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.

大规模的全基因组关联研究首次确定了遗传变异。 与严重的抑郁症有关。将这一进步转化为更好的诊断、监控和 治疗，关键的下一步是阐明与相关基因变异有关的行为机制 患有抑郁症。不幸的是，已确定相关变异的大规模研究通常 采用了不适合研究机制的单一时间点和有限表型评估 将基因与抑郁症联系起来，这是一种慢性多模式疾病。我们的长期目标是澄清 抑郁症潜在的病理生理架构，以促进改进治疗的发展。 我们在这项应用中的目标是了解基因变异是如何与 抑郁症发挥了它们的作用。医学实习是专业医生培训的第一年，它提供了一个 一种独特的情况，在这种情况下，我们可以前瞻性地预测一种统一的慢性应激源的开始，并遵循 出现抑郁症状。我们发现，抑郁症的发病率从4%急剧上升 实习前为26%，实习期间为26%。目前，该研究每年招收3000至3500名实习生。我们的 实习生队列是一个理想的人群，可以通过最近的流动健康来密切监测抑郁症的发展 技术作为一种工具，实时跟踪这些人，并采取客观措施。在拟议的研究中， 我们将结合尖端基因组学、移动健康技术和未来的实习生压力设计来 确定与抑郁相关的基因变异导致抑郁的机制。我们 假设抑郁症相关基因变异通过以下途径增加抑郁症的风险 特定的移动测量的行为表型。为了验证这一假设，我们提出了以下三个假设 具体目标：1)确定数据驱动的行为表型，源自移动数据元素， 预测情绪变化和抑郁发作的短期风险；2)识别基因变异 与压力下的抑郁有关；以及3)阐明行为表型，通过 基因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 将自然产生的压力范例和新的实时客观评估工具相结合，以便 用一种方法阐明基因、客观、实时标记物与抑郁症的关系， 迄今为止，还没有人尝试过。这个项目意义重大，因为它有可能确定关键 压力下抑郁的遗传关联的潜在机制，这一进展 在预测治疗反应和确定抗抑郁药物开发的新靶点方面承诺。