Mobile Technology to Identify Behavorial Mechanisms Linking Genetic Variation and Depression

移动技术识别与遗传变异和抑郁症相关的行为机制

• 批准号: 10399597
• 负责人: SRIJAN SEN
• 金额: $ 69.7万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10399597
• 关键词: Affect; American; Antidepressive Agents; Architecture; Assessment tool; Behavioral Mechanisms; Biological; Chronic; Chronic stress; Code; Complex; Country; Data; Data Element; Development; Devices; Diagnosis; Disease; Early Diagnosis; Emotional; Enrollment; Epidemiology; Funding; Genes; Genetic Risk; Genetic Variation; Genome; Genomics; Goals; Health Technology; Heart Rate; Hour; Individual; Infrastructure; Intention; Internships; Intervention; Lead; Link; Major Depressive Disorder; Measures; Mediating; Medical; Mental Depression; Modeling; Monitor; Moods; Neurocognition; Neurocognitive; Pathway interactions; Phase; Phenotype; Physical activity; Physicians; Population; Prediction of Response to Therapy; Progress Reports; Public Health; Publishing; Risk; Sampling; Sleep; Sleep Deprivation; Specific qualifier value; Stress; Testing; Time; Training; Translating; Untranslated RNA; Variant; Work; World Health Organization; base; behavior measurement; behavioral health; behavioral phenotyping; biosignature; circadian; cohort; depressive symptoms; design; disability; fitbit; genetic association; genetic variant; genome wide association study; genome-wide; heart rate variability; improved; innovation; mHealth; mobile computing; multimodality; novel; prospective; public health priorities; stressor; therapy development; tool; trait

Large scale genome-wide association studies, for the first time, have identified genetic variation definitively associated with major depression. To translate this advancement into improved diagnosis, monitoring, and treatment, a critical next step is to elucidate the behavioral mechanisms linking the implicated genetic variation with depression. Unfortunately, the large-scale studies that have identified associated variants have typically employed single-time point and limited phenotypic assessments that are not suited to study mechanisms linking genes and depression, a chronic multi-modal disease. Our long-term goal is to elucidate the pathophysiological architecture underlying depression to facilitate the development of improved treatments. Our objective in this application is to understand how genetic variants associated with the development of depression exert their effect. Medical internship, the first year of professional physician training, presents a unique situation in which we can prospectively predict the onset of a uniform, chronic stressor and follow the development of depressive symptoms. We have found that rates of depression increase dramatically, from 4% prior to internship to 26% during internship year. Currently, the study enrolls 3,000-3,500 interns annually. Our intern cohort is an ideal population to closely monitor the development of depression with recent mobile health technology as a tool to follow these individuals in real-time, with objective measures. In the proposed study, we will combine, cutting edge-genomics, mobile health technology, and the prospective intern stress design to identify the mechanisms through which depression-related genetic variation lead to depression. We hypothesize that depression-associated genetic variation acts to increase the risk of depression through specific mobile measured behavioral phenotypes. To test this hypothesis, we propose the following three specific aims: 1) Identify data driven behavioral phenotypes, derived from mobile data elements, that predict short-term risk for mood changes and depressive episodes; 2) Identify genetic variants associated with depression under stress; and 3) Elucidate behavioral phenotypes through which genetic variants may act to increase the risk of depression. Our approach is innovative because it combines a naturally occurring stress paradigm and new real-time objective assessment tools in order to elucidate the relationship between genes, objective, real-time markers and depression with an approach that, to date, has not been attempted. This project is significant because it has the potential to identify key mechanisms underlying genetic associations involved in depression under stress, an advancement that holds promises in predicting treatment response and identifying novel targets for antidepressant development.

大规模全基因组关联研究首次确定了遗传变异 与重度抑郁症有关。将这一进步转化为改进的诊断、监测和 治疗中，下一步关键是阐明与相关遗传变异相关的行为机制 患有抑郁症。不幸的是，已经识别出相关变异的大规模研究通常 采用不适合研究机制的单时间点和有限的表型评估 将基因与抑郁症（一种慢性多模式疾病）联系起来。我们的长期目标是阐明 抑郁症的病理生理结构有助于开发改进的治疗方法。 我们在此应用中的目标是了解遗传变异如何与发育相关 抑郁症发挥其作用。医学实习是专业医师培训的第一年，提出了 在这种独特的情况下，我们可以前瞻性地预测统一的、慢性的压力源的发生，并遵循 抑郁症状的发展。我们发现抑郁症发病率急剧上升，从 4% 实习前至实习期间的 26%。目前，该研究每年招收 3,000-3,500 名实习生。我们的 实习生队列是通过最近的移动健康密切监测抑郁症发展的理想人群 技术作为一种工具，可以通过客观的措施实时跟踪这些人。在拟议的研究中， 我们将结合尖端基因组学、移动健康技术和前瞻性实习压力设计， 确定与抑郁症相关的遗传变异导致抑郁症的机制。我们 假设与抑郁症相关的遗传变异通过以下方式增加患抑郁症的风险 特定移动测量的行为表型。为了验证这个假设，我们提出以下三个 具体目标： 1) 识别源自移动数据元素的数据驱动的行为表型， 预测情绪变化和抑郁发作的短期风险； 2) 识别遗传变异 与压力下的抑郁有关； 3）阐明行为表型，通过这些表型 遗传变异可能会增加患抑郁症的风险。我们的方法是创新的，因为它 结合了自然发生的压力范例和新的实时客观评估工具，以便 通过以下方法阐明基因、客观、实时标记与抑郁症之间的关系： 迄今为止，尚未尝试过。该项目意义重大，因为它有可能确定关键的 与压力下抑郁症相关的遗传关联的潜在机制，这一进展表明 有望预测治疗反应并确定抗抑郁药物开发的新靶点。