Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jul;15(4):916-960.
doi: 10.1177/19322968211016480.

Diabetes Technology Meeting 2020

Trisha Shang  1 , Jennifer Y Zhang  1 , B Wayne Bequette  2 , Jennifer K Raymond  3 , Gerard Coté  4 , Jennifer L Sherr  5 , Jessica Castle  6 , John Pickup  7 , Yarmela Pavlovic  8 , Juan Espinoza  3 , Laurel H Messer  9 , Tim Heise  10 , Carlos E Mendez  11 , Sarah Kim  12 , Barry H Ginsberg  13 , Umesh Masharani  12 , Rodolfo J Galindo  14 , David C Klonoff  15
Affiliations

Diabetes Technology Meeting 2020

Trisha Shang et al. J Diabetes Sci Technol. 2021 Jul.

Abstract

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.

Keywords: diabetes; glucose; insulin; meeting; software; technology.

PubMed Disclaimer

Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: TS, JZ, BWB, JKR, GC, YP, CEM, SK, BHG, and UM have nothing to disclose. JLS has had research support from the NIH, JDRF, and the Helmsley Charitable Trust and her institution has had research support from Medtronic and Insulet. JLS serves as a consultant to Cecelia Health, Lexicon, Lilly, Insulet, Medtronic, and Sanofi. She also is a member of the advisory board for Bigfoot Biomedical, Cecelia Health, Insulet, Medtronic, and the T1D Fund. JC is a shareholder of Pacific Diabetes Technologies, has received research support from Dexcom, and participates in the advisory board for Insulet. JP has been an advisory board member for BD and Medtronic, and a speaker for Insulet. JE’s efforts were supported by the Food and Drug Administration under award number P50FD006425 for The West Coast Consortium for Technology & Innovation in Pediatrics. The funding source had no involvement in the development of this manuscript or in the decision to submit the paper for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the FDA. LHM is a consultant and speaker for Tandem Diabetes, a speaker for Dexcom, and a consultant for Capillary Biomedical. TH’s employer has received research grants from Adocia, Biocon, Eli Lilly, Gan Lee Pharmaceuticals, Mylan, Novo Nordisk and Sanofi. TH has also received speaker honoraria from Novo Nordisk, and is an advisory board member for Novo Nordisk. RJG is supported in part by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institute of Health (NIH) under award numbers P30DK111024-04S2 and 5K23DK123384-02. RJG received unrestricted research support (to Emory University) for investigator-initiated studies from Novo Nordisk, Eli Lilly and Dexcom, and consulting fees from Abbott Diabetes Care, Sanofi, Novo Nordisk, Eli Lilly and Valeritas. DCK is a consultant for EOFlow, Fractyl, Lifecare, Novo, Roche, Samsung, and Thirdwayv.

Figures

Figure 1.
Figure 1.
The ecosystem of devices that feed data into CGM-AI, which are then used for a variety of clinical services. Figure provided by Boris Kovatchev, PhD, University of Virginia, Charlottesville, Virginia, USA.
Figure 2.
Figure 2.
An example of building and testing a calibration model. Figure provided by Mark Arnold, PhD, University of Iowa, Iowa City, Iowa, USA.
Figure 3.
Figure 3.
Only two metrics are required for characterization of quality of glycemic control: a measure of safety and one of efficacy. LBGI, Low Blood Glucose Index (or Low Glucose Index); %TAR, %time above range; %TBR, %time below range; %TIR, %time in range. Figure provided by David Rodbard MD, Biomedical Informatics Consultants LLC, Potomac, Maryland, USA
Figure 4.
Figure 4.
A principal component analysis of CGM metrics. Figure provided by Boris Kovatchev, PhD, University of Virginia, Charlottesville, Virginia, USA.
Figure 5.
Figure 5.
Examples of parameters that may be considered as part of a holistic view of the body for patient management. Figure provided by Andreas Pfützner, MD, PhD, Pfützner Science and Health Institute, Mainz, Germany.
Figure 6.
Figure 6.
Framework for evaluating RWD and RWE for use in regulatory decisions. Figure provided by Kenneth Quinto, MD, MPH, FDA, Silver Spring, Maryland, USA.
Figure 7.
Figure 7.
A diagram of a diabetes digital health ecosystem. Figure provided by David Kerr, MBChB, DM, FRCP, FRCPE, Sansum Diabetes Research Institute, Santa Barbara, California, USA.
Figure 8.
Figure 8.
Analysis of data from the 2017 to 2018 T1D Exchange Registry comparing the number of daily boluses in patients with T1D using insulin pumps with or without a CGM. Figure provided by Sam Collaudin, PhD, jMBA, Independent consultant, Marburg, Germany.
Figure 9.
Figure 9.
Diagram of a hepatic-directed insulin vesicle. Figure provided by Bruce Bode, MD, Atlanta Diabetes Associates, Atlanta, Georgia, USA.
Figure 10.
Figure 10.
Body compartment and calibration differences. Figure provided by Guido Freckmann, MD, Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany.
Figure 11.
Figure 11.
NIH SBIR phase 1 applications from WOSBs and non-WOSBs, fiscal years 2005 to 2014. Figure provided by Keesha M. Crosby, MS, Tri-Guard Risk Solutions, LTD, Arlington, Virginia, USA. Reproduced from National Academies Press (USA).
Figure 12.
Figure 12.
Flow of CGM and physical activity tracker data from a device to the EHR. Figure provided by Dessi Zaharieva, PhD, Stanford University, Stanford, California, USA and adapted from Prahalad et al.
Figure 13.
Figure 13.
An example of CGM data and the types of short-term and long-term predictions that can be extrapolated from the data. Figure provided by Chiara Fabris, PhD, University of Virginia, Charlottesville, Virginia, USA.
Figure 14.
Figure 14.
Ways that the values of thermography can be used to prevent DFU. Figure provided by Bijan Najafi, PhD, Baylor College of Medicine, Houston, Texas, USA.
See this image and copyright information in PMC

References

    1. Frankenfield J. How artificial intelligence works. Investopedia. Accessed March 25, 2021. https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp
    1. Battelino T, Danne T, Bergenstal RM, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42(8):1593-1603. doi: 10.2337/dci19-0028 - DOI - PMC - PubMed
    1. Bergenstal RM, Garg S, Weinzimer SA, et al. Safety of a hybrid closed-loop insulin delivery system in patients with type 1 diabetes. JAMA. 2016;316(13):1407-1408. doi: 10.1001/jama.2016.11708 - DOI - PubMed
    1. Brown SA, Kovatchev BP, Raghinaru D, et al. Six-month randomized, multicenter trial of closed-loop control in type 1 diabetes. N Engl J Med. 2019;381(18):1707-1717. doi: 10.1056/NEJMoa1907863 - DOI - PMC - PubMed
    1. Breton MD, Kanapka LG, Beck RW, et al. A randomized trial of closed-loop control in children with type 1 diabetes. N Engl J Med. 2020;383(9):836-845. doi: 10.1056/NEJMoa2004736 - DOI - PMC - PubMed

Publication types