Clinico-mathematical Approach to the Fog of Alzheimer's Disease: Application of Novel Mathematical Methods to Large Health Databases.

Alzheimer's disease (AD) is the most common form of dementia and is a major health concern for today's aging population. Although many researchers have focused on this degenerative disorder, the disease's etiology remains clouded in mystery. Typically, biomedical research utilizes a reductionist approach and traditional statistical methods to understand data from population studies of aging adults. However, these efforts have fallen short of producing much needed results. One major reason for the lack of tangible findings on this disease to date is the complexity that is involved in AD: densely networked aging brains, multiple interacting/nonlinear risk factors, variable rates of progression, and multiple comorbidities. This complexity can be called the "Fog of Alzheimer's disease" and can be extended to include the non-AD dementias that arise in aging brains and bodies. This research project will focus on understanding the complexity of AD and other dementias through innovative mathematical methods on data collected within three separate aging studies (1) Canadian Study on Health and Aging; (2)the Gothenburg Study - H70; (3) Honolulu-Asia Aging Study, to which the team from Dalhousie University has gained access. These three datasets follow a large number of adults as they age over extended periods of time and have the potential to provide valuable insights into the aging process, Alzheimer's disease, and other dementias. The overarching objective of this proposed research fellowship is to produce new insights into AD and other dementias by using quantitative tools that have been recently developed in disciplines outside of the health sciences (e.g., computer science, applied mathematics, data mining and machine learning). The clinico-mathematical approach that will be used in this project can be defined as a feedback loop where clinical knowledge is used to guide the creation of mathematical models, and the findings of these mathematical models can then be translated back to clinical practice in order to improve our knowledge of the health, well-being, and care of older adults (clinical knowledge ? mathematical modeling ? clinical knowledge). Mathematical models will include multistate stochastic transition models and data mining models (i.e., clustering, artificial neural nets). These will be focused on developing clinical knowledge of the complexity and heterogeneity of AD and other dementias. To further separate this proposed research from past reductionist approaches in large longitudinal datasets, this quantitative research will be guided by complex systems theory. The human brain and the human body are not machines that can be reduced and be understood by examining factors in isolation (this is where reductionist approaches has failed). By combining complex systems theory with state of the art mathematical methods in large health databases, it will be possible to clear some of the fog of Alzheimer's disease and other dementias. Research findings from this work will influence the investigations of other researchers by promoting and furthering the understanding of AD as a heterogeneous and complex degenerative disorder. Results will be disseminated through traditional knowledge translation activities. Novel knowledge translation methods such as interactive web technologies and digital media will also be considered.