The Eindhoven Diabetes Education Simulator (e-DES) - incorporating different food products and...
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Incorporating different food products and composite meals in the Eindhoven Diabetes Education Simulator (E-DES) Anne Maas, MSc ([email protected]) Researcher at Máxima Medical Center, Eindhoven
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Background: Diabetes education is mainly based on one-on-one patient-health care provider contact. This is costly, time-consuming and gives the patient no room for practice. We want to address these issues by creating the Eindhoven Diabetes Education Simulator, which uses a physiology-based mathematical model to predict glucose and insulin concentrations for patients with diabetes type 1 and 2 over a 2-4 hour time period after intake of food and/or insulin. In our current model food is entered in the form of carbohydrate content. The goal of this study was to incorporate different food products and composite meals for healthy persons, since different food types will elicit different glucose responses. Methods: A literature search was performed for datasets of different food products using (combinations of) the following search terms: healthy, mixed meal, glucose, insulin, glycemic response, glycemic index, and looking for cross-references. We included any dataset for which glucose ánd insulin concentrations were measured on at least 5 time points after food ingestion in healthy subjects. Healthy was defined as normal glucose tolerant, normal insulin sensitive, normotensive, normal HbA1c, non-obese (BMI< 30 kg/m2), no family history of diabetes, not pregnant, and free of apparent diseases and medication. Our model was fitted to the different datasets using a non-linear least squares algorithm. Results: We have fitted our model to 57 separate datasets (from 18 publications including 220 subjects, references available on request). For 35 of these datasets we obtained a model fit that described the dataset well, of which five are shown in Figure 1. In the cases that we could not obtain a good fit, there usually were a limited number of data points available. Conclusion: The Eindhoven Diabetes Education Simulator is able to simulate postprandial glucose and insulin concentrations for healthy persons for 35 different food products and composite meals.
Transcript of The Eindhoven Diabetes Education Simulator (e-DES) - incorporating different food products and...
Incorporating different food products and composite meals in the Eindhoven Diabetes Education Simulator (E-DES)
Anne Maas, MSc ([email protected])
Researcher at Máxima Medical Center, Eindhoven
The problem…
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Eindhoven Diabetes Educational Simulator (E-DES)
Objective:Practice safely, independently at home!
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Simplified physiological model
• Gut, plasma and remote compartments
• In- and outflow of glucose and insulin
In equations
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Model calculates glucose and insulin
Fit model on data
Fit model on data
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error
optimal model
Predicting OGTT data for healthy people
Plasma glucose Plasma insulin
From clinical to home setting: food inclusion
• Hypothesis: capture differences in dynamic profile by adjusting mathematical model
Graph from Sadler 2011, ILSI Europe Concise Monograph Series
Meal ingestion data from literature
• Literature search glucose + insulin data• 18 publications• 57 meals, 220 subjects total
• 35 meals included, … subjects total
Resulting model fits for different meals
Plasma glucose
Adjustments in the model
Conclusion & outlook
• Conclusion:– 35 different food products/meals included– Wide variety of meals– Only food intake part of model adjusted
• Outlook:– Dinner data largely missing measure 6
dinners in new research– Method & food profiles transferable to
model for diabetes type 1 and 2
• Y Rozendaal• N van Riel• P Hilbers• W Cottaar
• C van Pul• H Haak
Sponsored by Novo Nordisk
The team
