Quantifying the Effect of Fat and Protein on the Postprandial Glucose Excursion in Individuals With Type 1 Diabetes Using an Automated Insulin Delivery System

Abstract

Background:

Quantifying the effect of meal composition (MC) on postprandial glucose excursions would allow optimizing insulin therapy, accounting for fat and protein that can affect gastric retention (GR), glucose rate of appearance (R_a), and insulin sensitivity (S_I). Such variables can be estimated from continuous glucose monitor (CGM) and continuous subcutaneous insulin infusion (CSII) data using the Minimally-Invasive Oral Minimal Model (MI-OMM). In this work, we aim to quantify the effect of MC on those variables by applying the MI-OMM on a data set of prandial CGM and CSII profiles where MC information was available.

Methods:

A total of 120 individuals with type 1 diabetes (age = 15.5 ± 11.5 years, weight = 51.3 ± 28.0 kg) were monitored under free-living conditions while using CGM and CSII, and MC was carefully recorded. We extracted 353 CGM and CSII traces using predefined criteria and classified them into low or high fat content and low or high protein content. Finally, the MI-OMM was used to estimate GR, R_a, and S_I in each meal.

Results:

MI-OMM was able to fit CGM profiles and provided precise and physiologically plausible parameter estimates. Comparison among different classes of meals showed that a high content of fat and protein in the meal significantly slowed both GR (P < .01) and R_a (P < .01), and reduced S_I (P < .05).

Conclusions:

In this work, the effect of MC on postprandial glucose excursion was quantified in real-life conditions with the help of a model-based methodology. These results are usable for redesigning current insulin therapies, accounting for the presence of fat and protein in meals.