MINDS Seminar Series | Joon Ha (Howard University) - Prediction and Estimation of Diabetes with a Mathematical Model

period : 2022-05-31 ~ 2022-05-31
time : 16:40:00 ~ 17:20:00
개최 장소 : Math Bldg 100 & Online streaming (Zoom)
Topic : Prediction and Estimation of Diabetes with a Mathematical Model
Date 2022-05-31 ~ 2022-05-31 Time 16:40:00 ~ 17:20:00
Speaker Joon Ha Affiliation Howard University
Place Math Bldg 100 & Online streaming (Zoom) Streaming link ID : 688 896 1076 / PW : 54321
Topic Prediction and Estimation of Diabetes with a Mathematical Model
Contents Type 2 diabetes (T2D) is a chronic, progressive disorder in glucose homeostasis. A common form of pathophysiology of the disease is the failure of insulin-secreting pancreatic β-cells to increase levels of insulin, demanded mainly by obesity and aging. Such increased insulin levels are utilized to maintain a normal range of blood glucose concentration. Prediabetes (PDM) is an intermediate state between normal glucose tolerance and diabetes. About 70% of individuals with prediabetes will develop diabetes during their lifetimes. Identifying early signs of prediabetes is therefore essential to initiate therapy to prevent or slow disease progression. Our mathematical model of progression to diabetes (Ha et al. Endocrinology, 2016 and Ha and Sherman, AJP 2020) predicts that the threshold of one-hour glucose during an oral glucose test, which is not a current prediabetes criterion, is crossed before the threshold of two-hour glucose, the current standard criterion. To test our predictions, a linear mixed effect model (LME) was applied to analyze a longitudinal data set from a cohort of Pima Indians. The LME analysis confirmed that one-hour glucose passes its threshold about two years before two-hour glucose, a clinically significant difference. The results indicate that one-hour glucose should be considered as a new criterion for prediabetes. Moreover, the mathematical model predicts that weak early-phase insulin secretion, which is common among East Asians and cystic fibrosis patients, contributes to earlier abnormality of one-hour glucose. In clinical settings, it is beneficial to know patients’ metabolic parameters for an optimal treatment. As such, we also applied the mathematical model to identify underlying metabolic dysfunction in a cross-sectional data set of African Americans by estimating two major parameters of the mathematical model, pancreatic β-cell function and insulin sensitivity led by obesity and aging. The estimated parameters provide personalized treatments for diabetes patients.
MinDS MinDS · 2022-05-19 14:40 · Views 680