Diabetes Progression: From Statistical Analysis to Mathematical Modeling
Abstract
Diabetes constitutes one of the leading neuroendocrine conditions with nearly 9.4 % of the US population showing symptoms of it. Noteworthy, type 2 diabetes (T2D) accounts for 90 to 95 % of all cases. It is a chronic disease, and the clinical manifestations include hyperglycemia (prolonged blood glucose levels above normal), hyperinsulinemia (excess levels of insulin circulating in the blood) along with insulin resistance. This presentation includes a statistical analysis based on a cohort of 440 individuals where both, demographic and laboratory measurements are included. Based on these results, a mathematical model accounting for the obtained associations is solved. It includes the level of glucose, insulin, and the functional beta cells mass as an indicator of beta cell functions. Additionally, an external factor is included to account for obesity and the association with the BMI. The adopted approach constitutes the first step in developing a model that integrates glucose and insulin dynamics with the behavior of the Hypothalamic Pituitary Thyroid (HPT) and Hypothalamic Pituitary Adrenal (HPA) axes. Such an integrated systems modeling will permit analyzing the connection of diabetes with metabolic, endocrine, and neurological responses and finding strategies for a better health management of this condition.
Faculty Sponsors
Dr. David Quesada-Sáliba
Project Type
Event
Location
Alvin Sherman Library
Start Date
4-3-2024 12:30 PM
End Date
4-4-2024 1:30 PM
Diabetes Progression: From Statistical Analysis to Mathematical Modeling
Alvin Sherman Library
Diabetes constitutes one of the leading neuroendocrine conditions with nearly 9.4 % of the US population showing symptoms of it. Noteworthy, type 2 diabetes (T2D) accounts for 90 to 95 % of all cases. It is a chronic disease, and the clinical manifestations include hyperglycemia (prolonged blood glucose levels above normal), hyperinsulinemia (excess levels of insulin circulating in the blood) along with insulin resistance. This presentation includes a statistical analysis based on a cohort of 440 individuals where both, demographic and laboratory measurements are included. Based on these results, a mathematical model accounting for the obtained associations is solved. It includes the level of glucose, insulin, and the functional beta cells mass as an indicator of beta cell functions. Additionally, an external factor is included to account for obesity and the association with the BMI. The adopted approach constitutes the first step in developing a model that integrates glucose and insulin dynamics with the behavior of the Hypothalamic Pituitary Thyroid (HPT) and Hypothalamic Pituitary Adrenal (HPA) axes. Such an integrated systems modeling will permit analyzing the connection of diabetes with metabolic, endocrine, and neurological responses and finding strategies for a better health management of this condition.
