Abstract
Background and Aims
Most insulin bolus calculators model carbohydrates, insulin, and the rise in sensor glucose(SG) as a linear relationship, which does not account for meal-to-meal variations in insulin sensitivity, glycemic index, or nutrient content. The variation between carbohydrates, insulin, and SG level was evaluated in a population of real-world individuals using a sensor-augmented pump(SAP) to manage diabetes.
Methods
Data from 1825 SAP system users were voluntarily uploaded to CareLink™ Personal between January 2014 and October 2018 and retrospectively analyzed if ≥20 valid meals were logged. Glucose response to insulin and carbohydrates taken with a meal were measured using the Sensor Glucose Response Metric(SGRM) calculated as (SGΔ+Insulin*ISF)/Carbohydrate. SGΔ is the change in SG level from meal start to the first SG level peak. Insulin Sensitivity Factor(ISF) is the estimated glucose-to-insulin response(mg dL-1/unit). The variance in SGRM between meals users, and time of day was also determined.
Results
The mean±SD of meals for the 1825 users was 221±390. 402,941 meals were analyzed. The mean±SD of the SGRM across users was 8.4±4.3mgdL-1/gram and the average intra-user SD was 5.7mgdL-1/gram. The SGRM mean and standard deviation decreased from 3am-9pm. However, intra-user SGRM SD hit a max of 5.4mgdL-1/gram from 6am-9am.
Conclusions
The calculated SGRM of SAP therapy users revealed the potential impact of metabolic and meal-component factors(e.g., glycemic index, nutrient content, time of day, and other contextual factors) that influence glucose response in a non-linear fashion. These preliminary findings, on factors outside of carbohydrate and insulin, build toward the development of more advanced automated insulin delivery and predictive CGM systems.
