Parkinson's disease (PD) is a relatively common, debilitating neurodegenerative disorder of uncertain etiology. Recent studies of PD concordance in twins indicate that PD experienced after the age of 50 years (i.e., > 90 % of PD) must be either largely environmental in etiology, or the result of still undiscovered gene-environment interactions. Exposure to metals, particularly lead, has been associated with the development of PD in a few but highly-suggestive studies. This topic has not yet been studied epidemiologically using state-of-the-art biological marker techniques for measuring metals exposure and accumulation. In addition, there is reason to believe that gene-metal interactions may greatly increase the risk of PD. In particular, some evidence suggests that increased exposure to lead interacts with the altered iron metabolism cause PD. Given that iron metabolism is dependent to as large degree on genetic factors, mutations that alter iron metabolism may be associated with PD. In this application, our research team will take advantage of well- described, highly motivated and geographically convenient populations of PD patients and potential controls, state-of-the-art methods for measuring biological markers of metals exposure, polymerase chain reaction (PCR) assays, assessment of pesticide exposure, and a highly- experienced team of investigators to conduct a new case control study. In this study, we will evaluate PD patients > 50 years old as well as controls matched by age, gender, ethnicity, and geographic region. We will test several hypothesis, the main two of which will be that (1) among the cases and controls, higher levels of lead in bone are associated with a higher odds of being a PD cases; and (2) the effect of bone lead will be highest in individuals with at least one copy of the C282Y or H63d hemochromatosis (HFE) gene mutation. We will also explore several subsidiary hypothesis related to the relationship of PD to levels of manganese and copper in toenails and to pesticide exposures, assessed by self-reported history and by linking job histories with a job- exposure matrix.
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