↓ Skip to main content

Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach

Overview of attention for article published in Molecular Psychiatry, August 2015
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
11 news outlets
blogs
9 blogs
twitter
43 tweeters
facebook
11 Facebook pages
googleplus
3 Google+ users
reddit
2 Redditors

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
224 Mendeley
Title
Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach
Published in
Molecular Psychiatry, August 2015
DOI 10.1038/mp.2015.112
Pubmed ID
Authors

A B Niculescu, D F Levey, P L Phalen, H Le-Niculescu, H D Dainton, N Jain, E Belanger, A James, S George, H Weber, D L Graham, R Schweitzer, T B Ladd, R Learman, E M Niculescu, N P Vanipenta, F N Khan, J Mullen, G Shankar, S Cook, C Humbert, A Ballew, M Yard, T Gelbart, A Shekhar, N J Schork, S M Kurian, G E Sandusky, D R Salomon

Abstract

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.Molecular Psychiatry advance online publication, 18 August 2015; doi:10.1038/mp.2015.112.

Twitter Demographics

The data shown below were collected from the profiles of 43 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 224 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
Spain 3 1%
Australia 2 <1%
Netherlands 2 <1%
Luxembourg 1 <1%
Brazil 1 <1%
Belgium 1 <1%
Argentina 1 <1%
Ireland 1 <1%
Other 0 0%
Unknown 205 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 42 19%
Student > Ph. D. Student 35 16%
Student > Master 26 12%
Unspecified 24 11%
Other 20 9%
Other 77 34%
Readers by discipline Count As %
Medicine and Dentistry 62 28%
Unspecified 41 18%
Psychology 41 18%
Neuroscience 22 10%
Agricultural and Biological Sciences 19 8%
Other 39 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 180. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 April 2017.
All research outputs
#73,102
of 13,216,380 outputs
Outputs from Molecular Psychiatry
#74
of 2,644 outputs
Outputs of similar age
#1,712
of 238,105 outputs
Outputs of similar age from Molecular Psychiatry
#1
of 42 outputs
Altmetric has tracked 13,216,380 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,644 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 28.3. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 238,105 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.