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

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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.