Fully integrated artificial pancreas in type 1 diabetes: modular closed-loop glucose control maintains near normoglycemia

Marc Breton, Anne Farret, Daniela Bruttomesso, Stacey Anderson, Lalo Magni, Stephen Patek, Chiara Dalla Man, Jerome Place, Susan Demartini, Simone Del Favero, Chiara Toffanin, Colleen Hughes-Karvetski, Eyal Dassau, Howard Zisser, Francis J Doyle 3rd, Giuseppe De Nicolao, Angelo Avogaro, Claudio Cobelli, Eric Renard, Boris Kovatchev, International Artificial Pancreas Study Group, Marc Breton, Anne Farret, Daniela Bruttomesso, Stacey Anderson, Lalo Magni, Stephen Patek, Chiara Dalla Man, Jerome Place, Susan Demartini, Simone Del Favero, Chiara Toffanin, Colleen Hughes-Karvetski, Eyal Dassau, Howard Zisser, Francis J Doyle 3rd, Giuseppe De Nicolao, Angelo Avogaro, Claudio Cobelli, Eric Renard, Boris Kovatchev, International Artificial Pancreas Study Group

Abstract

Integrated closed-loop control (CLC), combining continuous glucose monitoring (CGM) with insulin pump (continuous subcutaneous insulin infusion [CSII]), known as artificial pancreas, can help optimize glycemic control in diabetes. We present a fundamental modular concept for CLC design, illustrated by clinical studies involving 11 adolescents and 27 adults at the Universities of Virginia, Padova, and Montpellier. We tested two modular CLC constructs: standard control to range (sCTR), designed to augment pump plus CGM by preventing extreme glucose excursions; and enhanced control to range (eCTR), designed to truly optimize control within near normoglycemia of 3.9-10 mmol/L. The CLC system was fully integrated using automated data transfer CGM→algorithm→CSII. All studies used randomized crossover design comparing CSII versus CLC during identical 22-h hospitalizations including meals, overnight rest, and 30-min exercise. sCTR increased significantly the time in near normoglycemia from 61 to 74%, simultaneously reducing hypoglycemia 2.7-fold. eCTR improved mean blood glucose from 7.73 to 6.68 mmol/L without increasing hypoglycemia, achieved 97% in near normoglycemia and 77% in tight glycemic control, and reduced variability overnight. In conclusion, sCTR and eCTR represent sequential steps toward automated CLC, preventing extremes (sCTR) and further optimizing control (eCTR). This approach inspires compelling new concepts: modular assembly, sequential deployment, testing, and clinical acceptance of custom-built CLC systems tailored to individual patient needs.

Figures

FIG. 1.
FIG. 1.
Design and profile of randomized clinical trials and timeline of inpatient admissions.
FIG. 2.
FIG. 2.
Modular architecture of CTR.
FIG. 3.
FIG. 3.
Primary outcomes of sCTR: Time in near normoglycemia (3.9–10 mmol/L), average glucose, intrasubject variability, and occurrence of hypoglycemia (hypo) during open- and closed-loop admissions, contrasted by overall and overnight periods. *P < 0.05. Open-loop CSII, gray bar; sCTR, black bar.
FIG. 4.
FIG. 4.
Primary outcomes of eCTR: Time in near normoglycemia (3.9–10 mmol/L) and tight control (4.4–7.78 mmol/L), average glucose, and intrasubject variability during open- and closed-loop admissions, contrasted by overall and overnight periods. *P < 0.05. Open-loop CSII, gray bar; eCTR, black bar.
FIG. 5.
FIG. 5.
Mean (curves) and 25–75% quantiles (shaded areas) of plasma glucose for each algorithm comparing open-loop CSII and closed-loop admissions. Glycemic ranges are depicted by the bounds (plain: near normoglycemia; dotted: tight glucose control).

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Source: PubMed

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