PREDICTORS OF SEVERITY IN ALZHEIMERS DISEASE
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The ability to predict the length of time from disease onset to major disease outcomes in individual patients with Alzheimer's disease (AD) has implications for patient care, the development of interventions, and public health. To address this issue, the major aim of the Predictors Study was to develop prediction algorithms. The investigators, who represent 3 collaborating sites, have collected prospective clinical data on 236 individuals with AD followed every 6 months for almost 9 years. These efforts culminated in the formulation of published algorithms that, for the first time, can reliably predict the time until an individual patient will require nursing home care or die. We now propose to continue to follow the remaining 97 patients in our cohort, as well as to follow a new cohort of 240 patients with mild AD that we have begun to recruit. In our new cohort, we will begin to assess additional clinical features suggested to be associated with Lewy bodies, including neuropsychological performance profiles. The proposed new research has two primary goals: 1) To validate, refine and extend the prediction algorithms that we have developed. Using data from both cohorts, we will refine the algorithms to further increase their predictive accuracy by incorporating: clinical features specifically associated with Lewy bodies, based on our proposed clinical-pathologic studies; genetic modifiers such as APOE; and agents recently shown to modulate AD progression, including antioxidants, anti-inflammatory agents and estrogen. We will also extend our algorithms to predict new outcomes, including quality of life and the economic impact of AD, and conduct validation studies of the algorithms. 2) To characterize the relationship of clinical presentation and course to the presence of Lewy bodies found post mortem. 61 patients in our cohort have come to autopsy to date, and 43 percent of those meeting pathologic criteria for AD also had Lewy bodies. Using semiquantitative and quantitative measures based on stereological methodology, we will quantify Lewy bodies, Lewy neurites, Abeta, neuritic plaques, neuropil threads, and neurofibrillary tangles, as well as neuronal loss and synaptic integrity, in 10 brain areas. We will use our prospectively collected clinical database to determine which clinical features, such as extrapyramidal signs or psychosis, are associated with the presence of Lewy Bodies and these other pathologic measures. We will then determine whether incorporating these clinical features into our prediction algorithms increases their accuracy.
