Inflammatory and Glutamatergic Mechanisms of Sustained Threat in Adolescents with Depression: Toward Predictors of Treatment Response and Clinical Course

抑郁症青少年持续威胁的炎症和谷氨酸机制：治疗反应和临床过程的预测因素

• 批准号: 10445166
• 负责人: TIFFANY CHEING HO
• 金额: --
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10445166
• 关键词: 17 year old; Accounting; Address; Adolescent; Age; Amygdaloid structure; Anterior; Antidepressive Agents; Behavioral; Blood specimen; Brain; Cardiovascular Diseases; Chronic; Clinical; Clinical assessments; Cluster Analysis; Data; Development; Disease remission; Evaluation; Functional Magnetic Resonance Imaging; Functional disorder; Future; Glutamates; Goals; Heterogeneity; Hippocampus (Brain); Immune; Inflammation; Inflammatory; Inflammatory Response; Knowledge; Laboratories; Life Stress; Longitudinal Studies; MRI Scans; Magnetic Resonance Spectroscopy; Maintenance; Measures; Mediating; Mediator of activation protein; Mental Depression; Mental Health; Mentored Research Scientist Development Award; Moods; Outcome; Peripheral; Pharmaceutical Preparations; Prediction of Response to Therapy; Prefrontal Cortex; Prevalence; Production; Psychosocial Stress; Public Health; Role; Scanning; Selective Serotonin Reuptake Inhibitor; Severities; Stimulus; Stress; Stressful Event; Subgroup; Suicide; Symptoms; Testing; Therapeutic; Time; Trier Social Stress Test; Vulnerable Populations; Work; base; behavioral response; child depression; cingulate cortex; cytokine; depressive symptoms; design; experience; high risk; improved; machine learning method; multimodal neuroimaging; neurobiological mechanism; novel; open label; peer; physical conditioning; preclinical study; prospective; relating to nervous system; response; social; social stress; social stressor; therapeutically effective; treatment effect; treatment optimization; treatment planning; treatment risk; treatment-resistant depression

ABSTRACT Despite the prevalence and public health significance of depression, up to 40% of depressed adolescents do not respond to first-line antidepressants (i.e., serotonin selective reuptake inhibitors [SSRIs]). Adolescents with treatment non-response (TNR) are at high risk for physical and mental health difficulties associated with ineffectively treated depression, including cardiovascular disease and suicide. Thus, identifying the neurobiological mechanisms that underlie TNR in adolescents is a critical step toward optimizing treatment plans for those who do not respond to first-line treatments. In this context, sustained threat to social stressors, as measured by elevated inflammatory profiles to stressful stimuli, has been shown to drive the onset and maintenance of depression among adolescents and is associated with TNR. The mechanisms by which elevated inflammation impact the brain in depressed adolescents, however, are unclear. To address these gaps in our knowledge, we will test our central hypothesis that excessive glutamate (Glu) in depression-related corticolimbic circuits—including the anterior cingulate cortex, ventromedial prefrontal cortex, amygdala, and hippocampus—is a critical mediator between peripheral inflammation and TNR in depressed adolescents. Specifically, we will conduct a prospective 18-month study of 160 unmedicated treatment-seeking depressed adolescents (ages 14-18) using state-of-the-art multimodal neuroimaging data at 7 Tesla. At Time 1 (prior to SSRI treatment) and Time 2 (after an open-label 12-week SSRI trial), we will assess peripheral measures of pro-inflammatory cytokines and glutamate in corticolimbic circuits before and after a well-validated adolescent- version of the Trier Social Stress Test (TSST). We also will use a well-validated fMRI task designed to probe behavioral and neural responses to negative peer evaluation, a salient form of social threat for adolescents. At Time 1, we will test if TSST induces increases in inflammation and glutamate in corticolimbic circuits in unmedicated adolescents with depression. At Time 2, we will use machine learning methods to identify multi- level predictors of TNR based on behavioral, inflammatory, and neural indicators of sustained threat to social stress; we will also test whether glutamate in corticolimbic circuits mediates the association between baseline levels of inflammation and TNR. Finally, we will continue to clinically assess depression symptoms and collect information on social stressors (e.g., context, severity, duration) every 3 months for 15 months following Time 2 (i.e., from Time 3 to Time 7), which will enable us to use functional clustering analyses to identify subgroups of adolescents on the basis of depression trajectories (e.g., persistent depression, gradual remission, etc), and identify predictors of these subgroups and other related clinical outcomes (e.g., remission status), while accounting for the effects of TNR status and any changes in treatment (and other related factors, including stressful life events). Results from this work will motivate future studies testing alternative therapeutics for depressed adolescents at risk for treatment resistant depression.

抽象的 尽管抑郁症很普遍且对公共健康具有重要意义，但高达 40% 的抑郁青少年确实患有抑郁症 对一线抗抑郁药（即选择性血清素再摄取抑制剂 [SSRI]）没有反应。青少年有 治疗无反应 (TNR) 面临与以下相关的身心健康困难的高风险 抑郁症治疗无效，包括心血管疾病和自杀。因此，识别 青少年 TNR 背后的神经生物学机制是优化治疗的关键一步 为那些对一线治疗没有反应的人制定计划。在这种情况下，社会压力源的持续威胁， 通过应激刺激引起的炎症升高来衡量，已被证明可以驱动炎症的发作和 青少年抑郁症的持续存在，并且与 TNR 相关。其机制 然而，炎症升高对抑郁青少年大脑的影响尚不清楚。为了解决这些 由于我们的知识差距，我们将检验我们的中心假设，即过量的谷氨酸 (Glu) 与抑郁症相关 皮质边缘回路——包括前扣带皮层、腹内侧前额叶皮层、杏仁核和 海马体——是抑郁青少年周围炎症和 TNR 之间的关键介质。 具体来说，我们将对 160 名未接受药物治疗的抑郁症患者进行一项为期 18 个月的前瞻性研究 青少年（14-18 岁）使用 7 特斯拉最先进的多模态神经影像数据。在时间 1（之前 SSRI 治疗）和时间 2（开放标签 12 周 SSRI 试验后），我们将评估以下外围指标： 经过充分验证的青春期前后皮质边缘回路中的促炎细胞因子和谷氨酸 特里尔社会压力测试（TSST）的版本。我们还将使用经过充分验证的功能磁共振成像任务来探测 对同伴负面评价的行为和神经反应，这是青少年社会威胁的一种突出形式。在 第 1 次，我们将测试 TSST 是否会诱导皮质边缘回路中炎症和谷氨酸的增加 未接受药物治疗的抑郁症青少年。在时间 2，我们将使用机器学习方法来识别多 基于社会持续威胁的行为、炎症和神经指标的 TNR 水平预测因子 压力;我们还将测试皮质边缘回路中的谷氨酸是否介导基线之间的关联 炎症和 TNR 水平。最后，我们将继续临床评估抑郁症状并收集 在时间 2 之后的 15 个月内，每 3 个月提供一次有关社会压力源的信息（例如背景、严重程度、持续时间） （即从时间 3 到时间 7），这将使我们能够使用功能聚类分析来识别子组 根据青少年的抑郁轨迹（例如，持续抑郁、逐渐缓解等），以及 确定这些亚组和其他相关临床结果（例如缓解状态）的预测因素，同时 考虑 TNR 状态的影响和治疗的任何变化（以及其他相关因素，包括 生活压力事件）。这项工作的结果将推动未来的研究测试替代疗法 抑郁青少年有患难治性抑郁症的风险。