System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Review Ppublish 2 citations

PubMed: 40590459 DOI: 10.1177/19322968251338300

Metabolic Models, in Silico Trials, and Algorithms.

J Diabetes Sci Technol 19(4):895-907

Authors: Ali Cinar, Ananda Basu, B Wayne Bequette, Marc D Breton, Bruce Buckingham, Eda Cengiz, Claudio Cobelli, Eyal Dassau, Francis J Doyle, Chiara Fabris, Andrea Facchinetti, Irl Hirsch, Roman Hovorka, Peter G Jacobs, Boris P Kovatchev, Chiara Dalla Man, Laurie Quinn, Jay Skyler

Affiliations

  • Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA.
  • The University of Alabama at Birmingham, Birmingham, AL, USA.
  • Rensselaer Polytechnic Institute, Troy, NY, USA.
  • University of Virginia, Charlottesville, VA, USA.
  • Stanford University, Stanford, CA, USA.
  • University of California, San Francisco, San Francisco, CA, USA.
  • University of Padova, Padova, Italy.
  • Harvard University, Boston, MA, USA.
  • Brown University, Providence, RI, USA.
  • University of Washington, Seattle, WA, USA.
  • University of Cambridge, Cambridge, UK.
  • CamDiab Ltd. Cambridge, UK.
  • Oregon Health and Science University, Portland, OR, USA.
  • Oregon State University, Corvallis, OR, USA.
  • University of Illinois Chicago, Chicago, IL, USA.
  • University of Miami Diabetes Research Institute, Miami, FL, USA.

Abstract

Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis-meals, physical activities, acute stress, and variations in sleep characteristics. A section is devoted to discuss the unique glycemic management challenges faced by women with diabetes across the lifespan (menstrual cycle, menopause, pregnancy) and summarize progress made to reduce their impact on glycemic management.

MeSH

Diabetes Mellitus, Exercise, Pancreas, Artificial, Insulin, Algorithms, Meals