Genetic, clinical and environmental factors and cognitive decline in AD

 
   Lead Investigator:    Ekaterina Galkina
   Institution      :    UMass Lowell
   E-Mail           :    ecleary@bentley.edu
   Proposal ID      :    304


   Publications:


1  Cleary EG, Cifuentes M, Grinstein G, Brugge D, Shea TB
   Association of Low-Level Ozone with Cognitive Decline in Older Adults
   Journal Article  J Alzheimers Dis,61:67-78,2018,PMC5755393

   PUBLISHED



2  Galkina E, Grinstein G
   Regional differences in diagnostic conversion to dementia
   2014   Abstract  8th International Conference on Information Visualization, Paris , 

   ACCEPTED



3  Galkina E, Grinstein G
   Comparison of interpolation methods for estimating spatially aggregated pollution exposure
   2014   Abstract  8th International Conference on Information Visualization, Paris , 

   ACCEPTED



4  Kolman S, Galkina E, Dufilie AS, Luo YF, Gupta V, Grinstein G
   Linked visual analysis of structured datasets and document collections
   2014   Abstract  Visualization and Data Analysis 2014 Proceedings of SPIE , 

   ACCEPTED





   Proposal Description:
   In the proposed research we will examine the relationship between two dozen clinical and demographic factors, including environmental exposure to air pollutants, and the variability of progression in AD and its prodromal stage, mild cognitive impairment (MCI). We hypothesize that a combination of these risk factors will predict the path of dementia progression.    Specific Aim. To define cognitive decline trajectories for NACC's cohort of study volunteers build and train a model of AD trajectory type dependence on the various risk factors implicated in AD. Alzheimer's patients exhibit various cognitive decline behaviors over time, showing distinct patterns of disease development. These individual-specific cognitive decline trajectories may be described as slow, steady, or rapid. Mixture of Gaussian Process Regression will be used to first cluster the study volunteers as belonging to one of the progression classes. Then, risk factors explaining membership to a particular class will be further investigated. The causes model will include a combination of individual level (i.e. smoking, co-morbidities, life-style variables, APOE allele) and community level factors (air pollution estimates for PM2.5/10, ozone, carbon monoxide, nitrous oxide, sulfur oxide, and lead spatially interpolated in regions of poor EPA monitor distribution) for a total of over two dozen predictors modeled simultaneously. Additionally, residence in regions determined to be primarily urban or primarily rural (or suburban-type) will be investigated for prediction of conversion from MCI to probable/possible AD diagnosis. Knowledge about which factors are important in predicting the type of decline (steady, slow, rapid) may be used to delay progression of individuals in the early stages of the disease.