (CYB561), LOC100128288
(Uncharacterized LOC100128288), Coiled-Coil Domain Containing 163
Pseudogene (CCDC163P), 5-Hydroxytryptamine (Serotonin) Receptor 2A,
G Protein-Coupled (HTR2A), Annexin A11 (ANXA11), Uncharacterized
LOC100131662 (LOC100131662), Prolylcarboxypeptidase (Angiotensinase
C; PRCP), and combinations thereof. See, FIG. 9 for a list of
biomarkers identified as showing a difference in expression
level.
[0064] In another aspect, the present disclosure is directed to a
method for mitigating suicidality in a subject in need thereof. The
method includes: obtaining an expression level of a blood biomarker
in a sample obtained from the subject; obtaining a reference
expression level of the blood biomarker; identifying a difference
in the expression level of the blood biomarker in the sample as
compared to the reference expression level of the blood biomarker;
and administering a treatment, wherein the treatment reduces the
difference between the expression level of the blood biomarker in
the sample as compared to the reference expression level of the
blood biomarker to mitigate suicidality in the subject. As used
herein, "mitigate", "mitigating", and the like refer to making a
condition less severe and/or preventing a condition. More
particularly, the phrase "mitigate suicidality" refers to reducing
suicide ideation in a subject and/or preventing suicide
completion.
[0065] Suitable treatments can be a lifestyle modification,
administering a therapy, and combinations thereof.
[0066] Suitable therapy can be a nutritional, a drug and
psychotherapy.
[0067] Particularly suitable nutritionals can be omega-3 fatty
acids, including, by way of example, docosahexaenoic acid
(DHA).
[0068] Particularly suitable drugs include, for example, ketamine,
lithium, clozapine, selegeline, tocilizumab, siltuximab,
enkephalin, methionine, gevokizumab, gallium nitrate, vemurafenib,
dabrafenib, oblimersen, rasagiline,(-)-gossypol, navitoclax,
gemcitabine/paclitaxel, bortezomib/paclitaxel, ABT-199,
paclitaxel/trastuzumab, paclitaxel/pertuzumab/trastuzumab,
lapatinib/paclitaxel, doxorubicin/paclitaxel,
epirubicin/paclitaxel, paclitaxel/topotecan, paclitaxel,
canakinumab, tesevatinib, enzastaurin, fomepizole, miglitol,
anakinra, and combinations thereof. Other suitable drugs, as well
as biomarkers found to be changed in opposite direction in suicide
versus in treatments with omega-3 fatty acids, lithium, clozapine,
or antidepressants (MAOIs) as listed in Tables 4 & 5. These
biomarkers could potentially be used to stratify patients to
different treatment approaches, and monitor their responses.
TABLE-US-00004 TABLE 4 Top candidate biomarker genes - drugs that
modulate expression of these markers in the opposite direction in
male subjects Discovery (Change) Gene symbol/ Method/ Modulated by
Modulated by Modulated by Other Gene Name Score Omega-3 Lithium
Clozapine Drugs CCDC136 (D) (I) coiled-coil domain AP4 Mouse
VT.sup.356 containing 136 CD44 (D) (I) CD44 molecule (Indian DE2
Mouse Blood.sup.356 blood group) IL6 (I) (D) tocilizumab
interleukin 6 (interferon, AP2 Human Blood.sup.357 siltuximab beta
2) SAT1 (I) (D) spermidine/spermine N1- DE2 Mouse Blood.sup.358
acetyltransferase 1 DE1 MAOB (I) selegiline monoamine oxidase B DE1
ARHGAP26 (I) (D) Rho GTPase activating DE1 Mouse VT.sup.356 protein
26 BCL2 (D) (I) (I) B-cell CLL/lymphoma 2 DE1 Human Blood.sup.153
Rat Dentate gyrus Hippocampus.sup.359 EHBP1 (D) (I) VT.sup.356 EH
domain binding protein DE 4 1 FAM49B (I) (D) family with sequence
AP2 Mouse Blood.sup.358 similarity 49, member B HPCAL1 (I) (D)
hippocalcin-like 1 DE2 Mouse VT.sup.356 MAPK9 (I) (D)
mitogen-activated protein DE2 Mouse VT.sup.356 kinase 9 NEAT1 (I)
(D) nuclear paraspeckle DE2 Mouse VT.sup.356 assembly transcript 1
(non- protein coding) RASL11B (I) (D) RAS-like, family 11, AP2
Mouse Caudate member B putamen.sup.356 TRAK2 (D) (I) (I)
trafficking protein, kinesin DE2 Mouse Blood.sup.358 Mouse
PFC.sup.360 binding 2 ADRBK1 adrenergic, beta, (D) (I) receptor
kinase 1 DE1 Mouse PEC.sup.361 BRAE (I) Vemurafenib v-raf murine
sarcoma viral DE1 Dabrafenib oncogene homolog B CAMK2B (I) (D)
calcium/calmodulin- DE1 Mouse striatum.sup.362 dependent protein
kinase II beta CNP (D) (I) (I) 2',3'-cyclic nucleotide 3' AP1 Mouse
Hippocampus.sup.358 Mouse AMY.sup.356 phosphodiesterase CTTN
cortactin (I) (D) (D) DE1 Mouse Blood.sup.358 Mouse VT.sup.356 G2E3
(D) (I) G2/M-phase specific E3 AP1 Mouse Hippocampus.sup.358
ubiquitin protein ligase GABARAPL1 GABA(A) (I) (D)
receptor-associated protein DE1 Mouse Blood.sup.358 like 1 HELZ
helicase with zinc (I) (D) finger DE1 Mouse Blood.sup.358 IL1B (I)
(D) canakinumab interleukin 1, beta DE1 Mouse Blood.sup.358
gevokizumab gallium nitrate LHFP lipoma HMGIC (I) (D) fusion
partner DE1 Mouse Blood.sup.358 LPAR1 lysophosphatidic (D) (I) (I)
acid receptor 1 AP1 Mouse Hippocampus, Mouse AMY.sup.356
Blood.sup.358 MBP myelin basic protein (D) (I) (I) (I) AP1 Mouse
Blood.sup.358 Oligodendrocy Mouse AMY and tes.sup.363 Blood.sup.356
Mouse Brain.sup.360 MEF2C myocyte enhancer (D) (I) factor 2C DE1
Mouse Hippocampus and VT.sup.356 NDRG1 (I) (D) N-myc downstream DE1
Mouse Blood.sup.358 regulated 1 OGFR (D) enkephalin opioid growth
factor DE1 methionine receptor PCDH9 protocadherin 9 (D) (I) AP1
Mouse VT.sup.356 PHF20L1 (I) (D) (D) PHD finger protein 20-like 1
DE1 Mouse Blood.sup.358 Mouse Hippocampus.sup.356 PRKCB protein
kinase C, (D) (I) beta DE1 Mouse PEC.sup.360 AP1 AMY.sup.364 RBMX
RNA binding motif (D) (I) protein, X-linked DE1 Mouse NAC,
Blood.sup.358 RNASEL ribonuclease L (D) (I)
(2',5'-oligoisoadenylate AP1 Mouse Blood.sup.358
synthetase-dependent) SNAP23 synaptosomal- (D) (I) associated
protein, 23 kDa AP1 Mouse Blood.sup.356 TM4SF1 transmembrane 4 (I)
(D) L six family member 1 DE1 Mouse Blood.sup.358 TSPAN33
tetraspanin 33 (D) (I) (I) AP1 Mouse Blood.sup.358 Mouse VT.sup.356
VMP1 (I) (D) vacuole membrane protein 1 DE1 Mouse Blood.sup.358
ZFP36 (I) (D) (D) ZFP36 ring finger protein DE1 Mouse Blood.sup.358
Rat Brain.sup.365 BTBD3 (I) (D) BTB (POZ) domain DE 4 Mouse
AMY.sup.358 containing 3 CADM1 (I) (D) cell adhesion molecule 1 DE4
Mouse VT.sup.356 CTBS (I) (D) chitobiase, di-N-acetyl- DE 4
VT.sup.356 LAMB1 (I) (D) laminin, beta 1 AP4 Mouse HIP.sup.358 PLEC
(D) (I) plectin DE 4 Mouse VT.sup.356 RAD23A (D) (I) RAD23 homolog
A DE 4 Mouse Blood.sup.358 (S. cerevisiae) SETD8 (D) (I) SET domain
containing DE 4 Mouse Blood.sup.358 (lysine methyltransferase) 8
TXNRD2 (D) (I) thioredoxin reductase 2 AP4 Mouse Blood.sup.356 (I):
increase in biomarker expression; (D): decrease in biomarker
expression
TABLE-US-00005 TABLE 5 Top candidate biomarker genes - drugs that
modulate expression of these markers in the opposite direction in
female subjects Discovery (Change) Gene Symbol/ Method/ Modulated
by Modulated by Modulated by Gene Name Score Omega-3 Lithium
Clozapine Other Drugs Out of Validated Biomarkers (Bonferroni) (49
genes, 50 probesets) BCL2 (D) (I) (I) oblimersen, rasagiline, (-)-
B-cell CLL DE/2 FC Hip gossypol, navitoclax, (Chen, Zeng et al.
(Bai, Zhang et al. gemcitabine/paclitaxel, 1999) 2004)
bortezomib/paclitaxel, ABT-199, (I) paclitaxel/trastuzumab,
cerebellar granule paclitaxel/pertuzumab/trastuzumab, cells
lapatinib/paclitaxel, (Chen and Chuang doxorubicin/paclitaxel,
1999) epirubicin/paclitaxel, (I) paclitaxel/topotecan, paclitaxel
Human Blood (Lowthert, Leffert et al. 2012) (I) Astrocyte
(Keshavarz, Emamghoreishi et al. 2013) (I) HIP (Chen, Rajkowska et
al. 2000) (I) Dentate gyrus, HIP(Hammonds and Shim 2009) GSK3B (D)
(I) enzastaurin glycogen synthase DE/1 FC (Fatemi, kinase 3 beta
Reutiman et al. 2009) CAT (D) Oxidative Stress BP fomepizole
catalase DE/2 (I) Plasma (de Sousa, Zarate et al. 2014) JUN (I) (D)
(D) jun proto-oncogene DE/2 leukocytes FC DE/1 (Watanabe, Iga et
al. (MacDonald, Eaton et 2014) al. 2005) MOB3B (D) (I) MOB kinase
activator DE/1 PFC (females) (Le- 3B Niculescu, Case et al. 2011)
NDRG1 (I) (D) N-myc downstream DE/1 Blood(Le-Niculescu, regulated 1
Case et al. 2011) SPON1 (D) (I) spondin 1, DE/1 VT extracellular
matrix (Le-Niculescu, protein Balaraman et al. 2007) FOXP1 (I) (D)
forkhead box P1 DE/4 Blood(Le-Niculescu, Case et al. 2011) HAVCR2
(I) (D) hepatitis A virus DE/4 PFC cellular receptor 2
(Jakovcevski, Bharadwaj et al. 2013) GJA1 (I) (D) (D) gap junction
protein, DE/1 HIP (females) (Le- VT alpha 1, 43 kDa Niculescu, Case
et al. (Le-Niculescu, 2011) Balaraman et al. 2007) CD84 (D) (I)
CD84 molecule DE/2 Blood (Le-Niculescu, Balaraman et al. 2007)
DCAF5 (D) (I) DDB1 and CUL4 DE/2 VT associated factor 5
(Le-Niculescu, Balaraman et al. 2007) GANC (D) miglitol
glucosidase, alpha; DE/2 neutral C IL1R1 (I) anakinra interleukin 1
receptor, AP/1 type I INPP4A (D) (I) inositol polyphosphate- DE/1
VT 4-phosphatase, type I, (Le-Niculescu, 107 kDa Balaraman et al.
2007) JRK (D) (I) Jrk homolog (mouse) AP/2 Brain(Hammamieh,
Chakraborty et al. 2014) PDXDC1 (I) (D) pyridoxal-dependent DE/2 VT
decarboxylase domain (Le-Niculescu, containing 1 Balaraman et al.
2007) SMARCA2 (D) (I) SWI DE/1 HIP (males) (Le- Niculescu, Case et
al. 2011) Out of Top Discovery and Prioritization Biomarkers(Non
Bonferroni Validated, 65 genes) CLTA (I) (D) clathrin, light chain
A DE/4 FC (MacDonald, Eaton et al. 2005) PPM1B (D) (I) protein
phosphatase, DE/4 VT Mg2+ (Le-Niculescu, Balaraman et al. 2007)
AFF3 (I) (D) AF4/FMR2 family, AP/4; Blood (Le- member 3 (I)
Niculescu, Case et al. DE/1 2011) WAC (D) (I) WW domain DE/4 VT
containing adaptor (Le-Niculescu, with coiled-coil Balaraman et al.
2007) AKT3 (I) enzastaurin v-akt murine thymoma AP/4 viral oncogene
homolog 3 ARID4B (D) (I) AT rich interactive DE/4 HIP (males) (Le-
domain 4B (RBP1- Niculescu, Case et al. like) 2011) ATXN1 (I) (D)
ataxin 1 DE/4 Blood(Le-Niculescu, Case et al. 2011) BRE (I) (D)
Brain and AP/4 VT reproductive organ- (Le-Niculescu, expressed
(TNFRSF1A Balaraman et al. 2007) modulator) CSNK1A1 (D) (I) casein
kinase 1, alpha DE/4 Blood(Le-Niculescu, 1 Case et al. 2011) ENTPD1
(I) (D) (D) ectonucleoside AP/4 Blood(Le-Niculescu, PFC
triphosphate Case et al. 2011) (Jakovcevski, diphosphohydrolase 1
Bharadwaj et al. 2013) EPHB4 (I) tesevatinib EPH receptor B4 DE/4
ETNK1 (D) (I) ethanolamine kinase 1 AP/4 PFC (males)(Le- Niculescu,
Case et al. 2011) ITIH5 (I) (D) (D) inter-alpha-trypsin AP/4 PFC
inhibitor heavy chain Blood(Le-Niculescu, (Jakovcevski, family,
member 5 Case et al. 2011) Bharadwaj et al. 2013) LARP4 (D) (I) La
ribonucleoprotein DE/4 VT domain family, (Le-Niculescu, member 4
Balaraman et al. 2007) MBNL1 (D) (I) (I) muscleblind-like DE/4 HIP
(males) (Le- Blood splicing regulator 1 Niculescu, Case et al.
(Le-Niculescu, 2011) Balaraman et al. 2007) MR1 (I) Anti-Lymphocyte
serum major DE/4 histocompatibility complex, class I- related PRDX3
(D) (I) peroxiredoxin 3 DE/4 Blood(Le-Niculescu, Case et al. 2011)
RAB22A (D) (I) RAB22A, member DE/4 Blood RAS oncogene family
(Le-Niculescu, Balaraman et al. 2007) SNX27 (I) (D) sorting nexin
family AP/4 AMY member 27 (Le-Niculescu, Balaraman et al. 2007)
SSBP2 (I) (D) (D) single-stranded DNA AP/4 Blood(Le-Niculescu, VT
binding protein 2 Case et al. 2011) (Le-Niculescu, Balaraman et al.
2007) WAPAL (D) (I) (I) wings apart-like DE/4 SK-N-AS cells VT
homolog (Drosophila) (ATCC derived from (Le-Niculescu, a human
Balaraman et al. 2007) neuroblastoma cell (Seelan, Khalyfa et al.
2008) (I): increase in biomarker expression; (D): decrease in
biomarker expression
[0069] More particularly, it has been found that BCL2, JUN, GHA1,
ENTPD1, ITIH5, MBNL1, and SSBP2 are changed in expression by the
above listed treatments, and in particular therapies such as
nutritionals and drugs, suggesting these biomarkers may be core to
the anti-suicidal mechanism of these drugs. Further, BCL2, CAT, and
JUN may be useful blood pharmacogenomic markers of response to
lithium. CD84, MBNL1, and RAB22A may be useful blood
pharmacogenomic markers of response to clozapine. NDRG1, FOXP1,
AFF3, ATXN1, CSNK1A1, ENTPD1, ITIH5, PRDX3, and SSBP2 may be useful
blood pharmacogenomic markers of response to omega-3 fatty acids.
Three existing drugs, used for other indications, have been
identified as targeting the top suicide biomarkers identified in
the present disclosure, and could potentially be re-purposed for
testing in treatment of acute suicidality: anakinra (inhibiting
ILR1), enzastaurin (inhibiting AKT3), and tesevatinib (inhibiting
EPHB4). Additionally, Connectivity Map analyses (FIGS. 34A-34C)
identified novel compounds that induce gene expression signatures
that are the opposite of those present in suicide, and might
generate leads and/or be tested for use to treat/prevent
suicidality: betulin (an anti-cancer compound from the bark of
birch trees), zalcitabine (an anti-HIV drug), and atractyloside (a
toxic glycoside). Other common drugs identified by the Connectivity
Map analyses are nafcillin, lansoprazole, mifepristone, LY294002,
minoxidil, acetysalicilic acid, estradiol, buspirone,
dicloxacillin, corticosterone, metformin, diphenhydramine,
haloperidol, metaraminol, yohimbine, trimethadione and fluoxetine
(see also Table 6, 7, and 8).
TABLE-US-00006 TABLE 6 Therapeutic Compounds for Suicidality across
Gender Therapeutic compound/Drug Score* fluoxetine -0.812 betulin
-0.812 dl-alpha tocopherol -0.821 haloperidol -0.823 hesperidin
-0.824 calcium folinate -0.825 harpagoside -0.826 trimipramine
-0.836 rilmenidine -0.845 tenoxicam -0.851 chlorpromazine -0.852
harman -0.858 homatropine -0.863 ramifenazone -0.864 clozapine
-0.866 diphenhydramine -0.873 prochlorperazine -0.874 pirenperone
-0.876 asiaticoside -0.886 adiphenine -0.923 verapamil -0.922
metaraminol -0.936 vohimbine -0.958 metformin -0.983 trimethadione
-1 chlorogenic acid -1 *Score of -1 means maximum opposite
effect.
TABLE-US-00007 TABLE 7 Therapeutic Compounds for Suicidality in Men
Therapeutic compound/drug Score* thiamine -0.778 homatropine -0.789
vitexin -0.794 ergocalciferol -0.801 tropicamide -0.801
(-)-atenolol -0.817 betulin -0.905 spaglumic acid -1 *Score of -1
means maximum opposite effect.
TABLE-US-00008 TABLE 8 Therapeutic Compounds for Suicidality in
Women Therapeutic compound/drug Score* mifepristone -0.797
lansoprazole -0.888 nafcillin -0.895 betulin -1 *Score of -1 means
maximum opposite effect.
[0070] In another aspect, the subject can further be diagnosed with
a psychiatric disorder. The psychiatric disorder can be any
psychiatric disorder known in the art, including, for example,
bipolar disorder, major depressive disorder, schizophrenia, and
schizoaffective disorder, post-traumatic stress disorder, and
combinations thereof.
[0071] In another aspect, the present disclosure is directed to a
questionnaire and/or a computer-implemented method for assessing
mood, anxiety, and combinations thereof in the subject using a
computer-implemented method for assessing mood, anxiety, and the
like, and combinations thereof. In one aspect, the method is
implemented using a computer device coupled to a memory device. The
method implemented using a first computer device coupled to a
memory device includes receiving mood information, anxiety
information, and combinations thereof into the first computer
device; storing, by the first computer device, the mood
information, anxiety information, and combinations thereof in the
memory device; presenting, by the first computer device, in visual
form the mood information, anxiety information, and combinations
thereof to a second computer device; receiving a request from the
second computer device for access to the mood information, anxiety
information, and combinations thereof and transmitting, by the
first computer device, the mood information, anxiety information,
and combinations thereof to the second computer device to assess
mood, anxiety, and combinations thereof in the subject.
[0072] Mood information includes information relating to a
subject's mood, motivation, movement, thinking, self-esteem,
interest, appetite, and combinations thereof Anxiety information
includes information relating to a subjects anxiety, uncertainty,
fear, anger, and combinations thereof. Particular mood and anxiety
information assessed can include: determining how good is the
subject's mood; determining the subject's motivation, drive,
determination to do things right now; determining how high is the
subject's physical energy and the amount of moving about that the
subject feels like doing right now; determining how high is the
subject's mental energy and thinking activity going on in the
subject's mind right now; determining how good the subject feels
about himself/herself and his/her accomplishments right now;
determining how high the subject's interest to do things that are
fun and enjoyable right now; determining how high the subjects
appetite and desire for food is right now; determining how anxious
the subject is right now; determining how uncertain about things
the subject is right now; determining how frightened about things
the subject feels right now; determining how angry about things the
subject feels right now; determining events or actions the subject
thinks are influencing how the subject feels right now; determining
additional feelings the subject has right now; and combinations
thereof. As illustrated in FIG. 6, the mood and anxiety information
can be assessed by having the subject rate each piece of
information on a scale of lowest to highest.
[0073] The subject of the method can further be diagnosed as having
a psychiatric disorder selected from bipolar disorder, major
depressive disorder, schizophrenia, and schizoaffective disorder,
post-traumatic stress disorder, and combinations thereof.
[0074] In another aspect, the present disclosure is directed to a
computer-implemented method for assessing
socio-demographic/psychological suicidal risk factors in the
subject using a computer-implemented method for assessing
socio-demographic/psychological suicidal risk factors in the
subject, the method implemented using a computer device coupled to
a memory device. The method includes: receiving
socio-demographic/psychological suicidal risk factor information
into the first computer device; storing, by the first computer
device, the socio-demographic/psychological suicidal risk factor
information in the memory device; presenting, by the first computer
device, in visual form the socio-demographic/psychological suicidal
risk factor information to a second computer device; receiving a
request from the second computer device for access to
socio-demographic/psychological suicidal risk factor information;
and transmitting, by the first computer device, the
socio-demographic/psychological suicidal risk factor information to
the second computer device to assess the
socio-demographic/psychological suicidal risk factors in the
subject.
[0075] Socio-demographic and clinical risk factors for suicide
includes items for assessing the influence of mental health
factors, as well as of life satisfaction, physical health,
environmental stress, addictions, cultural factors known to
influence suicidal behavior, and two demographic factors, age and
gender. Socio-demographic/psychological suicidal risk factors
assessed can include: lack of coping skills when faced with stress;
dissatisfaction with current life; lack of hope for the future;
current substance abuse; acute loss/grief; psychiatric illness
diagnosed and treated; poor treatment compliance; family history of
suicide in blood relatives; personally knowing somebody who
committed suicide; history of abuse (such as physical abuse, sexual
abuse, emotional abuse, and neglect); acute/severe medical illness
(including acute pain); chronic stress (including perceived
uselessness, not feeling needed, and burden to extended kin);
history of excessive introversion/conscientiousness (including
planned suicide attempts); past history of suicidal acts/gestures;
lack of religious beliefs; rejection; lack of positive
relationships/social isolation; history of excessive extroversion
and impulsive behavior (including rage, anger, physical fights and
seeking revenge); lack of children/not in touch with children/not
helping care for children; history of command hallucinations of
self-directed violence; age (older than 60 years or younger than 25
years); gender; and combinations thereof.
[0076] The socio-demographic/psychological suicidal risk factors
can be assessed by having the subject provide an answer to the
above factors such as a yes answer, a no answer and a not
applicable answer.
[0077] The subject of the method can further be diagnosed as having
a psychiatric disorder selected from bipolar disorder, major
depressive disorder, schizophrenia, and schizoaffective disorder,
post-traumatic stress disorder, and combinations thereof.
[0078] In another aspect, the present disclosure is directed to a
method for predicting suicidality in a subject. The method
includes: identifying a difference in the expression level of a
blood biomarker in a sample obtained from a subject and a reference
expression level of the blood biomarker by obtaining the expression
level of the blood biomarker in a sample obtained from a subject;
obtaining a reference expression level of a blood biomarker;
analyzing the blood biomarker in the sample obtained from the
subject and the reference expression level of the blood biomarker
to detect the difference between the blood biomarker in the sample
and the reference expression level of the blood biomarker;
assessing mood, anxiety, and combinations thereof, using a first
computer device coupled to a memory device, wherein the first
computer device receives mood information, anxiety information, and
combinations thereof into the first computer device; storing, by
the first computer device, the mood information, anxiety
information, and combinations thereof in the memory device;
presenting, by the first computer device, in visual form the mood
information, anxiety information, and combinations thereof to a
second computer device; receiving a request from the second
computer device for access to the mood information, anxiety
information, and combinations thereof; and transmitting, by the
first computer device, the mood information, anxiety information,
and combinations thereof to the second computer device to assess
mood, anxiety, and combinations thereof in the subject; assessing
socio-demographic/psychological suicidal risk factors in the
subject using the first computer device coupled to a memory device,
wherein the first computer device receives
socio-demographic/psychological suicidal risk factor information
into the first computer device; storing, by the first computer
device, the socio-demographic/psychological suicidal risk factor
information in the memory device; presenting, by the first computer
device, in visual form the socio-demographic/psychological suicidal
risk factor information to the second computer device; receiving a
request from the second computer device for access to
socio-demographic/psychological suicidal risk factor information;
and transmitting, by the first computer device, the
socio-demographic/psychological suicidal risk factor information to
the second computer device to assess the
socio-demographic/psychological suicidal risk factors in the
subject; and predicting suicidality in the subject by the
combination of the difference between the expression level of the
biomarker in the subject and the reference expression level of the
blood biomarker; the assessment of mood, anxiety, and combinations
thereof; and the assessment of socio-demographic/psychological
suicidal risk factor information.
[0079] As used herein, while the methods are described as using a
first and second computer device, it should be understood that more
or less than two computer devices may be used to perform the
methods of the present disclosure. Particularly, three computer
devices, or four computer devices or even five or more computer
devices can be used to perform the methods without departing from
the scope of the present disclosure.
[0080] In one aspect, the present disclosure is directed to a
method for predicting future hospitalization of a subject due to
suicidality. The method includes: identifying a difference in the
expression level of a blood biomarker in a sample obtained from a
subject and a reference expression level of the blood biomarker by
obtaining the expression level of the blood biomarker in a sample
obtained from a subject; obtaining a reference expression level of
a blood biomarker; analyzing the blood biomarker in the sample
obtained from the subject and the reference expression level of the
blood biomarker to detect the difference between the blood
biomarker in the sample and the reference expression level of the
blood biomarker; assessing mood, anxiety, and combinations thereof,
using a first computer device coupled to a memory device, wherein
the first computer device receives mood information, anxiety
information, and combinations thereof into the first computer
device; storing, by the first computer device, the mood
information, anxiety information, and combinations thereof in the
memory device; presenting, by the first computer device, in visual
form the mood information, anxiety information, and combinations
thereof to a second computer device; receiving a request from the
second computer device for access to the mood information, anxiety
information, and combinations thereof; and transmitting, by the
first computer device, the mood information, anxiety information,
and combinations thereof to the second computer device to assess
mood, anxiety, and combinations thereof in the subject; assessing
socio-demographic/psychological suicidal risk factors in the
subject using the first computer device coupled to a memory device,
wherein the first computer device receives
socio-demographic/psychological suicidal risk factor information
into the first computer device; storing, by the first computer
device, the socio-demographic/psychological suicidal risk factor
information in the memory device; presenting, by the first computer
device, in visual form the socio-demographic/psychological suicidal
risk factor information to a second computer device; receiving a
request from the second computer device for access to
socio-demographic/psychological suicidal risk factor information;
and transmitting, by the first computer device, the
socio-demographic/psychological suicidal risk factor information to
the second computer device to assess the
socio-demographic/psychological suicidal risk factors in the
subject; and predicting future hospitalization of the subject due
to suicidality by the combination of the difference between the
expression level of the biomarker in the subject and the reference
expression level of the blood biomarker; the assessment of mood,
anxiety, and combinations thereof; and the assessment of
socio-demographic/psychological suicidal risk factor
information.
[0081] Suitable biomarkers for use in the method for predicting
suicide ideation in a subject and the method for predicting future
hospitalization a subject due to suicidality include those
described herein.
[0082] Mood information for use in the method for predicting
suicide ideation in a subject and the method for predicting future
hospitalization of a subject due to suicidality includes
information relating to a subject's mood, motivation, movement,
thinking, self-esteem, interest, appetite, and combinations thereof
as described herein. Anxiety information includes information
relating to a subjects anxiety, uncertainty, fear, anger, and
combinations thereof as described herein.
[0083] Socio-demographic and clinical risk factors for suicide for
use in the method for predicting suicide ideation in a subject and
the method for predicting future hospitalization of a subject due
to suicidality include items for assessing the influence of mental
health factors, as well as of life satisfaction, physical health,
environmental stress, addictions, cultural factors known to
influence suicidal behavior, and two demographic factors, age and
gender as described herein.
EXAMPLES
[0084] Methods
[0085] Human Blood Gene Expression Experiments and Analyses
[0086] RNA extraction. Whole blood (2.5-5 ml) was collected into
each PaxGene tube by routine venipuncture. PaxGene tubes contain
proprietary reagents for the stabilization of RNA. RNA was
extracted and processed.
[0087] Microarrays. Biotin-labeled aRNAs were hybridized to
Affymetrix HG-U133 Plus 2.0 GeneChips (Affymetrix; with over 40 000
genes and expressed sequence tags), according to the manufacturer's
protocols. Arrays were stained using standard Affymetrix protocols
for antibody signal amplification and scanned on an Affymetrix
GeneArray 2500 scanner with a target intensity set at 250.
Quality-control measures, including 30/50 ratios for glyceraldehyde
3-phosphate dehydrogenase and .beta.-actin, scale factors,
background and Q-values, were within acceptable limits.
[0088] Analysis. The participant's SI scores at the time of blood
collection (0--no suicidal ideation (SI) compared with 2 and
above--high SI) were used. Gene expression differences between the
no SI and the high SI visits were analyzed using a
within-participant design, then an across-participants summation
(FIGS. 1C and 10C).
[0089] Gene Expression Analysis in the Discovery Cohort
[0090] Data was analyzed in two ways: an Absent-Present (AP)
approach and a differential expression (DE) approach. The AP
approach may capture turning on and off of genes, and the DE
approach may capture gradual changes in expression. For the AP
approach, Affymetrix Microarray Suite Version 5.0 (MASS) was used
to generate Absent (A), Marginal (M), or Present (P) calls for each
probe set on the chip (Affymetrix U133 Plus 2.0 GeneChips) for all
participants in the discovery cohort. For the DE approach, all
Affymetrix microarray data was imported as Cel. files into Partek
Genomic Suites 6.6 software package (Partek Incorporated, St Louis,
Mo., USA). Using only the perfect match values, a robust
multi-array analysis (RMA) was run, background corrected with
quantile normalization and a median polish probe set summarization,
to obtain the normalized expression levels of all probe sets for
each chip. RMA was performed independently for each of the 6
diagnoses used in the study, to avoid potential artefacts due to
different ranges of gene expression in different diagnoses
(Niculescu et al. MP 2015). Then the participants' normalized data
was extracted from these RMA and assembled for the different
cohorts used in the Example.
[0091] A/P analysis. For the longitudinal within participant AP
analysis, comparisons were made within participant between
sequential visits to identify changes in gene expression from
Absent to Present that track changes in phene expression (suicidal
ideation, "SI") from No SI to High SI. For a comparison, if there
was a change from A to P tracking a change from No SI to High SI,
or a change from P to A tracking a change from High SI to No SI,
that was given a score of +1 (increased biomarker in High SI). If
the change was in opposite direction in the gene vs the phene (SI),
that was given a score of -1 (decreased biomarker in High SI). If
there was no change in gene expression between visits, despite a
change of phene expression (suicidal ideation), or a change in gene
expression between visits, despite no change in phene expression
(suicidal ideation), that was given a score of 0 (not tracking as a
biomarker). If there was no change in gene expression and no change
in suicidal ideation between visits, that was given a score of +1
if there was concordance (P-P with High SI-High SI, or A-A with No
SI-No SI), or a score of -1 if there was the opposite (A-A with
High SI-High SI, or P-P with No SI-No SI). If the changes were to M
(moderate) instead of P, the values used were 0.5 or -0.5. These
values were then summed up across the comparisons in each
participant, resulting in a participant score for each
gene/probeset in each participant. A perfection bonus was also
used. If the gene expression perfectly tracked the suicidal
ideation in a participant that had at least two comparisons (3
visits), that probe set was rewarded by a doubling of its
participant score. Additionally, a non-tracking correction was
used. If there was no change in gene expression in any of the
comparisons for a particular participant, that overall participant
score for that probe set in that participant was zero.
[0092] DE analysis. For the longitudinal within participant DE
analysis, fold changes (FC) in gene expression were calculated
between sequential visits within each participant. Scoring
methodology was similar to that used above for AP. Probe sets that
had a FC.gtoreq.1.2 were scored+1 (increased in High SI) or -1
(decreased in High SI). FC.gtoreq.1.1 were scored+0.5 or -0.5. FC
lower than 1.1 were considered no change. The only difference
between the DE and the AP analyses was when scoring comparisons
where there was no phene expression (SI) change between visits and
no change in gene expression between visits (FC lower than 1.1). In
that case, the comparison received the same score as the nearest
preceding comparison where there was a change in SI from visit to
visit. If no preceding comparison with a change in SI was
available, then it was given the same score as the nearest
subsequent comparison where there was a change in SI. Also for DE,
a perfection bonus and a non-tracking correction was used. If the
gene expression perfectly tracked the suicidal ideation in a
participant who had at least two comparisons (3 visits), that probe
set was rewarded by a doubling of its score. If there was no change
in gene expression in any of the comparisons for a particular
participant, that overall participant score for that probe set in
that participant was zero.
[0093] Internal score. Once scores within each participant were
calculated, an algebraic sum across all participants was obtained
for each probe set. Probe sets were then given internal CFG points
based upon these algebraic sum scores. Probe sets with scores above
the 33% of the distribution (for increased probe sets and decreased
probe sets) received 1 point, those above 50% of the distribution
received 2 points, and those above 80% of the distribution received
4 points.
[0094] In Example 1, for AP analyses, 23 probe sets received 4
points, 581 probe sets received 2 points, and 2077 probe sets
received 1 point, for a total of 2681 probe sets. For DE analyses,
31 probe sets received 4 points, 1294 probe sets received 2 points,
and 5839 probe sets received 1 point, for a total of 7164 probe
sets. The overlap between the two discovery methods is shown in
FIG. 2A. For Example 2, for AP analyses, 30 probesets received 4
points, 647 probesets with 2 points, and 2596 probesets with 1
point, for a total of 3273 probesets. For DE analyses, 95 probesets
received 4 points, 2215 probesets with 2 points, and 7520 probesets
with 1 point, for a total of 9829 probesets. The overlap between
the two discovery methods for probesets with an internal score of 1
is shown in FIG. 11A.
[0095] Different probe sets may be found by the two methods due to
differences in scope (DE capturing genes that were present in both
visits of a comparison (i.e. PP, but are changed in expression),
thresholds (what makes the 33% change cutoff across participants
varies between methods), and technical detection levels (what is
considered in the noise range varies between the methods).
[0096] In total, 9413 probe sets were identified with an internal
CFG score of 1. Gene names for the probe sets were identified using
NetAffyx (Affymetrix) and Partek for Affymetrix HG-U133 Plus 2.0
GeneChips, followed by GeneCards to confirm the primary gene
symbol. In addition, for those probe sets that were not assigned a
gene name by NetAffyx or Partek, the UCSC Genome Browser was used
to directly map them to known genes, with the following
limitations. In case the probe set fell in an intron, that
particular gene was assumed to be implicated. Only one gene was
assigned to each probe set. Genes were then scored using manually
curated CFG databases as described below (FIGS. 2C and 11C).
[0097] Convergent Functional Genomics
[0098] Databases. Manually curated databases of all the human gene
expression (postmortem brain, blood and cell cultures), human
genetics (association, copy number variations and linkage), and
animal model gene expression and genetic studies published to date
on psychiatric disorders was established (Laboratory of
Neurophenomics, Indiana University School of Medicine,
www.neurophenomics.info). The databases include only primary
literature data and do not include review papers or other secondary
data integration analyses to avoid redundancy and circularity.
These large and constantly updated databases have been used for CFG
cross validation and prioritization (FIGS. 2B, 2C, 11B and 11C).
For Example 2, data from 442 papers on suicide were present in the
databases at the time of the CFG analyses (genetic studies-164,
brain studies-192, peripheral fluids-86).
[0099] Human postmortem brain gene expression evidence. Converging
evidence was scored for a gene if there were published reports of
human postmortem data showing changes in expression of that gene or
changes in protein levels in brains from participants who died from
suicide.
[0100] Human blood and other peripheral tissue gene expression
data. Converging evidence was scored for a gene if there were
published reports of human blood, lymphoblastoid cell lines, CSF,
or other peripheral tissue data showing changes in expression of
that gene or changes in protein levels in participants who had a
history of suicidality or who died from suicide.
[0101] Human genetic evidence (association and linkage). To
designate convergence for a particular gene, the gene had to have
independent published evidence of association or linkage for
suicide. For linkage, the location of each gene was obtained
through GeneCards (http://www.genecards.org), and the sex averaged
cM location of the start of the gene was then obtained through
http://compgen.rutgers.edu/mapinterpolator. For linkage
convergence, the start of the gene had to map within 5 cM of the
location of a marker linked to the disorder.
[0102] CFG scoring. For CFG analysis (FIGS. 2C and 11C), the
external cross-validating lines of evidence were weighted such that
findings in human postmortem brain tissue, the target organ, were
prioritized over peripheral tissue findings and genetic findings,
by giving them twice as many points. Human brain expression
evidence was given 4 points, whereas human peripheral evidence was
given 2 points, and human genetic evidence was given a maximum of 2
points for association and 1 point for linkage. Each line of
evidence was capped in such a way that any positive findings within
that line of evidence resulted in maximum points, regardless of how
many different studies support that single line of evidence, to
avoid potential popularity biases. In addition to the external CFG
score, genes were also prioritized based upon the initial gene
expression analyses used to identify them. Probe sets identified by
gene expression analyses could receive a maximum of 4 points. Thus,
the maximum possible total CFG score for each gene was 12 points (4
points for the internal CFG score and 8 points for the external CFG
score). The scoring system was decided upon before the analyses
were carried out. Twice as much weight was given to external CFG
than to internal CFG in order to increase generalizability and
avoid fit to cohort of the prioritized genes. It is recognized that
other ways of scoring the lines of evidence may give slightly
different results in terms of prioritization, if not in terms of
the list of genes per se. Nevertheless, it is believed that this
simple scoring system provides a good separation of genes based on
gene expression evidence and on independent cross-validating
evidence in the field (FIGS. 2B and 11B).
[0103] Pathway Analyses
[0104] IPA 9.0 (Ingenuity Systems, Redwood City, Calif., USA),
GeneGO MetaCore (Encinitas, Calif.), and Kyoto Encyclopedia of
Genes and Genomes (through the Partek Genomics Suite 6.6 software
package) were used to analyze the biological roles, including top
canonical pathways, and diseases, of the candidate genes resulting
from this work, as well as to identify genes in the dataset that
are the target of existing drugs (FIGS. 8, 15 and 17). The analyses
was run together for all the AP and DE probe sets with a total CFG
score.gtoreq.4, then for those of them that showed stepwise change
in the suicide completers validation cohort, then for those of them
that were nominally significant, and finally for those of them that
survived Bonferroni correction.
[0105] Validation Analyses
[0106] For validation of the candidate biomarker genes, which of
the top candidate genes (CFG score of 4 or above) that were
stepwise changed in expression from the No SI group to the High SI
group to the suicide completers group, were examined. The empirical
cutoff of 33% of the maximum possible CFG score of 12 was used,
which also permits the inclusion of potentially novel genes with
maximal internal CFG score, but no external CFG score. Statistical
analyses were performed in SPSS using one-way ANOVA and Bonferonni
corrections.
[0107] For the AP analyses, the Affymetrix microarray data files
were imported from the participants in the validation cohort of
suicide completers into MAS5, alongside the data files from the
participants in the discovery cohort.
[0108] For the DE analyses, Cel. files were imported into Partek
Genomic Suites. A RMA was then run, background corrected with
quantile normalization, and a median polish probe set summarization
of all the chips from the validation cohort to obtain the
normalized expression levels of all probe sets for each chip.
Partek normalizes expression data into a log base of 2 for
visualization purposes. Expression data was non-log-transformed by
taking 2 to the power of the transformed expression value. The
non-log-transformed expression data was then used to compare
expression levels of biomarkers in the different groups.
[0109] Testing Analyses
[0110] The test cohort for suicidal ideation and the test cohort
for future hospitalizations analyses were assembled out of data
that was RMA normalized by diagnosis. Phenomic (clinical) and gene
expression markers used for predictions were z-scored by diagnosis,
to be able to combine different markers into panels and to avoid
potential artefacts due to different ranges of phene expression and
gene expression in different diagnoses. Markers were combined by
computing the average of the increased risk markers minus the
average of the decreased risk markers. Predictions were performed
using R-studio.
[0111] Predicting Suicidal Ideation. Receiver-operating
characteristic (ROC) analyses between marker levels and suicidal
ideation (SI) were performed by assigning participants with a HAMD
SI score of 0-1 into the no SI category, and participants with a
HAMD-SI score of 2 and greater into the SI category. Additionally,
ANOVA was performed between no (HAMD-SI 0), moderate (HAMD-SI 1),
and high SI participants (HAMD-SI 2 and above) and Pearson R
(one-tail) was calculated between HAMD-SI scores and marker
levels.
[0112] Predicting Future Hospitalizations for Suicidality. Analyses
for hospitalizations in the first year following testing were
conducted on data for all the participants for which data was
collected. For each participant in the test cohort for future
hospitalizations, the Example visit with highest levels for the
marker or combination of markers was selected as index visit (or
with the lowest levels, in the case of decreased markers). ROC
analyses between marker levels and future hospitalizations were
performed based on assigning if participants had been hospitalized
for suicidality (suicide ideation, suicide attempts) or not
following the index testing visit. Additionally, a one tailed
t-test with unequal variance was performed between groups of
participants with and without hospitalizations for suicidality.
Pearson R (one-tail) correlation was performed between
hospitalization frequency (number of hospitalizations for
suicidality divided by duration of follow-up) and biomarker score.
The correlation analysis for hospitalizations frequency was also
conducted for all future hospitalizations due to suicide beyond one
year, as this calculation, unlike the ROC and t-test, accounts for
the actual length of follow-up, which varied beyond one year from
participant to participant.
Example 1
[0113] In this Example, male subjects were analyzed for predicting
suicidal ideation and future hospitalizations for suicidality.
[0114] Human Participants
[0115] Data was obtained from four cohorts: one live psychiatric
participants discovery cohort (within-participant changes in
suicidal ideation; n=37 out of 217); one postmortem coroner's
office validation cohort (suicide completers; n=26); and two live
psychiatric participants test cohorts--one for predicting suicidal
ideation (n=108) and one for predicting future hospitalizations for
suicidality (n=157).
[0116] Live psychiatric participants were recruited from the
patient population at the Indianapolis VA Medical Center. All
participants understood and signed informed consent forms detailing
the research goals, procedure, caveats and safeguards. Participants
completed diagnostic assessments by an extensive structured
clinical interview--Diagnostic Interview for Genetic Studies--at a
baseline visit, followed by up to six testing visits, 3-6 months
apart or whenever a hospitalization occurred. At each testing
visit, they received a series of psychiatric rating scales,
including the Hamilton Rating Scale for Depression-17, which
includes a suicidal ideation (SI) rating item (FIGS. 1A-1C), and
blood was drawn. Whole blood (10 ml) was collected in two
RNA-stabilizing PAXgene tubes, labeled with an anonymized ID
number, and stored at -80 degrees C. in a locked freezer until the
time of future processing. Whole-blood (predominantly lymphocyte)
RNA was extracted for microarray gene expression studies from the
PAXgene tubes, as detailed below. This Example focused on a male
population because of the demographics of the catchment area
(primarily male in a VA Medical Center), and to minimize any
potential gender-related effects on gene expression, which would
have decreased the discriminative power of the analysis given the
relatively small sample size.
[0117] The within participant discovery cohort, from which the
biomarker data were derived, consisted of 37 male participants with
psychiatric disorders, with multiple visits, who each had at least
one diametric change in SI scores from no SI to high SI from one
testing visit to another testing visit. There was 1 participant
with 6 visits, 1 participant with 5 visits, 1 participant with 4
visits, 23 participants with 3 visits each, and 11 participants
with 2 visits each, resulting in a total of 106 blood samples for
subsequent microarray studies (FIG. 1B).
[0118] The postmortem cohort, in which the top biomarker findings
were validated, consisted of a demographically matched cohort of 24
male violent suicide completers obtained through the Marion County
coroner's office (FIG. 9). A last observed alive postmortem
interval of 24 hours or less was required, and the cases selected
had completed suicide by means other than overdose, which could
affect gene expression. 14 participants completed suicide by
gunshot to head or chest, 8 by hanging, 1 by electrocution and 1 by
slit wrist. Next of kin signed informed consent at the coroner's
office for donation of tissues and fluids for research. The samples
were collected as part of the INBRAIN initiative (Indiana Center
for Biomarker Research in Neuropsychiatry).
[0119] The independent test cohort for predicting suicidal ideation
consisted of 108 male participants with psychiatric disorders,
demographically matched with the discovery cohort with one or
multiple testing visits in the lab, with either no SI, intermediate
SI, or high SI, resulting in a total of 223 blood samples in whom
whole-genome blood gene expression data were obtained.
[0120] The test cohort for predicting future hospitalizations
consisted of male participants in whom whole-genome blood gene
expression data were obtained at testing visits over the years as
part of a longitudinal study. If the participants had multiple
testing visits, the visit with the highest marker (or combination
of markers) levels was selected for the analyses. The participants'
subsequent number of psychiatric hospitalizations, with or without
suicidality, was tabulated from electronic medical records. All
participants had at least one year of follow-up or more at the VA
Medical Center since the time of the testing visits in the lab.
Participants were evaluated for the presence of future
hospitalizations for suicidality, and for the frequency of such
hospitalizations. A hospitalization was deemed to be without
suicidality if suicidality was not listed as a reason for
admission, and no SI was described in the admission and discharge
medical notes. Conversely, a hospitalization was deemed to be
because of suicidality if suicidal acts or intent was listed as a
reason for admission, and SI was described in the admission and
discharge medical notes.
[0121] Medications
[0122] The participants in the discovery cohort were all diagnosed
with various psychiatric disorders (e.g., BP, MDD, SZA, SZ, PTSD).
The participants were on a variety of different psychiatric
medications: mood stabilizer, antidepressants, antipsychotics,
benzodiazepines and others. Medications can have a strong influence
on gene expression. However, the identification of differentially
expressed genes was based on within-participant analyses, which
factor out not only genetic background effects but also medication
effects, as the participants had no major medication changes
between visits. Moreover, there was no consistent pattern in any
particular type of medication, or between any change in medications
and SI, in the rare instances where there were changes in
medications between visits.
[0123] Results
[0124] The top increased and decreased biomarkers after the
discovery for ideation (CADM1, CLIP4, DTNA, KIF2C), prioritization
with CFG for prior evidence (SAT1, SKA2, SLC4A4), and validation
for behavior in suicide completers (IL6, MBP, JUN, KLHDC3) steps
were tested in a completely independent test cohort of psychiatric
participants for prediction of suicidal ideation (n=108), and in a
future follow-up cohort of psychiatric participants (n=157) for
prediction of psychiatric hospitalizations due to suicidality. The
best individual biomarker across psychiatric diagnoses for
predicting suicidal ideation was SLC4A4, with 72% accuracy. For
bipolar disorder in particular, SLC4A4 predicted suicidal ideation
with 93% accuracy, and future hospitalizations with 70% accuracy.
Two new clinical information apps, one for affective state
(Simplified Affective Scale, SASS) and one for suicide risk factors
(Convergent Functional Information for Suicide, CFI-S) are
disclosed, and how well they predict suicidal ideation across
psychiatric diagnoses (85% accuracy for SASS, 89% accuracy for
CFI-S). Also disclosed is that the integration of the top
biomarkers and the clinical information into a universal predictive
measure (UP-Suicide) was able to predict suicidal ideation across
psychiatric diagnoses with 92% accuracy. For bipolar disorder, it
was able to predict suicidal ideation with 98% accuracy and future
hospitalizations with 94% accuracy.
[0125] For discovery, two differential expression methodologies
were used: Absent/Present (AP) (reflecting on/off of transcription)
and Differential Expression (DE) (reflecting more subtle gradual
changes in expression levels). Genes that tracked suicidal ideation
in each participant were identified. Three thresholds were used for
increased in expression genes and for decreased in expression
genes: .gtoreq.33% (low), .gtoreq.50% (medium), and .gtoreq.80%
(high) of the maximum scoring increased and decreased gene across
participants. These differentially expressed genes were then
prioritized using a Bayesian-like Convergent Functional Genomics
(CFG) approach (FIGS. 2A-2C), integrating all the previously
published human genetic evidence, postmortem brain gene expression
evidence, and peripheral fluids evidence for suicide available in
the field as of September 2014 to identify and prioritize disease
relevant genomic biomarkers, extracting generalizable signal out of
potential cohort-specific noise and genetic heterogeneity. For
validation, genes whose levels of expression were changed stepwise
significantly from no suicidal ideation to high suicidal ideation
to suicide completion, and who survived Bonferroni correction for
multiple comparisons, were carried forward. The overall best
biomarkers for suicidal ideation across diagnostic groups was
identified. The top genes after discovery were DTNA and KIF2C from
AP, CADM1 and CLIP4 from DE. The top genes after prioritization
with CFG were SLC4A4 and SKA2 from AP; and SAT1 and SKA2 from DE.
The top genes after validation in suicide completers were IL6 and
MBP from AP; and JUN and KLHDC3 from DE (FIG. 2C). Notably, the
SAT1 finding is a replication and expansion of previously reported
results identifying SAT1 as a biomarker for suicidality
(Le-Niculescu et al. 2013), and the SKA2 finding is an independent
replication of a previous report identifying SKA2 as a biomarker
for suicidality (Kaminsky et al. 2014). A number of other genes
identified are completely novel in terms of their involvement in
suicidality.
[0126] To understand the biology represented by the biomarkers
identified, and derive some mechanistic and practical insights,
unbiased biological pathway analyses and hypothesis driven
mechanistic queries, overall disease involvement and specific
neuropsychiatric disorders queries, and overall drug modulation
along with targeted queries for omega-3, lithium and clozapine were
conducted. Administration of omega-3s in particular may be a
mass-deployable therapeutic and preventive strategy.
[0127] The sets of biomarkers identified have biological roles in
immune and inflammatory response, growth factor regulation, mTOR
signaling, stress, and perhaps overall the switch between cell
survival and proliferation vs. apoptosis (FIG. 8). An extrapolation
can be made and model proposed whereas suicide is a whole body
apoptosis (or "self-poptosis") in response to perceived stressful
life events.
[0128] Evidence for the involvement of the biomarkers for
suicidality was also examined for involvement in other psychiatric
disorders, allowing for analysis of context and specificity FIGS. 8
and 9). SKA2, HADHA, SNORA68, RASL11B, CXCL11, HOMEZ, LOC728543,
AHCYL1, LDLRAP1, NEAT1 and PAFAH1B2 appeared to be relatively
specific for suicide, based on the evidence to date. SAT1, IL6,
FOXN3 and FKBP5 were less specific for suicide, having equally high
evidence for involvement in suicide and in other psychiatric
disorders, possibly mediating stress response as a common
denominator. CADM1, discovered in this Example as a top biomarker
for suicide, had previous evidence for involvement in other
psychiatric disorders, such as ASD and BP. Interestingly, it was
identified in a previous study as a blood biomarker increased in
expression in low mood states in bipolar participants, and it is
increased in expression in the current Example in high suicidal
ideation states. Increased expression of CADM1 is associated with
decreased cellular proliferation and with apoptosis, and this gene
is decreased in expression or silenced in certain types of
cancers.
[0129] A 22-item scale and app for suicide risk, Convergent
Functional Information for Suicidality (CFI-S), was also developed,
which integrates information about known life events, mental
health, physical health, stress, addictions, and cultural factors
that can influence suicide risk. Clinical risk predictors and
scales are of high interest in the military and in the general
population at large. The scale disclosed herein builds on those
excellent prior achievements, while aiming for comprehensiveness,
simplicity and quantification similar to a polygenic risk score.
CFI-S is able to distinguish between individuals who committed
suicide (coroner's cases, information obtained from the next of
kin, n=35) and those high risk participants who did not, but had
experienced changes in suicidal ideation (e.g., the discovery
cohort of psychiatric participants described herein). Items of the
CFI-S scale that were the most significantly different were
analyzed. Seven (7) items that were significantly different were
identified, 5 of which survived Bonferroni correction: lack of
coping skills when faced with stress (p=3.35E-11), dissatisfaction
with current life (p=2.77E-06), lack of hope for the future
(4.58E-05), current substance abuse (p=1.25E-04), and acute
loss/grief (p=9.45E-4). The top item was inability to cope with
stress, which was independently consistent with the biological
mechanistic results discussed above.
[0130] CFI-S provided good accuracy (ROC AUC 0.70, p-value 0.006)
at predicting future hospitalizations for suicidality in the first
year, across diagnostic groups. CFI-Suicide had very good accuracy
(AUC 0.89, p-value 3.53E-13) at predicting suicidal ideation in
psychiatric participants across diagnostic groups. Within
diagnostic groups, in affective disorders, the accuracy was even
higher. CFI-S had excellent accuracy at predicting high suicidal
ideation in bipolar participants (AUC 0.97, p-value 1.75E-06) and
in depression participants (AUC 0.95, p-value 7.98E-06).
[0131] Previously, the TASS (Total Affective State Scale) was
developed and described for measuring mood and anxiety. The wording
used in TASS was simplified and a new app was developed for an 11
item scale for measuring mood and anxiety, the Simplified Affective
State Scale (SASS). The SASS is a set of 11 visual analog scales (7
for mood, 4 for anxiety) that provides a number ranging from 0 to
100 for mood state and for anxiety state.
[0132] SASS had very good accuracy (AUC 0.85, 9.96E-11) at
predicting suicidal ideation in psychiatric participants across
diagnostic groups. Within diagnostic groups, in affective
disorders, the accuracy was even higher (AUC 0.87) in both bipolar
disorder and depression. SASS also had good accuracy (AUC 0.71,
p-value 0.008) at predicting future hospitalizations for
suicidality in the first year following testing.
[0133] The best single biomarker predictor for suicidal ideation
state across all diagnostic groups was SLC4A4, the top increased
biomarker from AP CFG prioritization (AUC 0.72, p-value 2.41E-05).
Within diagnostic groups, the accuracy was even higher. SLC4A4 had
very good accuracy at predicting future high suicidal ideation in
bipolar participants (AUC 0.93, p-value 9.45E-06) and good accuracy
in schizophrenia participants (AUC 0.76, p-value 0.030). SLC4A4 is
a sodium-bicarbonate co-transporter that regulates intracellular
pH, and possibly apoptosis. Very little is known to date about its
roles in the brain, thus representing a completely novel
finding.
[0134] SKA2, the top decreased biomarker from AP and DE CFG, had
good accuracy at predicting suicidal ideation across all diagnostic
groups (AUC 0.69), and even better accuracy in bipolar participants
(AUC 0.76, p-value 0.011) and schizophrenia participants (AUC
0.82).
[0135] The best single top biomarker predictor for future
hospitalizations for suicidal behavior in the first year across all
diagnostic groups was SAT1, the top increased biomarker from the DE
CFG (AUC 0.55). Within diagnostic groups, in affective disorders,
the SAT1 prediction accuracies were higher (depression AUC 0.62,
bipolar AUC 0.63).
[0136] The a priori primary endpoint was a combined universal
predictor for suicide (UP-Suicide), composed of the top biomarkers
from discovery, prioritization and validation (n=11), along with
CFI-Suicide, and SASS. UP-Suicide is an excellent predictor of
suicidal ideation across all disorders in the independent cohort of
psychiatric participants (AUC 0.92). UP-Suicide also has good
predictive ability for future psychiatric hospitalizations for
suicidality in the first year of follow-up (AUC 0.70). The
predictive ability of UP-Suicide is notably higher in affective
disorder participants (bipolar, depression) (FIGS. 4A &
4B).
Example 2
[0137] In this Example, female subjects were analyzed for
predicting suicidal ideation and future hospitalizations for
suicidality.
[0138] Human Participants
[0139] Four cohorts were used: one live psychiatric participants
discovery cohort (within-participant changes in suicidal ideation;
n=12 out of 51); one postmortem coroner's office validation cohort
(suicide completers; n=6); and two live psychiatric participants
test cohorts--one for predicting suicidal ideation (n=33), and one
for predicting future hospitalizations for suicidality (n=24).
[0140] The live psychiatric participants were part of a larger
longitudinal cohort that was continuously being collected.
Participants were recruited from the patient population at the
Indianapolis VA Medical Center and Indiana University School of
Medicine through referrals from care providers, the use of
brochures left in plain sight in public places and mental health
clinics, and through word of mouth. All participants understood and
signed informed consent forms detailing the research goals,
procedure, caveats and safeguards. Participants completed
diagnostic assessments by an extensive structured clinical
interview--Diagnostic Interview for Genetic Studies--at a baseline
visit, followed by up to six testing visits, 3-6 months apart or
whenever a new psychiatric hospitalization occurred. At each
testing visit, they received a series of psychiatric rating scales,
including the Hamilton Rating Scale for Depression-17, which
includes a suicidal ideation (SI) rating item (FIG. 10A), and the
blood was drawn. Whole blood (10 ml) was collected in two
RNA-stabilizing PAXgene tubes, labeled with an anonymized ID
number, and stored at -80 degrees C. in a locked freezer until the
time of future processing. Whole-blood (predominantly lymphocyte)
RNA was extracted for microarray gene expression studies from the
PAXgene tubes, as detailed below. This Exampled focused on a female
population.
[0141] The within participant discovery cohort, from which the
biomarker data were derived, consisted of 12 female participants
with psychiatric disorders and multiple visits in the lab, who each
had at least one diametric change in SI scores from no SI to high
SI from one testing visit to another. There were 7 participants
with 3 visits each, and 5 participants with 2 visits each,
resulting in a total of 31 blood samples for subsequent microarray
studies (FIGS. 10B and 10C).
[0142] The postmortem cohort, in which the top biomarker findings
were validated for behavior, consisted of a demographically matched
cohort of 6 female violent suicide completers obtained through the
Marion County coroner's office (FIG. 14). A last observed alive
postmortem interval of 24 hours or less was required, and the cases
selected had completed suicide by means other than overdose, which
could affect gene expression. 5 participants completed suicide by
gunshot to head or chest, and 1 by asphyxiation. Next of kin signed
informed consent at the coroner's office for donation of blood for
research. The samples were collected as part of the INBRAIN
initiative (Indiana Center for Biomarker Research in
Neuropsychiatry).
[0143] The independent test cohort for predicting suicidal ideation
(FIG. 14) consisted of 33 female participants with psychiatric
disorders, demographically matched with the discovery cohort, with
one or multiple testing visits in the lab, with either no SI,
intermediate SI, or high SI, resulting in a total of 74 blood
samples in whom whole-genome blood gene expression data were
obtained (FIG. 14).
[0144] The test cohort for predicting future hospitalizations (FIG.
14) consisted of 24 female participants in whom whole-genome blood
gene expression data were obtained at testing visits over the years
as part of a longitudinal study. If the participants had multiple
testing visits, the visit with the highest marker (or combination
of markers) levels was selected for the analyses (so called "high
watermark" or index visit). The participants' subsequent number of
psychiatric hospitalizations, with or without suicidality (ideation
or attempt), was tabulated from electronic medical records.
Participants were evaluated for the presence of future
hospitalizations for suicidality, and for the frequency of such
hospitalizations. A hospitalization was deemed to be without
suicidality if suicidality was not listed as a reason for
admission, and no SI was described in the admission and discharge
medical notes. Conversely, a hospitalization was deemed to be
because of suicidality if suicidal acts or intent was listed as a
reason for admission, and/or SI was described in the admission and
discharge medical notes.
[0145] Medications
[0146] The participants in the discovery cohort were all diagnosed
with various psychiatric disorders (FIG. 14). Their psychiatric
medications were listed in their electronic medical records, and
documented at the time of each testing visit. The participants were
on a variety of different psychiatric medications: mood
stabilizers, antidepressants, antipsychotics, benzodiazepines and
others (data not shown). Medications can have a strong influence on
gene expression. However, discovery of differentially expressed
genes was based on within--participant analyses, which factor out
not only genetic background effects but also medication effects, as
the participants had no major medication changes between visits.
Moreover, there was no consistent pattern in any particular type of
medication, or between any change in medications and SI, in the
rare instances where there were changes in medications between
visits.
[0147] Clock Gene Database
[0148] In this Example, a database was compiled of genes associated
with circadian function, by using a combination of review papers
(Zhang et al. Cell 2009; 139(1):19-210, McCarthy and Welsh Journal
of biological rhythms 2012; 27(5):339-352) and searches of existing
databases CircaDB (circadb.hogeneschlab.org), GeneCards
(www.genecards.org), and GenAtlas
(genatlas.medecine.univ-paris5.fr). Using the data, a total of 1280
genes were identified that show circadian functioning. The genes
were classified into "core" clock genes, i.e. those genes that are
the main engine driving circadian function (n=18), "immediate"
clock genes, i.e. the genes that directly input or output to the
core clock (n=331), and "distant" clock genes, i.e. genes that
directly input or output to the immediate clock genes
(n=1,119).
[0149] Clinical Measures
[0150] The Simplified Affective State Scale (SASS) is an 11-item
scale for measuring mood and anxiety. The SASS has a set of 11
visual analog scales (7 for mood, 4 for anxiety) that ends up
providing a number ranging from 0 to 100 for mood state, and the
same for anxiety state. Also developed is an Android app
version.
[0151] In some embodiments, the systems and methods described
utilize a computer implemented method for assessing suicidal risk
factors based upon patient psychiatric information further
including mood information, anxiety information, and other
psychiatric symptom information. Any and all such patient
psychiatric information may be represented as a quantitative rating
on a defined analog scale, such as the ratings and scales described
above. Further, as used herein, such patient psychiatric
information may be processed using an associated processing
algorithm. The associated processing algorithm may include
calculating mean values for each component of patient psychiatric
information and then assigning a suitable weighting to each
calculated mean value. The processing algorithm may thus use the
quantitative ratings of the patient psychiatric information as
inputs to calculate a diagnostic output score. The diagnostic
output score may be used to compare to reference scores (from a
diagnostic database) associated with patients having psychiatric
symptom information (e.g., psychiatric disorder diagnosis or lack
thereof) similar to the patient. By such comparison, the diagnostic
output score may be assigned a percentile. The diagnostic output
score may also be compared to the reference scores in the
diagnostic database associated with individuals with no suicidality
and high suicidality. Thus, if the diagnostic output score meets or
exceeds a high suicidality reference score, a patient may be marked
as at risk for suicide. Conversely, if the diagnostic output score
meets or falls below a low suicidality reference score, a patient
may be marked as not at risk for suicide.
[0152] Convergent Functional Information for Suicidality (CFI-S) is
a 22-item scale and Android app for suicide risk, which integrates,
in a simple binary fashion (Yes-1, No-0), similar to a polygenic
risk score, information about known life events, mental health,
physical health, stress, addictions, and cultural factors that can
influence suicide risk. The scale was administered at participant
testing visits (n=39), or scored based on retrospective electronic
medical record information and Diagnostic Interview for Genetic
Testing (DIGS) information (n=48). When information was not
available for an item, it was not scored (NA).
[0153] In other embodiments, the systems and methods described
utilize a computer implemented method for assessing suicidal risk
factors based upon socio-demographic/psychological suicidal risk
factors. Any and all such socio-demographic/psychological suicidal
risk factors may be represented as a quantitative rating on a
defined analog scale, such as the ratings and scales described
above. Further, as used herein, such
socio-demographic/psychological suicidal risk factors may be
processed using an associated processing algorithm. The associated
processing algorithm may include calculating mean values for each
component socio-demographic/psychological suicidal risk factor and
then assigning a suitable weighting to each calculated mean value.
The processing algorithm may thus use the quantitative ratings of
the socio-demographic/psychological suicidal risk factors as inputs
to calculate a diagnostic output score. The diagnostic output score
may be used to compare to reference scores (from a diagnostic
database) associated with patients having
socio-demographic/psychological suicidal risk factors similar to
the patient. By such comparison, the diagnostic output score may be
assigned a percentile. The diagnostic output score may also be
compared to the reference scores in the diagnostic database
associated with individuals with no suicidality and high
suicidality. Thus, if the diagnostic output score meets or exceeds
a high suicidality reference score, a patient may be marked as at
risk for suicide. Conversely, if the diagnostic output score meets
or falls below a low suicidality reference score, a patient may be
marked as not at risk for suicide.
[0154] In some computer-implemented methods described above and
herein, multiple computing devices may interact with one another
(e.g., first and second computer devices). To protect data and
privacy, such requests and transmissions are made using data
encryption.
[0155] Combining Gene Expression and Clinical Measures
[0156] The Universal Predictor for Suicide (UP-Suicide) construct,
the primary endpoint, was decided upon as part of a apriori study
design to be broad-spectrum, and combine the top Bonferroni
validated biomarkers with the phenomic (clinical) markers (SASS and
CFI-S).
[0157] Results
[0158] Discovery of Biomarkers for Suicidal Ideation
[0159] A whole-genome gene expression profiling was conducted in
the blood samples from a longitudinally followed cohort of female
participants with psychiatric disorders that predispose to
suicidality. The samples were collected at repeated visits, 3-6
months apart. State information about suicidal ideation (SI) was
collected from a questionnaire (HAMD) administered at the time of
each blood draw. Out of 51 female psychiatric participants (with a
total of 123 visits) followed longitudinally in this Example, with
a diagnosis of BP, MDD, SZ and SZA, there were 12 participants that
switched from a no SI (SI score of 0) to a high SI state (SI score
of 2 and above) at different visits, which was the intended
discovery group (FIG. 10B). A within-participant design was used to
analyze data from these 12 participants and their 31 visits. A
within-participant 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. Another benefit of a within-participant design may be
accuracy/consistency of self-report of psychiatric symptoms (`phene
expression`), similar in rationale to the signal detection benefits
it provides in gene expression.
[0160] For discovery, two differential expression methodologies
were used: Absent/Present (AP) (reflecting on/off of
transcription), and Differential Expression (DE) (reflecting more
subtle gradual changes in expression levels). The genes that
tracked suicidal ideation in each participant were identified in
the analyses. Three thresholds were used for increased in
expression genes and for decreased in expression genes:
.gtoreq.33.3% (low), .gtoreq.50% (medium), and .gtoreq.80% (high)
of the maximum scoring increased and decreased gene across
participants. Such a restrictive approach was used as a way of
minimizing false positives, even at the risk of having false
negatives. For example, there were genes on each of the two lists,
from AP and DE analyses, that had clear prior evidence for
involvement in suicidality, such as AKAP10 (31.7%) and MED28
(31.8%) from AP, and S 100B (31.7%) and SKA2 (31.4%) for DE, but
were not included in subsequent analyses because they did not meet
the apriori set 33.3% threshold. Notably, SKA2 reproduces the
results in males (Example 1).
[0161] Prioritization of Biomarkers Based on Prior Evidence in the
Field
[0162] These differentially expressed genes were then prioritized
using a Bayesian-like Convergent Functional Genomics (CFG) approach
(FIGS. 11B and 11C) integrating all the previously published human
genetic evidence, postmortem brain gene expression evidence, and
peripheral fluids evidence for suicide in the field available at
the time of this analyses (i.e., September 2015). This is a way of
identifying and prioritizing disease relevant genomic biomarkers,
extracting generalizable signal out of potential cohort-specific
noise and genetic heterogeneity. The manually curated databases of
the psychiatric genomic and proteomic literature to date were used
in CFG analyses. The CFG approach is thus a de facto field-wide
collaboration.
[0163] Validation of Biomarkers for Behavior in Suicide
Completers
[0164] For validation in suicide completers, 1471 genes were used
that had a CFG score of 4 and above, from AP and DE, reflecting
either maximum internal score from discovery or additional external
literature cross-validating evidence. Out of these, 882 did not
show any stepwise change in suicide completers
(NC--non-concordant). As such, they may be involved primarily in
ideation and not in behavior. The remaining 589 genes (40.0%) had
levels of expression that were changed stepwise from no suicidal
ideation to high suicidal ideation to suicide completion. 396 of
these genes (26.9%) were nominally significant, and 49 genes (50
probesets--two for JUN) (3.33%) survived Bonferroni correction for
multiple comparisons (FIG. 11C). These genes are likely involved in
suicidal ideation and suicidal behavior. (A person can have
suicidal ideation without suicidal behavior, but cannot have
suicidal behavior without suicidal ideation).
[0165] Selection of Biomarkers for Testing of Predictive
Ability
[0166] For testing, Bonferroni validated biomarkers (49 genes, 50
probesets) were focused on. A secondary analysis of the top scoring
biomarkers from both discovery and prioritization (65 genes) was
conducted so as to avoid potential false negatives in the
validation step due to possible postmortem artefacts or extreme
stringency of statistical cutoff. The top CFG scoring genes after
the Bonferroni validation step were BCL2 and GSK3B. The top CFG
scoring genes from the discovery and prioritization steps were
FAM214A, CLTA, HSPD1, and ZMYND8. Notably, all have co-directional
gene expression changes evidence in brains of suicide completers in
studies form other groups.
[0167] Biological Understanding
[0168] Unbiased biological pathway analyses and hypothesis driven
mechanistic queries, overall disease involvement and specific
neuropsychiatric disorders queries, and overall drug modulation
along with targeted queries for omega-3, lithium and clozapine were
studied (FIGS. 15 and 17). Administration of omega-3s in particular
may be a mass-deployable therapeutic and preventive strategy.
[0169] The sets of biomarkers identified have biological roles in
inflammation, neurotrophins, inositol signaling, stress response,
and perhaps overall the switch between cell survival and
proliferation vs. apoptosis (FIG. 15).
[0170] The involvement of these biomarkers for suicidality in other
psychiatric disorders were also analyzed. FAM214A, MOB3B, ZNF548,
and ARHGAP35 were relatively specific for suicide, based on the
evidence to date in the field, and were also identified
co-directionally in the previous male work (Example 1). BCL2,
GSK3B, HSPD1, and PER1 were less specific for suicide, having
equally high evidence for involvement in suicide and in other
psychiatric disorders. BCL2 was also identified co-directionally in
Example 1.
[0171] HSPD1, found to be a top biomarker in this Example,
increased in expression in suicidality, and was also increased in
expression in the blood following anti-depressant treatment. Thus,
this may be a useful biomarker for treatment-emergent suicidal
ideation (TESI).
[0172] Further, a number of the genes changed in expression in
opposite direction in suicide in this Example vs. high mood in
Example 1--SSBP2, ZNF596, suggesting that suicidal participants are
in a low mood state. Also, some of the top suicide biomarkers are
changed in expression in the same direction as in high psychosis
participants in a previous psychosis biomarker study--HERC4,
PIP5K1B, SLC35B3, SNX27, KIR2DL4, NUDT10, suggesting that suicidal
participants may be in a psychosis-like state. Taken together, the
data indicates that suicidality could be viewed as a psychotic
dysphoric state. This molecularly informed view is consistent with
the emerging clinical evidence in the field.
[0173] A number of top biomarkers identified have biological roles
that are related to the core circadian clock (such as PER1), or
modulate the circadian clock (such as CSNK1A1), or show at least
some circadian pattern (such as HTRA1). To be able to ascertain all
the genes in the dataset that were circadian and do estimates for
enrichment, a database from literature was compiled of all the
known genes that fall into these three categories, numbering a
total of 1468 genes. Using an estimate of about 21,000 genes in the
human genome, that gives about 7% of genes having some circadian
pattern. Out of the 49 Bonferroni validated biomarker genes, 7 had
circadian evidence (14.3%), suggesting a 2-fold enrichment for
circadian genes.
[0174] Additionally, biological pathway analyses were conducted on
the genes that, after discovery and prioritization, were stepwise
changed in suicide completers (n=882) and may be involved in
ideation and behavior vs. those that were not stepwise changed
(n=589), and that may only be involved in ideation. The genes
involved in ideation map to pathways related to PI3K signaling. The
genes involved in behavior map to pathways related to
glucocorticoid receptor signaling. This is consistent with ideation
being related to neurotrophic factors, and behavior being related
to stress.
[0175] Clinical Information
[0176] A 22-item scale and app were used for suicide risk,
Convergent Functional Information for Suicidality (CFI-S), which
scores in a simple binary fashion and integrates information about
known life events, mental health, physical health, stress,
addictions, and cultural factors that can influence suicide risk.
Determining which items of the CFI-S scale were the most
significantly different between no and high suicidal ideation live
participants was analyzed (FIG. 12A). Seven items were identified
that were significantly different: lack of positive
relationships/social isolation (p=0.004), substance abuse
(p=0.0071), history of impulsive behaviors (p=0.015), lack of
religious beliefs (p=0.018), past history of suicidal acts/gestures
(p=0.025), rejection (p=0.029), and history of command auditory
hallucinations (p=0.045) (FIG. 12B). It is noted that lack of
positive relationships/social isolation was the second top item in
males as well. Social isolation increases vulnerability to stress,
which is independently consistent with the biological marker
results.
[0177] Testing for Predictive Ability
[0178] The best single increased (risk) biomarker predictor for
suicidal ideation state was EPB41L5 (ROC AUC 0.68, p-value 0.06;
Pearson Correlation 0.22, p-value 0.03), an increased in
expression, Bonferroni validated biomarker (FIG. 16). This
biomarker was also identified co-directionally in Example 1, and
has no evidence for involvement in other psychiatric disorders. The
best single decreased (protective) biomarker predictor for suicidal
ideation is PIK3C3 (ROC AUC 0.65, p-value 0.1; Pearson Correlation
-0.21, p-value 0.037), a decreased in expression, Bonferroni
validated biomarker (FIG. 16). PIK3C3 is also decreased in
expression in postmortem brains in depression.
[0179] The best single increased (risk) biomarker predictor for
future hospitalizations for suicidality was HTRA1 (ROC AUC 0.84,
p-value 0.01; Cox Regression Hazard Ratio 4.55, p-value 0.01), an
increased in expression, Bonferroni validated biomarker (FIG. 16).
HTRA1 is also increased in expression in the blood of
schizophrenics. The best single decreased (protective) biomarker
predictor for future hospitalizations for suicidality was CSNK1A1
(ROC AUC 0.96, p-value 0.0007; Cox Regression Hazard Ratio 620.5,
p-value 0.02), a top discovery and prioritization, non-Bonferroni
validated biomarker (FIG. 16). This biomarker was also identified
co-directionally in Example 1. CSNK1A1 (casein kinase 1, alpha 1)
is a circadian clock gene, part of the input into the core clock.
It decreased in expression in suicidality, and decreased in
postmortem brains of alcoholics. It has further been found to be
increased in expression by mood stabilizers and by omega-3 fatty
acids. PIK3C3 was also found to be a good predictor for future
hospitalizations for suicidality (ROC AUC 0.9, p-value 0.011) (FIG.
16).
[0180] BCL2, the top CFG scoring biomarker from validation, had
good accuracy at predicting future hospitalizations for suicidality
(ROC AUC 0.89, p-value 0.007; Cox Regression Hazard Ratio 3.08,
p-value 0.01) (FIG. 16). In the panel of 50 validated biomarkers,
BioM-50, had even better accuracy at predicting future
hospitalizations for suicidality (ROC AUC 0.94, p-value 0.002; Cox
Regression Hazard Ratio 89.46, p-value 0.02) (FIG. 16). Overall, in
women, blood biomarkers seemed to perform better for predicting
future hospitalizations for suicidality (trait) than for predicting
suicidal ideation (state). This is different than the trend seen in
Example 1, where blood biomarkers were somewhat better predictors
of state than of trait.
[0181] CFI-S had very good accuracy (ROC AUC 0.84, p-value 0.002;
Pearson Correlation 0.39, p-value 0.001) at predicting suicidal
ideation in psychiatric participants across diagnostic groups. The
other app, SASS, also had very good accuracy (ROC AUC 0.81, p-value
0.003; Pearson Correlation 0.38, p-value 0.0005) at predicting
suicidal ideation in women psychiatric participants. The
combination of the apps was synergistic (ROC AUC 0.87, p-value
0.0009; Pearson Correlation 0.48, p-value 0.0001). Thus, even
without the benefit of potentially more costly and labor intensive
blood biomarker testing, clinically useful predictions could be
made with the apps.
[0182] The apriori primary endpoint was a combined universal
predictor for suicide (UP-Suicide), composed of CFI-S and SASS,
along with the Bonferroni validated biomarkers (n=50) resulting
from the sequential discovery for ideation, prioritization with
CFG, and validation for behavior in suicide completers steps.
UP-Suicide was a good predictor of suicidal ideation (ROC AUC 0.82,
p-value 0.003; Pearson Correlation 0.43, p-value 0.0003) (FIGS.
13A, 13B and 16). UP-Suicide also had good predictive ability for
future psychiatric hospitalizations for suicidality (ROC AUC 0.78,
p-value 0.032; Cox Regression Hazard Ratio 9.61, p-value 0.01).
[0183] Discussion
[0184] The present Example identified markers involved in suicidal
ideation and suicidal behavior, including suicide completion, in
females. Markers involved in behavior may be on a continuum with
some of the markers involved in ideation, varying in the degree of
expression changes from less severe (ideation) to more severe
(behavior). One cannot have suicidal behavior without suicidal
ideation, but it may be possible to have suicidal ideation without
suicidal behavior.
[0185] 50 biomarkers were found to have survived Bonferroni
correction (49 genes; one gene, JUN, had two different probesets
that validated). Additionally, 65 other biomarkers that were non
Bonferroni, but had maximum internal score of 4 in discovery and a
CFG score of 6 and above, meaning that in addition to strong
evidence in this Example, they also had prior independent evidence
of involvement in suicide from other studies, were also studied.
These additional biomarkers are likely involved in suicide, but did
not make the Bonferroni validation cutoff due to its stringency or
potential technical/postmortem artefact reasons (FIGS. 26 and
30).
[0186] Data validating the CFI-S in women in the combined discovery
and test cohort of live psychiatric participants was analyzed
(FIGS. 12A and 12B) and compared with similar analyses in men
(Example 1). The chronic stress of lack of positive
relationships/social isolation was identified as the top
differential item in women, which is consistent with biological
data from the biomarker side of this Example.
[0187] In assessing anxiety and mood, it was shown that anxiety
measures cluster with suicidal ideation and CFI-S, and mood
measures are in the opposite cluster, suggesting that the
participants have high suicidal ideation when they have high
anxiety and low mood (FIG. 10C).
[0188] The rationale for identifying blood biomarkers as opposed to
brain biomarkers is a pragmatic one--the brain cannot be readily
accessed in live individuals. Other peripheral fluids, such as CSF,
require more invasive and painful procedures. Nevertheless, it is
likely that many of the peripheral blood transcriptomic changes are
not necessarily mirroring what is happening in the brain, and
vice-versa. The keys to finding peripheral biomarkers are, first,
to have a powerful discovery approach, such as the
within-participant design, that closely tracks the phenotype you
are trying to measure and reduces noise. Second, cross-validating
and prioritizing the results with other lines of evidence, such as
brain gene expression and genetic data, are important in order to
establish relevance and generalizability of findings. Third, it is
important to validate for behavior in an independent cohort with a
robust and relevant phenotype, in this case suicide completers.
Fourth, testing for predictive ability in independent/prospective
cohorts is a must.
[0189] Biomarkers that survive such a rigorous step-wise discovery,
prioritization, validation and testing process are likely directly
relevant to the disorder studied. As such, whether they are
involved in other psychiatric disorders or are relatively specific
for suicide, and whether they are the modulated by existing drugs
in general, and drugs known to treat suicidality in particular were
evaluated.
[0190] A series of biomarkers have been identified that seem to be
changed in opposite direction in suicide vs. in treatments with
omega-3 fatty acids, lithium, clozapine. These biomarkers could
potentially be used to stratify patients to different treatment
approaches, and monitor their response.
[0191] BCL2, JUN, GHA1, ENTPD1, ITIH5, MBNL1, and SSBP2 were
changed in expression by two of these three treatments, suggesting
they may be core to the anti-suicidal mechanism of these drugs.
BCL2, CAT, and JUN may be useful blood pharmacogenomic markers of
response to lithium. CD84, MBNL1, and RAB22A may be useful blood
pharmacogenomic markers of response to clozapine. NDRG1, FOXP1,
AFF3, ATXN1, CSNK1A1, ENTPD1, ITIH5, PRDX3, and SSBP2 may be useful
blood pharmacogenomic markers of response to omega-3 fatty acids.
Three existing drugs used for other indications have been
identified as targeting the top suicide biomarkers identified, and
could potentially be re-purposed for testing in treatment of acute
suicidality: anakinra (inhibiting ILR1), enzastaurin (inhibiting
AKT3), and tesevatinib (inhibiting EPHB4). Additionally,
Connectivity Map (ref) analyses identified compounds that induced
gene expression signatures that were the opposite of those present
in suicide, and might generate leads and/or be tested for use to
treat/prevent suicidality: betulin (an anti-cancer compound from
the bark of birch trees), zalcitabine (an anti-HIV drug), and
atractyloside (a toxic glycoside). Other common drugs identified by
the Connectivity Map analyses were nafcillin, lansoprazole,
mifepristone, LY294002, minoxidil, acetysalicilic acid, estradiol,
buspirone, dicloxacillin, corticosterone, metformin,
diphenhydramine, haloperidol, and fluoxetine.
[0192] Of note, a number of biomarkers from the current Example in
women reproduced and were co-directional with previous findings in
Example 1 (BCL2, ALDH3A2, FAM214A, CLTA, ZMYND8, JUN), whereas
others had changes in opposite directions (GSK3B, HSPD1, AK2, CAT),
underlying the issue of biological context and differences in
suicidality between the two genders.
[0193] Disclosed herein are instruments (biomarkers and
applications) for predicting suicidality, that do not require
asking the person assessed if they have suicidal thoughts, as
individuals who are truly suicidal often do not share that
information with people close to them or with clinicians. The
widespread use of such risk prediction tests as part of routine or
targeted healthcare assessments will lead to early disease
interception followed by preventive lifestyle modifications or
treatment. Biomarkers identified herein for suicidal ideation are
enriched for genes involved in neuronal connectivity and
schizophrenia. Biomarkers identified herein also validated for
suicide behavior are enriched for genes involved in neuronal
activity and mood.
[0194] Worldwide, one person dies every 40 seconds through suicide,
a potentially preventable tragedy. A limiting step in the ability
to intervene is the lack of objective, reliable predictors. A
powerful within-participant discovery approach is disclosed herein
in which genes that change in expression between no suicidal
ideation and high suicidal ideation states were identified. The
methods disclosed herein do not require asking the person assessed
if they have thoughts of suicide, as individuals who are truly
suicidal often do not share that information with clinicians. The
widespread use of such risk prediction tests as part of routine or
targeted healthcare assessments will lead to early disease
interception followed by preventive lifestyle modifications and
proactive treatment.
[0195] In view of the above, it will be seen that the several
advantages of the disclosure are achieved and other advantageous
results attained. As various changes could be made in the above
methods without departing from the scope of the disclosure, it is
intended that all matter contained in the above description and
shown in the accompanying drawings shall be interpreted as
illustrative and not in a limiting sense.
[0196] When introducing elements of the present disclosure or the
various versions, embodiment(s) or aspects thereof, the articles
"a", "an", "the" and "said" are intended to mean that there are one
or more of the elements. The terms "comprising", "including" and
"having" are intended to be inclusive and mean that there may be
additional elements other than the listed elements.
* * * * *
References