Novel Tests May Earlier ID Those at Risk of Suicide, Postpartum Depression
Clinicians have long sought a way to identify which patients are at greatest risk for potentially disastrous consequences resulting from suicidal thoughts or postpartum depression. But finding an objective measure to assess psychiatric states, without asking a patient directly if they’re suicidal, has proven challenging. Now, two new studies are providing hope that simple blood […]
Clinicians have long sought a way to identify which patients are at greatest risk for potentially disastrous consequences resulting from suicidal thoughts or postpartum depression. But finding an objective measure to assess psychiatric states, without asking a patient directly if they're suicidal, has proven challenging. Now, two new studies are providing hope that simple blood tests could hold the answer as to which patients could most benefit from early intervention—prior to exhibiting outward symptoms. The two studies highlight the tremendous ongoing work to identify reliable biomarkers of psychiatric conditions.
RNA Test Can Predict Suicide Risk
The combination of blood-based RNA biomarkers in a simple questionnaire app can predict which psychiatric patients are at the greatest risk of suicide, according to a study published online Aug. 18 in Molecular Psychiatry. The researchers had previously shown in a proof-of-principle study that blood-based gene expression biomarkers could predict future hospitalizations due to suicide in male bipolar disorder patients. The present study broadens validation of an improved set of markers across major psychiatric disorders (bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and combines the quantitative markers with an app-based questionnaire to achieve greater than 90 percent accuracy in identifying which patients are most likely to attempt suicide.
The multi-step study first identified changes in gene expression associated with progression from no suicidal ideation (SI) to high SI states among 37 participants of a longitudinal psychiatric cohort (n=217). Expression changes were assessed using both absent/present (reflecting on/off of transcription) and differential expression (more subtle gradual changes in expression levels). Candidate markers were then prioritized using the group's Convergent Functional Genomics approach. Next, these top biomarkers were validated using blood from a demographically matched cohort of suicide completers from the coroner's office (n=26 male violent suicide completers with samples collected within 24 hours of death). Lastly, the top biomarkers were tested in an independent cohort of psychiatric participants for prediction of suicidal ideation (n=108) and in a follow-up cohort of psychiatric patients (n=157) for prediction of hospitalizations due to suicidality.
The researchers found that biomarkers for suicidal ideation only are enriched for genes involved in neuronal connectivity and schizophrenia, while biomarkers also validated for suicidal behavior are enriched for genes involved in neuronal activity and mood. Among the top biomarkers for suicidal behavior, 14 percent were tied to psychological stress response, and 19% for involvement in programmed cell death. The best individual biomarker across psychiatric diagnoses for predicting suicidal ideation was SLC4A4, with an area under the curve (AUC) of 72 percent. It performed even better for patients with bipolar disorder, predicting suicidal ideation and future hospitalizations with an AUC of 93 percent and 70 percent, respectively.
Two new 22-item clinical questionnaire-based apps, one for affective state and one for suicide risk factors (life events, mental health, physical health, stress, addictions, and cultural factors) predict suicidal ideation across psychiatric diagnoses (AUC range of 85 and 89 percent, respectively). Integrating the top biomarkers and the clinical questionnaire information into a universal predictive measure (UP-Suicide) showed broad-spectrum predictive ability across psychiatric diagnoses with an AUC of 92 percent, which improved to 98 percent for bipolar disorder.
"We propose that the widespread use of such risk prediction tests as part of routine or targeted health care assessments will lead to early disease interception followed by preventive lifestyle modifications and proactive treatment," writes lead author Alexander B. Niculescu III, M.D., Ph.D., from Indiana University in Indianapolis.
In a statement, Niculescu said he believes the apps are ready to be deployed and tested by medical professionals, particularly in emergency department settings, while the biomarkers are undergoing further validation both in women and in people without a psychiatric diagnosis.
Gene Expression Marker May Predict Postpartum Depression
Epigenetic regulation of the oxytocin receptor gene (OXTR) appears to be tied to development of postpartum depression (PPD), according to a study published July 21 in Frontiers in Genetics. The hormone oxytocin has long been known to play a role in mood regulation and maternal bonding, but factors contributing to decreased expression of OXTR may contribute to PPD and identifying this variability prior to symptom onset is central to predicting risk of PPD.
"Identification of genetic and epigenetic susceptibility to depression in pregnancy may be one key element in a multidisciplinary approach to reduce the development of PPD and hence the adverse sequelae of depression," write the authors. Jessica Connelly, Ph.D., senior author from University of Virginia, Charlottesville adds in a statement, "We know that women who have experienced depression before pregnancy are at higher risk of developing depression in the postpartum period. However, women who have never experienced depression also develop postpartum depression. These markers we identified may help to identify them, in advance."
Data from women (269 PPD cases and 276 controls matched for age, prior parity, and presence of depressive symptoms in pregnancy) participating in the Avon Longitudinal Study of Parents and Children (date of delivery between April 1991 and December 1992) were analyzed for symptoms of depression (both at 32 weeks gestation and 8 weeks postpartum). Additionally, blood was evaluated for OXTR DNA methylation and genotype (rs53576_GG and rs2254298_A).
The researchers found that no genetic/epigenetic interactions predicted PPD in the total sample. However, there was a significant interaction between rs53576 genotype and methylation in the OXTR gene amongst women who did not have depression prenatally, but developed PPD. Those women with GG genotype showed more than 2.6 greater odds of PPD for every 10 percent increase in methylation level, whereas methylation was unrelated to PPD amongst "A" genotype carriers. There was no such interaction among women with PPD and prenatal depression.
"Findings from this study argue for the integrative use of genetic and epigenetic markers in the oxytocin pathway to better understand and predict risk of psychological disorders in the postnatal period, a critical period for healthy mother–infant interaction," writes lead author Aleeca Bell, Ph.D., University of Illinois at Chicago. "Early identification of susceptibility might allow clinical vigilance for the possible development of PPD, and targeting of preventative interventions. The biologically at-risk women in this study did not display elevated symptoms of depression in pregnancy, but went on to display an increased risk of PPD after birth."
Takeaway: Researchers are moving closer to identifying quantifiable markers that can effectively predict risk of harm from psychiatric conditions. Targeting preventive interventions and therapies at high-risk individuals may ultimately cut rates of suicide and postpartum depression.
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