Continuous Glucose Monitoring and Insulin Informed Advisory System with Automated Titration and Dosing of Insulin Reduces Glucose Variability in Type 1 Diabetes Mellitus

Marc D Breton, Stephen D Patek, Dayu Lv, Elaine Schertz, Jessica Robic, Jennifer Pinnata, Laura Kollar, Charlotte Barnett, Christian Wakeman, Mary Oliveri, Chiara Fabris, Daniel Chernavvsky, Boris P Kovatchev, Stacey M Anderson, Marc D Breton, Stephen D Patek, Dayu Lv, Elaine Schertz, Jessica Robic, Jennifer Pinnata, Laura Kollar, Charlotte Barnett, Christian Wakeman, Mary Oliveri, Chiara Fabris, Daniel Chernavvsky, Boris P Kovatchev, Stacey M Anderson

Abstract

Background: Glucose variability (GV) remains a key limiting factor in the success of diabetes management. While new technologies, for example, accurate continuous glucose monitoring (CGM) and connected insulin delivery devices, are now available, current treatment standards fail to leverage the wealth of information generated. Expert systems, from automated insulin delivery to advisory systems, are a key missing element to richer, more personalized, glucose management in diabetes.

Methods: Twenty four subjects with type 1 diabetes mellitus (T1DM), 15 women, 37 ± 11 years of age, hemoglobin A1c 7.2% ± 1%, total daily insulin (TDI) 46.7 ± 22.3 U, using either an insulin pump or multiple daily injections with carbohydrate counting, completed two randomized crossover 48-h visits at the University of Virginia, wearing Dexcom G4 CGM, and using either usual care or the UVA decision support system (DSS). DSS consisted of a combination of automated insulin titration, bolus calculation, and CHO treatment advice. During each admission, participants were exposed to a variety of meal sizes and contents and two 45-min bouts of exercise. GV and glucose control were assessed using CGM.

Results: The use of DSS significantly reduced GV (coefficient of variation: 0.36 ± 08. vs. 0.33 ± 0.06, P = 0.045) while maintaining glycemic control (average CGM: 155.2 ± 27.1 mg/dL vs. 155.2 ± 23.2 mg/dL), by reducing hypoglycemia exposure (%<70 mg/dL: 3.8% ± 4.6% vs. 1.8% ± 2%, P = 0.018), with nonsignificant trends toward reduction of significant hyperglycemia overnight (%>250 mg/dL: 5.3% ± 9.5% vs. 1.9% ± 4.6%) and at mealtime (11.3% ± 14.8% vs. 5.8% ± 9.1%).

Conclusions: A CGM/insulin informed advisory system proved to be safe and feasible in a cohort of 24 T1DM subjects. Use of the system may result in reduced GV and improved protection against hypoglycemia.

Keywords: Continuous glucose monitoring; Decision support systems; Expert systems; Insulin titration.; Treatment advisory systems; Type 1 diabetes.

Conflict of interest statement

No competing financial interests exist.

M.D.B. reports consulting/honorarium from Dexcom, Roche, Ascensia, and Sanofi; as well as research support from Ascensia, Roche, Tandem, Sanofi, NovoNordisk, and Dexcom; and equity form TypeZero Technologies. B.P.K. reports consulting/honorarium from Dexcom and Sanofi; as well as research support from Sanofi, Roche, Tandem, and Dexcom; and equity from TypeZero Technologies. S.D.P. reports salary coverage and equity from TypeZero Technologies. D.C. reports salary coverage from TypeZero Technologies. S.M.A. reports research support from Ascensia, Tandem, Roche, NovoNordisk, and Dexcom.

Figures

FIG. 1.
FIG. 1.
Design of the protocol. After randomization to branch A (experimental then control) or B (control then experimental) participants were admitted to two identical days with standardized meals and activity (bottom of figure) using either their own treatment paradigm or following the advice given by the DSS. The data collection needed to power the DSS occurred during the 4 weeks before the experimental admission. DSS, decision support system.
FIG. 2.
FIG. 2.
Evolution of average glycemia (Y-axis) and exposure to hypoglycemia (X-axis) from the control admission (gray circle) to the experimental admission (black circle). The gray areas with dotted perimeters represent the 95% confidence interval for the control (light gray) and experimental (dark gray) admissions.
FIG. 3.
FIG. 3.
Glycemic average (line) and 95th percentile (shaded area) for SoC (blue) and DSS (red), during the exercise bouts (stripped). (A) Shows the results for the exercise 2 h post breakfast, and (B) focuses on exercise 3 h post breakfast. (C) Shows the distribution (box plot: mean: x, median: horizontal bar, 25–75 quartiles: gray box, and range: whiskers) of the minimum BG reached during exercise. BG, blood glucose; SoC, standard of care.
FIG. 4.
FIG. 4.
Histogram of the area under the glucose curve during the 4 h following meals. (A) describes all lunches and dinners (breakfasts are excluded because of the exercise bouts), whereas (B) focuses on meals with high fat/protein contents, and (C) on larger meals (∼1 g/kg body weight).

Source: PubMed

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