The ability to predict suicidal ideation by looking at the amount of expression of certain genes is quite amazing in and of itself, but what is really amazing is that lithium alters sodium transport and may interfere with ion exchange mechanisms and nerve conduction. Fluid and electrolyte metabolism are believed to be altered in affective disorders and this may be related to the therapeutic action of lithium. SLC4A4 is involved in sodium transport, and it is this protein which is a biomarker for suicidal ideation. Could this also be one of the key proteins involved in mental illness? Why isn’t anyone but me asking this question? Oh my goodness, I’m going to call my psychiatrist and run this by him. These relationships are not random, there is something quite important going on here. Na+ transporter protein predicts chances of suicide, Na+ influx and outflux, possibly the pumps that do this, are affected by lithium which treats bipolar d/o! Read on please!
Researchers identified biomarkers of suicidal ideation across multiple psychiatric disorders and developed an application to help clinicians accurately screen for suicide risk. These researchers looked at the expression of different genes and changes in their expression between a no suicidal state to a high suicidal state, to see if one of them would signal suicidal ideation in bipolar d/o, major depressive d/o, schizoaffective d/o and schizophrenia. They looked at 217 males with bipolar d/o, major depressive d/o, schizoaffective d/o or schizophrenia.
What they found was that SLC4A4 was the best biomarker for suicidal ideation across all illnesses! This gene encodes a membrane protein transporter which transports sodium bicarbonate across cell membranes. Sodium bicarbonate cotransporters, like SLC4A4, mediate the coupled movement of sodium and bicarbonate ions across the plasma membrane of many cells. This is an electrogenic process with an apparent stoichiometry of 3 bicarbonate ions per sodium ion. SLC4A4 predicted suicidal ideation among participants with bipolar disorder with an area under the curve (AUC) of 93% and future hospitalizations with an AUC of 70%. Using questionnaires such as the simplified affective state scale (SASS) and Convergent Functional Information for Suicide (CFI-S) which measure suicidal ideation along with the biomarker SLC4A4, the AUC was 92% for all psychiatric diagnoses, 98% for bipolar disorder and 94% for future hospitalizations.
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.Molecular Psychiatry advance online publication, 18 August 2015; doi:10.1038/mp.2015.112.