Objective: Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (R a), and insulin sensitivity (S I). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM. Methods: Forty-seven individuals with T1D (weight =78±13kg, age =42±10yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data. Results: The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R a model parameters were not significantly different using the MI-OMM and R-OMM (p 0.05) and the correlation between the two S I was satisfactory ( ρ =0.77). Conclusion: The MI-OMM is usable to estimate GR, R a, and S I from data collected in real-life conditions with minimally-invasive technologies. Significance: Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R a, and S I. DSS could finally exploit this information to improve diabetes management.
The Minimally-Invasive Oral Glucose Minimal Model: Estimation of Gastric Retention, Glucose Rate of Appearance, and Insulin Sensitivity from Type 1 Diabetes Data Collected in Real-life Conditions
Faggionato, Edoardo;Schiavon, Michele;Dalla Man, Chiara
2023
Abstract
Objective: Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (R a), and insulin sensitivity (S I). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM. Methods: Forty-seven individuals with T1D (weight =78±13kg, age =42±10yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data. Results: The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R a model parameters were not significantly different using the MI-OMM and R-OMM (p 0.05) and the correlation between the two S I was satisfactory ( ρ =0.77). Conclusion: The MI-OMM is usable to estimate GR, R a, and S I from data collected in real-life conditions with minimally-invasive technologies. Significance: Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R a, and S I. DSS could finally exploit this information to improve diabetes management.
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