Randomized Crossover Comparison of Automated Insulin Delivery Versus Conventional Therapy Using an Unlocked Smartphone with Scheduled Pasta and Rice Meal Challenges in the Outpatient Setting

Abstract

Background: Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of prescribed meals. We evaluated performance of our interoperable artificial pancreas system (iAPS) in the at-home setting, running on an unlocked smartphone, with scheduled meal challenges in a randomized crossover trial. Methods: Ten adults with type 1 diabetes completed 2 weeks of AID-based control and 2 weeks of conventional therapy in random order where they consumed regular pasta or extra-long grain white rice as part of a complete dinner meal on six different occasions in both arms (each meal thrice in random order). Surveys assessed satisfaction with AID use. Results: Postprandial differences in conventional therapy were 10,919.0 mg/dL × min (95% confidence interval [CI] 3190.5-18,648.0, P = 0.009) for glucose area under the curve (AUC) and 40.9 mg/dL (95% CI 4.6-77.3, P = 0.03) for peak continuous glucose monitor glucose, with rice showing greater increases than pasta. White rice resulted in a lower estimate over pasta by a factor of 0.22 (95% CI 0.08-0.63, P = 0.004) for AUC under 70 mg/dL. These glycemic differences in both meal types were reduced under AID-based control and were not statistically significant, where 0-2 h insulin delivery decreased by 0.45 U for pasta (P = 0.001) and by 0.27 U for white rice (P = 0.01). Subjects reported high overall satisfaction with the iAPS. Conclusions: The AID system running on an unlocked smartphone improved postprandial glucose control over conventional therapy in the setting of challenging meals in the outpatient setting. Clinical Trial Registry: clinicaltrials.gov NCT03767790.

Keywords: Artificial pancreas; Automated insulin delivery; Glycemic control; Nutrition; Pasta; Rice; Type 1 diabetes.

Conflict of interest statement

J.E.P. reports receiving grant support, provided to his institution, consulting fees, and speaker fees from Tandem Diabetes Care, grant support, provided to his institution, and advisory board fees from Medtronic, grant support, provided to his institution, and consulting fees from Eli Lilly, grant support and supplies, provided to his institution, from Insulet, and supplies, provided to his institution, from Dexcom, Inc. (San Diego, CA). F.J.D. reports product support from Dexcom, Inc., and Tandem Diabetes Care, as well as patent royalties from Insulet, Inc., Dexcom, Inc., Mode AGC, and Roche, and is a Scientific Advisor to Mode AGC. D.M.E. is a Scientific Advisor to the Health and Wellness Advisory Committee of the Barilla Center for Food & Nutrition (Italy). E.D. reports consulting fees from Eli Lilly, speaker bureau fees from Roche Diabetes Care, and product support from Dexcom, Inc., and Tandem Diabetes Care, as well as patent royalties from Insulet, Inc., Dexcom, Inc., Mode AGC, and Roche. No conflicts of interest relevant to this project are reported for the rest of the authors.

Figures

FIG. 1.

Sensor glucose in mg/dL (top row) and microbolus insulin in units (bottom row) from start of the study meals (Time Zero) through 360 min, stratified by pasta and white rice. Data are shown as median (IQR) across subjects for each time point. The CGM profiles are adjusted at baseline by subtracting the CGM value at the start of the meal at each CGM time point for each subject. CGM, continuous glucose monitor; IQR, interquartile range.

FIG. 2.

Paired comparison of mean glucose and time in 70–180 mg/dL range during SAP/PLGS and AID arms. The solid lines connect individual subjects, with the bubble size proportional to time in hypoglycemia below 70 mg/dL, while dashed lines show mean change with annotation. Use of AID resulted in decrease of percent time spent below 70 mg/dL in 8 out of 10 subjects. AID, Automated Insulin Delivery; PLGS, predictive low-glucose suspend; SAP, sensor-augmented pump.