Edoardo Faggionato (Italy)
University of Padova
Department of Information Engineering
Edoardo Faggionato received the bachelor’s degree in biomedical engineering, in 2018, and the master’s degree cum laude in bioengineering, in 2020, from the University of Padova, Padova, Italy. During his master studies, he took part at the Erasmus+ exchange program spending a semester at the Chalmers University of Technology, Göteborg, Sweden. He is currently a second year PhD student with the Department of Information Engineering of the University of Padova. His research focus on the development of nonlinear mixed effects models for the kinetics of substrates and hormones involved in the glycemic control. At the moment, he is a visiting researcher at the University of Cape Town, South Africa, where he collaborates with the pharmacometrics group.

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REAL LIFE ESTIMATION OF POSTPRANDIAL GASTRIC RETENTION, GLUCOSE ABSORPTION AND INSULIN SENSITIVITY IN TYPE 1 DIABETES USING MINIMALLY INVASIVE TECHNOLOGIES AND COMPUTATIONAL MODELING

Session Name
0820 - ORAL PRESENTATIONS SESSION 8 (ID 58)
Session Type
Oral Presentations Session
Date
Sat, 30.04.2022
Session Time
13:00 - 14:30
Room
Hall 120
Presenter
Lecture Time
13:32 - 13:40

Abstract

Background and Aims

Understanding the effect of meal composition on glucose excursion would be key in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, the amount of carbohydrates, lipids and proteins in a meal significantly affects postprandial gastric retention (GR), glucose absorption (GA) and insulin sensitivity (SI). Such variables can be estimated, in hospitalized setting, from plasma glucose and insulin data using the Oral Minimal Model (OMM, Dalla Man et al., 2002). Here, we developed a model to estimate those quantities in daily life conditions, using minimally invasive technologies (MI-OMM), and validated it against OMM.

Methods

Data collected from 47 patients with T1D (weight=77±10kg, age=41±13yr) in a closed loop clinical trial (Luijf et al., 2013) were used for model development and validation. Each participant underwent three randomized 23-hour visits (one open- and two closed-loop), during which plasma glucose and insulin were frequently collected, together with continuous glucose monitoring (CGM) and insulin pump (IP) data.

MI-OMM was identified from CGM and IP data using a Bayesian maximum a posteriori estimator, while OMM, identified from plasma glucose and insulin data, was used as reference.

Results

Both models fitted the data well and provided precise parameter estimates. Estimated GR, GA, and SI, obtained with the two methods, are compared in Figure 1.

faggionato_attd2022_figure1.jpg

Conclusions

MI-OMM provided accurate estimates of GR, GA and SI using CGM and IP data. Therefore, it is usable to assess the effect of meal composition on those quantities in daily life conditions and potentially exploitable in DSS for T1D management.

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