The Eindhoven Diabetes Education Simulator (e-DES) - incorporating different food products and composite meals
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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.
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The Eindhoven Diabetes Education Simulator (e-DES) - incorporating different food products and composite meals
- 1. Incorporating different food products and composite meals in the Eindhoven Diabetes Education Simulator (E-DES) Anne Maas, MSc (anne.maas@mmc.nl) Researcher at Máxima Medical Center, Eindhoven
- 3. Eindhoven Diabetes Educational Simulator (E-DES) Objective: Practice safely, independently at home! ??
- 4. Simplified physiological model • Gut, plasma and remote compartments • In- and outflow of glucose and insulin
- 5. In equations Glucose in gut Glucose in plasma Insulin in plasma Insulin in remote compartment ( ) ( ) gut 2 pl meal 1 1 1 meal gutplmealmeal gut ))(exp( , d d GG G GGG G Mkm Dtktkm MmtDm t M = −= −= − σσσ σ ( ) ( ) ( ) ( ) ( ) ( ) ( ) ≤ >− = + = + + = == +−−= −−−+= − − plpl plplplpl1 ren pl pl rem 5 it pl pl pl pl liv 0 itnon gut b2 pl b gut rem 4 plpl 3 livliv plrenremplitplitnongutgutremplliv pl ,0 , ,, d d th ththb G M Mb bM G G G G bb G GGif GGifGG Mν c g GK G Ikg GK G G GK gg M Mν f km Mν f g IkGGkgg GgIGgGgMgIGg t G β β ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )( ) ( )plpl 12 rem pl 11 liv la 5.0 la 2 5.0 b 1 5.0la sc2 9 sc1 2 sc2 sc1 10 sa sc1 sc2 b 9sa pl d8 plpl i 7plpl 6 1pnc plrempllivlalasc2sc1saplpnc pl d d d d d d d ,, d d b hh I hh II I I I I I bb II IIki Iki baUt U ttMv tth i UkUc t U Uku t U U Mv k i t G ktGG k GGki IiIitUiUUiGi t I −= = += + = −= −= = +− +−= −−++= − − ∫ τ τ β ( ) ( ) ( ) rem 13 it plpl 12 rempl remitplpl rem d d Iki IIkii IiIi t I b = −== −=
- 6. Model calculates glucose and insulin
- 7. Fit model on data
- 8. Fit model on data time concentration magnitude error peak (simulation) peak (data) data original model error optimal model
- 9. Predicting OGTT data for healthy people Plasma glucose Plasma insulin
- 10. 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
- 11. Meal ingestion data from literature • Literature search glucose + insulin data • 18 publications • 57 meals, 220 subjects total • 35 meals included, … subjects total
- 12. Resulting model fits for different meals Plasma glucose
- 13. Adjustments in the model
- 14. 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
- 15. • Y Rozendaal • N van Riel • P Hilbers • W Cottaar • C van Pul • H Haak Sponsored by Novo Nordisk The team
Editor's Notes
- Good morning everyone. My name is Anne Maas and I am a researcher at the Máxima Medical Center in Eindhoven. In the next ten to fifteen minutes I want to introduce you to the Eindhoven Diabetes Education Simulator, or E-DES for short, and the incorporation of food intake into this simulator.
- As you all know, diabetes education is crucial to the glycemic regulation of our patients. Currently, in most hospitals this education is given in one-on-one conversations with the health care specialists. Although this method has its merits, it is also very costly and gives the patient no opportunity to practice with the given information.
- To overcome these issues, we have developed the Eindhoven Diabetes Educational Simulator. With this simulator, patients can practice safely and independently at home with all influences on their blood glucose values: food, medication including insulin, and in the future also exercise and strong emotions.
- The heart of E-DES is a simplified physiological model, constructed from several literature models. This model can calculate glucose and insulin concentrations as they change over time after ingestion of food or intake of medication. It consists of three main compartments, for which it calculates the inflow and outflow of glucose and insulin. Glucose enters the plasma via the gut through food intake or is released from the liver. From the plasma it is used by insulin-independent tissues like the brain and red blood cells, by insulin-dependent tissues such as muscles, liver and fat under the influence of insulin, or it is excreted by the kidneys, but only if the current levels are too high. Insulin enters the system either from the pancreas or from insulin injections, and is partly filtered out by the liver and partly released into a remote compartment, from where it can mediate glucose uptake by the insulin-dependent tissues. These in- and outflows have been described using mathematic equations, which look as follows:
- But instead of showing you these equations, I’d rather show you what they can calculate:
- The model generates graphs of glucose and insulin concentrations over time, as shown in these two plots. In this situation, we calculated what happens after a healthy person ingests 50 grams of glucose solution. As you can see, the glucose concentration goes up, and so does the insulin concentration. But we can only be sure that our predictions are correct, by comparing them to data. This is what we have done in the next graph. As you can see, the model does not completely follow the data points.
- To fix this, we fit our model on the data, by adjusting certain values in the mathematical equations. We search for a combination of values that makes the error between the data points and our model as small as possible. We also pay special attention to the height and timing of the glucose peak; we try to make sure that this is the same for the model and the data. By doing this, we obtain an optimal model that can better predict the glucose and insulin profile. We performed this model adjustment for healthy people undergoing an OGTT using data from different papers in the literature, and the results are shown in the next slide.
- As you can see, the model adjustment worked and the models coincide with the data quite well. We have tested the same model on data sets for 50 grams and 75 grams of glucose, and see no difference in performance. This shows that our model is flexible enough to predict different situations. By fitting the model to a data set of type 1 or type 2 patients, we should also be able to model these phenotypes. And if we have data for an individual, we could also create a personalized version of the model.
- But, an OGTT is of course not the same as eating a meal. We want to bring our model from a clinical setting to the home setting, by enabling the model to predict glucose profiles after eating different meals and food products. Because, as every patient has experienced, eating vegetable soup is not the same as eating a granola bar, even if the glucose content is the same. This is nicely shown in this graph, where glucose profiles are shown for different food products with the same glucose content. Not only the peak, but the whole dynamic profile of the glucose concentration is different. We hypothesize that we can capture these differences in dynamic profile by adjusting specific values in our mathematical model.
- To test this hypothesis, we performed a literature search to look for glucose and insulin data for various different food products and composite meals. This resulted in 18 publications, containing data on 57 meals for a total of 220 subjects. Unfortunately, not all these data sets were suitable for usage in our data fitting; in some cases the timing between data points was off and the glucose peak was not captured in the data. Without a clear glucose peak, we can not fit our model to that specific data set, so we had to exclude these, leaving us with 35 different meals. We then followed the same procedure as I described before: we adjusted certain values in the model to make the error between the data and the model prediction as small as possible. We did this for every meal separately, and I will show some example meals in the next slide.
- (grafieken animeren, 1 voor 1 laten verschijnen) As you can see, we have included a number of very different meals, ranging from oatmeal to spaghetti with meat sauce. But as you can also see, the model follows the data fairly well for most of these meals, and the differences in dynamic profile are clearly visible. I have shown only 5 example graphs here, but we have done this for all 35 meals and the results are comparable.
- And what’s more, using these results we can also check if our physiological model still holds. Because we checked which values in the model we needed to change in order to make it fit to the data, and it turned out we only adjusted the mathematical model in the gut compartment; just as it should be.
- Concluding, this means that we can indeed simulate dynamic profiles of different food products, by only adjusting the food intake part of the model. We have shown this for 35 different data sets of a wide variety of meals. The only type of meal that is still largely missing is different types of dinner; we did model spaghetti, but we have no data on a potatoes/meat/vegetables meal or something with a higher fat content, like pizza. Therefore, the next step in our research will be to measure glucose and insulin profiles after eating these different meals and adjusting our model for those food types as well. We will also obtain glucose and insulin data for patients with diabetes type 1 and 2, and adjust our model for these phenotypes as well. But we assume that the model values we now found for healthy people, will also be valid in patients with diabetes type 1 and 2, giving us one less thing to measure explicitly. I hope that in a year, I will be able to show you the modeling results for diabetes patients.
- And with this final remark I would like to end my presentation, but not before thanking the whole E-DES team for their feedback and help, and before thanking Novo Nordisk for providing us with the funds necessary to continue this work.