Next-generation precision medicine for suicidality prevention

Abstract

Suicidality remains a clear and present danger in society in general, and for mental health patients in particular. Lack of widespread use of objective and/or quantitative information has hampered treatment and prevention efforts. Suicidality is a spectrum of severity from vague thoughts that life is not worth living, to ideation, plans, attempts, and completion. Blood biomarkers that track suicidality risk provide a window into the biology of suicidality, as well as could help with assessment and treatment. Previous studies by us were positive. Here we describe new studies we conducted transdiagnostically in psychiatric patients, starting with the whole genome, to expand the identification, prioritization, validation and testing of blood gene expression biomarkers for suicidality, using a multiple independent cohorts design. We found new as well as previously known biomarkers that were predictive of high suicidality states, and of future psychiatric hospitalizations related to them, using cross-sectional and longitudinal approaches. The overall top increased in expression biomarker was SLC6A4, the serotonin transporter. The top decreased biomarker was TINF2, a gene whose mutations result in very short telomeres. The top biological pathways were related to apoptosis. The top upstream regulator was prednisolone. Taken together, our data supports the possibility that biologically, suicidality is an extreme stress-driven form of active aging/death. Consistent with that, the top subtypes of suicidality identified by us just based on clinical measures had high stress and high anxiety. Top therapeutic matches overall were lithium, clozapine and ketamine, with lithium stronger in females and clozapine stronger in males. Drug repurposing bioinformatic analyses identified the potential of renin-angiotensin system modulators and of cyclooxygenase inhibitors. Additionally, we show how patient reports for doctors would look based on blood biomarkers testing, personalized by gender. We also integrated with the blood biomarker testing social determinants and psychological measures (CFI-S, suicidal ideation), showing synergy. Lastly, we compared that to machine learning approaches, to optimize predictive ability and identify key features. We propose that our findings and comprehensive approach can have transformative clinical utility.

Fig. 1. Steps 1-3: discovery, prioritization, validation and testing of biomarkers for suicidality.

A Cohorts used in study, depicting flow of discovery, prioritization, and validation of biomarkers from each step. B Prioritization using Convergent Functional Genomics (CFG). C Validation -biomarkers are assessed for stepwise change from discovery subjects with no symptoms, high symptoms to the validation subjects where samples were collected from suicide completers, using ANOVA. The histograms depict a top increased (I) and a top decreased biomarker (D). Number of probesets and scoring at each of the Steps. Step 1 -Discovery probesets are identified based on their score for tracking symptoms and ranked 33.3% (2 pt), 50% (4 pt) and 80% (6 pt). Step 2- Prioritization with CFG for prior evidence of involvement in Suicidality. Maximum of 6 pt. Genes scoring at least 6 pt out of a maximum possible of 18 pt after Discovery and Prioritization are carried forward to the validation step. Step 3- Validation in an independent cohort of suicide completers. We selected the top CFE score ≥8 (n = 2340) for further testing and characterization. E Predictions for State—High Suicidality. Top cross-sectional and longitudinal markers are shown in all subjects, males, and females. Table below displays number of significant markers within each prediction group by AUC. F Predictions for Trait—Hospitalizations in the First Year. Top cross-sectional and longitudinal markers are shown in all subjects, males, and females. Table below displays number of significant markers within each prediction group by AUC. G Predictions for Trait—All Future Hospitalizations. Top cross-sectional and longitudinal markers are shown in all subjects, males, and females. Table below displays number of significant markers within each prediction group by odds ratio.

Fig. 2. Prototype reports and population Radar Plot.

Subject phchp328 (female, 37 years old) died by suicide by overdose a year after being tested by us. Phchp385 (male, 47 years old) died by suicide by hanging three years after being tested by us. A Prototype Report for Phchp328v1. B Prototype Report for phchp385v1. Reports based on panels of top predictive biomarkers for that gender. C, D Radar plots of Hospitalizations in the First Year following testing. Our individual subject scores (black line), as well as average scores for high risk subjects (red, n = 768) and average scores for low risk subjects (blue, n = 176).

Fig. 3. Machine learning analysis.

A, C, E, G Positive predictive value and ROC AUC of occurrence of hospitalizations as well as time to first hospitalization for various machine learning models utilizing all three aspects of the biopsychosocial model (biomarkers, CFIS, and HAMD-SI). B, D, F, H Salience analysis of which features of the model are the most important.