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. 2022 Jul;16(4):1016-1056.
doi: 10.1177/19322968221090279. Epub 2022 May 2.

Diabetes Technology Meeting 2021

Nicole Y Xu  1 , Kevin T Nguyen  1 , Ashley Y DuBord  2 , John Pickup  3 , Jennifer L Sherr  4 , Hazhir Teymourian  5 , Eda Cengiz  2 , Barry H Ginsberg  6 , Claudio Cobelli  7 , David Ahn  8 , Riccardo Bellazzi  9 , B Wayne Bequette  10 , Laura Gandrud Pickett  11 , Linda Parks  2 , Elias K Spanakis  12   13 , Umesh Masharani  2 , Halis K Akturk  14 , John S Melish  15 , Sarah Kim  2 , Gu Eon Kang  16 , David C Klonoff  17
Affiliations

Diabetes Technology Meeting 2021

Nicole Y Xu et al. J Diabetes Sci Technol. 2022 Jul.

Abstract

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.

Keywords: diabetes; digital health; glucose; insulin; software; technology.

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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: NYX is a consultant to Abbott Diabetes Care. KTN is a consultant to Abbott Diabetes Care. JP reports consultancy fees from Medtronic and BD. JLS has conducted clinical trials for Eli Lilly, Insulet, and Medtronic and has received in-kind support for research studies from Dexcom and Medtronic. She has consulted for Eli Lilly, Lexicon, Medtronic, and Sanofi. She has been a member of advisory boards for Bigfoot Biomedical, Cecelia Health, Eli Lilly, Insulet, the T1D Fund, and Vertex. EC is an advisory board member and consultant for Novo Nordisk, Adocia, Eli Lilly, and Arecor. DA has received speaker’s honoraria from Lilly Diabetes, Novo, Xeris Pharmaceuticals, and Zealand Pharma. He has received consulting fees from Mannkind, Ascensia Diabetes Care, Roche Diagnostics, and Senseonics. RB is shareholder of Biomeris s.r.l, a university spin-off dealing with clinical research informatics. Moreover, he has received consulting fees from Merck Healthcare to support the design of decision support tools. EKS reports that this work was supported in part by a VA MERIT award from the U.S. Department of Veterans Affairs Clinical Sciences Research and Development Service (1I01CX001825). He has received unrestricted research support from Dexcom (to the Baltimore VA Medical Center and to the University of Maryland) for the conduction of clinical trials. UM reports research funding from Clementia Pharmaceutical and is an advisor for Ryse Health. HKA received research support through University of Colorado from Dexcom, Senseonics, Medtronic, Eli Lilly, Mannkind. DCK is a consultant to AI Health, Dexcom, Eli Lilly, EOFlow, Integrity, Lifecare, Medtronic, Novo, Roche Diagnostics, Rockley Photonics, and Thirdwayv. AYD, HT, BHG, CC, LGP, LP, RB, BWB, JSM, SK, and GEK have nothing relevant to disclose.

Figures

Figure 1.
Figure 1.
The chemical reaction for a new optical transmission absorbance system for blood glucose monitoring. Abbreviation: FADGDH, flavin adenine dinucleotide-dependent glucose dehydrogenase. Source: Figure reprinted from Moriuchi et al.
Figure 2.
Figure 2.
Sampling arrangements of different types of continuous glucose monitors compared with self-monitoring of blood glucose. Interstitial fluid reference measurements are currently not feasible. Abbreviation: SMBG, self-monitoring of blood glucose. Source: Figure reprinted from Freckmann et al, with permission from Elsevier.
Figure 3.
Figure 3.
A metrological traceability diagram where the glucose concentration of interstitial fluid measured by a continuous glucose monitor is not necessarily comparable to the glucose concentration of simultaneously obtained capillary or venous blood.
Figure 4.
Figure 4.
Plot of interstitial fluid ketone values measured by the wearable continuous ketone monitor sensors against capillary ketone strip reference measurements. Source: Figure reprinted from Alva et al.
Figure 5.
Figure 5.
Hybrid healthcare promotes simplified access to healthcare. Abbreviations: ER, emergency room; PCP, primary care physician. Source: Image courtesy of CareHive Health Inc.
Figure 6.
Figure 6.
The four domains to address as part of the roadmap to diabetes technology adoption. Source: Figure provided by Robert Gabbay, MD, PhD, American Diabetes Association, Arlington, VA, USA.
Figure 7.
Figure 7.
A glucose frequency distribution with a histogram for all times of day (blue bar chart), which is asymmetrical and skewed to the right, and the corresponding cumulative frequency distribution (pink curve). Source: Figure reprinted from Rodbard.
Figure 8.
Figure 8.
A diabetes technology integration framework consisting of eight concepts for integration of CGM data into the EHR. Abbreviations: CGM, continuous glucose monitor; EHR, electronic health record; FHIR, Fast Healthcare Interoperability Resources; IRB, Institutional Review Board; IT, information technology; SMART, Substitutable Medical Applications, Reusable Technologies. Source: Figure reprinted from Espinoza et al.
Figure 9.
Figure 9.
The total product lifecycle for medical devices. Abbreviation: IDE, Investigational Device Exemption. Source: Figure provided by Mike Waters, PhD, FDA, Silver Spring, MD, USA.
Figure 10.
Figure 10.
The diabetes optimization plane for continuous glucose monitoring data is defined by two principal dimensions or components, hypoglycemia and hyperglycemia risk, which explain around 90% of the variance of diabetes data. Source: Figure provided by Boris Kovatchev, PhD, University of Virginia, Charlottesville, Virginia, USA.
Figure 11.
Figure 11.
The current trend of data (current day’s evolving pattern) can be compared with historical data to find the most similar scenarios for the realization of unknown and unmodeled disturbances. Source: Figure reprinted from Askari et al, with permission from Elsevier.
Figure 12.
Figure 12.
Comparison of different insulin pen needle wall thicknesses. Source: Figure reprinted from Aronson et al, with permission from Elsevier.
Figure 13.
Figure 13.
Left: a patch test with (a) continuous glucose monitor adhesive that sticks to the skin, (b) double layer of adhesive, and (c) thin-layer adhesive that binds continuous glucose monitor to the adhesive. Right: a positive skin reaction from the patch test. Source: Figure reprinted from Kamann et al.
Figure 14.
Figure 14.
Metabesity is the constellation of chronic diseases and cancer, which all share metabolic root causes and can therefore be targeted collectively. Abbreviation: NASH, non-alcoholic steatohepatitis. Source: Figure provided by Alexander Fleming, MD, Kinexum Services LLC, Harpers Ferry, WV, USA.
Figure 15.
Figure 15.
Technology uses in pregnancy. Abbreviation: EMR, Electronic Medical Record. Source: Figure provided by Lynn Yee, MD, MPH, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Figure 16.
Figure 16.
Apparatus and app for taking a Foot Selfie. Source: Figure provided by Mark Swerdlow, MS, University of Southern California, Los Angeles, CA, USA.
See this image and copyright information in PMC

References

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