. The identification of Alzheimer?s disease (AD) prior to the onset of clinical symptoms is critical in order to avert an impending public health crisis. Thus far, clinical trials of secondary prevention interventions have lacked cost- and time-efficiency. This is due to the absence of validated minimally invasive, cost effective, and widely accessible AD risk detection biomarkers. The development of an AD risk detection algorithm comprised of readily available information, including these biomarkers, is essential to identify potential targets for secondary prevention while minimizing cost. Thus far, development of these algorithms have been hampered by lack of collaboration across disciplines, failure to apply advanced and novel statistical techniques, and the complex pathophysiology of the underlying disease. Building on the parent Advance CTR study (U54GM115677; PI: Padbury) with the goal of generating translational research projects across Rhode Island Institutions, the proposed work fosters collaboration between the Brown Center for Biomedical Informatics, the Quantitative Sciences Program at the Alpert Medical School of Brown University, the Butler Hospital Memory & Aging Program (MAP), and the University of Rhode Island. Co-I?s Brick and Sarkar will apply advanced analytic and machine learning techniques to existing internal (Butler MAP Alzheimer?s Prevention Registry) and external (GAAIN) datasets to develop, test, and validate an AD risk detection algorithm. They will work with Co-I?s (Alber, Lee) on the MAP staff to collect prospective biomarker data (amyloid PET, retinal imaging, blood based- biomarkers) to improve and refine this algorithm, providing the springboard for a dynamic dataset based at Brown that can be used to develop and validate novel AD risk biomarkers in the state of Rhode Island.
. Over five million adults in the United States are affected by Alzheimer?s disease and this number is rising. The development of a widely accessible, minimally invasive, cost-effective method for early detection is a global public health imperative. The overarching goal of this study is to develop a sustainable database of AD risk markers to be used for collaborative research and to develop a risk algorithm that will identify individuals at risk for AD at the earliest pathologic stage, prior to the onset of clinical symptoms.
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