Optimizing stratification for trial design in Alzheimer’s disease

Alzheimer’s disease (AD) heterogeneity is associated with distinct risk factors, clinical manifestations, rates of cognitive decline, and comorbidities. However, this population variance is not properly incorporated in clinical trial design. Therefore, I will utilize the unique longitudinal BioFINDER dataset at Lund University to identify clusters or subtypes of amyloid-β (Aβ)-vascular pathology interplay, the two most common co-occurring and interacting pathologies in the aging brain. Next, the role of neuronal function, microglial activation, and novel targets through proteomics analyses based on CSF/plasma biomarkers on disease progression within these subgroups will be determined. Finally, resulting tau-PET accumulation patterns and cognitive decline across domains across will be assessed. Unravelling distinct disease trajectories is paramount to optimize trial population selection and stratification, directly increasing the changes of positive trial outcomes.