Assessing Novel Biomarkers for Cognitive Fluctuations in DLB

Age-related diseases, such as Alzheimer disease (AD) and Dementia with Lewy bodies (DLB), are defining public health concerns of the 21st century and are the leading cause of disability worldwide. Novel disease modifying therapies have opened a new horizon for managing these progressive diseases. However clinical trials assessing these therapeutic medications are bedeviled by small effect sizes and substantial intersubject variability that challenges evaluation of their effectiveness. To meet this challenge, additional factors mediating variability in cognitive performance are a key knowledge gap to overcome. Cognitive fluctuations (CFs) are intermittent episodes of decreased alertness, attention, and responsiveness that occur in some patients with dementia. The unpredictable nature of these events poses a major burden for patients and caregivers, as well as complicating the measurement of treatment efficacy in clinical trials, particularly when the treatment is intended to improve cognition. Unfortunately, methods to track CFs effectively and the pathophysiology of this condition are poorly understood. In this proposal, we aim to validate novel biomarkers that captures the most relevant features of cognitive fluctuations in AD and DLB. Towards this goal, we will provide a multimodal characterization of the frequency and severity of CFs and their longitudinal variability at annual visits. The Dementia with Lewy Body Consortium (DLBC) cohort presents a gold standard cohort for DLB biomarker diagnosis (CSF α-synuclein, aggregation assay), while the Alzheimer’s Disease Research Centers (ADRC) presents cohorts for AD diagnosis (CSF A-beta42,40, t-Tau, and p-Tau181) along with autopsy confirmation. In these cohorts, we will obtain both clinical (fluctuation scales, cognitive variability) and multiple objective digital biomarkers (actigraphy, EEG, sleep, resting state functional MRI) to evaluate the frequency and severity of CFs at baseline and longitudinal variability annually over four years. This project will enable us to help develop appropriate biomarkers that capture the most relevant features that can be scaled to track CFs effectively in large clinical trials. Further, this project will help us develop a model of the underlying neural features of CFs to enable future targeted therapies.