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Metabolomic Profiles of Depression and Social Isolation in Midlife.

Metabolomic Profiles of Depression and Social Isolation in Midlife.
中年抑郁症和社会孤立的代谢组学概况。
批准号:
9807468
负责人:
Jan Bressler
金额:
$7.7万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-08-15 至 2021-04-30

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项目成果

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中文摘要
翻译
项目摘要 重度抑郁症是一种精神障碍,据报道,2016年美国约有1620万18岁或以上的成年人受到影响。抑郁症和孤独症的遗传率估计分别为31-42%和48%,表明遗传和环境因素在其病因中起着重要作用。非靶向代谢组学是一种新兴的方法来定量生物样品中大量的低分子量化合物。多个途径和功能类的代表,提供了一个来源的生物标志物,可以揭示生理和细胞过程中涉及的协调响应DNA序列变异和环境的影响发生在整个生命周期。此前已证明,该技术可用于揭示患有常见疾病和病症的个体的代谢物水平变化,这些疾病和病症包括心血管疾病、2型糖尿病、慢性肾病、认知能力下降以及阿尔茨海默病和痴呆症。在这项研究中,我们建议分析的关联,血清代谢物和抑郁症,社会隔离,并在中年感知社会支持。这将在社区动脉粥样硬化风险(ARIC)研究的背景下完成,ARIC是一项动脉粥样硬化的前瞻性双盲队列研究,在1987-1989年基线时招募了15,792名45至64岁的欧洲裔美国人和非洲裔美国人男性和女性。ARIC研究人员和其他人以前已经表明,这些心理社会因素与成年后期慢性疾病的易感性增加有关,包括心血管疾病,阿尔茨海默病和痴呆症。为了确定代谢物水平的个体间差异是否是抑郁症和社会隔离的生物学决定因素,将追求以下具体目标:鉴定与抑郁症、社会隔离和社会支持相关的血清代谢物(目标1);评估常见和罕见DNA序列变异与抑郁症,社会隔离,通过分析ARIC研究中已经获得的全外显子组测序数据(Aim 2a),并通过检查目标2a中确定的遗传变异与相应的代谢物之间的关联,确定目标1中确定的代谢物组学特征是否存在因果关系。使用孟德尔随机化方法的心理社会因素(目标2b);研究发现与目标2b中的抑郁症、社会孤立和社会支持相关的遗传变异与心血管疾病、住院痴呆症、和流行性阿尔茨海默病分类的基础上,在所有ARIC参与者与外显子组测序的综合神经认知评估(目的3)。这项研究的长期目标是进一步了解潜在的生物学机制,并发现生物标志物,以识别高危人群,这些人将受益于干预措施,以防止在老年时临床上明显的不良健康后果。
英文摘要
Project Abstract Major depression is a mental disorder reported to affect approximately 16.2 million adults aged 18 years or older in the United States in 2016. Heritability estimates of 31-42% and 48% for depression and loneliness, respectively, indicate a substantial role for genetic and environmental factors in their etiology. Untargeted metabolomics is an emerging approach to quantify large numbers of low molecular weight compounds in a biological sample. Multiple pathways and functional classes are represented, providing a source of biomarkers that can reveal physiologic and cellular processes implicated in the coordinated response to DNA sequence variation and environmental influences occurring over the lifespan. It has previously been demonstrated that this technology can be used to reveal changes in metabolite levels in individuals with common diseases and conditions including cardiovascular disease, type 2 diabetes, chronic kidney disease, cognitive decline, and Alzheimer’s disease and dementia. In this study, we propose to analyze the association of serum metabolites and depression, social isolation, and perceived social support in midlife. This will be accomplished in the setting of the Atherosclerosis Risk in Communities (ARIC) Study, a prospective biracial cohort study of atherosclerosis that enrolled 15,792 European American and African American men and women 45 to 64 years at baseline in 1987-1989. ARIC investigators and others have previously shown that these psychosocial factors were associated with increased susceptibility to chronic diseases of late adulthood including cardiovascular disease, and Alzheimer’s disease and dementia. In order to determine whether interindividual variation in metabolite levels is a biological determinant of depression and social isolation the following specific aims will be pursued: identify serum metabolites that are associated with depression, social isolation, and social support (Aim 1); evaluate the relationship of common and rare DNA sequence variation with the metabolomic signatures associated with depression, social isolation, and social support in Aim 1 by analyzing whole exome sequencing data already available in the ARIC study (Aim 2a), and establish whether the metabolomic signatures identified in Aim 1 are causally related by examining the association between the genetic variants identified in Aim 2a and the corresponding psychosocial factor using a Mendelian randomization approach (Aim 2b); examine the association between the genetic variants found to be related to depression, social isolation, and social support in Aim 2b and incident cardiovascular disease, incident hospitalized dementia, and prevalent Alzheimer’s disease classified based on a comprehensive neurocognitive assessment in all ARIC participants with exome sequencing (Aim 3). The long-term goals of this research are to further understand the underlying biological mechanisms, and to discover biomarkers to identify high-risk individuals who would benefit from interventions to prevent the spectrum of adverse health consequences that become clinically apparent at older ages.
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