The ability to predict the length of time from disease onset to major disease outcomes in individual patients with Alzheimer's disease (AD) has important implications for patient care, the development of interventions and public health. The major aim of the Predictors Study is to further the understanding of disease progression in order to develop predictor algorithms to address this issue. Over the past funding periods, we have followed two clinic-based cohorts of AD patients recruited from three major medical centers, and have made major progress in characterizing the natural history of AD and identifying predictors of disease course. While the Predictors study has had a major impact on our understanding of AD and its progression, the patient cohorts are clinic-based and ethnically homogenous, and the true date of disease onset was unknown. We now propose to continue our studies using a well-characterized, population-based cohort of ethnically diverse elders with AD. These individuals were all followed from a point prior to the onset of AD, so the onset date of clinical AD is known. We propose to initiate intensive followup of this cohort in order to validate our previous Predictors study findings in this population-based cohort and to implement new research questions based on novel predictor and outcome variables. We will introduce telomere length and telomerase activity, estimates of biological age that have been linked to risk of dementia and death, as a potential predictor of disease course. We will take advantage of linkage to Medicare and Medicaid data to understand the economic impact of AD in this multiethnic community cohort. Finally, we propose to create and refine a new predictive approach by analyzing our data using longitudinal Grade of Membership (GoM) modeling, a statistically optimized method that allows large amounts of prospectively collected data on individual AD patients to be efficiently and accurately summarized using a small number of distinct variables. A preliminary version of a new prediction model has already been developed from the Predictors cohorts data. This analysis identified three "subtypes" of AD with different rates of progression. We now propose to refine this preliminary GoM , and then test and refine it further using data from the new AD cohort. This will enable a methodology for making predictions about the real-world outcomes of AD, an extremely important tool for use by clinicians and researchers.
The rate of progression of AD varies from patient to patient, making it impossible to provide patients or investigators with accurate estimates of time until disease endpoints. Differences in rates of progression and treatment efficacies in therapeutic trials are confounded by an inability to account for variability in rates of progression. Thus, the development of accurate prediction algorithms has major public health implications.
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