Methods for Insulin Bolus Adjustment Based on the Continuous Glucose Monitoring Trend Arrows in Type 1 Diabetes: Performance and Safety Assessment in an In Silico Clinical Trial

drCORRECT: An Algorithm for the Preventive Administration of Postprandial Corrective Insulin Boluses in Type 1 Diabetes Management

BACKGROUND

In type 1 diabetes therapy, precise tuning of postprandial corrective insulin boluses (CIBs) is crucial to mitigate hyperglycemia without inducing dangerous hypoglycemic events. Several heuristic formulas accounting for continuous glucose monitoring (CGM) trend have been proposed in the literature. However, these formulas suggest a lot of quantized CIB adjustments, and they lack personalization.

METHOD

drCORRECT algorithm proposed in this work employs a patient-specific time parameter and the "dynamic risk" (DR) measure to determine postprandial CIB suggestion. The expected benefits include the reduction of time in hyperglycemia, thanks to the preventive action exploited through DR. drCORRECT has been assessed retrospectively vs the literature methods proposed by Aleppo et al (AL), Bruttomesso et al (BR), and Ziegler et al (ZI) using a data set of 49 CGM daily traces recorded in free-living conditions. Retrospective evaluation of the algorithms is made possible by the use of ReplayBG, a digital twin-based tool that allows assessing alternative insulin therapies on already collected glucose data. Efficacy in terms of glucose control was measured by temporal, risk indicators, and dedicated hyperglycemic/hypoglycemic events metrics.

RESULTS

drCORRECT significantly reduces time spent in hyperglycemia when compared with AL and BR (33.52 [24.16, 39.89]% vs 39.76 [22.54, 48.15]% and 36.32 [26.91, 45.93]%, respectively); significantly reduces daily injected insulin (5.97 [3.80, 8.06] U vs 7.5 [5.21, 10.34] U), glycemia risk index (38.78 [26.58, 55.39] vs 40.78 [27.95, 70.30]), and time spent in hypoglycemia (0.00 [0.00, 1.74]% vs 0.00 [0.00, 10.23]%) when compared with ZI, resulting overall in a safer strategy.

CONCLUSIONS

The proposed drCORRECT algorithm allows preventive actions thanks to the personalized timing configuration and the introduction of the innovative DR-based CIB threshold, proving to be a valid alternative to the available heuristic literature methods.

Postprandial glucose variability in type 1 diabetes: The individual matters beyond the meal

AIM

To explore intraindividual (between-meals) and interindividual (between-subjects) variability of postprandial glucose response (PGR) in type 1 diabetes (T1DM).

METHODS

Data were taken from five cross-over trials in 61 subjects with T1DM on insulin pump wherein the effects of different dietary components or the intraindividual-variability of PGR to the same meal were evaluated by CGM. Predictors (type of meal or nutrient composition) of early (iAUC_0-3h), late (iAUC_3-6h), total (iAUC_0-6h), and time-course of postprandial blood glucose changes (iAUC_{3-6hminus0-3h}) were evaluated using two mixed-effect linear regression models considering the patient's identification number as random-effect.

RESULTS

High-glycemic-index (HGI) and low-glycemic-index meals were the best positive and negative predictors of glucose iAUC_0-3h, respectively. A Low-Fat-HGI meal significantly predicted iAUC_{3-6hminus0-3h} (Estimate 3268; p = 0.017). Among nutrients, dietary fiber was the only significant negative predictor of iAUC_0-3h (Estimate -550; p < 0.001) and iAUC_0-6h (Estimate -742; p = 0.01) and positive predictor of iAUC_{3-6hminus0-3h} (Estimate 336; p = 0.043). For all models, the random-effect patient was statistically significant (p < 0.001 by ANOVA).

CONCLUSION

Beyond the meal characteristics (including glycemic index, fat and fiber content), individual traits significantly influence PGR. Specific interindividual factors should be further identified to properly predict glucose response to meals with different composition in individuals with T1DM.