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2016 Grants - Samieri
Network Science Tools to Identify Novel Diet Patterns in Prodromal Dementia
Cécelia Samieri, Ph.D.
Association for the Development of Education and Research with Universities, Research Centers, and Enterprises of Aquitaine
2016 Alzheimer’s Association Research Grant (AARG)
Can novel methods to analyze complex datasets reveal how diet impacts the risk of Alzheimer’s disease?
Network science is the study of how multiple factors may interact in a complex system with the goal of making predictive models. Network science offers a promising way to integrate and evaluate the complex interaction of many lifestyle and biological risk factors that may contribute to Alzheimer’s disease. One such factor is diet, which previous research has shown can impact the risk of cognitive decline and Alzheimer’s disease. However, there are many factors to a person’s diet that may show important patterns, such as exact foods and nutrients consumed and differences in metabolism. Standard research methods that do not use network science may miss associations between dietary factors and disease-related changes that could help inform the development of new interventions.
Cécelia Samieri, Ph.D. and colleagues will use network science methods to identify diet patterns that may contribute to increased or decreased risk of developing Alzheimer’s disease. The researchers will use data from a large French study on dementia called the 3-City (3C) Study, to analyze dietary patterns over the last 10 years in 500 healthy adults and compare them to patterns identified in 250 individuals who went on to develop dementia. They will use network science approaches to integrate a variety of factors including different types of nutrients in the diet and levels of molecules related to metabolic function.
If successful, this study may provide a new way to identify previously unknown risk factors for Alzheimer’s disease in large and complex datasets. These may include modifiable lifestyle factors, like specific diet characteristics, that could be altered to slow or prevent disease progression. In addition, these network science tools could be used in future studies to incorporate other factors, such as physical activity levels, or genetic variations into models to predict a person’s risk of Alzheimer’s or other neurodegenerative diseases.