Statins, statin-related gene, and Parkinson's disease risk

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DESCRIPTION (provided by applicant): Parkinson disease (PD) is the second most common neurodegenerative disease in the United States (US) and affects over one million Americans; this number is growing due to population aging. Most patients eventually develop severe disability, which markedly affects their quality of life and that of their caregivers. There is thu a compelling need to develop disease modifying or neuroprotective treatments. In our previous study, we found that PD risk is lower among individuals who regularly use statins. However a major limitation of our previous analyses is that we did not have information on use of specific statins, which was collected in our cohorts only starting in 2004. As statins differ in their abiliy to cross the blood brain barrier, it is important to examine their specific associations with PD risk. Further, previous studies have shown that there is considerable interindividual variation in statin responsiveness, which is strongly influenced by genetic variations. However there are no studies to examine the role of these statin-related genetic factors in PD risk to date. We therefore propose to expand our previous study to examine the potential effects of specific statins on PD risk among ~170,000 active participants in the Nurses' Health Study (NHS), and Health Professionals Follow-up Study (HPFS) during 10 years of follow-up (2004-2014). We expect to confirm 765 new incident cases of PD (283 Men and 482 women). We will further examine whether protective effects of statins remain among those with one or more PD pre-motor symptoms at the baseline. This analysis will provide the first evidence regarding whether statins could slow PD progression from pre-clinical to clinical stage. It is worth noting that the observed association between statins and PD could be confounded by indication, which is key to understanding of the therapeutic potential of statins in PD treatment. To answer this extremely important but unsolved question, we will also examine the interaction between use of statins and genes associated with statin responsiveness, in relation to PD risk. To test this gene-statin interaction, we will employ a novel method, the gene-environment set association test, in which all SNPs will be grouped together into a SNP-set using the kernel machine approach and thus the joint effect of these SNPs will be tested. This approach utilizes genetic information from all SNPs in a SNP-set simultaneously, leading to a more powerful test with reduced degrees of freedom. In contrast to methods that create a single genetic score from a set of proposed risk alleles, a benefit of the GESAT approach is that it does not require a priori knowledge of directionality for the variants. A better understanding of the potential role of statins, and statin-related genes, in PD risk will undoubtedly shed some light on the PD etiology and development of personalized prevention and early treatment strategies.