Creation of an Open Science Dataset from PREVENT-AD, a Longitudinal Cohort Study of Pre-symptomatic Alzheimer′s Disease.

Abstract

We describe the creation of an open science dataset from a cohort of cognitively unimpaired aging individuals with a parental or multiple-sibling history of Alzheimer′s disease (AD). Our purpose was to enable PResymptomatic EValuation of Novel or Experimental Treatments for AD (PREVENT-AD). To characterize this population, possibly progressing in the pre-symptomatic phase of AD, we studied genetic variants and obtained longitudinal measures of cognition, brain structure and function, blood and cerebral fluid biochemistry and neurosensory capacities. Two nested prevention trials were also conducted. Data were hosted in LORIS, a platform that facilitates data organization, curation and sharing. We initially assessed 425 individuals, 385 meeting criteria for sustained investigation and 330 remaining active for longitudinal follow-ups. Between 2011 and 2017, we obtained quality-controlled data from 1704 MRI scans, 532 CSF samples, and 1882 cognitive evaluations. To date, 310 active participants (94%) have agreed that their data be openly shared. In addition to being a living resource for continued data acquisition, therefore, PREVENT-AD offers shared data to facilitate understanding of AD pathogenesis.