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摩尼克标记来阐明慢性，长期农药暴露如何影响 古老的生活中的抑郁症和焦虑发生在粗糙的环境中，以及发展帕金森氏病的人 （PD）和抑郁。抑郁症和焦虑症是老年人最常见的精神障碍类型。 在长期护理环境中，抑郁症患病率高达25％，并为合并症有很大贡献 疾病1-5。 PD患者的主要抑郁发作和临床动画的发生率更高 PD诊断1,2。提高我们对抑郁和动画涉及的可修改风险因素和机制的理解 最古老的是紧急的公共卫生问题。在过去的二十年中，我们的团队生成了独特的数据 现在为我们提供了调查共同和重要的贡献的机会 环境暴露于抑郁和动画的环境暴露（农药），并通过 多维生物网络。通过先前的资助，我们已经收集了终生抑郁/焦虑诊断和 在较大人群中的抑郁和动画症状的治疗史以及当前（和随访）状态 - 加利福尼亚中央山谷居民中PD的基于病例控制研究，包​​括500多名PD患者我们 十多年来紧随其后。在这些人中，多达38％的PD患者和27％的人没有 PD报告了一生中的任何时候诊断出抑郁症或焦虑症。对于所有学习参与者，我们 可用的基因组（Illumina全局筛选阵列，660K标记）可用，而对于800个，我们生成了 表观基因组（Illumina Infinium 450K DNA平台；全基因组DNA甲基化）和未靶向的代谢组数据（AT AT 300名参与者的两个时间点）。我们开发了基于纵向的地理信息系统（GIS）评估 有关农药的曝光，该曝光与所有农药的类型，日期和位置联系起来 自1974年以来，在加利福尼亚的申请记录了土地使用地图，并研究参与者的住所和工作场所。 此外，我们收集了有关农药占用，家庭和园艺使用的广泛信息。生物学 过程，包括生物学（代谢，表观遗传）对慢性毒物暴露或疾病过程的反应 动态，但也取决于遗传敏感性。在这里，我们建议结合强大的系统生物学分析 质疑遗传学的表观遗传学和代谢组数据的方法，以识别特定农药的特定签名 暴露（有机磷酸盐，拟除虫菊酯，新烟碱）和与PD抑郁有关的外周疾病过程。 我们的数据独特地将我们定位为有效地进行高风险试验研究，该研究研究多维网络 使用监督的机器学习方法来识别不同不同的生物系统中的慢性响应模式 可以与暴露和/或疾病过程有关的分子层。将我们的多乐器数据组装到 多维网络将解决我们当前有关分子机制的知识的差距 PD中的抑郁症和焦虑症，这是一种在衰老社会中越来越重要的神经退行性疾病。