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. 2024 Sep;18(5):1208-1244.
doi: 10.1177/19322968241235205. Epub 2024 Mar 25.

Diabetes Technology Meeting 2023

Tiffany Tian  1 , Rachel E Aaron  1 , Ashley Y DuNova  1 , Johan H Jendle  2 , David Kerr  3 , Eda Cengiz  4 , Andjela Drincic  5 , John C Pickup  6 , Kong Y Chen  7 , Naomi Schwartz  8 , Douglas B Muchmore  9 , Halis K Akturk  10 , Carol J Levy  11 , Signe Schmidt  12 , Riccardo Bellazzi  13 , Alan H B Wu  14 , Elias K Spanakis  15 , Bijan Najafi  16 , James Geoffrey Chase  17 , Jane Jeffrie Seley  18 , David C Klonoff  19
Affiliations

Diabetes Technology Meeting 2023

Tiffany Tian et al. J Diabetes Sci Technol. 2024 Sep.

Abstract

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Keywords: artificial intelligence; automated insulin delivery systems; continuous glucose monitoring; diabetes drugs; diabetes technology.

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Conflict of interest statement

Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: JHJ was also a speaker for Eli Lilly, Boehringer Ingelheim, Nordic Infucare, Novo Nordisk, Sanofi, and an advisory board member for Abbott, Eli Lilly, and Medtronic. DK has received research support from Abbott Diabetes Care and has been a Consultant to Sanofi, Better Therapeutics and Abbott Diagnostics. EC is an advisory board member and consultant for Novo Nordisk, Eli Lilly, Adocia, MannKind, Lexicon, Arecor. EC was also a speaker for Novo Nordisk. JP reports consultancy fees from Medtronic. NS is an employee of Medcrypt. DM is a consultant for Capillary Biomedical and a shareholder in Halozyme Therapeutics. HKA received research grant support through University of Colorado from Medtronic, Tandem Diabetes, Eli Lilly, Mannkind, Dexcom and consultation fees through University of Colorado from Tandem Diabetes, Medtronic and Dexcom. CJL has received research support by the NIDDK and Helmsley Foundation and industry support paid to the Icahn School of Medicine at Mount Sinai from Abbott Diabetes, Dexcom, Insulet, Novo Nordisk, Mannkind, Senseonics, and Tandem. CJL has received consulting fees from Eli Lilly, and Dexcom outside of this work. RB is a shareholder of Biomeris s.r.l. and Engenome s.r.l. EKS was partially supported by the VA MERIT award (#1I01CX001825) and CSP #2002 from the US Department of Veterans Affairs. EKS has received unrestricted research support from Dexcom and Tandem (to Baltimore VA Medical Center and to University of Maryland) for the conduction of clinical trials. EKS has received fees from the Medscape and the Endocrine Society (ESAP). BN is serving as a consultant for BioSensics LLC. JJS is a consultant for Lifescan Diabetes Institute. DCK is a consultant for Afon, Better Therapeutics, Integrity, Lifecare, Nevro, Novo, and Thirdwayv. TT, REA, AYD, AD, KYC, SS, AHBW, JGC have nothing to disclose.

Figures

Figure 1.
Figure 1.
Mean absolute relative differences of the same CGM system observed in performance studies published between 2015 and 2022. Abbreviations: CGM, continuous glucose monitor; MARD, mean absolute relative difference. Source: Figure reproduced from the work of Freckmann et al.
Figure 2.
Figure 2.
Examples of the top iPhone diabetes applications in the Apple App Store as of January 11, 2024. Source: Apple App Store.
Figure 3.
Figure 3.
An image generated by artificial intelligence of (a) a chatbot and (b) a health care professional with the assistance of DALL·E 2. Source: Figure courtesy of David Kerr.
Figure 4.
Figure 4.
The Glycemia Risk Index Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (zeroth-20th percentile) to the worst (81st-100th percentile) overall quality of glycemia. Source: Glycemia Risk Index. Diabetes Technology Society. https://www.diabetestechnology.org/gri/.
Figure 5.
Figure 5.
A deep learning system predicts parameters and biomarker levels based on external eye photographs. The results are shown for experiments where different regions of the images are masked or the image color is removed. Abbreviations: ACR, albumin-to-creatinine ratio; AST, aspartate aminotransferase; AUC, area under the receiver-operating characteristic curve; DLS, deep learning system; eGFR, estimated glomerular filtration rate; N, number of positive datapoints; n, number of datapoints; TSH, thyroid stimulating hormone; WBC, white blood cells. Source: Figure reproduced from Babenko et al under the Creative Commons Attribution Non-Commercial No-Derivatives 4.0 International License (CC BY-NC-ND 4.0, https://creativecommons.org/licenses/by-nc-nd/4.0/).
Figure 6.
Figure 6.
Diabetes Data and Technology Integration Frame. Source: Figure reproduced from Espinoza et al (https://www.frontiersin.org/articles/10.3389/fcdhc.2022.867284/full) under the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0, https://creativecommons.org/licenses/by/4.0/).
Figure 7.
Figure 7.
A conceptual model of precision monitoring in diabetes. Source: Figure reproduced from Hermanns et al under the Creative Commons Attribution 4.0 International License (CC-BY 4.0, https://creativecommons.org/licenses/by/4.0/).
Figure 8.
Figure 8.
A Clarke error grid analysis of continuous glucose monitor (test) and point-of-care (reference) data pairs collected from patients with type 1 diabetes and type 2 diabetes in a non-intensive care unit hospital setting. Source: Figure reproduced from Spierling Bagsic et al.
Figure 9.
Figure 9.
Eight elements of a decentralized clinical trial.
Figure 10.
Figure 10.
Two approaches to combining glucose monitoring and insulin infusion at the same body site through (a) a single-port and (b) a dual-port device. Labels: a, integrated body-worn device; b, dermis; c, subcutaneous tissue; d, glucose sensor with glucose sensitive tip (red); e, insulin catheter. Source: Figure reproduced from Schoemaker et al.
Figure 11.
Figure 11.
Blood glucose data can be transferred from the glucose meter via Bluetooth, mobile health apps, and the cloud and be viewed in aggregator software and the electronic health record. Source: Figure reproduced from Crossen et al (https://diabetes.jmir.org/2022/1/e33639) under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Figure 12.
Figure 12.
Self-reported hemoglobin A1C and time in range of adults and children with diabetes, before and after an open-source automated insulin delivery system was implemented. Abbreviations: DIYAPS, do-it-yourself artificial pancreas system; HbA1C, hemoglobin A1c. Source: Figure reproduced from Braune et al (https://www.jmir.org/2021/6/e25409) under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Figure 13.
Figure 13.
A summary of the benefits of dual glucagon-like peptide-1 receptor agonist and glucose-dependent insulinotropic polypeptide receptor agonist therapy in different organ systems in type 2 diabetes. Source: Figure reproduced from Samms et al (https://www.sciencedirect.com/science/article/pii/S1043276020300485) under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Figure 14.
Figure 14.
Advancements in connected devices, artificial intelligence, and computing power promote the development of improved therapies for diabetes treatments. Source: Figure reproduced from Jacobs et al under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). Abbreviations: AGP, ambulatory glucose profile; AI, artificial intelligence; CGM, continuous glucose monitor; GLP-1, glucagon-like peptide 1; EHR, electronic health record; MDI, multiple daily injection; ML, machine learning; SGLT2i, sodium-glucose cotransporter 2 inhibitor.
Figure 15.
Figure 15.
Differences in average photoplethysmography cycles during low glucose (blue) and high glucose (red) periods for four subjects (S1-S4). The photoplethysmography signal was recorded from a non-invasive in-ear sensor. Shaded areas correspond to confidence intervals, and the green bar denotes statistical significance (P < .01). Source: Figure reproduced from Hammour and Mandic under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Figure 16.
Figure 16.
Commercial continuous glucose monitors discussed at the 2023 Diabetes Technology Meeting. -
Figure 17.
Figure 17.
A summary of the (a) number and (b) type of errors made by 147 people across Canada with type 1 diabetes or type 2 diabetes while injecting insulin. Source: Figure reproduced from Bari et al under the Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/).
Figure 18.
Figure 18.
A smart offloading system for people with diabetic foot ulcers, consisting of a smart removable cast walker, a sensor, and a watch that provides the user with adherence information, notifications, and feedback. Source: Figure reproduced from Finco et al under the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/).
Figure 19.
Figure 19.
Schematic diagram of the mobile version of the House of Carbs Personalized Carbohydrate Dispenser for People with Diabetes. Source: Figure courtesy of Eirik Årsand, PhD, UiT Arctic University of Norway, Tromsø, Norway.
See this image and copyright information in PMC

References

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