Identifying suicidal subtypes and dynamic indicators of increasing and decreasing suicide risk

摘要 在过去的20年里，美国一般人口的自杀率稳步上升。的人 在美国武装部队服役的人是一个高风险的亚群，其中发病率以更快的速度增长 与从未服过兵役的人相比。新的研究表明， 几种自杀状态的亚型不同亚型的个体可能遵循不同的途径， 国家，并可能以不同的方式应对治疗干预。到目前为止，研究还没有检查 类型学使用综合数据集，包括遗传，环境，医学和心理变量。 为了解决这一知识差距，我们建议利用南德克萨斯州地区的存档数据集 创伤和复原力组织网络指导研究（STRONG星星）储存库，其中包括 遗传、环境、医学和心理变量来自4000多名军事人员， 在部署之前和之后进行评估。使用这个数据集，我们将（a）确定自杀军人的亚组 人员和（B）识别增加、减少和静态自杀风险的不同模式。成果 分析将使我们能够识别自杀风险的离散基因型-表型表达，从而使我们能够 确定多种风险模型，可用于改善风险检测和完善自杀预防 干预措施。 新的研究进一步表明，随着时间的推移，自杀风险的过程在本质上是非线性的。不幸的是， 大多数研究随着时间的推移自杀风险的出现都采用了研究和数据 分析方法无法准确捕捉非线性变化过程。为了解决这个 知识差距，我们建议利用STRONG中包含的六项临床试验的存档数据集 星星储存库（N>800），每个储存库都包括对抑郁症的重复评估（总共多达13次）， 创伤后应激障碍和自杀意念。多变量潜在变化得分模型，由动力系统理论提供信息， 将用于模拟与低风险和高风险状态相关的非线性变化过程。成果 分析将产生后验概率，该后验概率可以估计给定患者转变为高风险的可能性。 风险状态在一个给定的时间点，这可能导致发展的“预警系统”，确定谁 会随着时间和时间的推移而增加风险。 虽然拟议的研究使用从军事人员收集的存档数据，但拟议的方法可以 将其转化为其他人群和环境，从而在检测和 自杀风险高的人。

ABSTRACT The U.S. general population suicide rate has increased steadily over the past 20 years. Those who have served in the U.S. Armed Forces are a high risk subgroup among which rates have increased at a faster rate as compared to those who have never served in the military. Emerging research suggests the existence of several subtypes of suicidal states. Individuals in different subtypes may follow different pathways to high risk states and may respond to treatment interventions in different ways. To date, studies have not examined typologies using integrated datasets that include genetic, environmental, medical, and psychological variables. To address this knowledge gap, we propose to leverage an archived dataset from the South Texas Region Organization Network Guiding Studies of Trauma and Resilience (STRONG STAR) Repository, which contains genetic, environmental, medical, and psychological variables from over 4000 military personnel who were assessed before and after deployment. Using this dataset, we will (a) identify subgroups of suicidal military personnel and (b) identify different patterns of increasing, decreasing, and static suicide risk. Results of this analysis will enable us to identify discrete genotype-phenotype expressions of suicide risk, thereby enabling us to identify multiple risk models that can be used to improve risk detection and refine suicide prevention interventions. Emerging research further indicates the process of suicide risk over time is nonlinear in nature. Unfortunately, the majority of studies examining the emergence of suicide risk over time have employed research and data analytic methods that are unable to accurately capture nonlinear change processes. To address this knowledge gap, we propose to leverage archived datasets from six clinical trials included in the STRONG STAR Repository (N>800), each of which includes repeated assessments (up to 13 total) of depression, PTSD, and suicide ideation. Multivariate latent change score models, informed by dynamical systems theory, will be used to model nonlinear change processes associated with low risk and high risk states. Results of this analysis will yield posterior probabilities that can estimate the likelihood of a given patient transitioning to a high risk state at a given point in time, which could lead to the development of “warning systems” that identify who will experience increased risk over time, and when. Although the proposed study uses archived data collected from military personnel, the proposed methods can be translated to other populations and settings, thereby leading to significant advances in the detection and individuals with elevated risk for suicide.