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Comparisons of continuous monitoring and 7-point self-monitoring of glucose for assessing HbA1c: a computer-based simulation study

Terrmin

HT21

Beskrivning

Background
Diabetes is a group of metabolic disorders characterized by a chronic hyperglycemia over a prolonged period of time. In 90% of cases it is type 2 diabetes (T2D). Mean plasma glucose (MPG) is one of the biomarkers that are commonly used in antihyperglycemic drug development. The MPG can be calculated in 2 ways:
1. Using 7-point self-monitoring of blood glucose (SMBG). Patients take 7 finger pricks during the day (before and after every meal (6 in total) and before going to bed). The MPG is then calculated as average of 7 measurements.
2. Using 24 hour measurements from the continuous glucose monitoring device (CGM). With this type of monitoring, the repeated measurements of glucose are collected every 5 minutes during the day. The MPG is then calculated as the average.
Scientific question
The recent research evidence shows that CGM is superior then 7-SMBG in terms of control over blood glucose variability in patients with diabetes. With this study we would like to compare the MPG values calculated with both methods with the real MPG values (“true” data), using modelling & simulation approach. Additionally, the affect of differences in health status and eating behavior on the glucose profile is going to be explored.
Methods
This study is simulation-based, when the data for multiple individuals (assuming T2D) is going to be simulated in advance (the “true” data). The MPG is then going to be predicted by the IGI model, and the AUC for glucose will be calculated using samples from either 7-SMBG or CGM approaches. The predicted AUCs are then going to be compared with the “true” values. Multiple datasets are going to be simulated, allowing to investigate the impact of the following factors on the variation of AUC:
1) glucose related: baseline glucose level (high/low)
2) meals related: size (amount of glucose consumed), frequency (amount of meals per day, presence/absence of snacks), time (when the meals were eaten) , glycemic index (GI) of the meals (high/low).
The modelling is going to be done with NONMEM. The data analysis and data visualization are going to be done with R.
Project is suitable for students who will take/have taken the course Models for biological systems.

Huvudområde

Farmaceutisk vetenskap

Ämne

Farmakokinetik

Typ

Laborativ studie

Företag

Uppsala Universitet

Ort/Plats

BMC, Uppsala

Handledarens namn

Hanna Kunina

Handledarens e-post

hanna.kunina@farmaci.uu.se

Institution

Program

Kurs

Omfattning/hp

30hp