DecipherAD
Worldwide, 50 million people suffer from dementia, which is caused by Alzheimer’s disease (AD) in 70% of the cases. There is no treatment to stop, reverse or prevent AD. AD is pathologically defined by amyloid plaques and tau tangles in the brain, implying that it is a single disease entity. Still, patients vary greatly in rate of decline and underlying pathophysiology, which hampers the search for cures. I have discovered and replicated 5 AD subtypes in patients based on cerebrospinal fluid proteomics (CSF). Subtypes had distinct pathophysiology, including differences in amyloid metabolism and clearance, as well as in cognitive decline. This indicates that they would need tailored treatments.The goal of this project is to understand AD subtype specific mechanisms, and how these are related to cognitive decline, taking CSF proteomics as a starting point. I will address the following scientific needs:1. Understand which AD subtype molecular processes change over time, and how those changes relate to cognitive decline. This requires large datasets of individuals with repeated CSF proteomics. I have access to two large, deeply phenotyped cohorts with already collected repeated CSF samples over 5 years from 700 individuals, in which I will measure proteomics.2. Study drivers of AD subtypes with genetics and in a subset of n=50 with tissue proteomics.3. Develop markers in blood for subtype detection, which would remove barriers for clinical use.4. Test if AD subtypes differently respond to amyloid modifying drugs, which is the strongest proof that AD subtypes need tailored therapy.If successful, DecipherAD will provide proof of concept that AD subtypes have use as a theragnostic tool, show which genetic factors drive molecular subtypes, how molecular signatures change over time and how these changes relate to cognitive decline, with tools in blood to facilitate implementation in clinical practice. This will catalyse AD subtype tailored treatment development.
STICHTING AMSTERDAM UMC (NL); UNIVERSITETET I BERGEN (NO)