Low glycemic index diets for subjects with diabetes by minimizing glycemic load of individual foods

Abstract

In this work, the authors develop and analyze a linear programming model to obtain an ideal diet for individuals with diabetes by setting the glycemic load as the objective function. Additionally, a standardized system is used in order to facilitate the substitutability of foods present in a diet since those are classified according to their macronutrient content (proteins, lipids, and carbohydrates) and these values are, on average, very similar. Finally, the diet glycemic index is calculated with the model’s outcome to corroborate that it is indeed a diet with low glycemic index and that, at the same time, it complies with the nutrient restrictions, which proves that the model can be a useful tool both to generate low glycemic index diets and to restrict certain nutrients from the diet.