The effect of insulin feedback on closed loop glucose control

Garry M Steil, Cesar C Palerm, Natalie Kurtz, Gayane Voskanyan, Anirban Roy, Sachiko Paz, Fouad R Kandeel, Garry M Steil, Cesar C Palerm, Natalie Kurtz, Gayane Voskanyan, Anirban Roy, Sachiko Paz, Fouad R Kandeel

Abstract

Context: Initial studies of closed-loop proportional integral derivative control in individuals with type 1 diabetes showed good overnight performance, but with breakfast meal being the hardest to control and requiring supplemental carbohydrate to prevent hypoglycemia.

Objective: The aim of this study was to assess the ability of insulin feedback to improve the breakfast-meal profile.

Design and setting: We performed a single center study with closed-loop control over approximately 30 h at an inpatient clinical research facility.

Patients: Eight adult subjects with previously diagnosed type 1 diabetes participated.

Intervention: Subjects received closed-loop insulin delivery with supplemental carbohydrate as needed.

Main outcome measures: Outcome measures were plasma insulin concentration, model-predicted plasma insulin concentration, 2-h postprandial and 3- to 4-h glucose rate-of-change following breakfast after 1 d of closed-loop control, and the need for supplemental carbohydrate in response to nadir hypoglycemia.

Results: Plasma insulin levels during closed loop were well correlated with model predictions (R = 0.86). Fasting glucose after 1 d of closed loop was not different from nighttime target (118 ± 9 vs. 110 mg/dl; P = 0.38). Two-hour postbreakfast glucose was 132 ± 16 mg/dl with stable values 3-4 h after the meal (0.03792 ± 0.0884 mg/dl · min, not different from 0; P = 0.68) and at target (97 ± 6 mg/dl, not different from 90; P = 0.28). Three subjects required supplemental carbohydrates after breakfast on d 2 of closed loop.

Conclusions/interpretation: Insulin feedback can be implemented using a model estimate of concentration. Proportional integral derivative control with insulin feedback can achieve a desired breakfast response but still requires supplemental carbohydrate to be delivered in some instances. Studies assessing more optimal control configurations and safeguards need to be conducted.

Figures

Fig. 1.
Fig. 1.
A, Average glucose profile obtained using the ePID system modified to include IFB. B, Insulin delivery during closed loop (left axis) with measured and model-predicted plasma insulin concentration (right axis). Model-predicted and measured insulin are shown in μU/ml (1 μU/ml = 6.00 pmol/liter); glucose concentration is shown in mg/dl (1 mg/dl = 0.055 mmol/liter). BG, Blood glucose; SG, sensor glucose; B1 and B2, breakfast on d 1 and 2, respectively; L, lunch; D, dinner.
Fig. 2.
Fig. 2.
Subject in which closed-loop control was initiated at gain proportional the subject's daily insulin requirement (shaded area) but subsequently decreased (not shaded). A, Glucose. B, Insulin delivery (left axis) and measured and predicted insulin concentration (right axis). BG, Blood glucose; SG, sensor glucose; CHO, supplemental carbohydrate.
Fig. 3.
Fig. 3.
Subject in which closed-loop control was initiated with one sensor but where the sensor was subsequently replaced (yellow shaded region). A, Glucose; and B, insulin delivery (left axis); measured and predicted insulin concentration (right axis). BG, Blood glucose; SG, sensor glucose; SG 2, replacement sensor; CHO, supplemental carbohydrate.
Fig. 4.
Fig. 4.
Glucose profiles obtained in four subjects requiring supplemental carbohydrate on at least one occasion (A–D) together with profiles obtained in two subjects with no interventions (E and F). BG, Blood glucose; SG, sensor glucose; CHO, supplemental carbohydrate.
Fig. 5.
Fig. 5.
Plasma cortisol (A) and FFA levels (B) during closed-loop control.
Fig. 6.
Fig. 6.
Comparison of open and closed-loop control during the nighttime.

Source: PubMed

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