Sunlight Exposure and Parkinsonʼs Disease in California

Background: Recently attention has turned to the question whether inadequate sunlight exposure may lead to deficient production of vitamin D, and consequently to Parkinson's disease (PD). However, this hypothesis has yet to be investigated in human populations, and thus to date, it is not known whether vitamin D might be neuroprotective against oxidative damage in PD. Aim(s): We are examining whether long-term sunlight exposure, which generates vitamin D, decreases the risk of PD among 368 incident PD cases and 341 population controls residing in three largely rural Central California counties and enrolled in the Parkinson's Environment and Genes (PEG) study. Method(s): Patients were diagnosed between January 1998 and January 2007 and confirmed as having either probable or possible idiopathic PD by PEG movement disorder specialists. Controls older than 65 years of age were identified from Medicare lists or recruited from randomly selected residential parcels. We developed a sophisticated exposure model using geographic information systems (GIS) tools to link geocoded residential and occupational address data obtained from study participants to available AVerage daily total GLObal solar radiation (AVGLO) data produced by the Department of Energy to determine historical spatially-oriented UV measures. Result(s): Average lifetime UV exposure levels for the PEG population were 5033 Wh/m2 (range: 3886 - 5460) with no appreciable differences between cases and controls; estimated levels were higher among non-whites, subjects with lower education levels and outdoor workers. In logistic regression models adjusted for age, gender, education, race/ethnicity, smoking, and pesticide exposures, high average lifetime UV levels weakly, if at all, protected against PD (OR = 0.81, 95% CI = 0.51, 1.30, comparing 4th to 1st quartile). Discussion(s): We will extend our analyses further by adding data for additional newly recruited subjects, adding UV data for international addresses, and using an additional approach to estimate UV levels in our GIS model.