Longitudinal Personalized Dynamics Among Anorexia Nervosa Symptoms, Core Dimensions, and Physiology Predicting Suicide Risk - PROJECT SUMMARY/ABSTRACT Anorexia nervosa (AN) is a severe mental illness with the highest mortality rate of any psychiatric disorder, with suicide as the second leading cause of death. Despite extremely high rates of suicide, risk factors for suicidal ideation (SI) and behaviors/attempts (SA) in this high-risk population are not well understood. While there is evidence that threat reactivity, stress-response, over-arousal, emotion dysregulation, and agitation contribute to suicide risk, the dynamic relations among these processes have not been characterized on a comprehensive, momentary basis. Our scientific premise, developed from our past work, is that the application of ideation-to-action and network theories will enable the identification of dynamic longitudinal interactions among core dimensions (e.g., arousal, threat), AN symptoms, and SI/SA both between and within individuals. Our study goals are to (1) identify symptom and dimension risk interactions of co-occurring AN and SI/SA between and within persons, (2) differentiate which risk factors predict SI vs SA and (3) test if these risk factors predict onset of SI/SA. These goals will ultimately identify which factors should be targeted in novel prevention and treatment efforts. We will use a multiple units of analysis approach, combined with novel, cutting-edge advances in suicide and network science. We will collect intensive real-time data on AN and suicide behaviors, anxiety, over-arousal, emotion regulation, and agitation using mobile technology, as well as psychophysiological assessment of emotion regulation (via heart-rate variability) and arousal (via electrodermal activity characterizing over-arousal and acceleration characterizing the sleep-wake cycle), from 230 individuals with a diagnosis of AN/Atypical AN (AAN). At 1-month, 6-month, and one year follow-up we will test if individual risk factors predict SI/SA. We expect 35-58 participants will have SA across our study period. Specific aims are to (1) test which symptoms and dimensions across time and between-persons maintain comorbid SI/SA and AN symptoms, (2) develop personalized network models to identify which suicide and AN features predict SI/SA within individuals and an exploratory aim (3) to test if there are differences between AN and AAN. The proposed research uses highly innovative methods, combining intensive longitudinal data collection methods, measurement of physiological data via wearable sensor technology, and novel advances in network science to answer previously unresolvable questions pinpointing which individual risk factors contribute to suicide outcomes. The proposed research has clinical impact. If we identify patterns that contribute to suicide risk, these data will provide a model of personalized medicine for the entire field of psychiatry, as well as providing novel intervention targets to prevent and treat AN spectrum illnesses. Additionally, the algorithms we develop can be used in both (a) clinician friendly software to identify treatment targets to prevent SI/SA and (b) in wearable alert devices that can disrupt SA before it occurs.
