A significant amount of prior research has been conducted to investigate type 2 diabetes, the most prevalent form afflicting over 90% of diabetic individuals [6]. Yet, gestational diabetes is an understudied form of diabetes that is thought to share various attributes with type 2 diabetes. It was the aim of this project to investigate a proposed mechanism of the disease, the contra-insulin effect, through a cell-culture experiment. To address the question of whether glycemic and hormonal conditions of cell-culture media affect Hs 795.Pl morphology, cellular growth, and glucose uptake, immunocytochemistry (ICC) and a glucose uptake assay was performed. It was hypothesized that higher the presence of hormones, specifically lactogen, in cell culture media will exacerbate the contra-insulin effect, decreasing the glucose uptake of the Hs 795.Pl cells and inducing abhorrent cell morphology. Qualitatively, estradiol and cortisol had a severe impact on cellular morphology indicative of stress and death. As for glucose uptake, it was decreased when the hormones were isolated compared to all together with estradiol thought to be majorly inhibitory to insulin’s proper functioning. It was concluded that cell morphology, growth, and glucose uptake were detrimentally impacted by the gestational hormones, especially those of cortisol and estrogen.

The burden of dementia and its primary cause, Alzheimer’s disease, continue to devastate many with no available cure although present research has delivered methods for risk calculation and models of disease development that promote preventative strategies. Presently Alzheimer’s disease affects 1 in 9 people aged 65 and older amounting to a total annual healthcare cost in 2023 in the United States of $345 billion between Alzheimer’s disease and other dementias making dementia one of the costliest conditions to society (“2023 Alzheimer’s Disease Facts and Figures,” 2023). This substantial cost can be dramatically lowered in addition to a reduction in the overall burden of dementia through the help of risk prediction models, but there is still a need for models to deliver an individual’s predicted time of onset that supplements risk prediction in hopes of improving preventative care. The aim of this study is to develop a model used to predict the age of onset for all-cause dementias and Alzheimer’s disease using demographic, comorbidity, and genetic data from a cohort sample. This study creates multiple regression models with methods of ordinary least squares (OLS) and least absolute shrinkage and selection operator (LASSO) regression methods to understand the capacity of predictor variables that estimate age of onset for all-cause dementia and Alzheimer’s disease. This study is unique in its use of a diverse cohort containing 346 participants to create a predictive model that originates from the All of Us Research Program database and seeks to represent an accurate sampling of the United States population. The regression models generated had no predictive capacity for the age of onset but outline a simplified approach for integrating public health data into a predictive model. The results from the generated models suggest a need for continued research linking risk factors that estimate time of onset.

This project uses All of Us Data to analyze how well of a predictor APOE ε4 is in the Latinx community, a high grandparent care community. APOE is used as a predictor for Alzheimer’s disease, but it is unknown, due to the lack of studies, how strong of a predictor it will be for Latinx individuals. This project aims to understand if the increased risk of Alzheimer’s disease among Hispanics is associated with a different level of ε4 gene frequency.