Anti-depressant response in neurobiologically defined psychiatric veteran groups

神经生物学定义的精神病退伍军人群体的抗抑郁反应

• 批准号: 10038794
• 负责人: ALAN N SIMMONS
• 金额: --
• 依托单位: VA SAN DIEGO HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10038794
• 关键词: Affect; Amygdaloid structure; Anterior; Antidepressive Agents; Arousal; Award; Behavior; Behavioral; Behavioral Symptoms; Biological; Biological Assay; Brain; Brain imaging; Cellular biology; Classification; Clinic; Clinical; Clinical Research; Clinical Treatment; Cognitive; Complex; Data; Diagnosis; Diagnostic; Disease; Distress; Drug Prescriptions; Economics; Emotional; FDA approved; Face; Family; Fostering; Fright; Future; Generalized Anxiety Disorder; Genes; Genetic; Goals; Grant; Grouping; Health Care Costs; Hippocampus (Brain); Individual; Insula of Reil; Interleukin-6; Lead; Learning; Link; Literature; Machine Learning; Major Depressive Disorder; Measures; Medical; Mental Depression; Mental Health; Mental disorders; Mission; Modeling; Molecular; Molecular Biology; Neurobiology; Neurotransmitters; Outcome; Patients; Pattern; Peripheral; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Physiological; Physiology; Population; Post-Traumatic Stress Disorders; Prediction of Response to Therapy; Predictive Value; Psychiatric Diagnosis; Psychiatric therapeutic procedure; Quality of life; Research; Risk; Running; Selective Serotonin Reuptake Inhibitor; Serotonin; Sertraline; Signal Transduction; Soldier; Startle Reaction; Statistical Methods; Stress; Subgroup; Symptoms; System; Testing; Treatment outcome; Veterans; Work; base; behavioral phenotyping; clinical practice; combat; comorbidity; disease classification; feasibility testing; heart rate variability; high risk; imaging study; improved; inflammatory marker; interest; mild traumatic brain injury; military veteran; multidisciplinary; novel strategies; optimal treatments; predict clinical outcome; predictive modeling; psychopharmacologic; random forest; response; success; supervised learning; treatment response; white matter; Affect; Amygdaloid structure; Anterior; Antidepressive Agents; Arousal; Award; Behavior; Behavioral; Behavioral Symptoms; Biological; Biological Assay; Brain; Brain imaging; Cellular biology; Classification; Clinic; Clinical; Clinical Research; Clinical Treatment; Cognitive; Complex; Data; Diagnosis; Diagnostic; Disease; Distress; Drug Prescriptions; Economics; Emotional; FDA approved; Face; Family; Fostering; Fright; Future; Generalized Anxiety Disorder; Genes; Genetic; Goals; Grant; Grouping; Health Care Costs; Hippocampus (Brain); Individual; Insula of Reil; Interleukin-6; Lead; Learning; Link; Literature; Machine Learning; Major Depressive Disorder; Measures; Medical; Mental Depression; Mental Health; Mental disorders; Mission; Modeling; Molecular; Molecular Biology; Neurobiology; Neurotransmitters; Outcome; Patients; Pattern; Peripheral; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Physiological; Physiology; Population; Post-Traumatic Stress Disorders; Prediction of Response to Therapy; Predictive Value; Psychiatric Diagnosis; Psychiatric therapeutic procedure; Quality of life; Research; Risk; Running; Selective Serotonin Reuptake Inhibitor; Serotonin; Sertraline; Signal Transduction; Soldier; Startle Reaction; Statistical Methods; Stress; Subgroup; Symptoms; System; Testing; Treatment outcome; Veterans; Work; base; behavioral phenotyping; clinical practice; combat; comorbidity; disease classification; feasibility testing; heart rate variability; high risk; imaging study; improved; inflammatory marker; interest; mild traumatic brain injury; military veteran; multidisciplinary; novel strategies; optimal treatments; predict clinical outcome; predictive modeling; psychopharmacologic; random forest; response; success; supervised learning; treatment response; white matter

The soldiers that face combat are at high risk for the potentially significant repercussions of combat stress. Combat stress can lead to a number of impactful emotional and cognitive conditions, most notably Posttraumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD), Generalized Anxiety Disorder (GAD), and mild Traumatic Brain Injury (mTBI). While there have been attempts to match a specific neurobiological pattern to a specific DSM identified disorder, the pursuit has met limited success. Clinically, different DSM diagnoses are often approached with similar treatments with similar response rates (~33-50%). Clinically groups are identified my sets behaviors (DSM disorders) and research has aimed to find the neurobiological underpinnings of these behavior defined groups. Our aim is to instead identify neurobiological groups in the context of underlying neurobiological and with the long term goal of improving response rates to medical trials be clustering of relevant features. However, due to the complex relationship between the neurobiological variables a simple linear relationship or risk score is not appropriate. Here we present a novel approach in which we define neurobiologically distinct subgroups — based on the most feasible, most robust, and most likely to relate to treatment outcomes — in these Veterans with combat related psychiatric distress. We have selected a set of brain imaging, molecular biology, and physiological markers such that measures will not be influenced by current clinical models. We will then seek to determine robust subgroups from this model-based hierarchical clustering approach. Next, we contrast our neurobiologically defined groups with traditional groups or general response. Finally, to help best understand the available data and feed forward for future studies, we will run a supervised machine learning (random forest) to determine the optimal variables and groups to predict treatment response. We have opted to solely test sertraline, as this is the most commonly prescribed medication in this population at the San Diego VA mental health clinics (FDA approved for MDD and PTSD). The model-based clustering approach allows us to look at the non-linear relationship between variables of interest and foster an attempt to better link clinical research and clinical practice to best benefit our Veteran population.

面对战斗的士兵面临着潜在的重大影响的高风险 战斗压力。战斗压力会导致许多有影响力的情绪和认知能力 疾病，最著名的是创伤后应激障碍（PTSD），重度抑郁症 （MDD），广义焦虑症（GAD）和轻度创伤性脑损伤（MTBI）。尽管 已经尝试将特定的神经生物学模式与特定的DSM匹配 确定的疾病，追求取得了有限的成功。临床上，不同的DSM诊断是 通常以相似的反应率（约33-50％）接近类似的治疗方法。临床 确定了我的集合行为（DSM疾病），研究旨在找到 这些行为定义的组的神经生物学基础。我们的目的是确定 神经生物学群体在潜在的神经生物学和长期目标的背景下 提高对医学试验的反应率的是相关特征的聚类。但是，到期 与神经生物学变量之间的复杂关系，简单的线性关系或 风险评分不合适。在这里，我们提出了一种我们定义的新方法 神经生物学上不同的子组 - 基于最可行，最强大，最有可能的 与治疗结果相关 - 在与战斗相关的精神病患者的这些退伍军人中。 我们选择了一组大脑成像，分子生物学和物理标记 该测量将不会受到当前临床模型的影响。然后，我们将寻求确定 来自此基于模型的分层聚类方法的强大子组。接下来，我们对比我们 具有传统群体或一般反应的神经生物学定义的群体。最后，为您提供帮助 最好理解可用的数据，并向未来的研究提出依据，我们将进行监督 机器学习（随机森林）确定最佳变量和组以预测 治疗反应。我们选择仅测试舍曲林，因为这是最常见的 圣地亚哥VA心理健康诊所（FDA）的该人群的处方药 批准用于MDD和PTSD）。基于模型的聚类方法使我们能够查看 感兴趣的变量与促进更好地联系临床的尝试之间的非线性关系 研究和临床实践，以最大程度地使我们的退伍军人人口受益。