The goal of this study is to validate a pragmatic method to predict impending cognitive decline in early- stage Alzheimer?s disease (AD) patients, prior to the development of hallmark symptoms. With the growing epidemic of AD dementia, the current focus of scientific research and clinical trials in Alzheimer?s disease and related disorders (ADRD) has shifted toward intervention during the asymptomatic and early- stages of the disease. However, such early-stage trials have struggled with an inability to identify and enroll subjects with no outward symptoms of cognitive decline. In addition, once treatments are approved to treat early-stage ADRD, identifying asymptomatic patients will be a significant obstacle in the delivery of timely care. Therefore, there is an urgent need for a pragmatic method to predict impending decline in cognitively normal subjects who could enroll in ADRD clinical trials and identify those who could potentially benefit from treatment with future ADRD therapies. Our preliminary studies have demonstrated that a Hierarchical Bayesian Cognitive Processing (HBCP) model of wordlist memory (WLM) test performance can (1) quantify cognitive processes which are not captured by traditional scoring of assessments such as the AVLT or ADAS-Cog, or by recent composite measures such as the ADCOMS; and (2) accurately classify cognitively normal individuals into two groups: those whose latent cognitive processes indicate cognitively normal aging (stable) and those whose latent cognitive processes indicate progression to MCI/AD (progressor). Using HBCP models to analyze WLM tests, we will enable quantitative estimations of latent cognitive processes that predict impending cognitive decline due to ADRD. Successful delivery of the proposed study will improve efficacy of ADRD drug development by expediting enrollment and shortening trial duration and by quantifying changes in cognitive processes to demonstrate meaningful treatment effects in asymptomatic subjects. This technology will also facilitate timely clinical intervention for early-stage ADRD patients when new treatments are approved.
The current focus of scientific research, clinical trials, and clinical care in Alzheimer?s disease and related disorders (ADRD) has shifted toward intervention during asymptomatic and early-stages of the disease. However, there are currently no pragmatic approaches for identifying cognitively normal subjects with impending cognitive decline. The proposed study will use a Hierarchical Bayesian Cognitive Processing model of wordlist memory task performance to enable quantitative estimations of latent cognitive processes that predict impending cognitive decline due to ADRD.
