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

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
$_\text{a}$
), and insulin sensitivity (S
$_\text{I}$
). 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
$=78\pm$
13 kg, age
$=42\pm$
10 yr) 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
$_\text{a}$
model parameters were not significantly different using the MI-OMM and R-OMM (p
$>$
0.05) and the correlation between the two S
$_\text{I}$
was satisfactory (
$\rho$
$=$
0.77).
Conclusion:
The MI-OMM is usable to estimate GR, R
$_\text{a}$
, and S
$_\text{I}$
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
$_\text{a}$
, and S
$_\text{I}$
. DSS could finally exploit this information to improve diabetes management.