Wildfires, Air Pollution, Metabolome and Cognitive Decline in Parkinson’s

University Of California Los Angeles, Los Angeles CA

Investigators

Abstract

In light of the increasing importance of heat and wildfires as growing contributors to air pollution (AP) in the US, there is a pressing need for investigations into effects they have on the cognitive and mental health of vulnerable elderly and to better understand biologic pathways. Here we propose to assess the impact of wildfire smoke and heat on cognition, mood, and the human metabolome in a vulnerable older population. Parkinson's Disease (PD), a common neurodegenerative disease with high rates of cognitive decline and depressive mood, affects older adults and has been linked to air pollution; those with PD may also be particularly susceptible to heat wave and wildfire smoke exposures as they may curtail physical activity, impair sleep, and affect mood. The California Central Valley, a region notorious for its high level of AP from traffic and agricultural sources, is under strong pressure from heat and wildfires, making it an ideal environment to investigate health impacts in older residents of rural communities. Our large case-control study (with follow-up) of ~1000 well-characterized PD patients and as many community controls has assembled comprehensive lifelong risk factor information, detailed residential histories, and biosamples over two decades. Data on cognitive status and decline (in patients) as well as depressive symptoms/diagnoses have been collected and, for the first time, will be used to assess heat and wildfire-related impacts while controlling for other pollutants (air pollutants, pesticides). Heat waves not only generate short term physiologic stress, but also prevent older adults from sleeping, exercising, and socializing which are important for healthy aging. Our approach relies on cutting-edge spatiotemporal modeling techniques, incorporating land-use regression (LUR) and harnessing the power of machine learning algorithms. These methods will allow us to generate exposure measures for a range of pollutants and wildfires. To generate heat measures, we will use the gridMET data and apply the U.S. National Weather Service Heat Index algorithm. For wildfire smoke, we will use the Community Multi-Scale Air Quality (CMAQ) model, a well-established tool for simulating air quality from specific sources as well as an innovative ensemble model that incorporates novel statistical techniques to capture the dynamic and evolving nature of wildfire smoke and its impact on air quality. We have generated untargeted metabolomics data for 919 PD patients and 419 controls with repeat samples available for 450 patients. These data will be used to explore pathways implicated in cognitive decline and mood – mainly depressive symptoms/diagnoses - in older subjects, many at high risk for the outcomes as they suffer from PD, with the aim to illuminate underlying biological mechanisms of wildfire smoke and heat stress exposures in rural older adults. We will be leveraging advanced modeling techniques and a robust systems biology analytical approach with our metabolomic data to identify short and long-term physiologic responses to these exposures in PD and older controls. Our approach will address the complex interplay between environmental factors and cognitive and mental health of older adults with a potential to shed light on metabolomic features and pathways that increase susceptibility or resilience as we face heat and increasing wildfire frequencies.