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.

项目概要/摘要大规模全基因组关联研究已确定遗传变异与严重抑郁症。为了将这一进步转化为改进的诊断、监测和治疗，一个关键的下一步是阐明将相关遗传变异与抑郁症联系起来的行为机制。不幸的是，已经识别相关变体的大规模研究通常采用单一的时间点和有限的表型评估不适合研究基因和基因之间的联系机制抑郁症，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架构，以促进改进治疗方法的开发。我们的目标是父 R01 应用程序是为了了解遗传变异如何与抑郁症的发展相关发挥他们的作用。医学实习是专业医师培训的第一年，呈现出独特的在这种情况下，我们可以前瞻性地预测一致的、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%。我们的实习生群体是密切监控情况的理想人群利用最新的移动医疗技术作为实时跟踪这些人的工具来控制抑郁症的发展时间，并采取客观措施。对于当前的资助期，我们提出了以下三个具体目标：1) 识别源自移动数据元素的数据驱动特征，可预测情绪变化的短期风险和抑郁发作。 2) 识别与压力下抑郁症相关的遗传变异。 3）识别抑郁症相关遗传变异与客观抑郁症相关移动之间的关系特征。迄今为止，资助期非常富有成效，从研究中收集的数据是主要的发表在《新英格兰医学杂志》、《BMJ》、《内科医学年鉴》、《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》等其他期刊。不幸的是，COVID-19 大流行以多种方式扰乱了这项研究并推迟了目标的完成情况。随着大学关闭和儿童保育服务的丧失，我们的可用性和带宽临床招聘和分析人员大幅减少。同样，随着 COVID-19 的负担在我们的培训医生招募人数上，我们的招募率也受到了影响。此外，由于 COVID-19，作为当前 R01 一部分提出的基因分型被推迟了 9 个月密歇根测序中心的相关延误。该补充文件旨在为人员提供资金避免困难并完成学习目标。