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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  $&gt;$  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.

ieee transactions on biomedical engineering

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