Integrating biobehavioral information to predict mood disorder suicide risk

The will to live and the ability to maintain one’s well-being are crucial for survival. Yet, almost a million people die by suicide globally each year (Aleman and Denys, 2014), making premature deaths due to suicide a significant public health problem (Saxena et al., 2013). The expression of suicidal behaviors is a complex phenotype with documented biological, psychological, clinical, and sociocultural risk factors (Turecki et al., 2019). From a brain disease perspective, suicide is associated with neuroanatomical, neurophysiological, and neurochemical dysregulations of brain networks involved in integrating and contextualizing cognitive and emotional regulatory behaviors. From a symptom perspective, diagnostic measures of dysregulated mood states like major depressive symptoms are associated with over sixty percent of suicide deaths worldwide (Saxena et al., 2013). This paper reviews the neurobiological and clinical phenotypic correlates for mood dysregulations and suicidal phenotypes. We further propose machine learning approaches to integrate neurobiological measures with dysregulated mood symptoms to elucidate the role of inflammatory processes as neurobiological risk factors for suicide. •Mood disorder symptoms are the highest risk factors for suicide.•The neurobiology of mood disorders and suicide risk behaviors are intricately linked.•Brain and peripheral inflammation in mood disorders are predictors of suicidal behaviors.•Our proposed framework illustrates links between environment and bodily homeostasis in influencing suicide risk biology.•Machine learning models can integrate bio-environmental and clinical risk prediction of suicide.