Glucose dynamics in Type I diabetes: insights from the classic and linear minimal models

This study demonstrates that the classic minimal model (MM) and the linear minimal model (LMM) are able to follow the dynamics of glucose in Type I diabetes. LMM precision is better than the MM with systematic lower mean values for the coefficient of variation (CV) in all characteristic model parameters. LMM S(I)(L)=7.40 is not significantly different from MM S(I)=10.71 (units 1/min per muU/ml, alpha=0.001) with a strong correlation (R(s) = 0.83, alpha=0.01). LMM S(G)(L)=0.0407 appears to be significantly different to S(G)=0.0266 (units 1/min, alpha=0.001) but correlates very well (R(s)=0.91,alpha=0.01). Since residuals appear to be heteroscedastic, further work is required to address the effect of modeling and signal processing on them. For the data under study, the models are not able to fit two-thirds of the data windows available. This is because none of the models are able to follow complex situations such as the presence of several bolus injections, the absence of insulin supply or inappropriate insulin dosage. A synthesis of the patterns found in these windows is presented which would be useful for the development of new models for fitting these data.