Geographic Model and Biomarker-derived Measures of Pesticide Exposure and Parkinson's Disease

For more than two decades, reports have suggested that pesticides and herbicides may be an etiologic factor in idiopathic Parkinson's disease (PD). To date, no clear associations with any specific pesticide have been demonstrated from epidemiological studies perhaps, in part, because methods of reliably estimating exposures are lacking. We tested the validity of a Geographic Information Systems (GIS)-based exposure assessment model that estimates potential environmental exposures at residences from pesticide applications to agricultural crops based on California Pesticide Use Reports (PUR). Using lipid-adjusted dichlorodiphenyldichloroethylene (DDE) serum levels as the "gold standard" for pesticide exposure, we conducted a validation study in a sample taken from an ongoing, population-based case-control study of PD in Central California. Residential, occupational, and other risk factor data were collected for 22 cases and 24 controls from Kern county, California. Environmental GIS-PUR-based organochlorine (OC) estimates were derived for each subject and compared to lipid-adjusted DDE serum levels. Relying on a linear regression model, we predicted log-transformed lipid-adjusted DDE serum levels. GIS-PUR-derived OC measure, body mass index, age, gender, mixing and loading pesticides by hand, and using pesticides in the home, together explained 47% of the DDE serum level variance (adjusted r(2) = 0.47). The specificity of using our environmental GIS-PUR-derived OC measures to identify those with high-serum DDE levels was reasonably good (87%). Our environmental GIS-PUR-based approach appears to provide a valid model for assessing residential exposures to agricultural pesticides.