Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach.
Worldwide, one person dies every 40 seconds by suicide, a potentially preventable tragedy. A limiting step in our ability to intervene is the lack of objective, reliable predictors. We have previously provided proof of principle for the use of blood gene expression biomarkers to predict future hospitalizations due to suicidality, in male bipolar disorder participants. We now generalize the discovery, prioritization, validation, and testing of such markers across major psychiatric disorders (bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) in male participants, to understand commonalities and differences. We used a powerful within-participant discovery approach to identify genes that change in expression between no suicidal ideation and high suicidal ideation states (n=37 participants out of a cohort of 217 psychiatric participants followed longitudinally). We then used a convergent functional genomics (CFG) approach with existing prior evidence in the field to prioritize the candidate biomarkers identified in the discovery step. Next, we validated the top biomarkers from the prioritization step for relevance to suicidal behavior, in a demographically matched cohort of suicide completers from the coroner's office (n=26). The biomarkers for suicidal ideation only are enriched for genes involved in neuronal connectivity and schizophrenia, the biomarkers also validated for suicidal behavior are enriched for genes involved in neuronal activity and mood. The 76 biomarkers that survived Bonferroni correction after validation for suicidal behavior map to biological pathways involved in immune and inflammatory response, mTOR signaling and growth factor regulation. mTOR signaling is necessary for the effects of the rapid-acting antidepressant agent ketamine, providing a novel biological rationale for its possible use in treating acute suicidality. Similarly, MAOB, a target of antidepressant inhibitors, was one of the increased biomarkers for suicidality. We also identified other potential therapeutic targets or biomarkers for drugs known to mitigate suicidality, such as omega-3 fatty acids, lithium and clozapine. Overall, 14% of the top candidate biomarkers also had evidence for involvement in psychological stress response, and 19% for involvement in programmed cell death/cellular suicide (apoptosis). It may be that in the face of adversity (stress), death mechanisms are turned on at a cellular (apoptosis) and organismal level. Finally, we tested the top increased and decreased biomarkers from the discovery for suicidal 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 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 a receiver operating characteristic (ROC) area under the curve (AUC) of 72%. For bipolar disorder in particular, SLC4A4 predicted suicidal ideation with an AUC of 93%, and future hospitalizations with an AUC of 70%. SLC4A4 is involved in brain extracellular space pH regulation. Brain pH has been implicated in the pathophysiology of acute panic attacks. We also describe two new clinical information apps, one for affective state (simplified affective state scale, SASS) and one for suicide risk factors (Convergent Functional Information for Suicide, CFI-S), and how well they predict suicidal ideation across psychiatric diagnoses (AUC of 85% for SASS, AUC of 89% for CFI-S). We hypothesized a priori, based on our previous work, that the integration of the top biomarkers and the clinical information into a universal predictive measure (UP-Suicide) would show broad-spectrum predictive ability across psychiatric diagnoses. Indeed, the UP-Suicide was able to predict suicidal ideation across psychiatric diagnoses with an AUC of 92%. For bipolar disorder, it predicted suicidal ideation with an AUC of 98%, and future hospitalizations with an AUC of 94%. Of note, both types of tests we developed (blood biomarkers and clinical information apps) do not require asking the individual assessed if they have thoughts of suicide, as individuals who are truly suicidal often do not share that information with clinicians. We propose that 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.
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.|Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA.|Indianapolis VA Medical Center, Indianapolis, IN, USA.
A B
AB
Niculescu
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.|Stark Neuroscience Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA.
D F
DF
Levey
Indianapolis VA Medical Center, Indianapolis, IN, USA.
P L
PL
Phalen
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
H
H
Le-Niculescu
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
H D
HD
Dainton
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
N
N
Jain
Indianapolis VA Medical Center, Indianapolis, IN, USA.
E
E
Belanger
Indianapolis VA Medical Center, Indianapolis, IN, USA.
A
A
James
Indianapolis VA Medical Center, Indianapolis, IN, USA.
S
S
George
Indianapolis VA Medical Center, Indianapolis, IN, USA.
H
H
Weber
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
D L
DL
Graham
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
R
R
Schweitzer
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
T B
TB
Ladd
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
R
R
Learman
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
E M
EM
Niculescu
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
N P
NP
Vanipenta
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
F N
FN
Khan
Advanced Biomedical IT Core, Indiana University School of Medicine, Indianapolis, IN, USA.
J
J
Mullen
Advanced Biomedical IT Core, Indiana University School of Medicine, Indianapolis, IN, USA.
G
G
Shankar
Marion County Coroner's Office, Indianapolis, IN, USA.
S
S
Cook
Marion County Coroner's Office, Indianapolis, IN, USA.
C
C
Humbert
Marion County Coroner's Office, Indianapolis, IN, USA.
A
A
Ballew
INBRAIN, Indiana University School of Medicine, Indianapolis, IN, USA.
M
M
Yard
Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, USA.
T
T
Gelbart
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
A
A
Shekhar
J. Craig Venter Institute, La Jolla, CA, USA.
N J
NJ
Schork
Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, USA.
S M
SM
Kurian
INBRAIN, Indiana University School of Medicine, Indianapolis, IN, USA.
G E
GE
Sandusky
Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, USA.
D R
DR
Salomon
D000328:Adult; D015415:Biomarkers; D015331:Cohort Studies; D030541:Databases, Genetic / Q000706:statistics & numerical data; D005260:Female; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods*; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000235:genetics / Q000378:metabolism / Q000523:psychology; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D011569:Psychiatric Status Rating Scales; D018570:Risk Assessment; D012307:Risk Factors; D013405:Suicide; D055815:Young Adult
D015415:Biomarkers