The topic of achieving optimal glycemic control through proper dosing of insulin has been extensively researched. While similar analyses of have been performed, most focus on finding a bolus method which works best for a particular meal composition, in a clinical setting. Figure 1 shows four examples of bolus methods examined in literature, summarized by Heinemann [9]. For example, Chase and colleagues [4] examined the glycemic response of nine subjects after a high-carbohydrate, high-fat meal. The four modes used in this study were a single bolus, two boluses of 50% of dose each and delayed bolus over 2 hours, and combination bolus of 70% immediately and the rest over 2 hours. This study concluded that the combination bolus was most successful in controlling post-mealtime blood glucose levels for meals with a high fat and high carbohydrate content.
A similar study has been carried out by Lee and colleagues which aimed to examine the glycemic control of 10 subjects with the following three meal and bolus mode combinations, a high-fat meal with normal bolus delivery, a high-fat meal with combination bolus, a control meal with single bolus delivery, and a control meal with single bolus delivery [11]. This study also reached the conclusion that for high-fat, high-carbohydrate meals, the combination bolus leads to better results than the single bolus. Both studies were carried out in a clinical setting and focused only on meals with a high-fat and high-carbohydrate content.
Several limitations of these studies make it difficult to translate to real-world, outpatient settings. Firstly, both studies were carried out in a clinical setting and focused only on meals with a high-fat and high-carbohydrate content. Secondly, subjects in both experiments had in-range blood glucose values in the hours prior to the meal, which may not always be the case in real life. Furthermore, neither study considered the timing of the bolus administration. As pointed out by Heinemann [9], both studies fails to present the full picture, as in real life, people tend to consume more than one meal a day, and hence may have active insulin in their system, highlighting the need of similar studies to be performed using real-world data.
The only similar analysis which uses was performed on users of closed-loop systems is a 2014 study by Chase and colleagues. In this study, the glycemic reaction of 53 patients was measured to the following four scenarios, standard bolus, standard bolus delivered 15 minutes prior to the meal, over-bolus of 30% and a purposefully omitted bolus. This study focused more on testing whether the artificial pancreas system is able to handle errors in bolus estimation, rather than finding the bolus patterns which lead to best outcomes [5]. One conclusion of the study was the need to perform similar analyses in real-life outpatient scenarios, in order to consider factors such as stress, exercise and different types of meals.
Other novel approaches for improving postprandial glycemic control, while also reducing the disease management burden, include the use of machine learning to predict insulin doses. For example, Cappon and colleagues propose an approach for personalizing bolus calculations using CGM data through the use of neural networks [3].
