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Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment

Abstract

Women are under-represented in research on suicidality to date. Although women have a lower rate of suicide completion than men, due in part to the less-violent methods used, they have a higher rate of suicide attempts. Our group has previously identified genomic (blood gene expression biomarkers) and clinical information (apps) predictors for suicidality in men. We now describe pilot studies in women. We used a powerful within-participant discovery approach to identify genes that change in expression between no suicidal ideation (no SI) and high suicidal ideation (high SI) states (n=12 participants out of a cohort of 51 women psychiatric participants followed longitudinally, with diagnoses of bipolar disorder, depression, schizoaffective disorder and schizophrenia). We then used a Convergent Functional Genomics (CFG) approach to prioritize the candidate biomarkers identified in the discovery step by using all the prior evidence in the field. Next, we validated for suicidal behavior the top-ranked biomarkers for SI, in a demographically matched cohort of women suicide completers from the coroner’s office (n=6), by assessing which markers were stepwise changed from no SI to high SI to suicide completers. We then tested the 50 biomarkers that survived Bonferroni correction in the validation step, as well as top increased and decreased biomarkers from the discovery and prioritization steps, in a completely independent test cohort of women psychiatric disorder participants for prediction of SI (n=33) and in a future follow-up cohort of psychiatric disorder participants for prediction of psychiatric hospitalizations due to suicidality (n=24). Additionally, we examined how two clinical instruments in the form of apps, Convergent Functional Information for Suicidality (CFI-S) and Simplified Affective State Scale (SASS), previously tested in men, perform in women. The top CFI-S item distinguishing high SI from no SI states was the chronic stress of social isolation. We then showed how the clinical information apps combined with the 50 validated biomarkers into a broad predictor (UP-Suicide), our apriori primary end point, predicts suicidality in women. UP-Suicide had a receiver-operating characteristic (ROC) area under the curve (AUC) of 82% for predicting SI and an AUC of 78% for predicting future hospitalizations for suicidality. Some of the individual components of the UP-Suicide showed even better results. SASS had an AUC of 81% for predicting SI, CFI-S had an AUC of 84% and the combination of the two apps had an AUC of 87%. The top biomarker from our sequential discovery, prioritization and validation steps, BCL2, predicted future hospitalizations due to suicidality with an AUC of 89%, and the panel of 50 validated biomarkers (BioM-50) predicted future hospitalizations due to suicidality with an AUC of 94%. The best overall single blood biomarker for predictions was PIK3C3 with an AUC of 65% for SI and an AUC of 90% for future hospitalizations. Finally, we sought to understand the biology of the biomarkers. BCL2 and GSK3B, the top CFG scoring validated biomarkers, as well as PIK3C3, have anti-apoptotic and neurotrophic effects, are decreased in expression in suicidality and are known targets of the anti-suicidal mood stabilizer drug lithium, which increases their expression and/or activity. Circadian clock genes were overrepresented among the top markers. Notably, PER1, increased in expression in suicidality, had an AUC of 84% for predicting future hospitalizations, and CSNK1A1, decreased in expression, had an AUC of 96% for predicting future hospitalizations. Circadian clock abnormalities are related to mood disorder, and sleep abnormalities have been implicated in suicide. Docosahexaenoic acid signaling was one of the top biological pathways overrepresented in validated biomarkers, which is of interest given the potential therapeutic and prophylactic benefits of omega-3 fatty acids. Some of the top biomarkers from the current work in women showed co-directionality of change in expression with our previous work in men, whereas others had changes in opposite directions, underlying the issue of biological context and differences in suicidality between the two genders. With this study, we begin to shed much needed light in the area of female suicidality, identify useful objective predictors and help understand gender commonalities and differences. During the conduct of the study, one participant committed suicide. In retrospect, when the analyses were completed, her UP-Suicide risk prediction score was at the 100 percentile of all participants tested.

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References

  1. Hawton K . Sex and suicide. Gender differences in suicidal behaviour. Br J Psychiatry 2000; 177: 484–485.

    CAS  Article  PubMed  Google Scholar 

  2. Niculescu AB, Levey DF, Phalen PL, Le-Niculescu H, Dainton HD, Jain N et al. Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach. Mol Psychiatry 2015; 20: 1266–1285.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Le-Niculescu H, Levey DF, Ayalew M, Palmer L, Gavrin LM, Jain N et al. Discovery and validation of blood biomarkers for suicidality. Mol Psychiatry 2013; 18: 1249–1264.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Niculescu AB, Levey DF, Phalen PL, Le-Niculescu H, Dainton HD, Jain N et al. Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach. Mol Psychiatry 2015; 20: 1266–1285.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Niculescu AB, Levey D, Le-Niculescu H, Niculescu E, Kurian SM, Salomon D . Psychiatric blood biomarkers: avoiding jumping to premature negative or positive conclusions. Mol Psychiatry 2015; 20: 286–288.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Niculescu AB, Le-Niculescu H . Convergent Functional Genomics: what we have learned and can learn about genes, pathways, and mechanisms. Neuropsychopharmacology 2010; 35: 355–356.

    Article  PubMed  Google Scholar 

  7. Zhang EE, Liu AC, Hirota T, Miraglia LJ, Welch G, Pongsawakul PY et al. A genome-wide RNAi screen for modifiers of the circadian clock in human cells. Cell 2009; 139: 199–210.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. McCarthy MJ, Welsh DK . Cellular circadian clocks in mood disorders. J Biol Rhythms 2012; 27: 339–352.

    CAS  Article  PubMed  Google Scholar 

  9. Niculescu AB, Lulow LL, Ogden CA, Le-Niculescu H, Salomon DR, Schork NJ et al. PhenoChipping of psychotic disorders: a novel approach for deconstructing and quantitating psychiatric phenotypes. Am J Med Genet B Neuropsychiatr Genet 2006; 141B: 653–662.

    Article  PubMed  Google Scholar 

  10. Borges G, Angst J, Nock MK, Ruscio AM, Kessler RC . Risk factors for the incidence and persistence of suicide-related outcomes: a 10-year follow-up study using the National Comorbidity Surveys. J Affect Disord 2008; 105: 25–33.

    Article  PubMed  Google Scholar 

  11. Nock MK . Future directions for the study of suicide and self-injury. J Clin Child Adolesc Psychol 53 2012; 41: 255–259.

    Article  Google Scholar 

  12. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Miriami E et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012; 148: 1293–1307.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. Sequeira A, Morgan L, Walsh DM, Cartagena PM, Choudary P, Li J et al. Gene expression changes in the prefrontal cortex, anterior cingulate cortex and nucleus accumbens of mood disorders subjects that committed suicide. PLoS One 2012; 7: e35367.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Falcone T, Fazio V, Lee C, Simon B, Franco K, Marchi N et al. Serum S100B: a potential biomarker for suicidality in adolescents? PLoS One 2010; 5: e11089.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Guintivano J, Brown T, Newcomer A, Jones M, Cox O, Maher BS et al. Identification and replication of a combined epigenetic and genetic biomarker predicting suicide and suicidal behaviors. Am J Psychiatry 2014; 171: 1287–1296.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kaminsky Z, Wilcox HC, Eaton WW, Van Eck K, Kilaru V, Jovanovic T et al. Epigenetic and genetic variation at SKA2 predict suicidal behavior and post-traumatic stress disorder. Transl Psychiatry 2015; 5: e627.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Mann JJ, Currier D . Medication in Suicide Prevention Insights from Neurobiology of Suicidal Behavior, In: Dwivedi Y (ed) The Neurobiological Basis of Suicide: Boca Raton, FL, USA, 2012.

    Google Scholar 

  18. Sublette ME, Hibbeln JR, Galfalvy H, Oquendo MA, Mann JJ . Omega-3 polyunsaturated essential fatty acid status as a predictor of future suicide risk. Am J Psychiatry 2006; 163: 1100–1102.

    Article  PubMed  Google Scholar 

  19. Lewis MD, Hibbeln JR, Johnson JE, Lin YH, Hyun DY, Loewke JD . Suicide deaths of active-duty US military and omega-3 fatty-acid status: a case-control comparison. J Clin Psychiatry 2011; 72: 1585–1590.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Hennings JM, Uhr M, Klengel T, Weber P, Putz B, Touma C et al. RNA expression profiling in depressed patients suggests retinoid-related orphan receptor alpha as a biomarker for antidepressant response. Transl Psychiatry 2015; 5: e538.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Martins-de-Souza D, Maccarrone G, Ising M, Kloiber S, Lucae S, Holsboer F et al. Blood mononuclear cell proteome suggests integrin and Ras signaling as critical pathways for antidepressant treatment response. Biol Psychiatry 2014; 76: e15–e17.

    CAS  Article  PubMed  Google Scholar 

  22. Le-Niculescu H, Kurian SM, Yehyawi N, Dike C, Patel SD, Edenberg HJ et al. Identifying blood biomarkers for mood disorders using convergent functional genomics. Mol Psychiatry 2009; 14: 156–174.

    CAS  Article  PubMed  Google Scholar 

  23. Kurian SM, Le-Niculescu H, Patel SD, Bertram D, Davis J, Dike C et al. Identification of blood biomarkers for psychosis using convergent functional genomics. Mol Psychiatry 2011; 16: 37–58.

    CAS  Article  PubMed  Google Scholar 

  24. Kelleher I, Lynch F, Harley M, Molloy C, Roddy S, Fitzpatrick C et al. Psychotic symptoms in adolescence index risk for suicidal behavior: findings from 2 population-based case-control clinical interview studies. Arch Gen Psychiatry 2012; 69: 1277–1283.

    Article  PubMed  Google Scholar 

  25. Le-Niculescu H, McFarland MJ, Ogden CA, Balaraman Y, Patel S, Tan J et al. Phenomic, convergent functional genomic, and biomarker studies in a stress-reactive genetic animal model of bipolar disorder and co-morbid alcoholism. Am J Med Genet B Neuropsychiatr Genet 2008; 147B: 134–166.

    CAS  Article  PubMed  Google Scholar 

  26. Goodwin RD, Marusic A . Association between short sleep and suicidal ideation and suicide attempt among adults in the general population. Sleep 2008; 31: 1097–1101.

    PubMed  PubMed Central  Google Scholar 

  27. Schoenbaum M, Kessler RC, Gilman SE, Colpe LJ, Heeringa SG, Stein MB et al. Predictors of suicide and accident death in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS): results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). JAMA Psychiatry 2014; 71: 493–503.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Posner K, Brown GK, Stanley B, Brent DA, Yershova KV, Oquendo MA et al. The Columbia-Suicide Severity Rating Scale: initial validity and internal consistency findings from three multisite studies with adolescents and adults. Am J Psychiatry 2011; 168: 1266–1277.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Gaiteri C, Guilloux JP, Lewis DA, Sibille E . Altered gene synchrony suggests a combined hormone-mediated dysregulated state in major depression. PLoS One 2010; 5: e9970.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sainz J, Mata I, Barrera J, Perez-Iglesias R, Varela I, Arranz MJ et al. Inflammatory and immune response genes have significantly altered expression in schizophrenia. Mol Psychiatry 2013; 18: 1056–1057.

    CAS  Article  PubMed  Google Scholar 

  31. Sokolov BP, Jiang L, Trivedi NS, Aston C . Transcription profiling reveals mitochondrial, ubiquitin and signaling systems abnormalities in postmortem brains from subjects with a history of alcohol abuse or dependence. J Neurosci Res 2003; 72: 756–767.

    CAS  Article  PubMed  Google Scholar 

  32. Yu Z, Ono C, Kim HB, Komatsu H, Tanabe Y, Sakae N et al. Four mood stabilizers commonly induce FEZ1 expression in human astrocytes. Bipolar Disord 2011; 13: 486–499.

    CAS  Article  PubMed  Google Scholar 

  33. Le-Niculescu H, Case NJ, Hulvershorn L, Patel SD, Bowker D, Gupta J et al. Convergent functional genomic studies of omega-3 fatty acids in stress reactivity, bipolar disorder and alcoholism. Transl Psychiatry 2011; 1: e4.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. Sood S, Gallagher IJ, Lunnon K, Rullman E, Keohane A, Crossland H et al. A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status. Genome Biol 2015; 16: 185.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006; 313: 1929–1935.

    CAS  Article  PubMed  Google Scholar 

  36. Wasserman D, Hoven CW, Wasserman C, Wall M, Eisenberg R, Hadlaczky G et al. School-based suicide prevention programmes: the SEYLE cluster-randomised, controlled trial. Lancet 2015; 385: 1536–1544.

    Article  PubMed  Google Scholar 

  37. Nock MK, Borges G, Bromet EJ, Cha CB, Kessler RC, Lee S . Suicide and suicidal behavior. Epidemiol Rev 2008; 30: 133–154.

    Article  PubMed  Google Scholar 

  38. Butler AW, Breen G, Tozzi F, Craddock N, Gill M, Korszun et al. A genomewide linkage study on suicidality in major depressive disorder confirms evidence for linkage to 2p12. Am J Med Genet B Neuropsychiatr Genet 2010; 153B: 1465–1473.

    Article  PubMed  Google Scholar 

  39. Smalheiser NR, Lugli G, Rizavi HS, Torvik VI, Turecki G, Dwivedi Y . MicroRNA expression is down-regulated and reorganized in prefrontal cortex of depressed suicide subjects. PLoS One 2012; 7: e33201.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. Fiori LM, Bureau A, Labbe A, Croteau J, Noel S, Merette C et al. Global gene expression profiling of the polyamine system in suicide completers. Int J Neuropsychopharmacol 2011; 14: 595–605.

    CAS  Article  PubMed  Google Scholar 

  41. Dick DM, Meyers J, Aliev F, Nurnberger J Jr., Kramer J, Kuperman S et al. Evidence for genes on chromosome 2 contributing to alcohol dependence with conduct disorder and suicide attempts. Am J Med Genet B Neuropsychiatr Genet 2010; 153B: 1179–1188.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Hesselbrock V, Dick D, Hesselbrock M, Foroud T, Schuckit M, Edenberg H et al. The search for genetic risk factors associated with suicidal behavior. Alcohol Clin Exp Res 2004; 28: 70 S–76 S.

    Article  Google Scholar 

  43. Menke A, Domschke K, Czamara D, Klengel T, Hennings J, Lucae S et al. Genome-wide association study of antidepressant treatment-emergent suicidal ideation. Neuropsychopharmacology 2012; 37: 797–807.

    CAS  Article  PubMed  Google Scholar 

  44. Sequeira A, Gwadry FG, Ffrench-Mullen JM, Canetti L, Gingras Y, Casero RA Jr. et al. Implication of SSAT by gene expression and genetic variation in suicide and major depression. Arch Gen Psychiatry 2006; 63: 35–48.

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

This work is, in essence, a field-wide collaboration. We acknowledge our debt of gratitude for the efforts and results of the many other groups, cited in our paper, who have conducted and published studies (clinical, genetic and biological) in suicidality. With their arduous and careful work, a convergent approach such as ours is possible. We thank David Welsh for advice on clock genes, Joseph Niezer and Tammy Jones for helpful clinical discussions, as well as Meghan Carpenter and Jay Natarajan for help with building literature databases. We also would particularly like to thank the participants who participated in these studies, their families and their caregivers. Without their contribution, such work to advance the understanding of suicide would not be possible. This work was supported by an NIH Directors’ New Innovator Award (1DP2OD007363) and a VA Merit Award (2I01CX000139) to ABN.

Author contributions

ABN designed the study and wrote the manuscript. DFL, EN, HLN, HD, PLP, TL and ECS analyzed the data. NV and FNK performed database work. HW, EB and DLG organized, conducted and scored testing in psychiatric participants. AB, MY, AS, GES and ABN organized and carried out postmortem samples collection. TG, NJS, SMK and DRS conducted microarray experiments and provided input on data analyses. All authors discussed the results and commented on the manuscript.

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Correspondence to A B Niculescu.

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ABN is listed as an inventor on a patent application being filed by Indiana University.

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Levey, D., Niculescu, E., Le-Niculescu, H. et al. Towards understanding and predicting suicidality in women: biomarkers and clinical risk assessment. Mol Psychiatry 21, 768–785 (2016). https://doi.org/10.1038/mp.2016.31

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