Principal Investigator: Dr Charles Marshall
Institution: Queen Mary, University of LondonTags: 59138, biomarker-discovery, Dementia, early diagnosis, EWAS, prevention, risk factors
Dementia is the most common neurodegenerative disorder worldwide affecting over 50 million people. There are currently no disease-modifying treatments and initially promising clinical trials keep failing. This has been partly attributed to the late recruitment of patients into trials at a point in which widespread neurodegeneration has already occurred. However, recent evidence indicates that there is a prodromal phase in dementia that begins up to 30 years before a diagnosis can be made. Being able to identify markers of such early changes is vital to enabling preventive treatments to be administered. Furthermore, a third of dementia cases are now believed to be preventable through lifestyle behaviours. This represents a promising direction for research focused on identifying modifiable risk factors that can be targeted to reduce dementia and on investigating what biological pathways are impacted at particular time points in the disease trajectory, to guide drug development and clinical care.
This project aims to conduct a preliminary hypothesis-free analysis (EWAS) of markers of dementia risk. Using linked genetic data the influence of genetic risk variants and gene-environment interactions will be explored. The timings of significantly associated exposures will then be mapped according to their proximity to a dementia diagnosis, to evaluate which factors could be potential candidates for detecting early dementia, and which should be prioritised as targets for dementia risk reduction earlier in life.
Another key aim of the project will be to model the synergistic effects of variables using multivariate statistical modelling to visualise and quantify the interactions and cumulative effects of multiple exposures. In order to establish whether these methodologies are useful in modelling the shared environmental architecture between dementia and known associated comorbidities, the same methods will also be conducted using depression as a trait. Any shared exposures and genetic architecture will then be analysed to identify potential aetiological mechanisms that drive the association between depression and dementia.
This work is anticipated to be completed within 36 months and will help to establish methods for modelling the combined effects of multiple environmental exposures in dementia. This is fundamental to understanding the complex interplay of exposures across the lifespan and how they interact internally leading to impaired biological pathways that occur before cognitive decline is evident. The ability to identify this early and to understand how comorbidities, such as depression, influence these pathways will help to develop treatments and preventive strategies at an individualised level.