A Multi-omics approach to Environment and Depression in Parkinsons disease (MOOD-PD)

帕金森病环境与抑郁症的多组学方法 (MOOD-PD)

• 批准号: 10493187
• 负责人: Beate R Ritz
• 金额: $ 19.5万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-24 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10493187
• 关键词: Address; Affect; Aging; Anxiety; Anxiety Disorders; Biological; Biological Process; California; Cardiovascular system; Case-Control Studies; Chronic; Communities; Complex; DNA; DNA Methylation; Data; Diagnosis; Disease; Disease Progression; Dose; Elderly; Environment; Environmental Exposure; Epigenetic Process; Exposure to; Funding; Genes; Genetic; Genetic Predisposition to Disease; Genetic Variation; Genome; Genomics; Geographic Information Systems; Health; Healthcare; Home; Human; Human Characteristics; Human Resources; Immune; Individual; Inflammatory; Knowledge; Lead; Life; Life Style; Link; Location; Long-Term Care; Maps; Mental Depression; Mental Health; Mental disorders; Metabolic; Methods; Modernization; Modification; Molecular; Molecular Profiling; Mood Disorders; Multiomic Data; Nerve Degeneration; Neurodegenerative Disorders; Neurotransmitters; Occupational; Onset of illness; Organophosphates; Parkinson Disease; Participant; Pathogenicity; Pathway Analysis; Pathway interactions; Patients; Pattern; Peripheral; Personal Satisfaction; Pesticides; Phenotype; Physical activity; Pilot Projects; Population Control; Population Study; Positioning Attribute; Predisposition; Prevalence; Process; Public Health; Quality of life; Quantitative Trait Loci; Recording of previous events; Reporting; Resistance; Resources; Risk Factors; Role; Sampling; Serum; Societies; System; Systems Analysis; Systems Biology; Technology; Time; Toxic Environmental Substances; Toxicant exposure; Work; Workplace; agricultural pesticide; anxiety symptoms; base; biological systems; brain health; cholinergic; clinical anxiety; comorbidity; cost; data resource; depressive symptoms; epigenome; epigenomics; follow-up; genetic makeup; genome analysis; genome-wide; high risk; improved; innovation; interest; land use; machine learning method; metabolome; metabolomics; minor depressive disorder; modifiable risk; motor symptom; multiple omics; neuropsychiatric disorder; neuropsychiatry; neurotoxic; non-motor symptom; novel; pesticide exposure; population based; pyrethroid; residence; response; rural environment; screening; single episode major depressive disorder; supervised learning; tool

Project Abstract We propose to use metabolic and multi-omic markers to elucidate how chronic, long-term pesticide exposures affect the occurrence of depression and anxiety in elderly living in a rural environment and those who develop Parkinson’s disease (PD) and depression. Depression and anxiety disorders are the most common types of mental disorders in older adults. Depression prevalence is as high as 25% in long-term care settings and strongly contributes to health care use for comorbid illnesses1-5. Major depressive episodes and clinical anxiety occur at a much higher rate among PD patients even well before PD diagnosis1,2. Improving our understanding of modifiable risk factors and mechanisms involved in depression and anxiety amongst the elderly is an urgent public health matter. Over the past two decades, our team has generated a unique data resource that now provides us with the opportunity to investigate the contributions of common and important environmental exposures (pesticides) to depression and anxiety and to explore disease processes through multidimensional biologic networks. With prior funding, we have collected lifetime depression/anxiety diagnosis and treatment histories as well as current (and follow-up) status of depressive and anxiety symptoms in a large population- based case control study of PD among residents of the California Central Valley, including more than 500 PD patients we closely followed for over a decade. Among these individuals, as many as 38% of PD patients and 27% of elderly without PD reported a diagnosis of depression or anxiety disorder at any time during their life. For all study participants we have genome (Illumina Global Screening Array, 660K markers) data available, and for ~800 additionally we generated epigenome (Illumina Infinium 450K DNA platform; genome-wide DNA methylation) and untargeted metabolomic data (at two time points for 300 participants). We developed a longitudinal geographic information system (GIS) based assessment for pesticide exposures that links state-mandated information on type, date, and location of all agricultural pesticide applications in California recorded since 1974 to land use maps and study participants’ residences and work places. Additionally, we collected extensive information on occupational, home and gardening use of pesticides. Biologic processes, including biologic (metabolic, epigenetic) responses to chronic toxicant exposures or disease processes are dynamic but also dependent on genetic susceptibilities. Here, we propose to combine a powerful systems biology analytic approach to interrogate epigenetic and metabolomic data anchored in genetics to identify signatures for specific pesticide exposures (organophosphates, pyrethroids, neonicotinoids) and peripheral disease processes related to depression in PD. Our data uniquely position us to efficiently conduct a high-risk pilot study that investigates multidimensional networks using supervised machine learning methods to identify chronic response patterns in biologic systems across different molecular layers that can be linked to exposure and/or disease processes. Assembling our multi-omic data into multidimensional networks will address gaps in our current knowledge concerning molecular mechanisms contributing to depression and anxiety disorders in PD, a major neurodegenerative disorder of growing importance in aging societies.

项目摘要 我们建议使用代谢和多组学标记来阐明长期农药暴露如何影响 农村老年人和帕金森病患者抑郁和焦虑的发生率 （PD）和抑郁症和焦虑症是老年人最常见的精神障碍类型。 在长期护理机构中，抑郁症患病率高达 25%，这极大地促进了针对共病的医疗保健使用 PD 患者中严重抑郁发作和临床焦虑的发生率要高得多，甚至早在这之前。 PD 诊断1,2。 在过去的二十年里，我们的团队生成了一份独特的数据。 资源现在为我们提供了调查共同和重要的贡献的机会 环境暴露（农药）导致抑郁和焦虑，并通过以下方式探索疾病过程 在先前的资助下，我们收集了终生抑郁/焦虑诊断和多维生物网络。 大量人群的治疗史以及抑郁和焦虑症状的当前（和后续）状态 - 基于加州中央山谷居民的帕金森病病例对照研究，包括我们的 500 多名帕金森病患者 在这些人中，多达 38% 的 PD 患者和 27% 的老年人没有受到密切关注。 对于我们所有的研究参与者，PD 在其一生中的任何时候都报告了抑郁症或焦虑症的诊断。 基因组（Illumina Global Screening Array，660K 标记）数据可用，另外我们还生成了约 800 个 表观基因组（Illumina Infinium 450K DNA 平台；全基因组 DNA 甲基化）和非目标代谢组数据（位于 300 名参与者的两个时间点）我们开发了基于纵向地理信息系统 (GIS) 的评估。 用于农药暴露，将国家规定的所有农业农药的类型、日期和位置信息联系起来 自 1974 年以来记录的加利福尼亚州土地利用地图申请并研究参与者的住宅和工作场所。 此外，我们还收集了有关生物农药的职业、家庭和园艺使用的大量信息。 过程，包括对慢性毒物暴露或疾病过程的生物（代谢、表观遗传）反应 动态但也依赖于遗传易感性，在这里，我们建议结合强大的系统生物学分析。 询问遗传学中的表观遗传和代谢组数据以识别特定农药特征的方法 暴露（有机磷、拟除虫菊酯、新烟碱类杀虫剂）和与 PD 抑郁相关的外周疾病过程。 我们的数据使我们能够有效地开展调查多维网络的高风险试点研究 使用监督机器学习方法来识别不同生物系统中的慢性反应模式 可以将我们的多组学数据组装成与暴露和/或疾病过程相关的分子层。 多维网络将弥补我们目前关于分子机制的知识空白 PD 中的抑郁症和焦虑症是一种在老龄化社会中日益重要的主要神经退行性疾病。