Patent Grant
11608532
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.
Based on the foregoing, objective and precise identification of individuals at risk, ways of monitoring response to treatments, and novel preventive therapeutics need to be discovered, employed, and widely deployed. Particularly, objective and quantitative markers would permit better and more precise assessment, tracking, and prediction of suicidal risk, which would enable preventive therapeutic interventions. Accordingly, the present disclosure is directed to identifying universal predictors, and in some embodiments, personalized predictors for suicidality. The present disclosure is generally directed at methods for assessing suicidality and early identification of risk for future suicidality, as well as methods for matching patients and drugs for prevention and mitigation of suicidality, and for monitoring response to treatment. Further, the present disclosure describes new methods of use for drugs and natural compounds repurposed for treating suicidality. All the above-mentioned methods are computer-assisted methods analyzing the expression of panels of genes, clinical measures, and drug databases. A universal approach in everybody, as well as a personalized approach by gender, and by diagnosis, are disclosed.
The present disclosure relates generally to compounds for mitigating suicidality. Particularly, novel drugs and natural compounds for treating and preventing suicidality (e.g., suicide ideation and actions, future hospitalization due to suicidality, and suicide completion) have now been identified through bioinformatics drug repurposing methods using novel gene expression biomarkers. The disclosure describes compounds for use in everybody (universal), as well as personalized by gender (males, females), diagnosis (bipolar, depression), and gender and diagnosis combined (male bipolar, male depression). Further, the present disclosure relates to gene expression biomarkers and their use for deciding in a particular person which drug or natural compound to use (precision medicine) for treating and preventing suicidality (e.g., suicide ideation and actions, future hospitalization due to suicidality, and suicide completion), as well as for tracking response to the drug or natural compound (pharmacogenomics). More particularly, the present disclosure relates to an algorithm composed of clinical measures and biomarkers for identifying subjects who are at risk of committing suicide, as well as for monitoring response to treatment. In some embodiments, the biomarkers used herein have been found to be more universal in nature, working across psychiatric diagnoses and genders. Such biomarkers may reflect and/or be a proxy for the core biology of suicide. In other embodiments, the present disclosure relates to biomarkers identified using a personalized approach; that is, by psychiatric diagnosis and/or gender, with a focus on bipolar males, the highest risk group. Such a personalized approach may be more sensitive to gender differences and to the impact of psychiatric co-morbidities and medications.
The present disclosure further relates to determining subtypes of suicidality using an app (SASS), based on mental state at the time of high suicidal ideation, and identified four subtypes: high anxiety, low mood, combined, and non-affective (psychotic). Such subtypes may delineate groups of individuals that are more homogenous in terms of biology and behavior.
The present disclosure further relates to a checklist of socio-demographic and psychological factors that influence the likelihood of becoming suicidal (CFI-S), with contributions from six domains (life events, mental health, physical health, environmental factors, cultural factors, and addictions). It can provide a likelihood score for an individual attempting that behavior (suicide) in the future. The items that are positive on the checklist can have differences in importance embodied as weight coefficients, based on specificity for suicide (Table 1), and based on empirical data, such as rank order in predictive datasets (
Biomarkers underlying propensity to behaviors can also be identified, as described in the present disclosure. They can be viewed as a checklist of biological measures. Again, the items/biomarkers that are positive/changed in levels on the checklist can have different weights of importance embodied as weight coefficients, based on specificity for suicide as reflected in a convergent functional genomics (CFG) score obtained during their discovery, prioritization and validation, (Table 1), and also based on other empirical data, such as strength in predictive datasets (
Besides the checklists of factors that influence behavior (such as CFI-S in the case of suicide), and the checklist of biomarkers that indicate propensity to a behavior, such as panels of predictive biomarkers, the state of mind of an individual is a major factor influencing whether a behavior will happen or not. So a checklist of measures of the mind domains (anxiety and mood (for example measured with SASS), psychosis (for example measured with PANSS Positive Scale), and a direct assessment of the severity of suicidal ideation (for example measured with the suicide item in HAMD (HAMD-SI), would be informative to include in the overall algorithm to predict suicidality, and as targets for intervention to facilitate or prevent behaviors.
The present disclosure is generally directed at methods for assessing suicidality and early identification of risk for future suicidality, as well as methods for matching patients and drugs for prevention and mitigation of suicidality, and for monitoring response to treatment. The present disclosure is further related to drugs for mitigating suicidality in subjects. Particular drugs have been found that can mitigate suicidality in subjects universally; that is, drugs that can be used for mitigating suicidality across psychiatric diagnoses, genders and subtypes of suicidality. Some drugs, however, have been found that can be used more effectively for mitigating suicidality dependent on gender, psychiatric diagnoses, subtypes and combinations thereof.
Additionally, the present disclosure relates to biomarkers and their use for predicting a subject's risk of suicidality. In some embodiments, the biomarkers used herein have been found to be more universal in nature, working across psychiatric diagnoses, genders and subtypes. In other embodiments, the present disclosure relates to biomarkers identified using a personalized approach; that is, by psychiatric diagnosis, gender and subtype.
The present disclosure further relates to determining subtypes of suicidality based on mental state at the time of high suicidal ideation, and identified four subtypes: high anxiety, low mood, combined, and psychotic (non-affective) such to delineate groups of individuals that are more homogenous in terms of biology and behavior.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.
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:
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.
The present disclosure is generally directed at methods for assessing suicidality and early identification of risk for future suicidality, as well as methods for matching patients and drugs for prevention and mitigation of suicidality, and for monitoring response to treatment. The methods may further include the generation of a report providing a risk score and/or personalized treatment options. Further, the present disclosure generally is directed to drugs for mitigating suicidality in subjects. Particular drugs have been found that can mitigate suicidality in subjects universally; that is, drugs that can be used for mitigating suicidality across psychiatric diagnoses and genders. Some drugs, however, have been found that can be used more effectively for mitigating suicidality dependent on gender, psychiatric diagnoses, and combinations thereof.
In additional embodiments, the present disclosure is directed to blood gene expression biomarkers that are more universal in nature; that is, blood biomarkers that can be used for predicting suicidality across psychiatric diagnoses and genders. Accordingly, a longitudinal within-participant design and large cohorts were used.
Additionally, subtypes of suicidality were identified based on mental state (anxiety, mood, psychosis) at the time of high suicidal ideation.
Furthermore, the predictive ability of the biomarkers discovered were examined, in a completely independent cohort, in all the participants in it, as well as divided by subtypes, and personalized by gender and diagnosis.
The top biomarkers were combined with scores from a clinical information measure of suicide risk (CFI-S), as well as anxiety and mood (SASS), to obtain a broader spectrum predictor (UP-Suicide) that puts the biomarkers in the context of the person and his/her mental state. This list was then leveraged for therapeutics and drug discovery purposes to see if some of the biomarkers identified could be modulated by existing compounds used to treat suicidality, and also to conduct bioinformatics drug repurposing analyses to discover new drugs and natural compounds that may be useful for treating suicidality.
As disclosed herein, “patient psychiatric information” may include mood information, anxiety information, psychosis 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 universal method for predicting suicidality in a subject; that is, a method for predicting suicidality across all psychiatric diagnoses and for either gender. 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 Tables 3A-3G 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 Tables 3A-3G 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 aspect, the subject is a female human. In another particular aspect, the subject is a male human, and in another particular aspect, the subject is a male bipolar human. In yet another particular aspect, the subject is a male depressed human.
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, psychosis and other like psychiatric symptoms, and combinations thereof in the subject using questionnaires and/or a computer-implemented method for assessing mood, anxiety, psychosis, 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, psychosis information and combinations thereof into the first computer device; storing, by the first computer device, the mood information, anxiety information, psychosis information and combinations thereof in the memory device; computing, by the first computer device, of the mood information, anxiety information, psychosis 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, psychosis 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, psychosis information and combinations thereof; and transmitting, by the first computer device, the mood information, anxiety information, psychosis information and combinations thereof to the second computer device to assess mood, anxiety, psychosis 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 embodiments of the present disclosure, as specifically seen in
Additionally, in accordance with another aspect of 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 universal method for future hospitalization due to suicidality in a subject; that is, a method for predicting future hospitalization due to suicidality across all psychiatric diagnoses and genders. 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 a blood biomarker expression level in a sample obtained from the subject.
In another aspect, the present disclosure is directed to further mitigating suicidality in the subject(s) identified above. 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, upon identifying a difference between the expression level of the blood biomarker in the sample obtained from the subject and the reference expression level of the blood biomarker, 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).
In some embodiments, the therapies can include drugs and natural compounds that have now been found to be effective in mitigating suicidality either universally or for a specific gender and/or psychiatric diagnosis. Exemplary repurposed drugs and natural compounds are found in Tables 6-18.
Various functions and advantages of these and other embodiments of the present disclosure will be more fully understood from the examples shown below. The examples are intended to illustrate the benefits of the present disclosure, but do not exemplify the full scope of the disclosure.
In this Example, blood biomarkers from three cohorts of subjects were analyzed.
Materials and Methods
Cohorts
Three independent cohorts were examined: discovery cohort (a live psychiatric participants cohort), validation cohort (a postmortem coroner's office cohort), and testing cohort (also referred to herein as “test cohort”) (an independent live psychiatric participants test cohort for predicting suicidal ideation, and for predicting future hospitalizations for suicidality) (
The live psychiatric participants are part of a larger longitudinal cohort of adults that are continuously being collected. Participants are 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, per IRB approved protocol. 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 (
The participant discovery cohort, from which the biomarker data were derived, consisted of 66 participants (49 males, 17 females) with psychiatric disorders and multiple testing visits, 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 2 participants with 6 visits each, 3 participants with 5 visits each, 5 participants with 4 visits each, 34 participants with 3 visits each, and 22 participants with 2 visits each resulting in a total of 193 blood samples for subsequent gene expression microarray studies (
The postmortem validation cohort, in which the top biomarker findings were validated for behavior, consisted of 38 male and 7 female violent suicide completers obtained through the Marion County coroner's office (Table 2). A last observed alive postmortem interval of 24 h or less was required, and the cases selected had completed suicide by means other than overdose, which could affect gene expression. Thirty-one participants completed suicide by gunshot to head or chest, 12 by asphyxiation, 1 by slit wrist, and 1 by electrocution. Next of kin signed informed consent at the coroner's office for donation of blood for research.
The independent test cohort for predicting suicidal ideation (Table 2) consisted of 184 male and 42 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 226 blood samples in which whole-genome blood gene expression data were obtained (
The test cohort for predicting future hospitalizations (
Medications. The participants in the discovery cohort were all diagnosed with various psychiatric disorders (Table 2). 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, the discovery of differentially expressed genes was based on within-participant analyses, which factor out not only genetic background effects but also minimizes medication effects, as the participants rarely had 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.
Blood Gene Expression Experiments
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 as described in Le-Niculescu et al., Mol Psychiatry 2013; 18(12): 1249-1264.
Microarrays. Microarray work was carried out using methodology described in Niculescu et al., Mol Psychiatry 2015; 20(11): 1266-1285.
Biomarkers
Discovery Cohort
The participant's suicidality score from the item in the Hamilton Rating Scale for Depression (HAMD SI) assessed at the time of blood collection (
The 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 probeset on the chip (Affymetrix U133 Plus 2.0 GeneChips) for all participants in the discovery cohort (Affymetrix Inc., Santa Clara, Calif.). 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, Mich., USA). Using only the perfect match values, a robust multi-array analysis (RMA) was conducted, background corrected with quantile normalization and a median polish probeset summarization, to obtain the normalized expression levels of all probesets for each chip. RMA was performed independently for each gender and diagnosis subgroup used in the Example, to avoid potential artefacts due to different ranges of gene expression in different gender and diagnoses. Then the participants' normalized data was extracted from these gender and diagnosis RMAs 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) from No SI to High SI, as described in Niculescu et al., Mol Psychiatry 2015; 20(11): 1266-1285 and Levey et al., Mol Psychiatry 2016; 21(6): 768-785. For a comparison between two sequential visits, 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 versus the phene (which is 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 (SI levels), or a change in gene expression between visits despite no change in phene expression (SI levels), 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 an overall 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 probeset was rewarded by a doubling of its overall 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 score for that probeset in that participant was zero. An R script was developed to conduct the calculations, and the analysis was double-checked manually using formulas/macros in Excel.
DE analysis. For the longitudinal within-participant DE analysis, fold changes (FC) in gene expression were calculated between sequential visits within each participant, as described in Niculescu et al., Mol Psychiatry 2015; 20(11): 1266-1285 and Levey et al., Mol Psychiatry 2016; 21(6): 768-785. Scoring methodology was similar to that used above for AP. Probesets 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. A perfection bonus and a non-tracking correction were also used for the DE analysis. If the gene expression perfectly tracked the suicidal ideation in a participant that had at least two comparisons (3 visits), that probeset 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 score for that probeset in that participant was zero. An R script was developed to conduct the calculations, and the analysis was double-checked manually using formulas/macros in Excel.
Internal score. Once scores within each participant were calculated, an algebraic sum across all participants was obtained, for each probeset. Probesets were then given internal points based upon these algebraic sum scores. Probesets with scores above the 33.3% of the maximum score (for increased probesets, respectively for decreased probesets) received 1 point, those above 50% received 2 points, and those above 80% received 4 points. For AP analyses, 35 probesets received 4 points, 754 probesets received 2 points, and 2197 probesets received 1 point, for a total of 2986 probesets. For DE analyses, 35 probesets received 4 points, 1477 probesets received 2 points, and 6450 probesets received 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
Gene Symbol for the probesets were identified using NetAffyx (Affymetrix) for Affymetrix HG-U133 Plus 2.0 GeneChips, followed by GeneCards to confirm the primary gene symbol. In addition, for those probesets that were not assigned a gene symbol by NetAffyx, GeneAnnot (https://genecards.weizmann.ac.il/geneannot/index.shtml) was used to obtain a gene symbol for these uncharacterized probesets, followed by GeneCard. Genes were then scored using manually curated CFG databases as described below (
Prioritization Using Convergent Functional Genomics (CFG)
Databases. Manually curated databases were established of the human gene expression/protein expression studies (postmortem brain, peripheral tissue/fluids: CSF, blood and cell cultures), human genetic studies (association, copy number variations and linkage), and animal model gene expression and genetic studies, published to date on psychiatric disorders. Only the findings deemed significant in the primary publication, using the particular experimental design and thresholds, are included in the databases. 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 in the CFG cross validation and prioritization platform (
Human postmortem brain gene expression/protein 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, CSF, and other peripheral tissue gene expression/protein expression evidence. Converging evidence was scored for a gene if there were published reports of human blood, lymphoblastoid cell lines, cerebrospinal fluid, 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, 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 physical positions (bp) of each gene were 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/map_interpolator.shtml. 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 (
Validation Analyses
For the AP analyses, the Affymetrix microarray .chp data files from the participants in the coroner validation cohort of suicide completers were imported into the MASS Affymetrix Expression Console, alongside the data files from the No SI and High SI groups in the live discovery cohort. The AP data was transferred to an Excel sheet and transformed: A into 0, M into 0.5, and P into 1. All data was then Z-scored together by gender. If a probeset would have showed no variance and thus gave a non-determined (0/0) value in Z-scoring in a gender, the values were excluded from that probeset for that gender from the analysis. All probesets, however, did show variance in this Example.
For the DE analyses, Affymetrix microarray .cel files were imported from the participants in the validation cohort of suicide completers into Partek Genomic Suites. An RMA was run by gender, background corrected with quantile normalization, and a median polish probeset summarization of the chips from the validation cohort was conducted to obtain the normalized expression levels of all probesets for each chip. The No SI and High SI groups from the discovery cohort were RMA by gender and diagnosis, as described above for Discovery. 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, and the non-log transformed coroner validation cohort expression data was transferred to an Excel sheet, alongside data from the No SI and High SI groups from the discovery cohort. All data was then Z-scored together by gender.
Validation analyses of the candidate biomarker genes were conducted separately for AP and for DE. The top candidate genes (total CFG score of 4 or above), were stepwise changed in expression from the No SI group to the High SI group to the suicide completers group. A CFG score of 4 or above reflects an empirical cutoff of 33.3% of the maximum possible CFG score of 12, which permits the inclusion of potentially novel genes with maximal internal score of 4, but no external evidence score. The Excel sheets with the Z-scored by gender expression data from AP were imported, respectively from DE, into Partek, and statistical analyses were performed using a one-way ANOVA for the stepwise changed probesets, and stringent Bonferroni corrections for all the probesets tested in AP and DE (stepwise and non-stepwise) (
Discovery and Validation in Male Bipolars
For male bipolar disorders, the discovery and validation were conducted as described above except that only male bipolar subjects from the discovery cohort (n=20 subjects, 65 visits) were used for discovery, and male suicide completers (n=38) were used for validation.
Phenotypic Measures
SASS. The Simplified Affective State Scale (SASS) is an 11-item scale for measuring mood state (SMS) and anxiety state (SAS), previously developed and described in Niculescu et al., Mol Psychiatry 2015; 20(11): 1266-1285 and Niculescu et al., American journal of medical genetics Part B, Neuropsychiatric genetics: the official publication of the International Society of Psychiatric Genetics 2006; 141B(6): 653-662. The SASS has a set of 11 visual analog scales (7 for mood, 4 for anxiety) each item ranging from 0 to 100 for mood state, and the same for anxiety state. The averaged 7 items for mood give the Mood score, and the averaged 4 items for anxiety give the Anxiety score.
CFI-S. Convergent Functional Information for Suicidality (CFI-S) (
Subtypes
In order to identify possible subtypes of suicidality, a two-way unsupervised hierarchical clustering of the high SI visits in the discovery cohort, based on measures of anxiety and mood (from the SASS), as well as psychosis (PANS S Positive) was used. The mood item was inverted for the purposes of this analysis so that higher values indicate low mood. This clustering was used to identify four distinct subtypes of suicidality/high suicidal ideation: a high anxiety subtype, a low mood subtype, a combined affective subtype, and a non-affective (psychotic) subtype (
The insight from the discovery cohort was used to divide the independent test cohort into the four subtypes, using anxiety and mood measures from SASS, which are on a scale of 0 to 100. The high anxiety subtype participant visits had anxiety above 50 and low mood below 50, the low mood subtype had low mood below 50 and anxiety below 50, the combined affective subtype had low mood above 50 and anxiety above 50, and the non-affective subtype had low mood below 50 and anxiety below 50.
Combining Biomarkers and Phenotypic Measures
The Universal Predictor for Suicidality (UP-Suicide) construct, the primary endpoint, was decided upon as part of the apriori study design. It combines the top biomarkers with the phenomic (clinical) measures (CFI-S score, Mood and Anxiety scores from SASS). It is calculated as the simple algebraic summation of the components included (averaged panel of biomarkers (BioM), CFI-S, Mood, Anxiety). All individual biomarkers and clinical measure scores are Z-scored by gender and diagnosis, to normalize for different ranges of values and be able to combine them into a composite predictor (UP-Suicide). Decreased biomarkers, and Mood, have a minus sign in front of them.
Diagnostics
The test cohort for predicting suicidal ideation (state), and the subset of it that is a test cohort for predicting future hospitalizations for suicidality (trait), were assembled out of data that was RMA normalized by gender and diagnosis. The cohort was completely independent, there was no subject overlap with the discovery cohort. Phenomic (clinical) and gene expression markers used for predictions were Z-scored by gender and diagnosis, to be able to combine different markers into panels and to avoid potential artefacts due to different ranges of expression in different gender and diagnoses. Markers were combined by simple summation of the increased risk markers minus the decreased risk markers. Predictions were performed using R-studio. For cross-sectional analyses, marker expression levels were used, z-scored by gender and diagnosis. For longitudinal analyses, four measures were combined: marker expression levels, slope (defined as ratio of levels at current testing visit vs. previous visit, divided by time between visits), maximum levels (at any of the current or past visits), and maximum slope (between any adjacent current or past visits). For decreased markers, the minimum, rather than the maximum, was used for level calculations. All four measures were Z-scored then combined in an additive fashion into a single measure. The longitudinal analysis was carried out in a sub-cohort of the testing cohort consisting of participants that had at least two test visits.
Predicting High Suicidal Ideation State. Receiver-operating characteristic (ROC) analyses between genomic and phenomic marker levels and suicidal ideation (SI) were performed by assigning participants with a HAMD-SI score of 2 and greater into the high SI category. The pROC function of the R studio was used. The Z-scored biomarker and phene scores were used, running them in this ROC generating program against the “diagnostic” groups in the independent test cohort (high SI vs. the rest of subjects). Additionally, ANOVA was performed between no SI (HAMD-SI 0), intermediate (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 (Tables 4A & 4B,
SLC4A4
0.65/1.86E−04
0.66/4.92E−05
0.77/5.93E−11
0.77/3.43E−11
0.81/5.55E−14
0.86/9.98E−18
0.89/2.59E−20
0.89/1.36E−20
Mood and Anxiety and CFI-S
All
52/523
0.90/3.87E−21
0.50/5.91−35
3.42E−19
and BioM 12(UP-Suicide)
0.89/4.56E−21
SPTBN1
Mood and Anxiety and CFI-S
M-BP
12/128
0.95/1.62E−07
0.62/1.92E−15
8.31E−07
and BioM 12
PSME4
0.69/3.70E−05
0.71/9.78E−06
0.75/1.79E−07
0.76/6.34E−08
0.81/5.27E−10
0.82/9.96E−11
0.76/6.65E−08
Mood and Anxiety and CFI-S
All
38/470
0.77/2.87E−08
0.23/2.81E−07
9.11E−07
1.40/5.31E−08
and BioM 12
0.76/3.87E−08
0.82/9.38E−11
Mood and Anxiety and CFI-S
M-BP
4/120
0.79/2.59E−02
0.19/1.88E−02
7.92E−02
1.44/4.72E−02
and BioM 12
Predicting Future Hospitalizations for Suicidality in First Year Following Testing. Analyses for predicting hospitalizations for suicidality in the first year following each testing visit were conducted in subjects that had at least one year of follow-up in the VA system, for which there was access to complete electronic medical records. ROC analyses between genomic and phenomic marker levels at a specific testing visit and future hospitalizations were performed as described above, based on assigning if participants had been hospitalized for suicidality (ideation, attempts) or not within one year following a testing visit. Additionally, a one tailed t-test with unequal variance was performed between groups of participant visits with and without future 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 marker levels.
A correlation analyses for hospitalization frequency for all future hospitalizations due to suicidality was also conducted, including those occurring beyond one year of follow-up, in the years following testing (on average 4.90 years per participant, range 0.40 to 10.42 years), as this calculation, unlike the ROC and t-test, accounts for the actual length of follow-up, which varied from participant to participant. The ROC and t-test might in fact, if used, under-represent the power of the markers to predict, as the more severe psychiatric patients are more likely to move geographically and/or be lost to follow-up.
Therapeutics
The individual top biomarkers known to be modulated by existing drugs were analyzed using the CFG databases, and using Ingenuity Drugs analyses (Tables 5A-5G). Drugs and natural compounds which are an opposite match for the gene expression profile of panels of the top biomarkers (top dozen biomarkers, Bonferroni corrected) were also analyzed using the Connectivity Map (Broad Institute, MIT) (Tables 6-18). For the top dozen universal biomarker panel, 7 of 12 probesets were present of the array used for the Connectivity Map; for the Bonferroni universal biomarker panel, 102 out of 148 probesets; for the top dozen male bipolar panel, 8 out of 12 probesets; and for the Bonferroni male bipolar panel, 31 out of 56 probesets.
chlorogenic acid
piracetam
dihydroergocristine
amoxapine
dl-alpha tocopherol
chlorpromazine
diphenhydramine
genistein
fluoxetine
chlorogenic acid
yohimbine
prazosin
amitriptyline
calcium folinate
chlorpromazine
thiamine
naringin
betulin
diphenhydramine
droperidol
vitexin
risperidone
fluoxetine
fluoxetine
cyanocobalamin
vitexin
hesperetin
kawain
ergocalciferol
phenelzine
baclofen
genistein
apigenin
amoxapine
apigenin
benfotiamine
cotinine
ergocalciferol
resveratrol
hesperetin
hyoscyamine
gabapentin
ginkgolide A
harmine
noscapine
thiamine
diphenhydramine
betulin
harmaline
acacetin
alpha-ergocryptine
myosmine
zuclopenthixol
benfotiamine
valproic acid
resveratrol
azacyclonol
allantoin
vincamine
fluvoxamine
calcium folinate
docosahexaenoic acid ethyl ester
calcium folinate
chlorogenic acid
dosulepin
thioproperazine
rolipram
citalopram
calcium folinate
prazosin
asiaticoside
trimipramine
chlorogenic acid
diphenhydramine
clozapine
dl-alpha tocopherol
calcium folinate
valproic acid
thioridazine
risperidone
trifluoperazine
thioproperazine
chlorpromazine
yohimbine
cotinine
prochlorperazine
chlorprothixene
boldine
dl-alpha tocopherol
serotonin
diphenhydramine
myricetin
promazine
lobelanidine
diphenhydramine
trimipramine
asiaticoside
chlorogenic acid
betulin
pirenperone
fluoxetine
Understanding
Pathway Analyses
IPA (Ingenuity Pathway Analyses, version 24390178, Qiagen), David Functional Annotation Bioinformatics Microarray Analysis (National Institute of Allergy and Infectious Diseases), and Kyoto Encyclopedia of Genes and Genomes (KEGG) (through DAVID) were used to analyze the biological roles, including top canonical pathways, and diseases, of the candidate genes, as well as to identify genes in that dataset that are the targets of existing drugs (Table 19). The pathway analyses were conducted for the combined AP and DE probesets with a total internal and external CFG prioritization score >4 that showed stepwise change in the suicide completers validation cohort and survived Bonferroni correction (130 genes, 148 probesets) (Table 4). For male bipolars, there were 50 genes, 54 probesets.
STRING Analysis
In order to examine potential network interactions between the biomarkers, the Search Tool for the Retrieval of Interacting Genes (STRING v10, string-db.org) was used. To run the analyses, the lists of genes were entered into the search box and Homo Sapiens was selected as the organism. The default (medium confidence) setting was used. (
CFG Beyond Suicide
A CFG approach was also used to examine evidence from other psychiatric and related disorders, for the top dozen biomarker genes and Bonferroni validated biomarker genes.
Clock Gene Database
For informational non-CFG scoring purposes, the suicide biomarker genes for involvement in the circadian clock were annotated. A database of genes associated with circadian function were compiled by using a combination of review papers (Zhang et al. 2009, McCarthy and Welsh 20129, 10) and searches of existing databases CircaDB (circadb.hogeneschlab.org), GeneCards (www.genecards.org), and GenAtlas (genatlas.medecine.univ-paris5.fr). Using the data compiled from these sources, a total of 1468 genes were identified that show circadian functioning. Genes were further 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).
Convergent Functional Evidence (CFE)
A convergent functional evidence (CFE) score tabulated all the evidence from discovery (up to 4 points), prioritization (up to 8 points), validation (up to 4 points), testing (2 points for SI predictions, 2 points for hospitalizations predictions), other psychiatric and related disorders (2 points), and drug evidence (2 points). The goal was to highlight, based on the totality of the data and of the evidence in the field to date, biomarkers that have all around evidence: track suicidality, predict suicidality, are reflective of psychiatric pathology, and are potential drug targets. Such biomarkers merit priority evaluation in future clinical trials.
Additionally, a convergent functional evidence (CFE) score was computed with all the evidence from discovery (up to 4 points), prioritization (up to 8 points), testing (High Suicide State and Trait Suicide Hospitalization Future (up to 4 points each if significantly predicts in all subjects, 2 points if predicts by gender, 1 points if predicts in gender/diagnosis subgroups). The goal was to highlight, based on the totality of the data and of the evidence in the field to date, biomarkers that have all-around evidence for tracking suicidality in discovery and validation steps, as well as to permit an objective assessment of state, and predict future clinical events (hospitalizations for suicidality) in the clinical utility testing step.
Results
From Universal to Subtypes and Personalized
Discovery
A powerful within-participant discovery approach to identify genes that: 1. change in expression in blood between no suicidal ideation (no SI) and high suicidal ideation (high SI) states, 2. track the SI state across visits in a participant, and 3. track the SI state in multiple participants. A longitudinally followed cohort of participants was used that showed diametric changes in SI between at least two testing visits (n=66 participants out of a cohort of 293 men and women psychiatric disorder participants followed longitudinally, with diagnoses of bipolar disorder, depression, mood disorder nos, schizophrenia, schizoaffective disorder, psychosis nos, and PTSD). Using a 33% of maximum raw score threshold (internal score of 1 pt), 10,468 unique probesets from AP and DE were found. (
It was then examined in the discovery cohort whether subtypes of suicidality can be identified based on mental state at the time of high suicidal ideation visits, using two way hierarchical clustering with anxiety, mood, and psychosis measures. The SI state self-report may be more reliable in this cohort, as the subjects demonstrated the aptitude and willingness to report different, and diametric, SI states. Four potential subtypes of suicidality were found: high anxiety, low mood, co-morbid, and non-affective (psychotic) (
Prioritization
A Convergent Functional Genomics (CFG) approach was used to prioritize the candidate biomarkers identified in the discovery step (internal score of >=1 pt.) by using all of the published prior independent evidence in the field (
Validation
Next, suicidal behavior was validated for these prioritized biomarkers in a demographically matched cohort of men and women suicide completers from the coroner's office (n=45), by assessing which markers were stepwise changed in expression from no SI to high SI to suicide completers (
Diagnostics
Diagnostic ability of the “universal” top dozen biomarkers (composed of the top increased and decreased biomarkers from AP and from DE from each step: discovery based on all participants, prioritization, and validation in all the coroner's cases) was tested, as well as all of the biomarkers that survived Bonferroni correction after the validation step (Table 3), in a completely independent test cohort of men and women psychiatric disorder participants (n=226), for prediction of suicidal ideation state, as well as for prediction of future psychiatric hospitalizations due to suicidality (
Additionally, two previously described clinical instruments in the form of apps, the Simplified Affective State Scale (SASS) that measures anxiety and mood, and the Convergent Functional Information for Suicidality (CFI-S) that measures risk for suicide indirectly, were used without asking about suicidal ideation. The scores from these apps showed good predictive ability for both state (suicidal ideation) and trait (future hospitalizations) (Table 4).
A panel of the dozen top biomarkers was combined with measures of anxiety and mood (SASS), and with the suicide risk scale (CFI-S), into a broad spectrum universal predictor (UP Suicide). The UP Suicide provides the biomarkers with mental state (SASS) and personal history context (CFI-S), enhancing precision of predictions (
Therapeutics
Pharmacogenomics. For phenomenology, the top CFI-S items distinguishing high SI from no SI states were past history of suicidality, social isolation, and dissatisfaction with one's life. The top CFI-S items distinguishing those that had future hospitalizations for suicidality vs. those that did not were past history of suicidality, command auditory hallucinations, and social isolation (
A number of individual top biomarkers are targets of medications in current clinical use for treating suicidality, such as lithium (HTR2A, GSK3B, ITGB1BP1, BCL2), clozapine (IL6, CD164, CD47, HTR2A, PGK1, DYRK2, IFNG, LPAR1), and omega-3 fatty acids (APOE, CD47, ACP1, GATM, LHFP, LPAR1) (Tables 4A-4G). In particular, HTR2A and CRYAB are at the overlap of lithium and clozapine, and MBP is at the overlap of all three treatments. Omega-3 fatty acids may be a widely depoyable preventive treatment, with minimal side-effects, including in women who are or may become pregnant.
Bioinformatics drug repurposing analyses using the gene expression biosignature of panels of top biomarkers identified new potential therapeutics for suicidality, such as ebselen (a lithium mimetic), piracetam (a nootropic), chlorogenic acid (a polyphenol from coffee), and metformin (an antidiabetic and possible longevity promoting drug) (Tables 6-18).
Understanding
Biological Pathways. Biological pathway analyses using the Bonferroni validated biomarkers was conducted, which suggested that neurotrophic factors, programmed cell death, and insulin signaling are involved in the biology of suicide (Table 19).
Networks and Interactions. STING analyses revealed groups of directly interactive genes, in particular HTR2A/ARRB1/GSK3B, and SLC4A4/AHCYL1/AHCYL2 (
A number of top biomarkers identified have biological roles that are related to the circadian clock (Table 20). To be able to ascertain all the genes in the dataset that were circadian and do estimates for enrichment, from the literature, a database 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 154 top biomarker genes, 18 had circadian evidence (11.7%) (Table 20), suggesting a 1.7 fold enrichment for circadian genes. Circadian clock abnormalities are related to mood disorders, and sleep abnormalities have been implicated in suicide.
Enrichment in suicide completers. Of the candidate biomarkers from the Prioritization step, 125/430 of the DE ones (29.1%) and 37/180 of the AP ones (20.6%) were Bonferroni validated in suicide completers. There is a 1.4 fold enrichment in DE vs. AP, which suggests that completion of suicide may be due more to an incremental change in expression of genes rather than the complete turning on and off of genes.
Overall evidence. For the top biomarkers identified, combining all the available evidence from this Example and published literature into a convergent functional evidence (CFE) score (
Biological pathway analyses were conducted using the top biomarkers, which suggest that neurotrophic factors, programmed cell death, and insulin signaling are involved in the biology of suicide (Table 19).
For the top biomarkers identified, combining all the available evidence from this current Example and the published literature, into a convergent functional evidence (CFE) score (
As a comparator to the universal approach across gender and diagnoses, in this Example, a within-participant longitudinal biomarker discovery analyses in male bipolars only, the largest subgroup (n=20 participants, 65 testing visits) in our discovery cohort, was conducted.
Male bipolars are the highest risk group for suicide clinically, and have been the focus of earlier suicide biomarker studies, with an N that was less than half of the current one (n=9). The discovery step was followed by prioritization, and by validation in male suicide completers. Some of the previous biomarker findings in bipolar disorder (Tables 3B and
This Example was successful in the identification of predictive biomarkers that might be more specific for suicidality in male bipolars. Also examined was whether biomarkers discovered using just male bipolar subjects yielded even better predictors for male bipolar subjects than using the universal biomarkers. It was found that to be the case for trait (hospitalizations) predictions (
A list/panel of 50 biomarkers (BioM50) was generated from the biomarkers with the best evidence from discovery, prioritization, validation, and testing in independent cohorts, obtained with additional data, longer follow-up, and longitudinal analyses (Table 23,
In this Example, the following abbreviations were utilized: validation: DE—differential expression, AP—Absent/Present. NS—Non-stepwise; Step 4 Predictions: C—cross-sectional (using levels from one visit), L—longitudinal (using levels, slope, as well as maximum levels and maximum slope from multiple prior visits); M—Males, F—Females. MDD—depression, BP—bipolar, SZ—schizophrenia, SZA—schizoaffective, PSYCHOSIS—schizophrenia and schizoaffective combined, PTSD-post-traumatic stress disorder. In ALL, by Gender, and personalized by Gender and Diagnosis. Score for predictions: 4 pts if in ALL, 2 pts Gender, 1 pts Gender/Dx. Bold name genes are also Bonferroni significant at Step 3 validation.
To generate the BioM50, the raw gene expression data was first Z-scored by gender and diagnosis, for normalization purposes. Then, each of the biomarkers in the panel was multiplied by a weight coefficient corresponding to their CFE (convergent functional evidence) score, and then an additive score of the 50 weighted biomarkers was obtained. This score can be used for (1) objective assessment of suicidality state and (2) predictive purposes for future clinical worsening, as reflected in hospitalizations for suicidality. Two types of analyses can be performed: cross-sectional, and longitudinal (Table 23,
As depicted in
The BioM-50 score of a new patient tested was compared against the scores of previously tested patients with known severity and outcomes. The thresholds were set based on averages of previous data, and on previous ROC AUC curves, choosing values for sensitivity and specificity. A report was generated with a raw score, a % score, and a risk classification (low, intermediate, high).
BioM50 scores can also be used in combination with quantitative phenotypic data from questionnaires/apps (such as CFI-S, SASS, others), in the UP-Suicide algorithm.
The biomarkers from the BioM50 panel can be used to (3) match patients to medications (Table 23,
The gene expression signature of the 50 biomarkers (BioM50) was used to identify repurposed drugs, for (5) new method of use in suicidality treatment and prevention (Table 24). The biological networks where these 50 biomarkers map offer additional targets for new drug development (
For the top biomarkers identified, combining all the available evidence from this current Example and the published literature, into a convergent functional evidence (CFE) score (Table 23), leads to a prioritization of biomarkers for future studies in the field.
PSME4
ACP1
ACSL6
MAGI3
PLPP3
SKA2
SOD2
CLN5
CLTA
DYRK2
ECHDC1
FBLN5
AIMP1
CLN5
ITGB1BP1
NR3C1
PER1
PIK3R1
PRKAR2B
SAE1
SPATA18
ZNF565
AIMP1
AIMP1
BCL2
CAT
ECHDC1
HDAC2
LPP
PSMB4
RPE
VTA1
AKAP13
CD164
CD47
CYP4V2
DNAJC15
FNTA
GIMAP4
GIMAP7
HACL1
HNRNPA0
MRPS14
PIK3C3
PRKCB
PSMB1
SAT1
SLC6A4
TMEM245
TPH2
PSME4
ALL
ALL
ALL
C:
C:
C:
Gender
Gender
L:
Males
Males
C:
C:
Gender
Females
Gender Dx
Gender Dx
L:
M-BP
M-MDD
C:
C:
Gender
Males
M-PTSD
M-PTSD
C:
C:
C:
Gender Dx
M-BP
C:
M-MDD
C:
M-PTSD
C:
M-SZA
C:
M-SZA
L:
ACP1
ALL
ALL
C:
L:
Gender
Gender
Males
Males
C:
L:
Gender Dx
Gender Dx
M-BP
M-PTSD
C:
C:
M-PSYCHOSIS
M-SZ
C:
L:
M-PTSD
C:
M-PTSD
L:
M-SZ
L:
M-SZA
C:
ACSL6
ALL
ALL
ALL
C:
C:
C:
Gender
Gender
Gender
Males
Males
Males
C:
C:
C:
Gender Dx
Gender Dx
M-BP
M-BP
C:
C:
M-PSYCHOSIS
C:
M-PTSD
C:
MAGI3
ALL
Gender
ALL
C:
Males
C:
C:
Gender
L:
Males
Gender Dx
C:
M-PSYCHOSIS
C:
Gender
Males
L:
C:
M-SZ
C:
Gender Dx
L:
M-BP
C:
Gender Dx
M-PSYCHOSIS
M-PSYCHOSIS
C:
C:
M-PSYCHOSIS
M-PSYCHOSIS
L:
L:
M-SZ
M-SZ
C:
C:
M-SZ
M-SZ
L:
L:
M-SZA
M-SZA
C:
C:
PLPP3
ALL
Gender
ALL
C:
Males
C:
C:
Gender
Gender
Males
Gender Dx
Males
C:
M-BP
C:
C:
Gender Dx
Gender Dx
M-BP
M-
M-BP
PSYCHOSIS
C:
C:
M-PSYCHOSIS
M-PSYCHOSIS
C:
M-SZA
C:
C:
M-SZA
C:
SKA2
ALL
Gender Dx
ALL
C:
M-SZA
C:
C:
Gender
Gender
Males
Males
C:
C:
Gender Dx
Gender Dx
M-BP
M-BP
C:
C:
M-MDD
L:
M-PSYCHOSIS
C:
M-PSYCHOSIS
L:
M-SZ
C:
M-SZ
L:
M-SZA
C:
SOD2
Gender
ALL
ALL
Males
C:
C:
C:
Gender
Gender
Gender Dx
Males
Females
M-PSYCHOSIS
C:
L:
C:
Gender Dx
Gender
M-BP
Males
C:
C:
M-PTSD
Gender Dx
C:
M-BP
C:
CLN5
ALL
Gender Dx
ALL
C:
M-SZA
C:
C:
Gender
L:
Males
C:
Gender
Males
Gender Dx
C:
M-BP
C:
L:
M-PSYCHOSIS
C:
Gender Dx
M-BP
C:
M-PSYCHOSIS
L:
M-SZ
C:
M-SZ
L:
M-SZA
C:
CLTA
ALL
Gender Dx
ALL
C:
M-SZA
L:
C:
Gender
Gender
Males
Males
C:
L:
Gender Dx
M-BP
C:
M-PSYCHOSIS
C:
M-SZA
C:
DYRK2
ALL
Gender Dx
ALL
C:
M-PTSD
L:
C:
L:
Gender
Males
L:
Gender
Males
C:
Gender Dx
M-PTSD
C:
L:
L:
Gender Dx
M-BP
C:
M-SZ
L:
M-PSYCHOSIS
C:
L:
M-SZ
C:
Gender Dx
M-SZ
L:
Gender Dx
M-SZA
C:
ECHDC1
ALL
Gender Dx
ALL
C:
M-PTSD
C:
C:
Gender
Gender
Males
Males
C:
C:
Gender Dx
Gender Dx
M-BP
M-PTSD
C:
C:
M-PSYCHOSIS
C:
M-SZ
L:
M-SZA
C:
FBLN5
Gender Dx
ALL
Gender
M-SZA
C:
Males
C:
C:
Gender
Males
Gender
C:
Males
L:
Gender Dx
M-PSYCHOSIS
Gender Dx
C:
M-PSYCHOSIS
C:
M-PTSD
C:
L:
M-SZ
M-SZ
L:
C:
M-SZ
L:
AIMP1
ALL
ALL
C:
C:
L:
Gender
Males
C:
Gender
Males
C:
Gender Dx
M-BP
C:
L:
M-PSYCHOSIS
Gender Dx
C:
M-BP
C:
M-SZ
C:
M-PSYCHOSIS
C:
M-PTSD
L:
M-SZA
C:
CLN5
ALL
Gender Dx
ALL
C:
M-PSYCHOSIS
C:
C:
Gender
L:
Males
M-SZA
C:
C:
Gender
Males
Gender Dx
C:
M-BP
C:
L:
M-PSYCHOSIS
C:
Gender Dx
M-PSYCHOSIS
C:
M-PSYCHOSIS
L:
L:
M-SZ
L:
M-SZ
L:
M-SZA
C:
M-SZA
C:
ITGB1BP1
ALL
ALL
C:
C:
Gender
L:
Males
C:
Gender
Males
C:
L:
Gender Dx
M-PSYCHOSIS
C:
M-SZA
C:
L:
NR3C1
ALL
Gender
C:
Males
L:
Gender
Males
C:
Gender Dx
F-MDD
C:
M-BP
C:
PER1
ALL
Gender Dx
ALL
C:
M-PSYCHOSIS
C:
C:
Gender
L:
Females
C:
Gender
Males
Gender
C:
Males
C:
L:
Gender Dx
Gender Dx
C:
M-PSYCHOSIS
C:
M-BP
C:
L:
M-SZ
Gender Dx
C:
M-SZ
C:
PIK3R1
Gender Dx
ALL
ALL
M-PTSD
C:
L:
C:
Gender
Gender Dx
Females
F-MDD
L:
C:
Gender Dx
M-PTSD
F-MDD
C:
C:
M-PTSD
L:
PRKAR2B
Gender Dx
Gender Dx
ALL
F-BP
M-BP
L:
C:
C:
Gender
Gender Dx
Males
M-BP
L:
L:
Gender Dx
M-BP
C:
L:
SAE1
Gender
ALL
ALL
Females
C:
C:
C:
Gender
Gender
Gender Dx
Males
Males
C:
C:
C:
Gender Dx
Gender Dx
M-BP
M-MDD
C:
C:
M-MDD
M-MDD
C:
L:
SPATA18
ALL
ALL
C:
L:
L:
Gender Dx
M-PSYCHOSIS
L:
Gender
Males
C:
M-SZ
L:
L:
Gender Dx
M-PSYCHOSIS
L:
M-PTSD
L:
ZNF565
ALL
Gender Dx
ALL
C:
M-SZA
C:
C:
Gender
L:
Males
C:
Gender
Males
Gender Dx
C:
M-PSYCHOSIS
C:
L:
M-SZA
C:
Gender Dx
M-PSYCHOSIS
C:
L:
M-SZ
L:
M-SZA
C:
AIMP1
ALL
Gender Dx
Gender
C:
M-SZA
Males
C:
C:
Gender
Males
L:
C:
Gender Dx
Gender Dx
M-BP
M-PSYCHOSIS
C:
C:
M-PTSD
M-SZ
C:
L:
M-SZA
C:
AIMP1
ALL
ALL
C:
C:
Gender
L:
Males
C:
Gender
Males
Gender Dx
C:
M-BP
C:
L:
M-PSYCHOSIS
C:
Gender Dx
M-PSYCHOSIS
C:
M-SZ
L:
M-SZA
C:
M-SZA
C:
BCL2
ALL
Gender Dx
C:
M-SZ
C:
Gender
Males
C:
Gender Dx
M-BP
C:
M-PSYCHOSIS
C:
M-SZ
C:
L:
CAT
ALL
Gender
Gender Dx
C:
Males
M-MDD
C:
C:
Gender
Females
Gender Dx
C:
M-MDD
C:
Gender
Males
M-SZA
C:
C:
Gender Dx
C:
M-BP
C:
ECHDC1
ALL
Gender Dx
Gender
C:
M-SZA
Males
C:
C:
Gender
Males
L:
C:
Gender Dx
M-BP
C:
M-SZA
C:
HDAC2
ALL
Gender Dx
ALL
C:
M-PTSD
L:
C:
Gender
Gender
Males
Males
C:
L:
Gender Dx
Gender Dx
M-BP
M-BP
C:
C:
M-PSYCHOSIS
C:
M-SZA
C:
LPP
ALL
Gender
Gender
C:
Females
Females
L:
L:
Gender
Females
Gender Dx
Gender Dx
C:
M-PTSD
C:
C:
Gender
Males
M-MDD
C:
L:
Gender Dx
M-PTSD
F-BP
C:
C:
M-PTSD
C:
PSMB4
ALL
Gender Dx
C:
M-SZA
C:
Gender
Males
C:
Gender Dx
M-BP
C:
M-PSYCHOSIS
C:
M-SZA
C:
RPE
ALL
Gender Dx
ALL
C:
M-PTSD
L:
C:
Gender
Gender
Males
Gender Dx
Males
C:
M-PTSD
L:
L:
Gender Dx
Gender Dx
M-BP
M-PTSD
C:
C:
M-PSYCHOSIS
L:
C:
M-SZ
L:
VTA1
ALL
Gender Dx
Gender
C:
M-SZA
Males
C:
L:
Gender
Males
C:
Gender Dx
M-BP
C:
M-PSYCHOSIS
C:
M-SZ
L:
M-SZA
C:
AKAP13
Gender Dx
Gender
ALL
M-PTSD
Females
L:
C:
L:
Gender
Gender Dx
Females
M-PTSD
L:
C:
Gender
Males
L:
Gender Dx
M-PTSD
C:
CD164
ALL
Gender Dx
Gender Dx
C:
M-PTSD
M-PTSD
C:
C:
Gender
Males
C:
Gender Dx
M-BP
C:
M-SZ
L:
CD47
ALL
Gender Dx
Gender Dx
C:
M-SZA
M-PTSD
C:
C:
Gender
Males
C:
M-BP
C:
M-PSYCHOSIS
C:
M-SZ
L:
M-SZA
C:
CYP4V2
ALL
ALL
C:
C:
Gender
Gender
Males
Males
C:
C:
Gender Dx
Gender Dx
M-BP
M-BP
C:
M-PSYCHOSIS
M-PSYCHOSIS
C:
C:
M-SZA
M-SZA
C:
C:
DNAJC15
ALL
Gender Dx
Gender Dx
C:
M-PTSD
M-PTSD
C:
C:
Gender
Males
C:
Gender Dx
M-PTSD
C:
FNTA
ALL
ALL
C:
L:
Gender
Gender
Males
Males
C:
L:
Gender Dx
M-BP
C:
M-PSYCHOSIS
C:
M-SZ
L:
GIMAP4
ALL
ALL
C:
C:
Gender
L:
Males
C:
Gender
Males
Gender Dx
C:
M-BP
C:
L:
Gender Dx
M-PTSD
L:
GIMAP7
ALL
ALL
C:
C:
Gender
Gender
Males
Males
C:
C:
Gender Dx
Gender Dx
M-BP
M-BP
C:
C:
M-PSYCHOSIS
M-PSYCHOSIS
C:
C:
M-PTSD
M-PTSD
C:
L:
M-SZ
L:
HACL1
Gender
Gender Dx
ALL
Males
M-SZA
C:
C:
C:
L:
Gender Dx
M-BP
C:
Gender
Males
C:
M-PSYCHOSIS
C:
L:
M-SZA
C:
Gender Dx
M-PSYCHOSIS
C:
M-SZ
L:
HNRNPA0
ALL
ALL
C:
L:
Gender
Gender
Males
Males
C:
L:
Gender Dx
M-BP
C:
M-PSYCHOSIS
C:
M-SZ
C:
M-SZ
L:
M-SZA
C:
MRPS14
ALL
Gender
C:
Males
C:
Gender
Males
Gender
C:
Males
L:
Gender Dx
M-BP
Gender Dx
C:
M-BP
C:
M-PSYCHOSIS
C:
M-SZ
C:
L:
M-SZA
C:
PIK3C3
ALL
Gender Dx
Gender
C:
M-PTSD
Males
C:
L:
Gender Dx
Gender Dx
Gender Dx
C:
M-SZA
M-PSYCHOSIS
C:
L:
M-BP
C:
M-PTSD
C:
PRKCB
ALL
C:
Gender
Males
C:
Gender Dx
M-BP
C:
PSMB1
Gender
Gender Dx
Gender Dx
Females
M-BP
C:
C:
L:
Gender Dx
M-SZA
L:
C:
M-PTSD
C:
L:
SAT1
ALL
Gender Dx
C:
M-SZ
C:
Gender
Males
C:
Gender Dx
M-SZ
C:
SLC6A4
ALL
Gender
C:
Females
C:
Gender
Males
C:
M-BP
C:
M-PSYCHOSIS
C:
M-SZ
C:
TMEM245
Gender
ALL
Males
C:
C:
Gender
Gender Dx
Males
M-BP
C:
C:
TPH2
ALL
Gender Dx
C:
M-BP
C:
Gender
Males
C:
Gender Dx
M-BP
C:
M-PSYCHOSIS
C:
M-SZA
C:
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.
This application is a continuation-in-part of and claims priority to PCT Application serial number PCT/US2018/032540, filed May 14, 2018, which claims priority to U.S. Provisional Application No. 62/505,197 filed on May 12, 2017, the contents of both of which are incorporated herein by reference in their entirety.
This invention was made with government support under OD007363 awarded by the National Institutes of Health and 2IO1CX000139 merit award by the Veterans Administration. The government has certain rights in the invention.
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| Number | Date | Country | |
|---|---|---|---|
| 20200318188 A1 | Oct 2020 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62505197 | May 2017 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2018/032540 | May 2018 | US |
| Child | 16677414 | US |
