US20200312425A1 - Predicting suicidality using a combined genomic and clinical risk assessment - Google Patents

Predicting suicidality using a combined genomic and clinical risk assessment Download PDF

Info

Publication number
US20200312425A1
US20200312425A1 US16/779,229 US202016779229A US2020312425A1 US 20200312425 A1 US20200312425 A1 US 20200312425A1 US 202016779229 A US202016779229 A US 202016779229A US 2020312425 A1 US2020312425 A1 US 2020312425A1
Authority
US
United States
Prior art keywords
score
subject
suicidality
biomarker
expression level
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US16/779,229
Inventor
Alexander Bogdan Niculescu
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Indiana University Research and Technology Corp
US Department of Veterans Affairs VA
Original Assignee
Indiana University Research and Technology Corp
US Department of Veterans Affairs VA
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Indiana University Research and Technology Corp, US Department of Veterans Affairs VA filed Critical Indiana University Research and Technology Corp
Priority to US16/779,229 priority Critical patent/US20200312425A1/en
Publication of US20200312425A1 publication Critical patent/US20200312425A1/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P25/00—Drugs for disorders of the nervous system
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P25/00—Drugs for disorders of the nervous system
    • A61P25/18—Antipsychotics, i.e. neuroleptics; Drugs for mania or schizophrenia
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P25/00—Drugs for disorders of the nervous system
    • A61P25/22—Anxiolytics
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P25/00—Drugs for disorders of the nervous system
    • A61P25/24—Antidepressants
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/30—Psychoses; Psychiatry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/30—Psychoses; Psychiatry
    • G01N2800/304—Mood disorders, e.g. bipolar, depression
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/50—Determining the risk of developing a disease
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
    • Y02A90/26—

Definitions

  • 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.
  • an algorithm for combining biomarkers and apps for identifying subjects who are at risk for committing suicide.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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 Simple 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 Young Mania Rating Scale.
  • 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 ( ⁇ 3) had three different probe sets with the same total score of 8.
  • 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.
  • FIG. 3C depicts CFI-S predictions for suicidal ideation in the independent test cohort and predicting future hospitalizations due to suicidality.
  • 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.
  • AUC Area Under the Curve
  • MDD major depressive disorder
  • SZ schizophrenia
  • SZA schizoaffective
  • 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 ⁇ 2 testing cohorts for SI and future hospitalizations in the first year ⁇ 5 diagnostic categories—all, BP, MDD, SZA, SZ). Pearson correlation data in the suicidal ideation test cohort is shown for HAMD-SI vs.
  • FIGS. 5A-5C depict prediction of Suicidal Ideation by UP-Suicide.
  • the graph in FIG. 5A depicts Receiver operating curve identifying participants with suicidal ideation against participants with No SI or intermediate SI.
  • the graph in FIG. 5A 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.
  • 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.
  • FIG. 6 depicts the Simplified Affective State Scale (SASS) questionnaire for measuring mood and anxiety.
  • SASS Simplified Affective State Scale
  • FIGS. 7A & 7B depict a screen image of the SASS mobile app ( FIG. 7A ) and CFI-S mobile app ( FIG. 7B ).
  • FIGS. 8A & 8B summarize biological pathways and diseases as analyzed in Example 1.
  • 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.
  • 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 Simple 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 Young Mania Rating Scale.
  • 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.
  • 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.
  • 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;
  • 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
  • Bottomtom 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.
  • FIG. 14 is a table depicting the cohorts used in Example 2.
  • FIG. 15 is a table depicting biological pathways and diseases as analyzed in Example 2.
  • 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.
  • 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.
  • 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.
  • 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.
  • SASS Simple Affective Scale
  • CFI-S Convergent Functional Information for Suicide
  • UP-Suicide 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.
  • patient psychiatric information may include mood information, anxiety information, and other psychiatric symptom information and combinations thereof
  • predicting suicidality in a subject is used herein to indicate in advance that a subject will attempt suicide and/or complete suicide.
  • “suicidal ideation” refers to thoughts, feelings, intent, external actions and behaviors about completing suicide. Suicidal ideation can vary from fleeting thoughts to unsuccessful attempts.
  • 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.
  • “suicidality” includes both suicide ideation and suicidal acts.
  • 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.
  • the change within a subject from visit to visit can indicate an increased or decreased risk for suicide.
  • 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.
  • 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.
  • 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.
  • 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.
  • a risk for suicide can refer to an increased (greater) risk that a subject will attempt to commit suicide and/or complete suicide
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • solute carrier family 4 sodium bicarbonate cotransporter
  • member 4 SLC4A4
  • cell adhesion molecule 1 CADM1 1, dystrobrevin, alpha
  • DTNA spermidine/spermine N1-acetyltransferase 1
  • SAT1 interleukin 6
  • IL6 interleukin 6
  • 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.
  • ERB41L5 erythrocyte membrane protein band 4.1 like 5
  • HTRA1 HtrA serine peptidase 1
  • DPCD primary ciliary dyskinesia homolog
  • GTF3C3C3 polypeptide 3, 102 kDa
  • 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.
  • 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.
  • SKA2 spindle and kinetochore associated complex subunit 2
  • CLIP4 CAP-GLY domain containing linker protein family
  • KIF2C kinesin family member 2C
  • KLHDC3 kelch domain containing 3
  • 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.
  • PIK3C3 catalytic subunit type 3
  • A2 aldehyde dehydrogenase 3 family
  • ARP3 actin-related protein 3 homolog yeast
  • BCL2 B-cell CLL
  • MOB3B MOB kinase activator
  • 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.
  • 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.
  • the subject is a female human. In another particular aspect, the subject is a male human.
  • the subject can further be diagnosed with a psychiatric disorder as known in the art.
  • the psychiatric disorder can be bipolar disorder, major depressive disorder, schizophrenia, and schizoaffective disorder, post-traumatic stress disorder, and combinations thereof.
  • 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.
  • the biomarker expression level can increase above a reference expression level of the biomarker or decrease below a reference expression level of the biomarker.
  • 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.
  • the biomarker expression level can increase above a reference expression level of the biomarker or decrease below a reference expression level of the biomarker.
  • 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.
  • SZA schizoaffective disorder
  • the biomarker expression level can increase above a reference expression level of the biomarker or decrease below a reference expression level of the biomarker.
  • 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.
  • the biomarker expression level can increase above a reference expression level of the biomarker or decrease below a reference expression level of the biomarker.
  • 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.
  • 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.
  • 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.
  • 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 here
  • 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.
  • 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.
  • 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.
  • 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.
  • EPB41L5 erythrocyte membrane protein band 4.1 like 5
  • HTRA1 HtrA serine peptidase 1
  • DPCD primary ciliary dyskinesia homolog
  • 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.
  • SKA2 spindle and kinetochore associated complex subunit 2
  • CLIP4 CAP-GLY domain containing linker protein family
  • KIF2C kinesin family member 2C
  • KLHDC3 kelch domain containing 3
  • 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.
  • PIK3C3 catalytic subunit type 3
  • A2 aldehyde dehydrogenase 3 family
  • ARP3 actin-related protein 3 homolog yeast
  • BCL2 B-cell
  • 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.
  • the subject is a female human. In another particular aspect, the subject is a male human.
  • 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.
  • 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.
  • 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) (CD
  • 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.
  • 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.
  • Suitable treatments can be a lifestyle modification, administering a therapy, and combinations thereof.
  • Suitable therapy can be a nutritional, a drug and psychotherapy.
  • Particularly suitable nutritionals can be omega-3 fatty acids, including, by way of example, docosahexaenoic acid (DHA).
  • DHA docosahexaenoic acid
  • 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, anakin
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • RAB22A D
  • I RAB22A
  • member DE/4 Blood RAS oncogene family Le-Niculescu, Balaraman et al. 2007
  • SNX27 I
  • sorting nexin family AP/4 AMY member 27 I
  • SSBP2 I
  • D D
  • D single-stranded DNA AP/4 Blood(Le-Niculescu, VT binding protein 2 Case et al. 2011) (Le-Niculescu, Balaraman et al.
  • WAPAL D
  • 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
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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;
  • 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.
  • a psychiatric disorder selected from bipolar disorder, major depressive disorder, schizophrenia, and schizoaffective disorder, post-traumatic stress disorder, and combinations thereof.
  • 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.
  • 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.
  • 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.
  • a psychiatric disorder selected from bipolar disorder, major depressive disorder, schizophrenia, and schizoaffective disorder, post-traumatic stress disorder, and combinations thereof.
  • 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
  • first and second computer device 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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 ⁇ -actin, scale factors, background and Q-values, were within acceptable limits.
  • Affymetrix Microarray Suite Version 5.0 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.
  • Affymetrix U133 Plus 2.0 GeneChips was imported as Cel. files into Partek Genomic Suites 6.6 software package (Partek Incorporated, St Louis, Mo., USA).
  • RMA multi-array analysis
  • 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).
  • 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.
  • FC fold changes
  • 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.
  • 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 .
  • 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).
  • 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.
  • 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 ).
  • 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.
  • association and linkage 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.
  • Probe sets identified by gene expression analyses could receive a maximum of 4 points.
  • 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 ).
  • 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 ⁇ 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.
  • 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.
  • 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.
  • ROC Receiver-operating characteristic 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.
  • 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 (S. 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.
  • SI suicidal ideation
  • 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.
  • 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 ).
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • the best individual biomarker across psychiatric diagnoses for predicting suicidal ideation was SLC4A4, with 72% accuracy.
  • 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.
  • UP-Suicide universal predictive measure
  • 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 ).
  • the SAT1 finding is a replication and expansion of previously reported results identifying SAT1 as a biomarker for suicidality (Le-Niculescu et al. 2013)
  • 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.
  • omega-3s in particular may be a mass-deployable therapeutic and preventive strategy.
  • 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.
  • 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.
  • CFI-S Convergent Functional Information for Suicidality
  • 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).
  • the TASS Total Affective State Scale
  • SASS Simplified Affective State Scale
  • 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.
  • 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.
  • SLC4A4 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.
  • SAT1 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).
  • 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 ).
  • 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.
  • 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.
  • 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 ).
  • 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).
  • 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 ).
  • 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.
  • 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.
  • the Simplified Affective State Scale 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.
  • 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.
  • patient psychiatric information may be represented as a quantitative rating on a defined analog scale, such as the ratings and scales described above.
  • 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.
  • CFI-S Convergent Functional Information for Suicidality
  • DIGS Diagnostic Interview for Genetic Testing
  • 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.
  • 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.
  • multiple computing devices may interact with one another (e.g., first and second computer devices).
  • first and second computer devices e.g., first and second computer devices.
  • requests and transmissions are made using data encryption.
  • the Universal Predictor for Suicide (UP-Suicide) construct 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).
  • 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 ).
  • SI suicidal ideation
  • 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.
  • Bonferroni validated biomarkers 49 genes, 50 probesets were focused on.
  • 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.
  • omega-3s in particular may be a mass-deployable therapeutic and preventive strategy.
  • 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 ).
  • 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.
  • 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).
  • TESI treatment-emergent suicidal ideation
  • Example 2 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.
  • 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.
  • 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.
  • 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).
  • circadian clock such as PER1
  • CSNK1A1 modulate the circadian clock
  • HTRA1 show at least some circadian pattern
  • CFI-S Convergent Functional Information for Suicidality
  • 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.
  • 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 ).
  • CSNK1A1 casein kinase 1, alpha 1
  • CSNK1A1 casein kinase 1, alpha 1
  • PIK3C3 was also found to be a good predictor for future hospitalizations for suicidality (ROC AUC 0.9, p-value 0.011) ( FIG. 16 ).
  • 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).
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Organic Chemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Molecular Biology (AREA)
  • Medical Informatics (AREA)
  • Analytical Chemistry (AREA)
  • Biomedical Technology (AREA)
  • Genetics & Genomics (AREA)
  • Biotechnology (AREA)
  • Public Health (AREA)
  • Biophysics (AREA)
  • Immunology (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Pathology (AREA)
  • Biochemistry (AREA)
  • Microbiology (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • Medicinal Chemistry (AREA)
  • Evolutionary Biology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Hematology (AREA)
  • Urology & Nephrology (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Food Science & Technology (AREA)
  • General Physics & Mathematics (AREA)
  • General Chemical & Material Sciences (AREA)
  • Veterinary Medicine (AREA)
  • Chemical Kinetics & Catalysis (AREA)

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.

Description

    CROSS REFERENCE TO RELATED APPLICATIONS
  • 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.
    • STATEMENT OF GOVERNMENT SUPPORT
  • 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.
    • BACKGROUND OF THE DISCLOSURE
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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:
  • 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.
  • 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 (×3) had three different probe sets with the same total score of 8.
  • 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.
  • 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×2 testing cohorts for SI and future hospitalizations in the first year×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).
  • 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.
  • FIG. 6 depicts the Simplified Affective State Scale (SASS) questionnaire for measuring mood and anxiety.
  • FIGS. 7A & 7B depict a screen image of the SASS mobile app (FIG. 7A) and CFI-S mobile app (FIG. 7B).
  • FIGS. 8A & 8B summarize biological pathways and diseases as analyzed in Example 1.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • FIG. 14 is a table depicting the cohorts used in Example 2.
  • FIG. 15 is a table depicting biological pathways and diseases as analyzed in Example 2.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • As disclosed herein, “patient psychiatric information” may include mood information, anxiety information, and other psychiatric symptom information and combinations thereof
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 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
  • 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.
  • 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 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
  • 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.
  • 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 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      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    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     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  0.000021/4 Male PTSD
    A kinase (PRKA) 0.78/0.0083
    anchor protein 13
    SECISBP2L 212450_at (D)/1  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     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    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    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  0.000015/4 All
    GDP dissociation 0.64/0.0006
    inhibitor 2 Male
    SZ/SZA
    0.72/0.0028
    UQCRC2 200883_at (D)/1    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   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   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    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   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    0.012/2 Male BP
    gamma- 0.74/0.0034
    aminobutyric
    acid (GABA) A
    receptor, beta 1
    CNP 1557943_at (D)/1    0.019/2
    2′,3′-cyclic
    nucleotide 3′
    phosphodiesterase
    RAP1A 202362_at (D)/1    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   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   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    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    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  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   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   0.0028/2 Male PTSD
    ELOVL fatty 0.76/0.012
    acid elongase 5
    KIF2C 211519_s_at (D)/4    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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Suitable treatments can be a lifestyle modification, administering a therapy, and combinations thereof.
  • Suitable therapy can be a nutritional, a drug and psychotherapy.
  • Particularly suitable nutritionals can be omega-3 fatty acids, including, by way of example, docosahexaenoic acid (DHA).
  • 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 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 VT356
    containing 136
    CD44 (D) (I)
    CD44 molecule (Indian DE2 Mouse Blood356
    blood group)
    IL6 (I) (D) tocilizumab
    interleukin 6 (interferon, AP2 Human Blood357 siltuximab
    beta 2)
    SAT1 (I) (D)
    spermidine/spermine N1- DE2 Mouse Blood358
    acetyltransferase 1 DE1
    MAOB (I) selegiline
    monoamine oxidase B DE1
    ARHGAP26 (I) (D)
    Rho GTPase activating DE1 Mouse VT356
    protein 26
    BCL2 (D) (I) (I)
    B-cell CLL/lymphoma 2 DE1 Human Blood153 Rat Dentate
    gyrus Hippocampus359
    EHBP1 (D) (I) VT356
    EH domain binding protein DE 4
    1
    FAM49B (I) (D)
    family with sequence AP2 Mouse Blood358
    similarity 49, member B
    HPCAL1 (I) (D)
    hippocalcin-like 1 DE2 Mouse VT356
    MAPK9 (I) (D)
    mitogen-activated protein DE2 Mouse VT356
    kinase 9
    NEAT1 (I) (D)
    nuclear paraspeckle DE2 Mouse VT356
    assembly transcript 1 (non-
    protein coding)
    RASL11B (I) (D)
    RAS-like, family 11, AP2 Mouse Caudate
    member B putamen356
    TRAK2 (D) (I) (I)
    trafficking protein, kinesin DE2 Mouse Blood358 Mouse PFC360
    binding 2
    ADRBK1 adrenergic, beta, (D) (I)
    receptor kinase 1 DE1 Mouse PEC361
    BRAE (I) Vemurafenib
    v-raf murine sarcoma viral DE1 Dabrafenib
    oncogene homolog B
    CAMK2B (I) (D)
    calcium/calmodulin- DE1 Mouse striatum362
    dependent protein kinase II
    beta
    CNP (D) (I) (I)
    2′,3′-cyclic nucleotide 3′ AP1 Mouse Hippocampus358 Mouse AMY356
    phosphodiesterase
    CTTN cortactin (I) (D) (D)
    DE1 Mouse Blood358 Mouse VT356
    G2E3 (D) (I)
    G2/M-phase specific E3 AP1 Mouse Hippocampus358
    ubiquitin protein ligase
    GABARAPL1 GABA(A) (I) (D)
    receptor-associated protein DE1 Mouse Blood358
    like 1
    HELZ helicase with zinc (I) (D)
    finger DE1 Mouse Blood358
    IL1B (I) (D) canakinumab
    interleukin 1, beta DE1 Mouse Blood358 gevokizumab
    gallium nitrate
    LHFP lipoma HMGIC (I) (D)
    fusion partner DE1 Mouse Blood358
    LPAR1 lysophosphatidic (D) (I) (I)
    acid receptor 1 AP1 Mouse Hippocampus, Mouse AMY356
    Blood358
    MBP myelin basic protein (D) (I) (I) (I)
    AP1 Mouse Blood358 Oligodendrocy Mouse AMY and
    tes363 Blood356
    Mouse Brain360
    MEF2C myocyte enhancer (D) (I)
    factor 2C DE1 Mouse Hippocampus
    and VT356
    NDRG1 (I) (D)
    N-myc downstream DE1 Mouse Blood358
    regulated 1
    OGFR (D) enkephalin
    opioid growth factor DE1 methionine
    receptor
    PCDH9 protocadherin 9 (D) (I)
    AP1 Mouse VT356
    PHF20L1 (I) (D) (D)
    PHD finger protein 20-like 1 DE1 Mouse Blood358 Mouse Hippocampus356
    PRKCB protein kinase C, (D) (I)
    beta DE1 Mouse PEC360
    AP1 AMY364
    RBMX RNA binding motif (D) (I)
    protein, X-linked DE1 Mouse NAC,
    Blood358
    RNASEL ribonuclease L (D) (I)
    (2′,5′-oligoisoadenylate AP1 Mouse Blood358
    synthetase-dependent)
    SNAP23 synaptosomal- (D) (I)
    associated protein, 23 kDa AP1 Mouse Blood356
    TM4SF1 transmembrane 4 (I) (D)
    L six family member 1 DE1 Mouse Blood358
    TSPAN33 tetraspanin 33 (D) (I) (I)
    AP1 Mouse Blood358 Mouse VT356
    VMP1 (I) (D)
    vacuole membrane protein 1 DE1 Mouse Blood358
    ZFP36 (I) (D) (D)
    ZFP36 ring finger protein DE1 Mouse Blood358 Rat Brain365
    BTBD3 (I) (D)
    BTB (POZ) domain DE 4 Mouse AMY358
    containing 3
    CADM1 (I) (D)
    cell adhesion molecule 1 DE4 Mouse
    VT356
    CTBS (I) (D)
    chitobiase, di-N-acetyl- DE 4 VT356
    LAMB1 (I) (D)
    laminin, beta 1 AP4 Mouse HIP358
    PLEC (D) (I)
    plectin DE 4 Mouse VT356
    RAD23A (D) (I)
    RAD23 homolog A DE 4 Mouse Blood358
    (S. cerevisiae)
    SETD8 (D) (I)
    SET domain containing DE 4 Mouse Blood358
    (lysine methyltransferase) 8
    TXNRD2 (D) (I)
    thioredoxin reductase 2 AP4 Mouse Blood356
    (I): increase in biomarker expression;
    (D): decrease in biomarker expression
  • 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
  • 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 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 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 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • Methods
  • Human Blood Gene Expression Experiments and Analyses
  • 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.
  • 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 β-actin, scale factors, background and Q-values, were within acceptable limits.
  • 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).
  • Gene Expression Analysis in the Discovery Cohort
  • 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.
  • 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.
  • 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≥1.2 were scored+1 (increased in High SI) or −1 (decreased in High SI). FC≥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.
  • 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.
  • 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.
  • 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).
  • 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).
  • Convergent Functional Genomics
  • 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).
  • 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.
  • 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.
  • 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.
  • 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).
  • Pathway Analyses
  • 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≥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.
  • Validation Analyses
  • 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.
  • 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.
  • 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.
  • Testing Analyses
  • 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.
  • 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.
  • 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
  • In this Example, male subjects were analyzed for predicting suicidal ideation and future hospitalizations for suicidality.
  • Human Participants
  • 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).
  • 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.
  • 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).
  • 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).
  • 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.
  • 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.
  • Medications
  • 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.
  • Results
  • 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.
  • 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: ≥33% (low), ≥50% (medium), and ≥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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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).
  • 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).
  • 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
  • In this Example, female subjects were analyzed for predicting suicidal ideation and future hospitalizations for suicidality.
  • Human Participants
  • 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).
  • 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.
  • 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).
  • 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).
  • 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).
  • 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.
  • Medications
  • 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.
  • Clock Gene Database
  • 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).
  • Clinical Measures
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • Combining Gene Expression and Clinical Measures
  • 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).
  • Results
  • Discovery of Biomarkers for Suicidal Ideation
  • 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.
  • 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: ≥33.3% (low), ≥50% (medium), and ≥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).
  • Prioritization of Biomarkers Based on Prior Evidence in the Field
  • 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.
  • Validation of Biomarkers for Behavior in Suicide Completers
  • 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).
  • Selection of Biomarkers for Testing of Predictive Ability
  • 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.
  • Biological Understanding
  • 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.
  • 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).
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • Clinical Information
  • 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.
  • Testing for Predictive Ability
  • 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.
  • 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).
  • 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.
  • 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.
  • 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).
  • Discussion
  • 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.
  • 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).
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Claims (21)

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.
US16/779,229 2015-06-12 2020-01-31 Predicting suicidality using a combined genomic and clinical risk assessment Abandoned US20200312425A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US16/779,229 US20200312425A1 (en) 2015-06-12 2020-01-31 Predicting suicidality using a combined genomic and clinical risk assessment

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
US201562174880P 2015-06-12 2015-06-12
US201662278707P 2016-01-14 2016-01-14
PCT/US2016/036985 WO2016201299A1 (en) 2015-06-12 2016-06-10 Predicting suicidality using a combined genomic and clinical risk assessment
US201715735304A 2017-12-11 2017-12-11
US16/779,229 US20200312425A1 (en) 2015-06-12 2020-01-31 Predicting suicidality using a combined genomic and clinical risk assessment

Related Parent Applications (2)

Application Number Title Priority Date Filing Date
PCT/US2016/036985 Continuation WO2016201299A1 (en) 2015-06-12 2016-06-10 Predicting suicidality using a combined genomic and clinical risk assessment
US15/735,304 Continuation US10991449B2 (en) 2015-06-12 2016-06-10 Predicting suicidality using a combined genomic and clinical risk assessment

Publications (1)

Publication Number Publication Date
US20200312425A1 true US20200312425A1 (en) 2020-10-01

Family

ID=57504404

Family Applications (2)

Application Number Title Priority Date Filing Date
US15/735,304 Active US10991449B2 (en) 2015-06-12 2016-06-10 Predicting suicidality using a combined genomic and clinical risk assessment
US16/779,229 Abandoned US20200312425A1 (en) 2015-06-12 2020-01-31 Predicting suicidality using a combined genomic and clinical risk assessment

Family Applications Before (1)

Application Number Title Priority Date Filing Date
US15/735,304 Active US10991449B2 (en) 2015-06-12 2016-06-10 Predicting suicidality using a combined genomic and clinical risk assessment

Country Status (9)

Country Link
US (2) US10991449B2 (en)
EP (1) EP3307910A4 (en)
JP (1) JP2018523979A (en)
KR (1) KR20180018706A (en)
CN (1) CN108291259A (en)
AU (1) AU2016274988A1 (en)
CA (1) CA2988578A1 (en)
IL (1) IL256123A (en)
WO (1) WO2016201299A1 (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10991449B2 (en) 2015-06-12 2021-04-27 Indiana University Research And Technology Corporation Predicting suicidality using a combined genomic and clinical risk assessment
US11047009B2 (en) 2013-02-28 2021-06-29 Indiana University Research And Technology Corporation Blood biomarkers for suicidality
US11608532B2 (en) 2017-05-12 2023-03-21 Indiana University Research And Technology Corporation Precision medicine for treating and preventing suicidality
KR20230105844A (en) * 2022-01-05 2023-07-12 전남대학교산학협력단 Assessment methods and prognostic kit for predicting suicidal behaviors in patients with depressive disorders using multimodal serum biomarkers

Families Citing this family (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU2018209164B2 (en) 2017-01-17 2021-11-04 Heparegenix Gmbh Protein kinase inhibitors for promoting liver regeneration or reducing or preventing hepatocyte death
US20180293682A1 (en) * 2017-04-05 2018-10-11 Global Tel*Link Corporation System and method for providing suicide prevention and support
US20200350056A1 (en) * 2017-07-27 2020-11-05 Harmonex Neuroscience Research Automated assessment of medical conditions
CN108410978A (en) * 2018-05-23 2018-08-17 北京泱深生物信息技术有限公司 Major depressive disorder related gene and its application in diagnosis
US20210255198A1 (en) * 2018-06-11 2021-08-19 The United States of America as Represented by the Department of Veterans Affairs Office Methods for objective assessment of stress, early detection of risk for stress disorders, matching individuals with treatments, monitoring response to treatment, and new methods of use for drugs
CN109030641B (en) * 2018-06-11 2021-04-06 合肥华润神鹿药业有限公司 Method for constructing fingerprint of Yangweishu granules
US11551787B2 (en) * 2018-07-02 2023-01-10 Pheramor, Inc. Personality genetics
KR102013692B1 (en) * 2018-11-09 2019-08-23 한국과학기술정보연구원 Apparatus and method for predicting suicide criticality
US20220259657A1 (en) * 2019-05-23 2022-08-18 Unist(Ulsan National Institute Of Science And Technology) Method for discovering marker for predicting risk of depression or suicide using multi-omics analysis, marker for predicting risk of depression or suicide, and method for predicting risk of depression or suicide using multi-omics analysis
KR20220052946A (en) * 2019-09-06 2022-04-28 각고호우징 게이오기주크 Method for producing cell aggregates containing glial progenitor cells
RU2726470C1 (en) * 2019-12-25 2020-07-14 Федеральное государственное бюджетное научное учреждение "Томский национальный исследовательский медицинский центр Российской академии наук" (Томский НИМЦ) Method for assessing the risk of suicidal behavior of patients with bipolar affective disorder
US20230333117A1 (en) * 2020-08-21 2023-10-19 Therapeutic Solutions International, Inc. Methods of determining risk of suicide by immunological assessment
CN112259234A (en) * 2020-10-30 2021-01-22 南通大学 Tumor patient suicide risk prediction model establishment method
CN114662261A (en) * 2020-12-22 2022-06-24 中核武汉核电运行技术股份有限公司 Multi-state isolation effect modeling method based on layered MMDD and MDD
WO2022182908A1 (en) * 2021-02-26 2022-09-01 California Institute Of Technology Probiotic therapies for social deficit and stress response
US12068066B2 (en) * 2021-03-01 2024-08-20 Kpn Innovations, Llc. System and method for generating an addiction nourishment program
CN116694644B (en) * 2023-07-13 2023-12-12 中国水产科学研究院珠江水产研究所 Female specific Rab35 gene of Chinese softshell turtle and application thereof

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DK1599576T3 (en) 2003-02-20 2016-08-01 Mayo Foundation Methods for selecting antidepressants
US8688385B2 (en) 2003-02-20 2014-04-01 Mayo Foundation For Medical Education And Research Methods for selecting initial doses of psychotropic medications based on a CYP2D6 genotype
US8609112B2 (en) * 2003-10-29 2013-12-17 Allergan, Inc. Botulinum toxin treatments of depression
UY28650A1 (en) 2003-12-05 2005-02-28 Forest Laboratories MEMANTINE FOR THE PREVENTION OR DECREASE OF SUICIDE CONDUCT AND FOR THE TREATMENT OF THE MAJOR DEPRESSION ASSOCIATED WITH THIS CONDUCT
EP3522172B1 (en) * 2009-04-27 2021-10-20 Children's Hospital Medical Center Method for assessing a neuropsychiatric condition of a human subject
CA2777843A1 (en) 2009-10-16 2011-04-21 University Of South Florida Treatment of suicidal ideation or behavior using inhibitors of nicotinic acetylcholine receptors
EP3949973A3 (en) * 2010-02-12 2022-07-06 Gentelon, Inc. Compositions and methods for treating depression
WO2013042054A1 (en) 2011-09-19 2013-03-28 Carmel - Haifa University Economic Corporation Ltd. Buprenorphine for the treatment of acute suicidality
US20140243211A1 (en) 2013-02-28 2014-08-28 Indiana University Research & Technology Corporation Blood biomarkers for suicidality
WO2015006645A1 (en) 2013-07-11 2015-01-15 The Johns Hopkins University A dna methylation and genotype specific biomarker of suicide attempt and/or suicide ideation
EP3604556A1 (en) 2014-12-12 2020-02-05 Medivation Prostate Therapeutics LLC Method for predicting response to breast cancer therapeutic agents and method of treatment of breast cancer
CA2988578A1 (en) 2015-06-12 2016-12-15 Indiana University Research & Technology Corporation Predicting suicidality using a combined genomic and clinical risk assessment
CA3105451A1 (en) 2017-05-12 2018-11-15 Indiana University Research And Technology Corporation Precision medicine for treating and preventing suicidality

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11047009B2 (en) 2013-02-28 2021-06-29 Indiana University Research And Technology Corporation Blood biomarkers for suicidality
US10991449B2 (en) 2015-06-12 2021-04-27 Indiana University Research And Technology Corporation Predicting suicidality using a combined genomic and clinical risk assessment
US11608532B2 (en) 2017-05-12 2023-03-21 Indiana University Research And Technology Corporation Precision medicine for treating and preventing suicidality
KR20230105844A (en) * 2022-01-05 2023-07-12 전남대학교산학협력단 Assessment methods and prognostic kit for predicting suicidal behaviors in patients with depressive disorders using multimodal serum biomarkers
KR102621049B1 (en) 2022-01-05 2024-01-03 전남대학교산학협력단 Assessment methods and prognostic kit for predicting suicidal behaviors in patients with depressive disorders using multimodal serum biomarkers

Also Published As

Publication number Publication date
CA2988578A1 (en) 2016-12-15
CN108291259A (en) 2018-07-17
WO2016201299A1 (en) 2016-12-15
KR20180018706A (en) 2018-02-21
EP3307910A4 (en) 2019-05-22
JP2018523979A (en) 2018-08-30
US20180181701A1 (en) 2018-06-28
AU2016274988A1 (en) 2017-12-21
US10991449B2 (en) 2021-04-27
IL256123A (en) 2018-02-28
EP3307910A1 (en) 2018-04-18

Similar Documents

Publication Publication Date Title
US20200312425A1 (en) Predicting suicidality using a combined genomic and clinical risk assessment
Kia et al. Identification of candidate Parkinson disease genes by integrating genome-wide association study, expression, and epigenetic data sets
Schreiner et al. Vegetarian or gluten-free diets in patients with inflammatory bowel disease are associated with lower psychological well-being and a different gut microbiota, but no beneficial effects on the course of the disease
Middeldorp et al. Preclinical assessment of young blood plasma for Alzheimer disease
Niculescu et al. Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach
Levey et al. Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment
Kebschull et al. Molecular differences between chronic and aggressive periodontitis
US11608532B2 (en) Precision medicine for treating and preventing suicidality
Kebschull et al. Gingival tissue transcriptomes identify distinct periodontitis phenotypes
Kohen et al. Association of serotonin transporter gene polymorphisms with poststroke depression
Xie et al. Interactive effect of stressful life events and the serotonin transporter 5-HTTLPR genotype on posttraumatic stress disorder diagnosis in 2 independent populations
Konradi et al. Molecular evidence for mitochondrial dysfunction in bipolar disorder
Sharp et al. The future of genomic profiling of neurological diseases using blood
Mercer et al. Acute and posttraumatic stress symptoms in a prospective gene× environment study of a university campus shooting
McCreary et al. Development and validation of a targeted next-generation sequencing gene panel for children with neuroinflammation
Redei et al. Blood transcriptomic markers for major depression: from animal models to clinical settings
Bhardwaj et al. MiR-4668 as a novel potential biomarker for eosinophilic esophagitis
Bluth et al. Use of gene expression profiles in cells of peripheral blood to identify new molecular markers of acute pancreatitis
Bitar et al. Apremilast in recalcitrant cutaneous dermatomyositis: a nonrandomized controlled trial
EP3972975A1 (en) Methods for objective assessment of memory, early detection of risk for alzheimer&#39;s disease, matching individuals with treatments, monitoring response to treatment, and new methods of use for drugs
US20210255198A1 (en) Methods for objective assessment of stress, early detection of risk for stress disorders, matching individuals with treatments, monitoring response to treatment, and new methods of use for drugs
Haghikia et al. Interferon-beta affects mitochondrial activity in CD4+ lymphocytes: Implications for mechanism of action in multiple sclerosis
Zilliox et al. An initial miRNA profile of persons with persisting neurobehavioral impairments and states of disordered consciousness after severe traumatic brain injury
Dhillon et al. Gene expression profiling in pediatric appendicitis
JP2021517892A (en) Precision Medicine for Pain: Diagnostic Biomarkers, Pharmacological Genomics, and Lipapas Drugs

Legal Events

Date Code Title Description
STPP Information on status: patent application and granting procedure in general

Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION

STPP Information on status: patent application and granting procedure in general

Free format text: NON FINAL ACTION MAILED

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION