Characterizing the Behavior Profile of Healthy Cognitive Aging

摘要

ABSTRACT Prevention of late life cognitive decline ranks among the most important public health challenges, and identification of the profile of healthy cognitive aging is an essential step. The proposed study will continue a highly successful program of research that has transformed the field’s understanding of healthy cognitive aging (R01AG34374). We conceptualize healthy cognitive aging as the late life cognitive change that is not due to known pathologic processes (e.g., AD/ADRD pathologies), and our research capitalizes on the detailed longitudinal cognitive and pathologic data from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). The central idea is that we can precisely characterize pathologic cognitive aging by linking pathologic indices to longitudinal cognitive trajectories and then identify healthy cognitive aging (i.e., residual change). In prior cycles, we reported that: a) common AD/ADRD neuropathologies account for much of the decline previously attributed to healthy cognitive aging but less than half of the variation in decline overall; b) nonlinear, terminal change represents a separate pathologic process and a major driver of decline; c) AD/ADRD neuropathologies differentially impact trajectories of specific cognitive systems; and d) our newly developed “cognitive age” metric is a robust prognostic indicator of adverse cognitive outcomes. The overall goal of the proposed continuation is to further elucidate the profile of healthy cognitive aging and translate our work in decedents into the living to prospectively distinguish healthy from pathologic cognitive aging. The proposed study will incorporate new neuropathologic indices, neuroimaging, and promising blood biomarkers of AD and neurodegeneration and apply a highly innovative statistical approach to precisely identify the profile of healthy cognitive aging. Building on prior work, we will first identify healthy cognitive aging among autopsied persons with detailed pathologic data. Importantly, we will then extend this approach to living persons. Finally, we will integrate new biomarker and ante-mortem neuroimaging data with our cognitive age metric to develop criteria for prediction of incident MCI and Alzheimer’s dementia and validate them in an independent dataset from a biracial, population-based sample, the Chicago Health and Aging Project (CHAP). Thus, the proposed study offers an innovative approach to address a fundamental and longstanding challenge in cognitive aging research. This work also will facilitate early and accurate identification of individuals at high risk of developing cognitive impairment, an urgent priority in aging research.