The main aim of the NIH Nutrition Obesity Research Center (NORC) is to assist researchers in advancing their research in the field of obesity and nutrition. The Precision Nutrition core, directed by Dr. Voruganti is one of the UNC NORC cores that provides an all-encompassing support system providing investigators with the design and implementation of nutrigenetic, nutrigenomics and microbiome research components of obesity studies. In this administrative supplement, we propose to a pilot study in which we will generate a model that can predict the risk for cognitive decline based on their genetic, dietary, metabolic and demographic variables. Cognitive decline is a key clinical feature of the neurodegenerative disease, Alzheimer?s disease (AD), an extremely debilitating condition. The prevalence of AD is expected to increase to 14 million by 2050 in the US and 130 million worldwide. Considering that there is no cure for AD, it is imperative that we take measures to predict its onset and design interventions which can either prevent and/or slow down the progression of its symptoms, particularly cognitive decline. Cognitive decline is influenced by multiple genetic and environmental factors and it has proven difficult to predict. Previous models predicting the risk for cognitive decline have either been based on invasive markers or focused on only a few sets of genotypes or phenotypes. In this project we will use an established cohort of elderly adults for developing a risk prediction model and then use data from the Baltomore Longitudinal Study of Aging (BLSA) data repository to test, validate and confirm this model. We expect that the resultant model will facilitate efficient and cost- effective nutritional interventions to prevent or slow the rate of progression of cognitive decline in middle- aged and elderly adults.
PROGRAM NARRATIVE Cognitive decline is a key clinical feature of and precedes the onset of Alzheimer?s disease (AD). As there is no cure for AD, it is important to predict its onset and design interventions that can prevent and/or slow down its progression. In this administrative supplement, we propose to generate a model that will be able to predict the risk for cognitive decline using persons? genetic, dietary, metabolic and demographic factors.
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