More than 50 million people worldwide suffer from dementia, 850,000 in the UK alone. These numbers are expected to more than double by 2050. There is currently no cure for dementia. Dementia is caused by different diseases, most commonly by Alzheimer's Disease. There is thus an urgent need to discover effective treatments for Alzheimers Disease. Numerous biological mechanisms in the human body have been associated with the development and severity of Alzheimers Disease. This research will examine whether several commonly used medications, that interact with these biological mechanisms, could be protective against Alzheimers Disease.
There is an urgent need to discover effective treatments for Alzheimers Disease (AD). Original research performed in the Unit of Clinical and Translational Neuroscience at the US National Institute of Aging Intramural Research Program (NIA IRP) has identified several molecular pathways that are associated with the severity of AD pathology in the brain and the expression of clinical symptoms. This research further identified several commonly used medications that interact with these pathways and thus may protect against AD. These medications are currently in use for several non-AD indications. Therefore, hypotheses of a potential protective role of these medications in AD can be tested (strengthened or refuted) using large-scale patient-level data collected during routine health care delivery, such as the CPRD. For each drug of interest, we will implement a new user, active comparator, observational cohort study. This design will minimize confounding by indication through comparisons to drugs with a shared indication that do not share interactions with the pathways related to AD. The approach further allows us to capture the effect of drug exposure on dementia risk throughout the treatment course and to address other key biases common to observational studies of prevalent users. For each comparison, propensity scores will be calculated as the predicted probability of initiating the exposure of interest (i.e., the repurposing candidate) versus the reference exposure conditional on baseline patient characteristics using multivariable logistic regression models. Pair matching will be conducted using a nearest-neighbour algorithm. For each comparison, adjusted relative risk estimates will be obtained by computing the hazard ratios and 95% confidence intervals using a Cox proportional hazard regression model in the propensity score matched population.
Alzheimers disease; vascular dementia; senile, pre-senile, or unspecified dementia; and dementia in other diseases classified elsewhere.
Tobias Gerhard - Chief Investigator - Rutgers, The State University of New Jersey
Haoqian Chen - Corresponding Applicant - Rutgers, The State University of New Jersey
- Collaborator -
Abner Nyandege - Collaborator - Rutgers, The State University of New Jersey
Cecilia Huang - Collaborator - Rutgers, The State University of New Jersey
Daniel B. Horton - Collaborator - Rutgers Robert Wood Johnson Medical School
Edward Nonnenmacher - Collaborator - Rutgers, The State University of New Jersey
Madhav Thambisetty - Collaborator - National Institutes of Health - USA
Mary Ritchey - Collaborator - Rutgers, The State University of New Jersey
Mufaddal Mahesri - Collaborator - Harvard Medical School
Rishi Desai - Collaborator - Harvard Medical School
Sebastian Schneeweiss - Collaborator - Aetion, Inc
Vijay Varma - Collaborator - US-NIH
Avinash Gabbeta - Collaborator - Rutgers, The State University of New Jersey
Kristyn Chin - Collaborator - Harvard Medical School
Priscilla Winston-Laryea - Collaborator - Rutgers, The State University of New Jersey
Patient Level Index of Multiple Deprivation