Mobile Technology to Identify Behavioral Mechanisms Linking Genetic Variation and Depression

移动技术识别遗传变异和抑郁症之间的行为机制

基本信息

批准号：
10728697
负责人：
SRIJAN SEN
金额：
$ 19.25万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10728697
关键词：
Affect American Antidepressive Agents Architecture Behavior Behavioral Mechanisms Biological Biological Psychiatry COVID-19 COVID-19 pandemic Child Care Chronic Clinical DNA Sequencing Facility Data Data Element Data Sources Development Diagnosis Disease Funding Genes Genetic Variation Genotype Goals Grant Health Technology Human Characteristics Human Resources Individual Internal Medicine Internships Journals Link Major Depressive Disorder Measures Medical Medicine Mental Depression Michigan Monitor Moods New England Paper Parents Pathway interactions Phenotype Physicians Population Productivity Psychiatry Publishing Risk Stress Time Training Translating Universities Variant World Health Organization cohort depressive symptoms design digital medicine disability genetic variant genome wide association study genome-wide improved mHealth mobile computing multimodality prospective recruit stressor tool

项目摘要

Project Summary/Abstract Large scale genome-wide association studies 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 the parent R01 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. 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. For the current grant period, we proposed the following three specific aims: 1) Identify data driven features, 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. 3) Identify relationship between depression-associated genetic variation and objective depression-associated mobile features. The grant period has been highly productive to date, with data collected from the study as primary data source for papers published in New England Journal of Medicine, BMJ, Annals of Internal Medicine, Nature Human Behavior, American Journal of Psychiatry, Biological Psychiatry, NPJ Digital Medicine among other journals. Unfortunately, the COVID-19 pandemic disrupted this study in multiple ways and has delayed completion of the Aims. With university shutdowns and loss of childcare, the availability and bandwidth for our clinical recruitment and analysis staff were substantially reduced. Similarly, with the burden COVID-19 placed on our recruitment population of training physicians, our recruitment rate was also compromised. Further, the genotyping proposed as part of the current R01 through was delayed by 9 months because of COVID-19 related delays at the Michigan Sequencing Core. This supplement seeks to provide funding for personnel to avoid hardship and allow completion of the study aims.

项目摘要/摘要大规模基因组关联研究已经确定了与重度抑郁症。为了将这一进步转化为改进的诊断，监测和治疗，这是一个关键的下一步是阐明将含义的遗传变异与抑郁症联系起来的行为机制。不幸的是，已经鉴定出相关变体的大规模研究通常使用时间点和有限的表型评估，这些评估不适合研究将基因和基因联系起来的机制抑郁症，一种慢性多模式疾病。我们的长期目标是阐明病理生理学抑郁症的基础建筑，以促进改善治疗的发展。我们的目标母体R01应用是要了解与抑郁发展相关的遗传变异发挥作用。医疗实习是专业医师培训的第一年，它提出了独特的我们可以前瞻性地预测统一，慢性压力源的发作，并遵循抑郁症状的发展。我们发现抑郁症发生率急剧增加，从4％在实习年度实习之前，至26％。我们的实习生队列是密切监视的理想人群以最近的移动卫生技术为工具来发展抑郁症时间，客观措施。在当前的赠款期间，我们提出了以下三个特定目标：1）识别来自移动数据元素的数据驱动特征，这些功能可以预测情绪变化的短期风险和抑郁情节。 2）确定与压力下与抑郁症相关的遗传变异。 3）识别抑郁症相关的遗传变异与客观抑郁相关的移动之间的关系特征。迄今为止，赠款期的生产力很高，从研究中收集了数据新英格兰医学杂志BMJ，内科年鉴，发表的论文数据来源，自然人类行为，美国精神病学杂志，生物精神病学，NPJ数字医学其他期刊。不幸的是，COVID-19大流行以多种方式破坏了这项研究，并延迟了目标的完成。随着大学的关闭和育儿损失，我们的可用性和带宽临床招聘和分析人员大大减少了。同样，将Covid-19放置在我们的培训医师招聘人群中，我们的招聘率也受到损害。此外，由于COVID-19 密歇根州测序核心的相关延迟。这种补充旨在为人员提供资金避免艰辛并允许完成研究的目的。