Dementia Risk Prediction Pooling Project

Alzheimer’s Disease and related dementias (ADRD) are a major public health problem which is increasing worldwide. Currently, over 5.8 million Americans are living with Alzheimer’s or other dementias and as our population continues to age this number will increase to over 13.8 million by 2050. Great strides are being made in targeted therapies which may be able to reduce the risk for dementia. However, identifying which patients should receive these prevention interventions is currently unknown and has been identified by NINDS and experts in the field as a critical area of research. Research done by our group and others has demonstrated that risk factor levels as early in life as the 20’s influence an individual’s future risk for dementia. Early intervention to prevent the accumulation of risk and targeted interventions for those at high risk could help to reduce dementia risk. Risk prediction methods can be used to accurately predict who is at high risk for developing dementia and target interventions; however, current risk prediction models for dementia have several critical limitations and none have taken into account longitudinal risk factor trajectories, a critical step to implementing precision prevention approaches to identify individuals at high risk for dementia.In this study we propose to develop the Dementia Risk Pooling Project (DRPP) by pooling and rigorously harmonizing 12 prospective observational cohorts of middle and older age adults, multiple in-person assessments of clinical, genetic and behavioral risk factors, follow-up of greater than 10 years and adjudicated dementia ascertainment. The DRPP will be completed and made available to researchers via a cloud-based computing platform as part of the R61 phase of this proposal meeting specific Go/No-Go Criteria outlined in the grant. Then, within the R33 phase of this project we will use the DRPP to develop and validate an accurate and personalized, dynamic dementia risk prediction model which incorporates longitudinal risk factor measurements and easily updates as new measurements are accrued. This tool can be used clinically to guide treatment decisions and will be made available to clinicians. The aims of this project are important and timely, they will provide a risk stratification tool for targeted intervention/prevention for high risk individuals.