Simulation of High-Fat High-Protein Meals Using the UVA/Padova T1D Simulator

Meals with a high content of fat (F) and protein (P) still represent a significant challenge for optimal glycemic control in individuals with type 1 diabetes (T1D). Current strategies dealing with such meals require patients to undergo a series of trial-and-error visits until satisfactory glycemic control is achieved. In this study, we updated the UVA/Padova T1D simulator by integrating F and P effects on model parameters to allow a realistic simulation of meals with varying content. Glycemic profiles of one-hundred virtual subjects were simulated after a nominal meal (75 g of carbohydrates, 20 g of F, and 24 g of P), a high-F meal (HF, +20 g of F to the nominal meal), a high-P meal (HP, +20 g of P), and a HF/HP meal (+20 g of F and +20 g of P) using the standard (carbohydrate-only based) meal insulin bolus. Simulations were evaluated in terms of average blood glucose (BG), time in range (TIR), and time below range (TBR). The simulated glucose time courses were compared against continuous glucose monitor profiles collected on real individuals who ate similar meals, showing comparable median profiles and variability. Then, the simulator was employed to test the efficacy of a dual-wave bolus (+30% of the dose, given 50% immediately and 50% over 2 h) over the standard bolus on the HF/HP meal challenge. The dual-wave bolus produced a significantly lower BG (p<0.0001) and higher TIR compared to the standard therapy (p<0.01), with TBR similar to that of the nominal meal (p=0.29), demonstrating its ability to restore normal glucose levels, without excessively increasing the time in hypoglycemia. However, the updated simulator still lacks proper validation against data that are free from any confounding factor. Therefore, the next step will involve the collection of data in a trial specifically designed to detect the effect of different macronutrients in the meal. Once validated, the proposed platform will be suitable for testing therapies involving meals with different macronutrient content, reducing the burden for clinical trials. Future work will also focus on exploring other dietary factors and integrating time-varying effects of F and P to improve precision and applicability of the simulator.