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BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI 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 (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Introduction: Better interoperability is essential to derive maximum benefit from connected wireless diabetes devices. The need for this feature of diabetes devices is supported by trends in designing better performance for three types of devices: nonmedical devices, medical devices, and diabetes devices., Framework: First, interoperability is a standard attribute for the performance of nonmedical devices contained in smart systems that can sense and actuate. Second, interoperability is now mandated by the US Department of Health and Human Services as carried out by the Office of the National Coordinator for Health Information technology (ONC) and the US Food and Drug Administration (FDA) to improve the performance of all medical devices. Third, new guidance from the FDA and nongovernmental professional organizations are intended to promote interoperability because this feature will improve the performance of all diabetes devices., Recommendations: Wireless devices perform best when they are interoperable, and this is particularly true for diabetes devices., Competing Interests: 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: DCK is a consultant for Afon, embecta, Glucotrack, Lifecare, Novo, Samsung, and Thirdwayv. JCE receives federal funding from FDA, NIMHD, and NCATS and is a consultant for Sanofi. REA and TT have no disclosures.

Introduction: Diabetes Technology Society virtually hosted the first meeting of the Integration of Connected Diabetes Device Data Into the Electronic Health Record #2 (iCoDE-2) Standards Project on May 31, 2023, via Zoom., Methods: Clinicians, patients, data aggregators, informaticists, manufacturers, attorneys, and cybersecurity experts discussed dosing data currently available from insulin delivery devices and data aggregators and the types of information that patients and clinicians want to see. This information along with technical and regulatory aspects of (1) data standards and (2) integration into the electronic health record (EHR) are the basis of iCoDE-2., Results: Six sessions were hosted discussing themes including (1) What's Currently Available: The Continuous Glucose Monitor Experience; (2) What's Currently Available: The Aggregator Experience; (3) What's Currently Available: The Insulin Pump Experience; (4) What's Currently Available: The Insulin Pen Experience; (5) What Do Clinicians and Patients Want Out of iCoDE-2 Data?; and (6) Technical and Regulatory Aspects of Data Aggregation., Conclusion: Additional working group meetings to discuss data standard and clinical workflow will be held to create additional technical specifications and clinical workflows to aid in insulin dosing data integration into the EHR. A mid-project meeting will convene in the second half of 2023., Competing Interests: 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: D.C.K. is a consultant for Better Therapeutics, EOFlow, Integrity, Lifecare, Nevro, NovoNordisk, Sanofi, and Thirdwayv. J.C.E. receives federal funding from FDA, NIMHD, and NCATS and is a consultant for Sanofi. R.E.A., T.T., A.M.Y., and J.H. have no disclosures.

Background: Continuous glucose monitor (CGM) systems were originally intended only for people with diabetes. Recently, there has been interest in monitoring glucose concentrations in a variety of other situations. As data accumulate to support the use of CGM systems in additional states unrelated to diabetes, the use of CGM systems is likely to increase accordingly., Methods: PubMed and Google Scholar were searched for articles about the use of CGM in individuals without diabetes. Relevant articles that included sufficient details were queried to identify what cohorts of individuals were adopting CGM use and to define trends of use., Results: Four clinical user cases were identified: (1) metabolic diseases related to diabetes with a primary dysregulation of the insulin-glucose axis, (2) metabolic diseases without a primary pathophysiologic derangement of the insulin-glucose axis, (3) health and wellness, and (4) elite athletics. Seven trends in the use of CGM systems in people without diabetes were idenfitied which pertained to both FDA-cleared medical grade products as well as anticipated future products, which may be regulated differently based on intended populations and indications for use., Conclusions: Wearing a CGM has been used not only for diabetes, but with a goal of improving glucose patterns to avoid diabetes, improving mental or physical performance, and promoting motivate healthy behavioral changes. We expect that clinicians will become increasingly aware of (1) glycemic patterns from CGM tracings that predict an increased risk of diabetes, (2) specific metabolic glucotypes from CGM tracings that predict an increased risk of diabetes, and (3) new genetic and genomic biomarkers in the future., Competing Interests: 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: DCK is a consultant for Eoflow, Eli Lilly, Lifecare, Integrity, Medtronic, Roche Diagnostics, Rockley Photonics, and Thirdwayv. KTN and NYX are consultants for Abbott Diabetes Care. AG is a contract consultant for the ProPharma Group and helps medical device companies with regulatory issues. JCE is a paid consultant for AI Health. JCE’s time is supported in part by the Food and Drug Administration under award number P50FD006425 for The West Coast Consortium for Technology & Innovation in Pediatrics (PI: Espinoza). APV receives CGM product in kind from Dexcom for research conduct. The content is solely the responsibility of the authors and does not necessarily represent the official views of the FDA.

Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.