Improved usability of the minimal model of insulin sensitivity based on automated approach and genetic algorithms for parameter estimation
Résumé
Minimal Model analysis of glucose and insulin data from an intravenous glucose tolerance test is widely used to estimate insulin sensitivity. However, the use of the model often requires intervention by a trained operator, and some problems in the estimation of the model parameters can occur. In this study a new method for Minimal Model analysis, GAMMOD, was developed based on Genetic Algorithms for the estimation of the model parameters. Such algorithm does not require fixing initial values for the parameters (that may lead to unreliable estimates). Our method also implements an automated weighting scheme not requiring manual intervention of the operator, thus improving the usability of the model. We studied a group of 170 women with a history of previous gestational diabetes. Results obtained by GAMMOD were compared to those obtained by a traditional gradient-based algorithm for minimal model analysis, MINMOD. Insulin sensitivity by GAMMOD was 3.86±0.19 ·10 -4} {mu}U ml -1} min -1} vs. 4.33±0.20 by MINMOD; glucose effectiveness was 0.0236±0.0005 min -1} vs. 0.0229±0.0005. The difference in the estimations by the two methods are within the precision expected for such kind of metabolic parameters and are of no clinical relevance. Moreover, both the coefficient of variations of the estimated parameters and the error of fit are generally lower in GAMMOD, despite the fact that it does not require manual intervention. In conclusion the GAMMOD approach for parameter estimation in the Minimal Model provides reliable estimation of the model parameters and improves the usability of the model, thus facilitating its further diffusion and application in a clinical contest.
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