Multi-cohort study of factors that influence Alzheimer's disease biomarker and dementia timing

PROJECT SUMMARY/ABSTRACT Advances in imaging and fluid-based biomarkers of Alzheimer’s disease (AD) including amyloid (A), tau (T) and neurodegeneration (N), allow detection of underlying disease pathology decades before the onset of dementia. Despite over a decade of AD biomarker research, the field still lacks the ability to accurately predict if and when individuals with preclinical AD (those with biomarker detectable pathology in the absence of cognitive symptoms) will experience dementia. Identifying factors that slow or quicken this preclinical timeframe is needed to improve dementia risk prediction for preclinical AD patients and to inform optimal treatment windows for clinical trials aiming to slow or prevent cognitive decline and impairment. Until recently, studying these factors was precluded by observing different people for short periods of time that began studies in different disease stages with no way to identify when disease began for individual participants. Our team developed and validated new methods that provide individualized estimated amyloid onset age (EAOA) from amyloid biomarkers. EAOA can be used to rearrange biomarker and clinical observations along an AD-specific timeline (i.e., an Amyloid Clock) anchored to the start of preclinical AD. This project will apply this novel approach to existing data from that Washington University Knight ADRC, the Wisconsin Alzheimer’s Disease Research Center, the Wisconsin Registry for Alzheimer’s Prevention, the, the Mayo Clinic Study of Aging and the Alzheimer’s Disease Neuroimaging Initiative to investigate factors across cohorts that influence the timing and trajectories of AD biomarkers and dementia. This study was initiated based on our preliminary findings showing considerable differences between individuals and cohorts regarding 1) when amyloid onset occurs, 2) the time between amyloid onset and dementia onset, and 3) factors that affect AD biomarker and dementia trajectories in AD. In addition, studies from our center and others have begun to link AD pathology, change in brain volume, and changes in cognition to social determinants of health (SDoH) like neighborhood disadvantage. However, possible links between SDoH and the timing and trajectories of AD biomarkers and dementia are not well-understood. Our hypothesis is that observed individual and cohort differences in AD trajectories are due to a combination of demographic, environmental, sociocultural, and biologic factors, and study design and sample composition. We will test this overall hypothesis in three specific aims: 1) identify common factors across multiple cohorts that influence the timing and trajectories of ATN biomarkers; 2) identify common factors across multiple cohorts that affect the time from amyloid onset to dementia; and 3) explore inter-cohort differences in AD biomarker and dementia trajectories. This study will leverage existing data in several well-characterized studies to provide new insights into mechanisms that explain when preclinical AD begins and how long this preclinical phase lasts. This is expected to improve AD dementia risk prediction for individuals and identify optimal windows for disease modifying and prevention therapies.