Identifying Predictors in the HPA Axis and Inflammatory Pathways for Suicidal Behavior in Youth

虽然自杀行为发生在许多精神疾病的背景下，但相对较少的受试者具有 精神障碍企图自杀对临床医生来说，最具挑战性的任务之一是确定 病人会继续尝试自杀研究发现，临床医生对癌症的预测 病人和他们实际的自杀行为因此，确定自杀的客观生物特征是至关重要的。 行为，青少年死亡的第二大原因。在一项R21试点研究中，我们发现， 第一次自杀未遂者的头发皮质醇浓度（HCC）低于第二次自杀未遂者。 自杀意念和健康对照。HCC提供了过去皮质醇水平的回顾性评估 几个月前，因此在我们的R21尝试之前。低肝癌也与增加的死亡率有关。 内部的尝试。自杀未遂者的糖皮质激素受体（GR）较低也有所不同 mRNA和炎症增加。在R 01中，我们建议招募大量精神科住院患者样本 （n=300），年龄18-30岁，既往无自杀行为史，自杀意念丰富;和 健康对照组（n=50）;并在摄入后3、6和12个月进行随访。自杀行为的风险是 尤其是在精神病住院后的第一年。我们将收集HCC的生物学数据， HPA轴和炎症途径中的基因表达，以及炎症的全身性标志物（例如，C型 反应蛋白、肿瘤坏死因子-α）;以及关于已确立的临床和行为预测因子的数据 自杀行为我们假设低HCC和HPA轴基因下调与 基线时炎症基因的上调和全身炎症的增加将预测未来的自杀行为 行为类似地，这些生物学改变随时间的轨迹将与以下情况的恶化相关： 临床（冲动，侵略，睡眠障碍）和行为措施（决策，记忆， 自杀特异性注意偏差和内隐认知）。该模型结合了生物学、临床和 行为测量将显示更好的性能，在预测尝试相比，模型结合 临床和行为测量。我们还建议从18-30岁的受试者中收集头发样本， 并将他们与死于HCC意外死亡的人进行比较，这反映了 死亡前皮质醇水平;我们还将比较HCC患者与无意念的患者， 想法，以及那些继续试图自杀的人，从而全面检查HCC与风险的关系。 一系列自杀行为这项研究是第一次检查生物标志物在HPA轴的能力 和炎症途径来预测自杀行为，并结合临床和 行为预测器这项研究将有助于更好地识别风险最高的患者， 更密切的监测和干预;并将提高我们对自杀的生物学途径的理解 行为，这将指导新的治疗目标。

While suicidal behavior occurs in the context of many psychiatric disorders, relatively few subjects with a psychiatric disorder attempt suicide. One of the most challenging tasks for clinicians is to identify which patients will actually go on to attempt suicide. Studies find no association between clinicians' prediction for a patient and their actual suicidal behavior. Thus, it is critical to identify objective biological signatures for suicidal behavior, the 2nd leading cause of death among youth. In an R21 pilot study, we found that inpatients admitted for their first suicide attempt had lower hair cortisol concentrations (HCC) compared to those admitted for suicidal ideation and healthy controls. HCC provides a retrospective assessment of cortisol levels over the past few months and thus prior to attempt in our R21. Lower HCC were also associated with increased lethality of the attempt within attempters. Suicide attempters also differed by their lower glucocorticoid receptor (GR) mRNA and increased inflammation. In this R01, we propose to recruit a large sample of psychiatric inpatients (n=300), aged 18-30 years, with no prior history of suicidal behavior and enriched for suicidal ideation; and healthy controls (n=50); and follow them at 3, 6, and 12 months from intake. The risk for suicidal behavior is especially high during the first year after psychiatric hospitalization. We will collect biological data on HCC, gene expression in the HPA axis and inflammatory pathways, and systemic markers of inflammation (e.g., C- Reactive Protein, Tumor Necrosis Factor-α); and data on already-established clinical and behavioral predictors for suicidal behavior. We hypothesize that low HCC and downregulation of HPA axis genes together with upregulation of inflammatory genes and increased systemic inflammation at baseline will predict future suicidal behavior. Similarly, the trajectories of these biological alterations over time will be associated with worsening of clinical (impulsivity, aggression, sleep disturbances) and behavioral measures (decision-making, memory, and suicide-specific attentional biases and implicit cognitions). The models combining biological, clinical, and behavioral measures will show better performance in predicting attempts compared to models combining clinical and behavioral measures. We also propose to collect hair samples from subjects, aged 18-30 years, who died by suicide and compare them to those who died from accidental deaths on HCC, which reflects cortisol levels prior to death; we will also compare them on HCC to patients with no ideation, those with ideation, and those who go on to attempt suicide and thus examine the relation of HCC to risk across the full spectrum of suicidal behavior. This study is the first to examine the ability of biological markers in the HPA axis and inflammatory pathways to predict suicidal behavior and to examine them combined with clinical and behavioral predictors. This study will help better identify patients at highest risk who can then be targeted for closer monitoring and interventions; and will improve our understanding of the biological pathways for suicidal behavior, which will guide new therapeutic targets.