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Objective: To determine whether continuous glucose monitoring (CGM)-derived glycemic patterns can characterize pregnancies with gestational diabetes mellitus (GDM) as diagnosed by standard oral glucose tolerance test at 24-28 weeks' gestation compared with those without GDM., Research Design and Methods: The analysis includes 768 individuals enrolled from two sites prior to 17 weeks' gestation between June 2020 and December 2021 in a prospective observational study. Participants wore blinded Dexcom G6 CGMs throughout gestation. Main outcome of interest was a diagnosis of GDM by oral glucose tolerance test (OGTT). Glycemic levels in participants with GDM versus without GDM were characterized using CGM-measured glycemic metrics., Results: Participants with GDM (n = 58 [8%]) had higher mean glucose (109 ± 13 vs. 100 ± 8 mg/dL [6.0 ± 0.7 vs. 5.6 ± 0.4 mmol/L], P < 0.001), greater glucose SD (23 ± 4 vs. 19 ± 3 mg/dL [1.3 ± 0.2 vs. 1.1 ± 0.2 mmol/L], P < 0.001), less time in range 63-120 mg/dL (3.5-6.7 mmol/L) (70% ± 17% vs. 84% ± 8%, P < 0.001), greater percent time >120 mg/dL (>6.7 mmol/L) (median 23% vs. 12%, P < 0.001), and greater percent time >140 mg/dL (>7.8 mmol/L) (median 7.4% vs. 2.7%, P < 0.001) than those without GDM throughout gestation prior to OGTT. Median percent time >120 mg/dL (>6.7 mmol/L) and time >140 mg/dL (>7.8 mmol/L) were higher as early as 13-14 weeks of gestation (32% vs. 14%, P < 0.001, and 5.2% vs. 2.0%, P < 0.001, respectively) and persisted during the entire study period prior to OGTT., Conclusions: Prior to OGTT at 24-34 weeks' gestation, pregnant individuals who develop GDM have higher CGM-measured glucose levels and more hyperglycemia compared with those who do not develop GDM., (© 2024 by the American Diabetes Association.)

Numerous studies have demonstrated the clinical value of continuous glucose monitoring (CGM) in type 1 diabetes (T1D) and type 2 diabetes (T2D) populations. However, the eligibility criteria for CGM coverage required by the Centers for Medicare & Medicaid Services (CMS) ignore the conclusive evidence that supports CGM use in various diabetes populations that are currently deemed ineligible. In an earlier article, we discussed the limitations and inconsistencies of the agency's CGM eligibility criteria relative to current scientific evidence and proposed practice solutions to address this issue and improve the safety and care of Medicare beneficiaries with diabetes. Although Medicaid is administered through CMS, there is no consistent Medicaid policy for CGM coverage in the United States. This article presents a rationale for modifying and standardizing Medicaid CGM coverage eligibility across the United States., 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: R.J.G. is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health (NIH) under award numbers 2P30DK1110246 and K23DK123384-3. R.J.G. received research support to Emory University for investigator-initiated studies from Novo Nordisk, Dexcom, and Eli-Lilly and consulting fees from Sanofi, Eli-Lilly, Boehringer, Pfizer, and Weight Watchers, outside of this work. G.A. has received research support to Northwestern University from Astra Zeneca, Dexcom, Eli-Lilly, Emmes, Fractyl Health, Insulet, Novo Nordisk, and WellDoc. G.A. has received consulting fees from Eli-Lilly, Dexcom, and Insulet outside of this work. C.G.P. has received consulting fees from Abbott Diabetes Care, CeQur, Dexcom, LifeScan, Mannkind, Roche Diabetes Care, and ProventionBio. D.A.B. has no disclosures associated with this manuscript. A.L.C. has received research support from UnitedHealthcare, Abbott, Dexcom, Eli-Lilly, Insulet, Medtronic, Novo Nordisk, and Sanofi, and is a consultant for Medtronic and Insulet, with all financial support going to his institution. In addition, ALC has a patent, Treatment of Hypoglycemia Unawareness with Intranasal Insulin, pending to HealthPartners Institute. EC is a scientific advisor for Eli-Lilly, Novo Nordisk, Adocia, and Arecor. G.P.F. received research support from Medtronic, Dexcom, Abbott, Tandem, Insulet, Beta Bionics, and Lilly, and has served as a consultant, speaker, and advisory board member for Medtronic, Dexcom, Abbott, Tandem, Insulet, Beta Bionics, and Lilly. D.F.K. institution has received research support from NIH, Helmsley Foundation, Novo Nordisk, Abbott Diabetes, and Dexcom. D.F.K. has received consulting fees from Novo Nordisk, Abbott Diabetes, Mannkind, CeQur, Sanofi, Medical Module, ProventionBio, and Pendulum. D.F.K. is the speaker for Dexcom, Novo Nordisk, Sanofi-Aventis, Xeris, and CeQur. D.F.K. has stock options not redeemed in Pendulum. C.J.L. 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, Senseonics, and Tandem. C.J.L. has received consulting fees from Eli-Lilly, and Dexcom outside of this work. J.B.M. has received research support from the NIH, Helmsley Foundation, JDRF, Novo Nordisk, and Beta Bionics. J.B.M. has received consulting fees from Bayer, Boehringer Ingelheim and Lilly, Mannkind, Novo Nordisk, and Thermo Fisher outside of this work. G.E.U. is partly supported by research grants from the National Institutes of Health (NIH/NATS UL 3UL1TR002378-05S2) from the Clinical and Translational Science Award program, and from National Institutes of Health and National Center for Research Resources (NIH/NIDDK 2P30DK111024-06). G.E.U. has received research support (to Emory University) from Astra Zeneca, Bayer, Abbott, and Dexcom.

Introduction: To characterize glucose levels during uncomplicated pregnancies, defined as pregnancy with a hemoglobin A1c <5.7% (<39 mmol/mol) in early pregnancy, and without a large-for-gestational-age birth, hypertensive disorders of pregnancy, or gestational diabetes mellitus (ie, abnormal oral glucose tolerance test)., Research Design and Methods: Two sites enrolled 937 pregnant individuals aged 18 years and older prior to reaching 17 gestational weeks; 413 had an uncomplicated pregnancy (mean±SD body mass index (BMI) of 25.3±5.0 kg/m 2 ) and wore Dexcom G6 continuous glucose monitoring (CGM) devices throughout the observed gestational period. Mealtimes were voluntarily recorded. Glycemic levels during gestation were characterized using CGM-measured glycemic metrics., Results: Participants wore CGM for a median of 123 days each. Glucose levels were nearly stable throughout all three trimesters in uncomplicated pregnancies. Overall mean±SD glucose during gestation was 98±7 mg/dL (5.4±0.4 mmol/L), median per cent time 63-120 mg/dL (3.5-6.7 mmol/L) was 86% (IQR: 82-89%), median per cent time <63 mg/dL (3.5 mmol/L) was 1.8%, median per cent time >120 mg/dL (6.7 mmol/L) was 11%, and median per cent time >140 mg/dL (7.8 mmol/L) was 2.5%. Mean post-prandial peak glucose was 126±22 mg/dL (7.0±1.2 mmol/L), and mean post-prandial glycemic excursion was 36±22 mg/dL (2.0±1.2 mmol/L). Higher mean glucose levels were low to moderately associated with pregnant individuals with higher BMIs (103±6 mg/dL (5.7±0.3 mmol/L) for BMI ≥30.0 kg/m 2 vs 96±7 mg/dL (5.3±0.4 mmol/L) for BMI 18.5-<25 kg/m 2 , r=0.35)., Conclusions: Mean glucose levels and time 63-120 mg/dL (3.5-6.7 mmol/L) remained nearly stable throughout pregnancy and values above 140 mg/dL (7.8 mmol/L) were rare. Mean glucose levels in pregnancy trend higher as BMI increases into the overweight/obesity range. The glycemic metrics reported during uncomplicated pregnancies represent treatment targets for pregnant individuals., Competing Interests: Competing interests: ALC reports no personal financial disclosures but reports that his institution has received funding on his behalf as follows: research support from Medtronic, Tandem, Insulet, Abbott, Dexcom, Eli Lilly, NovoNordisk, Sanofi and UnitedHealth Group and consultancy fees from Mannkind and NovoNordisk. RWB reports no personal financial disclosures but reports that his institution has received funding on his behalf as follows: grant funding and study supplies from Dexcom. RMB has received research support, has acted as a consultant, or has been on the scientific advisory board for Abbott Diabetes Care, Ascensia, Bigfoot Biomedical, CeQur, Dexcom, Eli Lilly, Embecta, Hygieia, Insulet, Medtronic, Novo Nordisk, Onduo, Roche Diabetes Care, Tandem Diabetes Care, Sanofi, UnitedHealthcare, Vertex Pharmaceuticals and Zealand Pharma. RMB’s employer, non-profit HealthPartners Institute, contracts for his services and he receives no personal income for any of these activities. CD reports advisory work for Dexcom for GDM patient-facing materials and system implementation., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: Evidence supporting use of continuous glucose monitoring in type 2 diabetes treated with basal insulin is unclear. This real-world study aimed to assess the impact on glycated hemoglobin (HbA1c) of flash glucose monitoring use in adults with type 2 diabetes managed with basal insulin., Research Design and Methods: Medical records were reviewed for adult individuals with type 2 diabetes using basal insulin for ≥1 year with or without additional antihyperglycemic medication, HbA1c 8.0%-12.0% prior to FreeStyle Libre Flash Glucose Monitoring use for ≥90 days and an HbA1c measurement recorded between 90 and 194 days after device use. Exclusion criteria included utilization of bolus insulin. Meta-analysis data are from the current study (USA) and a similar Canadian cohort., Results: Medical record analysis (n=100) from 8 USA study sites showed significant HbA1c decrease of 1.4%±1.3%, from 9.4%±1.0% at baseline to 8.0%±1.2% after device use, p<0.0001 (mean±SD).Meta-analysis of medical records from USA and Canada sites (n=191) showed HbA1c significantly decreased by 1.1%±0.14% (mean±SE), from baseline 9.2%±1.0% to 8.1%±1.1%, p≤0.0001, with moderate to high heterogeneity between sites (Q=43.9, I 2 =74.9, p<0.0001) explained by differences in baseline HbA1c between sites.The HbA1c improvement in both groups was observed by age group, body mass index, duration of insulin use and sex at birth., Conclusions: In a real-world retrospective USA study and a meta-analysis of a larger USA and Canada cohort, HbA1c significantly reduced in basal insulin-treated type 2 diabetes, without bolus insulin initiation and following the commencement of flash glucose monitoring technology., Competing Interests: Competing interests: ALC received research support, acted as a consultant or served on a scientific advisory board for Abbott Diabetes, Dexcom, Eli Lilly, Insulet, Medtronic, Novo Nordisk, Sanofi, Senseonics and United Healthcare, but did not receive any personal income. ALC is on the Board of Directors, JDRF Minnesota and Dakota chapter. TDD has served on a scientific advisory board for Abbott Diabetes. The healthcare system that ADeS works for receives research funding, but that money is not part of her current compensation arrangement, nor does she receive additional personal income. SJ, EKF and EM declare no relevant or material financial interests relating to the research described in this paper. DK has received research support and acted as a consultant for Abbott Diabetes. KO received research support from Abbott Diabetes but did not receive any personal income. KO received grants or served on a scientific advisory board for Eli Lilly, Novo Nordisk and Sanofi. KO is President of the Texas AACE chapter. TE has participated in advisory boards and received honoraria from Abbott Laboratories., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Use of telemedicine and remote monitoring technologies can significantly improve glycemic control in patients with type 1 diabetes and type 2 diabetes. Patients' ability to interact remotely with their health care providers via smartphones and other communication devices can increase their access to clinical care and online coaching and support programs. The establishment of metrics for clinical use of continuous glucose monitoring data and standardization of data reporting has enabled clinicians to maintain high-quality diabetes care through remote monitoring and telemedicine visits during the COVID-19 pandemic. This article discusses our experiences using remote monitoring and telemedicine visits during this time.

Background: As the use of continuous glucose monitoring (CGM) increases, there is a need to better understand key metrics of time in range 70-180 mg/dL (TIR 70-180 ) and hyperglycemia and how they relate to hemoglobin A1c (A1C)., Methods: Analyses were conducted utilizing datasets from four randomized trials encompassing 545 adults with type 1 diabetes (T1D) who had central-laboratory measurements of A1C. CGM metrics were calculated and compared with each other and A1C cross-sectionally and longitudinally., Results: Correlations among CGM metrics (TIR 70-180 , time >180 mg/dL, time >250 mg/dL, mean glucose, area under the curve above 180 mg/dL, high blood glucose index, and time in range 70-140 mg/dL) were typically 0.90 or greater. Correlations of each metric with A1C were lower (absolute values 0.66-0.71 at baseline and 0.73-0.78 at month 6). For a given TIR 70-180 percentage, there was a wide range of possible A1C levels that could be associated with that TIR 70-180 level. On average, a TIR 70-180 of 70% and 50% corresponded with an A1C of approximately 7% and 8%, respectively. There also was considerable spread of change in A1C for a given change in TIR 70-180 , and vice versa. An increase in TIR 70-180 of 10% (2.4 hours per day) corresponded to a decrease in A1C of 0.6%, on average., Conclusions: In T1D, CGM measures reflecting hyperglycemia (including TIR and mean glucose) are highly correlated with each other but only moderately correlated with A1C. For a given TIR or change in TIR there is a wide range of possible corresponding A1C values.

Purpose of Review: In this article, we consider advanced technologies for the management of diabetes., Recent Findings: Specifically, we pose the question of which should come first: an insulin pump (CSII) or a continuous glucose monitor (CGM)? Historical perspective on both insulin delivery and glucose measurement is provided. Recently published clinical trials are reviewed. Practical issues including quality of life, patient education, and out-of-pocket cost are discussed. Based on available evidence and clinical experience, we favor CGM as a first-line technology recommendation for the treatment of type 1 diabetes (T1D).

Use of continuous glucose monitoring (CGM) is recognized as a valuable component of diabetes self-management and is increasingly considered a standard of care for individuals with diabetes who are treated with intensive insulin therapy. As the clinical use of CGM technology expands, consistent and standardized glycemic metrics and glucose profile visualization have become increasingly important. A common set of CGM metrics has been proposed by an international expert panel in 2017, including standard definitions of time in ranges, glucose variability, and adequacy of data collection. We describe the core CGM metrics, as well as the standardized glucose profile format consolidating 2 weeks of CGM measurements, referred to as the ambulatory glucose profile (AGP), which was also recommended by the CGM expert panel. We present an updated AGP report featuring the core CGM metrics and a visualization of glucose patterns that need clinical attention. New tools for use by clinicians and patients to interpret AGP data are reviewed. Strategies based on the authors' experience in implementing CGM technology across the clinical care spectrum are highlighted.

Objective: Characterize the effectiveness of insulin glargine alone, exenatide alone, or combined in subjects taking stable doses of metformin and evaluate their impact on hemoglobin A1C, hypoglycemia, weight, and glucose variability. Methods: Open-label, randomized, parallel-arm study of adults with type 2 diabetes naïve to both insulin and glucagon-like peptide 1 (GLP-1) agonist who were not at A1C goal despite treatment with metformin. This prospective interventional study employed blinded continuous glucose monitoring ambulatory glucose profile (AGP) reports over 32 weeks. Subjects were randomized to treatment with glargine (Iglar), exenatide (GLP-1), or combination of glargine and exenatide (Iglar + GLP-1). At midpoint, those not at A1C target had the second medication added; those on Iglar + GLP-1 continued therapy optimization. Results: Decreases in A1C were: 7.6 to 6.2% for Iglar + GLP-1, 7.5 to 6.6% for Iglar, and 7.5 to 6.4% for GLP-1. Iglar + GLP-1 achieved A1C targets faster (14 to 16 weeks) but had more hypoglycemia. Hypoglycemia rates increased slightly for all arms. Weight loss was achieved in all regimens including GLP-1. Glucose variability was not reduced to the same extent in the Iglar arm as the GLP-1 arm. Conclusion: Addition of Iglar and/or GLP-1 to metformin for patients not at treatment goal was safe and effective. The order of medication addition needs to consider individualized AGP patterns and goals. Iglar + GLP-1 resulted in rapid A1C lowering, whereas GLP-1 was noted to have less hypoglycemia. Weight loss was most pronounced in GLP-1 monotherapy, suggesting that GLP-1 may mitigate the weight gain of Iglar. Any treatment with GLP-1 showed significant decreases in glucose variability. Abbreviations: A1C = hemoglobin A1c; AGP = ambulatory glucose profile; CGM = continuous glucose monitoring; GLM = general linear model; GLP-1 = glucagon-like peptide 1 (exenatide); Iglar = insulin glargine; SGLT2 = sodium-glucose cotransporter 2; SMBG = self-monitoring blood glucose; SU = sulfonylurea; T2D = type 2 diabetes mellitus.

Background: Hemoglobin A1c is an excellent population health measure for the risk of vascular complications in diabetes, while continuous glucose monitoring (CGM) is a tool to help personalize a diabetes treatment plan. The value of CGM in individuals with type 1 diabetes (T1D) has been well demonstrated when compared with utilizing self-monitoring of blood glucose (SMBG) to guide treatment decisions. CGM is a tool for patients and clinicians to visualize the important role that diet, exercise, stress management, and the appropriate selection of diabetes medications can have in managing type 2 diabetes (T2D). Several diabetes organizations have recently reviewed the literature on the appropriate use of CGM in diabetes management and concluded CGM may be a useful educational and management tool particularly for patients on insulin therapy. The indications for using CGM either as a clinic-based loaner distribution model for intermittent use (professional CGM) or a CGM system owned by the patient and used at home with real-time glucose reading (personal CGM) are only beginning to be addressed in T2D. Most summaries of CGM studies conclude that having a standardized glucose pattern report, such as the ambulatory glucose profile (AGP) report, should help facilitate effective shared decision-making sessions. The future of CGM indications for the use of CGM is evolving rapidly. In some instances, CGM is now approved for making medication adjustments without SMBG confirmation and it appears that some forms of CGM will be approved for use in the Medicare population in the United States in the near future. Many individuals with T1D and T2D and their care teams will come to depend on CGM as a key tool for diabetes management.