While reference ranges for glucose levels are well-established, no physiological benchmark exists for glucose rates of change (RoC), despite the association between rapid glycemic fluctuations and adverse health outcomes. We aimed to define normative RoC values by analyzing continuous glucose monitoring (CGM) data from 153 healthy, nondiabetic individuals (Dexcom G6, up to 10 days). We calculated the percentage of time spent exceeding various RoC thresholds over 5-, 15-, 30-, and 60-min intervals, stratifying results by age and time of day. Over 15 min, the median time with RoC exceeding ±2 mg/dL/min was minimal (1.4% rising, 1.0% falling). RoC was slower when measured over longer intervals, faster when rising than falling, faster during daytime hours, and exhibited modest differences across age groups. We propose a RoC of ±2 mg/dL/min over 15 min as a normative reference, analogous to the 70-140 mg/dL glucose range.

Aims: This study aimed to evaluate how pre-exercise glucose levels and menstrual cycle phase influence glucose responses to aerobic exercise in adults with type 1 diabetes (T1D) treated with multiple daily insulin injections, with the goal of improving personalized exercise management. Materials and Methods: We analyzed 51 moderate-intensity, 30-min aerobic sessions (17 male, 34 female) from the TAILOR/1a study. Glucose (plasma and interstitial), heart rate, and anthropometric data were collected. Female participants performed sessions in both the follicular and luteal phases. Glucose trends during exercise were clustered using k-medoids and Dynamic Time Warping. Features from clinical, glucose, and heart rate data were extracted and correlated with cluster assignment. Results: Two distinct glucose response patterns emerged: descending and stable. Higher pre-exercise glucose levels were associated with greater glucose decline and increased hypoglycemia risk, particularly in men. Female participants more frequently exhibited stable glucose profiles, particularly during the luteal phase. Fitness and body composition influenced cluster assignment: fitter individuals-particularly women-were more likely to exhibit stable glucose trends. Pre-exercise glucose was the strongest predictor of response. The menstrual cycle phase had a modest but noticeable effect on glucose dynamics. Conclusions: Glucose response to exercise in T1D is highly variable and influenced by pre-exercise glycemia, sex, fitness level, and menstrual cycle phase. Women, particularly during the luteal phase, demonstrated more stable glycemic responses. These findings support the need for individualized exercise recommendations and for integrating physiological and behavioral factors into predictive models for automated insulin dosing and exercise guidance in T1D.

Background: The rapid advancement of diabetes technology, including continuous glucose monitors (CGMs), insulin pumps, and automated insulin delivery systems, has revolutionized diabetes management. However, current care delivery paradigms have not kept pace, prolonging suboptimal health outcomes for youth with type 1 diabetes (T1D). A significant obstacle is the siloed nature of clinical data. This article explores integrating CGM data for multiple vendors into electronic health records (EHRs) to unify diabetes data in health care practices. Methods: This article describes the integration of diabetes device data, following Integration of Continuous Glucose Monitoring Data into the Electronic Health Record (iCoDE) specifications, in the EHR at an urban, tertiary, academic pediatric medical center serving approximately 500,000 pediatric lives in Southwest Ohio. The Diabetes Center provides specialized interdisciplinary care for about 2200 patients with diabetes, with an average of 200+ new onset cases/year. This project is part of the Cincinnati Children's Diabetes Clinic Initiative (ConnecT1D), funded by the Helmsley Charitable Trust, aiming to reorient diabetes care from quarterly visits to continuous, proactive care. Results: By evaluating 6 key factors for integration (data sources types, clinical workflows, level of integration, visualizations, sustainable account management, and optimization), we successfully achieved structural interoperability of CGM device data for 3 vendor platforms into the results section of the EHR using HL7 v2.x. Discussion: We present practical tips to optimize the integration experience: identify the problem, mobilize resources, negotiate contracts early, evaluate and optimize the workflow, celebrate early wins, prepare for (inevitable) stumbling blocks, keep asking questions, implement change management techniques, and evaluate integration acceptance, iterate, and monitor. Conclusion: While beneficial for patients and clinical workflows, integration of vendor CGM data into the EHR currently requires significant resources. Challenges remain in optimizing workflows, mapping data, and vendor variability. Ongoing monitoring, maintenance, and optimization are necessary as technology and workflows evolve.

Medicare's current coverage policy for continuous glucose monitoring (CGM) restricts their use to people with diabetes. This restriction is based on an older National Coverage Determination (NCD 40.2) that limits blood glucose testing to people with diabetes. The CGM coverage policy also requires that CGM be used only in accordance with an Food and Drug Administration (FDA) indication for its use. However, the law, regulation, and subregulatory guidance do not require such a restriction. Multiple conditions unrelated to diabetes are associated with risk of hypoglycemic events, such as postbariatric and other upper gastrointestinal surgery, glycogen storage diseases, kidney and liver failure, neuroendocrine tumors that secrete insulin, other forms of tumor-associated hyperinsulinism, and autoimmune conditions. To avoid life-threatening hypoglycemic events, these patients need access to CGM to monitor their glucose levels. Thus, the Centers for Medicare & Medicaid Services should rescind NCD 40.2. The durable medical equipment Medicare administrative contractors (MACs) responsible for establishing CGM coverage policy should remove the requirement that CGM be used only in accordance with an FDA indication for its use. This would allow the MACs to extend coverage for CGM to populations at high risk for hypoglycemia, as the evidence supports such an approach.

Purpose: Iscalimab is a fully human, monoclonal anti-CD40 antibody that blocks CD154-induced CD40 signaling. Method: In a Phase 2 (2a) study, new-onset stage 3 type 1 diabetes mellitus (T1DM) participants were randomized 2:1 to iscalimab or placebo administered as a single intravenous dose followed by weekly subcutaneous injections for 1 year to evaluate safety and effects on β cell function. Results: At 14 centers in 6 countries, 44 participants (29 M/15 F, mean age 16 years [range 12-21 years]) were randomized; 39 completed the study (26 active:13 placebo). Treatment was discontinued prematurely in seven, two of these due to a temporary trial halt during the COVID-19 pandemic. No difference in C-peptide area under the curve (C-peptide(AUC)) during a mixed meal tolerance test was observed after 52 weeks (ratio active:placebo 1.173 [80% confidence interval (CI) 0.94, 1.47], P(one-sided) = 0.18). The yearly rate of change of normalized stimulated C-peptide(AUC) suggests a slower decline of β cell function: iscalimab -0.14 (80% CI -0.23, -0.05) versus placebo -0.33 (-0.42, -0.23) nmol/L per year (P(one-sided) = 0.04). The estimated geometric mean ratio to baseline of hemoglobin A1c at week 52 was lower with iscalimab than placebo (0.95 [80% CI 0.92-0.99] versus 1.05 [80% CI 1.00-1.11], respectively). Leukocytes, neutrophils, and monocytes were lower, whereas T and B lymphocytes were higher in iscalimab-treated participants compared with placebo. Iscalimab was generally safe and well tolerated. Five serious adverse events (AEs) occurred under iscalimab (urinary tract infection, diabetic metabolic decompensation, traumatic fracture, hypoglycemia, and large intestine infection [3.4% each]) and one under placebo (mastoiditis [6.7%]). The most common AEs were hypoglycemia, nasopharyngitis, injection site reaction, COVID-19, and neutropenia. The majority of AEs were mild-to-moderate in intensity and resolved. Conclusion: Iscalimab has an acceptable safety and tolerability profile. The sample size limits interpretation of efficacy results. CD40:CD154 inhibition warrants further investigation in T1DM.

Introduction: The Omnipod® 5 Automated Insulin Delivery (AID) System is the first wearable, on-body, tubeless AID system in the United States. Clinical trials have demonstrated hemoglobin A1C (HbA1c) improvements with Omnipod 5 use. The aim of the study was to evaluate HbA1c changes after initiating Omnipod 5 in a real-world setting. Methods: This retrospective study utilized commercial and Medicare Advantage with Part D enrollees' claims data from the Optum Research Database between August 1, 2021, and December 31, 2023. The study included individuals diagnosed with type 1 diabetes (T1D) or type 2 diabetes (T2D) who initiated Omnipod 5. Participants were required to have continuous enrollment for 12 months pre- and post-initiation and at least two medical claims for diabetes during these periods. Demographic and clinical characteristics were measured at the index date, and HbA1c levels were compared pre- and post-initiation of Omnipod 5. Results: The final sample included 2,504 users, with 792 having pre- and post-initiation HbA1c results. The mean age was 54 years; 48% were enrolled in Medicare Advantage, 90% had prior continuous glucose monitoring, and 64% had prior pump therapy. A mean reduction in HbA1c of 0.4% was observed (P < 0.001); mean reductions were 0.4% and 0.5% among those with type 1 and type 2 diabetes, respectively. Among 135 users with pre-initiation HbA1c levels ≥9%, a reduction of 1.4% was noted (P < 0.001). Pre-initiation, 31% achieved the American Diabetes Association (ADA) target of <7% compared to 44% post-initiation (increase of 13%; P < 0.001). For the Healthcare Effectiveness Data and Information Set (HEDIS) target of <8%, 62% of individuals achieved the target pre-initiation compared to 78% post-initiation (increase of 16%; P < 0.001). Conclusions: In a real-world setting, Omnipod 5 was associated with reduced HbA1c and an increased proportion of individuals achieving ADA and HEDIS glycemic targets.

Background: Postprandial glucose control remains a challenge in people with type 1 diabetes (T1D), even when using advanced hybrid closed-loop systems (AHCL) such as the MiniMed™ 780G (MM780G) system. Missed mealtime boluses are common and can significantly impair glycemic outcomes. This study aimed to evaluate a corrective postprandial bolus strategy for missed meal boluses using the MM780G system. Methods: In a prospective, open-label, real-world study, 32 adults with T1D using the MM780G system completed three meal scenarios with standardized meals (∼60 g carbohydrates). The strategies were: (E1) premeal bolus (control), (E2) no bolus (relying on system automation), and (E3) delayed bolus (bolus covering 50% of carbohydrate content, 60 min postmeal). Each participant completed all strategies under remote supervision. Primary outcome was 4-h postprandial time in range (TIR, 70-180 mg/dL); secondary outcomes included time in tight range (TITR, 70-140 mg/dL), time above/below range, and adverse events. Results: Premeal bolus (E1) achieved the highest TIR (85.5% ± 18.8%), significantly outperforming both E2 (52.3% ± 25.3%) and E3 (63.5% ± 24.0%, P < 0.001). E3 also showed significant improvement over E2 (P < 0.001). TITR followed a similar pattern. Time between 54 and 70 mg/dL was slightly higher for E1 but remained within consensus goals. No significant differences in hypoglycemia (<54 mg/dL) were observed across strategies. Delayed bolus (E3) lowered time above 250 mg/dL compared with E2. No severe hypoglycemia or ketoacidosis occurred. Conclusion: Premeal bolusing yields optimal postprandial glycemic control with the MM780G. However, when a meal bolus is missed, administering a corrective bolus of 50% carbohydrate content 1 h postmeal significantly improves TIR and TITR without increasing hypoglycemia risk. If correction is not performed, the MM780G system can provide a reasonable partial compensation via autobasal and auto-correction.

Automated insulin delivery (AID) systems have been shown to be an effective therapeutic option for people with type 1 diabetes and, more recently, for people with type 2 diabetes. To date, the benefit of AID systems has been primarily assessed by glycemic parameters and has been shown to optimize glucose control for people living with diabetes. However, it is increasingly recognized that diabetes management extends beyond glucose levels alone, and there is a need to determine the effectiveness of AID systems in their entirety. This includes the need to also assess additional parameters including the impact of AID on chronic complications of diabetes, quality of life and burden of disease, reliability and durability of devices, health economics, and environmental sustainability. Surrogate endpoints to assess the future risk of microvascular and macrovascular complications and person-reported outcomes through a panel of standardized questionnaires that have been validated for AID systems may be beneficial in comprehensively assessing AID performance. The introduction of AID systems necessitates a balance between optimizing glycemia while simultaneously reducing the burden of diabetes itself and managing the challenges associated with technology use. This review aims to provide a comprehensive analysis of the need to establish endpoints beyond glycemic outcomes with AID use in people living with diabetes.

Objective: To characterize simplified meal bolus strategies in adults with insulin-treated type 2 diabetes using automated insulin delivery (AID). Research Design and Methods: In the 2IQP study, a 13-week randomized, controlled trial comparing Control-IQ+ AID to continuation of pre-study insulin regimen with continuous glucose monitoring, 201 participants in the AID arm were classified by meal bolus strategy. Glycemic outcomes were compared to baseline. Results: 68 participants' meal bolus strategies (33.8%) were classified as Carbohydrate Counting, 79 (39.3%) were classified as Preset Carbohydrate Amounts, 27 (13.4%) were classified as Fixed Insulin Doses, and 27 (13.4%) as Other Methods. All bolus strategies were associated with similar, significant improvements in HbA1c from baseline: -0.9% for Carbohydrate Counting (P < 0.001), -1.1% for Preset Carbohydrate Amounts (P < 0.001), -0.8% for Fixed Insulin Doses (P < 0.001), and -0.9% for Other Methods (P = 0.003). Hypoglycemia rates were low at baseline and remained low for all bolus strategies. As participants gained experience with the Control-IQ+ AID system, more participants opted to use a simplified bolus strategy in the second half of the study compared with the first half (63% vs. 52%). Conclusion: Simplified bolus strategies worked well for adults with type 2 diabetes using Control-IQ+ in the 2IQP trial. All bolus strategies led to substantial HbA1c improvements, without safety concerns.