Impact of Insulin Sensitivity and β-Cell Function Over Time on Glycemic Outcomes in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE): Differential Treatment Effects of Dual Therapy

OBJECTIVE

To compare the effects of insulin sensitivity and β-cell function over time on HbA1c and durability of glycemic control in response to dual therapy.

RESEARCH DESIGN AND METHODS

GRADE participants were randomized to glimepiride (n = 1,254), liraglutide (n = 1,262), or sitagliptin (n = 1,268) added to baseline metformin and followed for mean ± SD 5.0 ± 1.3 years, with HbA1c assessed quarterly and oral glucose tolerance tests at baseline, 1, 3, and 5 years. We related time-varying insulin sensitivity (HOMA 2 of insulin sensitivity [HOMA2-%S]) and early (0-30 min) and total (0-120 min) C-peptide (CP) responses to changes in HbA1c and glycemic failure (primary outcome HbA1c ≥7% [53 mmol/mol] and secondary outcome HbA1c >7.5% [58 mmol/mol]) and examined differential treatment responses.

RESULTS

Higher HOMA2-%S was associated with greater initial HbA1c lowering (3 months) but not subsequent HbA1c rise. Greater CP responses were associated with a greater initial treatment response and slower subsequent HbA1c rise. Higher HOMA2-%S and CP responses were each associated with lower risk of primary and secondary outcomes. These associations differed by treatment. In the sitagliptin group, HOMA2-%S and CP responses had greater impact on initial HbA1c reduction (test of heterogeneity, P = 0.009 HOMA2-%S, P = 0.018 early CP, P = 0.001 total CP) and risk of primary outcome (P = 0.005 HOMA2-%S, P = 0.11 early CP, P = 0.025 total CP) but lesser impact on HbA1c rise (P = 0.175 HOMA2-%S, P = 0.006 early CP, P < 0.001 total CP) in comparisons with the glimepiride and liraglutide groups. There were no differential treatment effects on secondary outcome.

CONCLUSIONS

Insulin sensitivity and β-cell function affected treatment outcomes irrespective of drug assignment, with greater impact in the sitagliptin group on initial (short-term) HbA1c response in comparison with the glimepiride and liraglutide groups.

A scalable application of Artificial Intelligence-Driven Insulin Titration Program to transform Type 2 Diabetes Management

BACKGROUND

Despite new pharmacotherapy, most patients with long-term Type 2 Diabetes are still hyperglycemic. This could have been solved by insulin with its unlimited potential efficacy, but its dynamic physiology demands frequent titrations which are overdemanding. This report provides a real-life account for a scalable transformation of diabetes care in a community-based endocrinology center by harnessing AI-based autonomous insulin titration.

METHODS

The center embedded the d-Nav® technology and its dedicated clinical support. Reported outcomes include treatment efficacy/safety in the first 600 patients and use of cardiorenal-risk reduction pharmacotherapy.

FINDINGS

Patients used d-Nav for 8.2±3.0 months with 82% retention. Age was 67.1±11.5 years and duration of diabetes was 19.8±11.0 years. During the last 3 years before d-Nav, HbA1c had been overall higher than 8% and at the beginning of the program it was as high as 8.6%±2.1% with 29.3% of the patients with HbA1c>9%. With d-Nav, HbA1c decreased to 7.3%±1.2% with 5.7% of patients with HbA1c>9%. During the first 3 months, d-Nav reduced total daily dose of insulin in 1 of every 5 patients due to relatively low glucose levels to minimize the risk of hypoglycemia. GLP-1 or dual GLP-1 and GIP receptor agonists were prescribed in about a half of the patients and SGLT2 inhibitor in a third. The frequency of hypoglycemia (<54mg/dl) was 0.4±0.6/month and severe hypoglycemia 1.7/100-patient-years.

INTERPRETATION

The use of d-Nav allowed for improvement in overall diabetes management with appropriate use of both insulin and non-insulin pharmacologic agents in a scalable way.

Control-IQ Technology Use in Individuals With High Insulin Requirements: Results From the Multicenter Higher-IQ Trial

BACKGROUND

Control-IQ technology version 1.5 allows for a wider range of weight and total daily insulin (TDI) entry, in addition to other changes to enhance performance for users with high basal rates. This study evaluated the safety and performance of the updated Control-IQ system for users with basal rates >3 units/h and high TDI in a multicenter, single arm, prospective study.

METHODS

Adults with type 1 diabetes (T1D) using continuous subcutaneous insulin infusion (CSII) and at least one basal rate over 3 units/h (N = 34, mean age = 39.9 years, 41.2% female, diabetes duration = 21.8 years) used the t:slim X2 insulin pump with Control-IQ technology version 1.5 for 13 weeks. Primary outcome was safety events (severe hypoglycemia and diabetic ketoacidosis (DKA)). Central laboratory hemoglobin A1c (HbA1c) was measured at system initiation and 13 weeks. Participants continued using glucagon-like peptide-1 (GLP-1) receptor agonists, sodium-glucose transport protein 2 (SGLT-2) inhibitors, or other medications for glycemic control and/or weight loss if on a stable dose.

RESULTS

All 34 participants completed the study. Fifteen participants used a basal rate >3 units/h for all 24 hours of the day. Nine participants used >300 units TDI on at least one day during the study. There were no severe hypoglycemia or DKA events. Time in range 70-180 mg/dL was 64.8% over the 13 weeks, with 1.0% time <70 mg/dL. Hemoglobin A1c decreased from 7.69% at baseline to 6.87% at 13 weeks (-0.82%, P < .001).

CONCLUSIONS

Control-IQ technology version 1.5, with wider range of weight and TDI input and enhancements for users with high insulin requirements, was safe in individuals with T1D in this study.

Is It Time to Move Beyond TIR to TITR? Real-World Data from Over 20,000 Users of Continuous Glucose Monitoring in Patients with Type 1 and Type 2 Diabetes

The growing use of continuous glucose monitoring (CGM) has been supported by expert consensus and clinical guidelines on glycemic management in diabetes with time in range (TIR 70-180 mg/dL) representing a key CGM-derived glucose metric. Time in tight range (TITR) has also been proposed for clinical use, spanning largely normal glucose levels of 70-140 mg/dL. However, keeping such narrow glucose ranges can be challenging, and understanding the factors modulating TITR can help achieve these tight glycemic targets. Our real-life study aimed to evaluate the relationship between average glucose (AG) and TIR/TITR in a large cohort (n = 22,006) of CGM users, divided into four groups: self-identified as having type 1 diabetes (T1D) treated with insulin using multiple daily injections (MDI) or pumps; type 2 diabetes (T2D) on MDI or insulin pumps; T2D on basal insulin only; and T2D not on insulin treatment. The T2D groups, regardless of treatment type, displayed the highest TIR and TITR values, associated with lowest glycemic variability measured as glucose coefficient of variation (CV; 23-30%). The T1D group showed the lowest TIR and TITR, associated with the highest CVs (36-38%). Overall, higher CV was associated with lower TIR and TITR for AG values below 180 and 140 mg/dL, respectively, with the reverse holding true for AG values above these thresholds. The discordance between AG and TIR/TITR was less pronounced in T2D compared with T1D, attributed to lower CV in the former group. It was also observed that TITR has advantages over TIR for assessing glycemia status and progress toward more stringent A1C, particularly when approaching normal glucose levels. The data detail how CV affects the AG relationship with TIR/TITR, which has implications for CGM interpretation. In many instances TITR, rather than TIR, may be preferable to employ once AG falls below 140 mg/dL and near-normal glucose levels are required clinically.

A Comparison of Continuous Glucose Monitoring-Measured Time-in-Range 70-180 mg/dL Versus Time-in-Tight-Range 70-140 mg/dL

Objective: To evaluate the relationship between continuous glucose monitoring (CGM)-measured time-in-range 70-180 mg/dL (TIR) and time-in-tight-range 70-140 mg/dL (TITR). Methods: TIR and TITR were calculated from CGM data collected using blinded or unblinded Dexcom sensors from 9 studies with 912 participants with type 1 diabetes (T1D) and 2 studies with 184 participants with type 2 diabetes (T2D). The TIR-TITR relationship was assessed overall and stratified by coefficient of variation (CV) and by time below range <70 mg/dL (TBR). Results: The correlation between TIR and TITR was 0.94. TITR was higher for a given TIR for T2D compared with T1D. However, after adjusting for the differences in CV or TBR, both of which were higher with T1D than T2D, the differences were minimized. The TIR-TITR relationship was nonlinear, with a higher ratio of TITR:TIR observed as TIR increased ranging from 0.42 when TIR was 20% to 0.66 when TIR was 80%. Similarly, as TITR increased, the ratio of TIR:TITR decreased, varying from 2.6 with TITR of 10% to 1.3 for TITR of 70%. The TIR-TITR relationship varied according to CV and TBR, such that the higher the CV or higher the amount of TBR the greater was TITR for a given TIR. Conclusions: TIR and TITR are highly correlated, although the relationship is nonlinear. With knowledge of TIR, TITR can be estimated with reasonable precision.

Decreasing the Burden of Carbohydrate Counting and Meal Announcement with Automated Insulin Delivery, Meal Recognition, and Autocorrection Doses: A Case Study

The use of automated insulin delivery (AID) has led to a decrease in the burden of diabetes, allowing for better sleep, decreased anxiety about hypoglycemia, and automatic corrections doses, and meal recognition algorithms have provided "forgiveness" for imprecise carbohydrate (CHO) entries and missed or late meal boluses. We provide a case report and review of the current literature assessing the effect of AID on the burden of meal bolus. The case also demonstrates how sensor and pump data provide insight into insulin bolus behavior, and access to integrated cloud-based data has allowed for virtual patient visits. Glucose sensor metrics provides time in range and time below range, and the sensor-derived glucose management indicator provides an assessment of the long-term risk of complications when a laboratory glycated hemoglobin is not available.

Time in Range, Time in Tight Range, and Average Glucose Relationships Are Modulated by Glycemic Variability: Identification of a Glucose Distribution Model Connecting Glycemic Parameters Using Real-World Data

Background: Time in range (TIR), time in tight range (TITR), and average glucose (AG) are used to adjust glycemic therapies in diabetes. However, TIR/TITR and AG can show a disconnect, which may create management difficulties. We aimed to understand the factors influencing the relationships between these glycemic markers. Materials and Methods: Real-world glucose data were collected from self-identified diabetes type 1 and type 2 diabetes (T1D and T2D) individuals using flash continuous glucose monitoring (FCGM). The effects of glycemic variability, assessed as glucose coefficient of variation (CV), on the relationship between AG and TIR/TITR were investigated together with the best-fit glucose distribution model that addresses these relationships. Results: Of 29,164 FCGM users (16,367 T1D, 11,061 T2D, and 1736 others), 38,259 glucose readings/individual were available. Comparing low and high CV tertiles, TIR at AG of 150 mg/dL varied from 80% ± 5.6% to 62% ± 6.8%, respectively (P < 0.001), while TITR at AG of 130 mg/dL varied from 65% ± 7.5% to 49% ± 7.0%, respectively (P < 0.001). In contrast, higher CV was associated with increased TIR and TITR at AG levels outside the upper limit of these ranges. Gamma distribution was superior to six other models at explaining AG and TIR/TITR interactions and demonstrated nonlinear interplay between these metrics. Conclusions: The gamma model accurately predicts interactions between CGM-derived glycemic metrics and reveals that glycemic variability can significantly influence the relationship between AG and TIR with opposing effects according to AG levels. Our findings potentially help with clinical diabetes management, particularly when AG and TIR appear mismatched.

Two Years with a Tubeless Automated Insulin Delivery System: A Single-Arm Multicenter Trial in Children, Adolescents, and Adults with Type 1 Diabetes

Background: The Omnipod® 5 Automated Insulin Delivery (AID) System was shown to be safe and effective following 3 months of use in people with type 1 diabetes (T1D); however, data on the durability of these results are limited. This study evaluated the long-term safety and effectiveness of Omnipod 5 use in people with T1D during up to 2 years of use. Materials and Methods: After a 3-month single-arm, multicenter, pivotal trial in children (6-13.9 years) and adolescents/adults (14-70 years), participants could continue system use in an extension phase. HbA1c was measured every 3 months for up to 15 months; continuous glucose monitor metrics were collected for up to 2 years. Results: Participants (N = 224) completed median (interquartile range) 22.3 (21.7, 22.7) months of AID. HbA1c was reduced in the pivotal trial from 7.7% ± 0.9% in children and 7.2% ± 0.9% in adolescents/adults to 7.0% ± 0.6% and 6.8% ± 0.7%, respectively, (P < 0.0001), and was maintained at 7.2% ± 0.7% and 6.9% ± 0.6% after 15 months (P < 0.0001 from baseline). Time in target range (70-180 mg/dL) increased from 52.4% ± 15.6% in children and 63.6% ± 16.5% in adolescents/adults at baseline to 67.9% ± 8.0% and 73.8% ± 10.8%, respectively, during the pivotal trial (P < 0.0001) and was maintained at 65.9% ± 8.9% and 72.9% ± 11.3% during the extension (P < 0.0001 from baseline). One episode of diabetic ketoacidosis and seven episodes of severe hypoglycemia occurred during the extension. Children and adolescents/adults spent median 96.1% and 96.3% of time in Automated Mode, respectively. Conclusion: Our study supports that long-term use of the Omnipod 5 AID System can safely maintain improvements in glycemic outcomes for up to 2 years of use in people with T1D. Clinical Trials Registration Number: NCT04196140.

Roadmap to the Effective Use of Continuous Glucose Monitoring: Innovation, Investigation, and Implementation

For 25 years, continuous glucose monitoring (CGM) has been evolving into what it is now: a key tool to both measure individuals' glycemic status and to help guide their day-to-day management of diabetes. Through a series of engineering innovations, clinical investigations, and efforts to optimize workflow implementation, the use of CGM is helping to transform diabetes care. This article presents a roadmap to the effective use of CGM that outlines past, present, and possible future advances in harnessing the potential of CGM to improve the lives of many people with diabetes, with an emphasis on ensuring that CGM technology is available to all who could benefit from its use.

The Role of Ultra-Rapid-Acting Insulin Analogs in Diabetes: An Expert Consensus

Ultra-rapid-acting insulin analogs (URAA) are a further development and refinement of rapid-acting insulin analogs. Because of their adapted formulation, URAA provide an even faster pharmacokinetics and thus an accelerated onset of insulin action than conventional rapid-acting insulin analogs, allowing for a more physiologic delivery of exogenously applied insulin. Clinical trials have confirmed the superiority of URAA in controlling postprandial glucose excursions, with a safety profile that is comparable to the rapid-acting insulins. Consequently, many individuals with diabetes mellitus may benefit from URAA in terms of prandial glycemic control. Unfortunately, there are only few available recommendations from authoritative sources for use of URAA in clinical practice. Therefore, this expert consensus report aims to define populations of people with diabetes mellitus for whom URAA may be beneficial and to provide health care professionals with concrete, practical recommendations on how best to use URAA in this context.

Assessing Time in Range with Postprandial Glucose-Focused Titration of Ultra Rapid Lispro (URLi) in People with Type 1 Diabetes

INTRODUCTION

To assess time in range (TIR) (70-180 mg/dL) with postprandial glucose (PPG)-focused titration of ultra rapid lispro (URLi; Lyumjev®) in combination with insulin degludec in people with type 1 diabetes (T1D).

METHODS

This phase 2, single-group, open-label, exploratory study was conducted in 31 participants with T1D on multiple daily injection therapy. Participants were treated with insulin degludec and Lispro for an 11-day lead-in and then URLi for a 46-day treatment period consisting of 35-day titration and 11-day endpoint maintenance period. Glucose targets for the titration period were PPG < 140 mg/dL or < 20% increase from premeal, fasting glucose 80-110 mg/dL, and overnight excursion ± 30 mg/dL or less. Participants used the InPen™ bolus calculator and Dexcom G6 continuous glucose monitoring (CGM).

RESULTS

Primary endpoint mean TIR (70-180 mg/dL) with URLi during the maintenance period was 70.2%. TIR (70-180 mg/dL) and times below/above range were not significantly different with URLi (maintenance) versus lispro (lead-in). HbA1c decreased from 7.1% at screening to 6.8% at endpoint (least squares mean [LSM] change from baseline, - 0.36%; P < 0.001). Fructosamine and 1,5-anhydroglucitol improved (P < 0.001). Mean hourly glucose using CGM was reduced from 8:00 AM to 4:00 PM with URLi. Overall highest PPG excursion across meals was significantly reduced at URLi endpoint compared with lispro lead-in (mean 56.5 vs 72.4 mg/dL; P < 0.001). Insulin-to-carbohydrate ratio (U/X g) was reduced (more insulin given) at breakfast at URLi endpoint vs lead-in (LSM 9.0 vs 9.7 g; P = 0.002) and numerically decreased at other meals. Total daily insulin dose (TDD) was higher at URLi endpoint compared with lispro lead-in (mean 50.2 vs 47.0 U; P = 0.046) with similar prandial/TDD ratio (mean 52.1% vs 51.2%). There were no severe hypoglycemia events during the study.

CONCLUSIONS

URLi in a basal-bolus regimen focusing on PPG targets demonstrated improved overall glycemic control and reduced PPG excursions without increased hypoglycemia in participants with T1D.

TRIAL REGISTRATION

ClinicalTrial.gov, NCT04585776.

Comprehensive Telehealth Model to Support Diabetes Self-Management

Importance

As the number of patients with diabetes continues to increase in the United States, novel approaches to clinical care access should be considered to meet the care needs for this population, including support for diabetes-related technology.

Objective

To evaluate a virtual clinic to facilitate comprehensive diabetes care, support continuous glucose monitoring (CGM) integration into diabetes self-management, and provide behavioral health support for diabetes-related issues.

Design, Setting, and Participants

This cohort study was a prospective, single-arm, remote study involving adult participants with type 1 or type 2 diabetes who were referred through community resources. The study was conducted virtually from August 24, 2020, to May 26, 2022; analysis was conducted at the clinical coordinating center.

Intervention

Training and education led by a Certified Diabetes Care and Education Specialist for CGM use through a virtual endocrinology clinic structure, which included endocrinologists and behavioral health team members.

Main Outcomes and Measures

Main outcomes included CGM-measured mean glucose level, coefficient of variation, and time in range (TIR) of 70 to 180 mg/dL, time with values greater than 180 mg/dL or 250 mg/dL, and time with values less than 70 mg/dL or 54 mg/dL. Hemoglobin A1c was measured at baseline and at 12 and 24 weeks.

Results

Among the 234 participants, 160 had type 1 diabetes and 74 had type 2 diabetes. The mean (SD) age was 47 (14) years, 123 (53%) were female, and median diabetes duration was 20 years. Median (IQR) CGM use over 6 months was 96% (91%-98%) for participants with type 1 diabetes and 94% (85%-97%) for those with type 2 diabetes. Mean (SD) hemoglobin A1c (HbA1c) in those with type 1 diabetes decreased from 7.8% (1.6%) at baseline to 7.1% (1.0%) at 3 months and 7.1% (1.0%) at 6 months (mean change from baseline to 6 months, -0.6%, 95% CI, -0.8% to -0.5%; P < .001), with an 11% mean TIR increase over 6 months (95% CI, 9% to 14%; P < .001). Mean HbA1c in participants with type 2 diabetes decreased from 8.1% (1.7%) at baseline to 7.1% (1.0%) at 3 months and 7.1% (0.9%) at 6 months (mean change from baseline to 6 months, -1.0%; 95% CI, -1.4% to -0.7%; P < .001), with an 18% TIR increase over 6 months (95% CI, 13% to 24%; P < .001). In participants with type 1 diabetes, mean percentage of time with values less than 70 mg/dL and less than 54 mg/dL decreased over 6 months by 0.8% (95% CI, -1.2% to -0.4%; P = .001) and by 0.3% (95% CI, -0.5% to -0.2%, P < .001), respectively. In the type 2 diabetes group, hypoglycemia was rare (mean [SD] percentage of time <70 mg/dL, 0.5% [0.6%]; and <54 mg/dL, 0.07% [0.14%], over 6 months).

Conclusions and Relevance

Results from this cohort study demonstrated clinical benefits associated with implementation of a comprehensive care model that included diabetes education. This model of care has potential to reach a large portion of patients with diabetes, facilitate diabetes technology adoption, and improve glucose control.

Coverage for Continuous Glucose Monitoring for Individuals with Type 2 Diabetes Treated with Nonintensive Therapies: An Evidence-Based Approach to Policymaking

Numerous studies have demonstrated the clinical benefits of continuous glucose monitoring (CGM) in individuals with type 1 diabetes (T1D) and type 2 diabetes (T2D) who are treated with intensive insulin regimens. Based on this evidence, CGM is now a standard of care for individuals within these diabetes populations and widely covered by commercial and public insurers. Moreover, recent clinical guidelines from the American Diabetes Association and American Association of Clinical Endocrinology now endorse CGM use in individuals treated with nonintensive insulin regimens. However, despite increasing evidence supporting CGM use for individuals treated with less-intensive insulin therapy or noninsulin medications, insurance coverage is limited or nonexistent. This narrative review reports key findings from recent randomized, observational, and retrospective studies investigating use of CGM in T2D individuals treated with basal insulin only and/or noninsulin therapies and presents an evidence-based rationale for expanding access to CGM within this population.

Prevalence, Cost, and Burden of Diabetic Ketoacidosis

Diabetic ketoacidosis (DKA) is a life-threatening complication, which is most common in individuals with type 1 diabetes (T1D) and is a significant risk for morbidity and mortality, and it is an economic burden on individuals, health care systems, and payers. Younger children, minority ethnic groups, and those with limited insurance are at the greatest risk for presentation of DKA at T1D diagnosis. Although monitoring ketone levels is an essential part of acute illness management and for both early detection and prevention of a DKA episode, studies have reported poor adherence to ketone monitoring. Ketone monitoring is particularly important for patients treated with sodium glucose cotransporter 2 inhibitor (SGLT2i) medications, in which DKA can present with only moderately elevated glucose levels, referred to as euglycemic DKA (euDKA). A majority of people with T1D and many with type 2 diabetes (T2D), particularly those using insulin therapy, are using continuous glucose monitoring (CGM) as their preferred method for measurement and management of glycemia. These devices provide a continuous stream of glucose data that enables users to take immediate action to mitigate and/or prevent severe hyperglycemic or hypoglycemic events. An international consensus of leading diabetes experts has recommended the development of continuous ketone monitoring systems, ideally a system that combines CGM technology with measurement of 3-β-OHB into a single sensor. In this narrative review of current literature, we report on the prevalence and burden of DKA, examine challenges to detecting and diagnosing this condition, and discuss a new monitoring option for DKA prevention.

Personalized Glycated Hemoglobin in Diabetes Management: Closing the Gap with Glucose Management Indicator

Glycated hemoglobin (HbA1c) has played a central role in the management of diabetes since the end of the landmark Diabetes Control and Complications Trial 30 years ago. However, it is known to be subject to distortions related to altered red blood cell (RBC) properties, including changes in cellular lifespan. On occasion, the distortion of HbA1c is associated with a clinical pathological condition affecting RBCs, however, the more frequent scenario is related to interindividual RBC variations that alter HbA1c-average glucose relationship. Clinically, these variations can potentially lead to over- or underestimating glucose exposure of the individual to the extent that may put the person at excess risk of over- or undertreatment. Furthermore, the variable association between HbA1c and glucose levels across different groups of people may become an unintentional driver of inequitable health care delivery, outcomes, and incentives. The subclinical effects within the normal expected physiological range of RBCs can be large enough to alter clinical interpretation of HbA1c and addressing this will help with individualized care and decision making. This review describes a new glycemic measure, personalized HbA1c (pA1c), that may address the clinical inaccuracies of HbA1c by taking into account interindividual variability in RBC glucose uptake and lifespan. Therefore, pA1c represents a more sophisticated understanding of glucose-HbA1c relationship at an individual level. Future use of pA1c, after adequate clinical validation, has the potential to refine glycemic management and the diagnostic criteria in diabetes.

Increased Derived Time in Range is Associated with Reduced Risk of Major Adverse Cardiovascular Events, Severe Hypoglycemia, and Microvascular Events in Type 2 Diabetes: A Post Hoc Analysis of DEVOTE

Time spent in glycemic target range (time in range [TIR]; plasma glucose 70-180 mg/dL [3.9-10.0 mmol/L]) as a surrogate endpoint for long-term diabetes-related outcomes requires validation. This post hoc analysis investigated the association between TIR, derived from 8-point glucose profiles (dTIR) at 12 months, and time to cardiovascular or severe hypoglycemic episodes in people with type 2 diabetes in the DEVOTE trial. At 12 months, dTIR was significantly negatively associated with time to first major adverse cardiovascular event (P = 0.0087), severe hypoglycemic episode (P < 0.0001), or microvascular event (P = 0.024). A non-significant trend was seen towards association between 12-month hemoglobin A1c (HbA1c) and these outcomes, but this was no longer seen after addition of dTIR to the model. The results support targeting TIR >70% and suggest dTIR could be used in addition to, or in some instances in place of, HbA1c as a clinical biomarker.

Advancing Diabetes Quality Measurement in the Era of Continuous Glucose Monitoring

PURPOSE

The purpose of this research is to develop a set of continuous glucose monitoring (CGM)-related measure concepts to be tested in a health care system. Existing measures assessing the quality of diabetes care do not include modern approaches to diabetes management, such as CGM. Continuous glucose monitors rival traditional methods of measuring diabetes management by providing real-time, longitudinal data and demonstrating glucose variability over time. The Improving Diabetes Quality Initiative seeks to address this gap in diabetes quality measurement.

METHODS

A Technical Expert Panel (TEP) was convened to curate a diabetes quality measures portfolio and conceptualize three new CGM-related quality measures within the portfolio. From the additional measure concepts identified in the portfolio, the TEP prioritized three for conceptualization. High-level measure concept specifications were made available during a public comment period.

RESULTS

The measure concepts prioritized by the TEP included a shared decision-making measure to assess the value of initiating CGM for disease management, a utilization measure to address disparities in access and use of CGM, and a patient-provider review of CGM data to promote routine consideration of these assessments in treatment and ongoing management. Clinical literature, public comments, and TEP feedback informed full measure specifications.

CONCLUSIONS

The evolution of diabetes technology reflects the need to shift diabetes quality of care. The measure concepts will be tested in a flexible pilot setting to understand the future of diabetes care and communicate the value of CGM to people with diabetes, providers, and payers.

Correlation Between Time in Range and HbA1c in People with Type 2 Diabetes on Basal Insulin: Post Hoc Analysis of the SWITCH PRO Study

INTRODUCTION

Use of continuous glucose monitoring (CGM) in people with diabetes may provide a more complete picture of glycemic control than glycated hemoglobin (HbA1c) measurements, which do not capture day-to-day fluctuations in blood glucose levels. The randomized, crossover, phase IV SWITCH PRO study assessed time in range (TIR), derived from CGM, following treatment with insulin degludec or insulin glargine U100 in patients with type 2 diabetes at risk for hypoglycemia. This post hoc analysis evaluated the relationship between TIR and HbA1c, following treatment intensification during the SWITCH PRO study.

METHODS

Correlation between absolute values for TIR (assessed over 2-week intervals) and HbA1c, at baseline and at the end of maintenance period 1 (M1; week 18) or maintenance period 2 (M2; week 36), were assessed by linear regression and using the Spearman correlation coefficient (r_s). These methods were also used to assess correlation between change in TIR and change in HbA1c from baseline to the end of M1, both in the full cohort and in subgroups stratified by baseline median HbA1c (≥ 7.5% [≥ 58.5 mmol/mol] or < 7.5% [< 58.5 mmol/mol]).

RESULTS

A total of 419 participants were included in the analysis. A moderate inverse linear correlation was observed between TIR and HbA1c at baseline (r_s -0.54), becoming stronger following treatment intensification during maintenance periods M1 (weeks 17-18: r_s -0.59) and M2 (weeks 35-36: r_s -0.60). Changes in TIR and HbA1c from baseline to end of M1 were also linearly inversely correlated in the full cohort (r_s -0.40) and the subgroup with baseline HbA1c ≥ 7.5% (r_s -0.43). This was less apparent in the subgroup with baseline HbA1c < 7.5% (r_s -0.17) (p-interaction = 0.07).

CONCLUSION

Results from this post hoc analysis of data from SWITCH PRO, one of the first large interventional clinical studies to use TIR as the primary outcome, further support TIR as a valid clinical indicator of glycemic control.

TRIAL REGISTRATION

ClinicalTrials.gov identifier, NCT03687827.

Consensus Recommendations for the Use of Automated Insulin Delivery Technologies in Clinical Practice

The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

Discordance Between Glucose Management Indicator and Glycated Hemoglobin in People Without Diabetes

Background: In recent years, continuous glucose monitor (CGM) use is increasing in people without diabetes to promote healthy lifestyle. CGM metrics such as glucose management indicator (GMI), a statistical formula to estimate glycated hemoglobin (HbA1c) from sensor glucose, is commonly used to approximate HbA1c. This study was aimed to evaluate discordance between GMI and HbA1c in people without diabetes. Methods: Children and nonpregnant adults (age ≥6 years) without diabetes (laboratory HbA1c <5.7% and negative islet antibodies) were invited to participate in a multicenter prospective study aimed to evaluate glycemic profiles in nondiabetic individuals. Each participant wore a blinded Dexcom G6 for up to 10 days. GMI was calculated from mean sensor glucose and discordance between GMI and HbA1c was analyzed. Results: Of 201 screened participants, 153 participants (mean age 31.2 ± 21.0 years, 66.0% female, HbA1c 5.1% ± 0.3%) were included in the analysis. Mean GMI was 0.59% higher than laboratory HbA1c in participants without diabetes. The discordance between GMI and HbA1c of 0.4% or greater was 71% in participants without diabetes compared with 39% in the original GMI development cohort. Conclusion: GMI does not accurately estimate HbA1c in healthy people without diabetes. Clinical trial registration number is: NCT00717977.

The Relationship Between Percent Time <70 mg/dL and Percent Time <54 mg/dL Measured by Continuous Glucose Monitoring

Objective: While it is recognized that there is a strong relationship between the amount of time glucose levels are <70 mg/dL (T^<70) and the amount of time <54 mg/dL (T^<54), the association has not been well quantified. Methods: Datasets with Dexcom continuous glucose monitoring (CGM) data from nine type 1 diabetes randomized trials were pooled to evaluate the relationship between CGM-measured T^<70 and T^<54. Penalized B-spline regression lines were fitted to assess the relationship between T^<70 and T^<54 for blinded CGM use, unblinded CGM use without an automated insulin delivery (AID) system, and unblinded CGM use with an AID system. Results: For blinded data, the T^<54 : T^<70 ratio varied from 19% when the amount of T^<70 was <1% to 44% when the amount of T^<70 was ≥7% whereas for unblinded data the ratio varied from 15% to 42%, respectively. When T^<70 was 4%, the predicted T^<54 was 1.18%, 0.94%, and 0.91% for the blinded, unblinded, and AID data, respectively (P<0.001 comparing blinded versus unblinded and AID). Conclusions: The T^<54 : T^<70 ratio increases with greater T^<70, and the ratio generally is higher with blinded than unblinded CGM data, with the latter appearing to be similar to AID system data. The finding of greater T^<54 for a given T^<70 with blinded CGM data is presumed to be due to an action being taken by the unblinded CGM user and/or by the AID system to minimize hypoglycemia which will have the effect of reducing the amount of T^<54.

A Roadmap to an Equitable Digital Diabetes Ecosystem

OBJECTIVES

Diabetes management presents a substantial burden to individuals living with the condition and their families, health care professionals, and health care systems. Although an increasing number of digital tools are available to assist with tasks such as blood glucose monitoring and insulin dose calculation, multiple persistent barriers continue to prevent their optimal use.

METHODS

As a guide to creating an equitable connected digital diabetes ecosystem, we propose a roadmap with key milestones that need to be achieved along the way.

RESULTS

During the Coronavirus 2019 pandemic, there was an increased use of digital tools to support diabetes care, but at the same time, the pandemic also highlighted problems of inequities in access to and use of these same technologies. Based on these observations, a connected diabetes ecosystem should incorporate and optimize the use of existing treatments and technologies, integrate tasks such as glucose monitoring, data analysis, and insulin dose calculations, and lead to improved and equitable health outcomes.

CONCLUSIONS

Development of this ecosystem will require overcoming multiple obstacles, including interoperability and data security concerns. However, an integrated system would optimize existing devices, technologies, and treatments to improve outcomes.

A Comparison of Continuous Glucose Monitoring Estimated Hemoglobin A1c in Adults with Type 1 or Type 2 Diabetes

Background: The relationship of mean glucose measured with continuous glucose monitoring (CGM) and hemoglobin A1c (HbA1c) shows considerable variability between individuals with diabetes and may be influenced by race-related factors. Whether the relationship of mean glucose with HbA1c varies according to type 1 diabetes (T1D) or type 2 diabetes (T2D) has not been well evaluated. Methods: Data from 343 participants in four clinical trials (191 with T1D and 152 with T2D) were analyzed. Least squares linear regression models were fit with HbA1c as the dependent variable and mean glucose as the independent variable. Results: Mean age was 57 ± 15 years in the T1D cohort and 58 ± 10 years in the T2D cohort. The T1D cohort was 89% White non-Hispanic, 5% African American, 3% Hispanic, and 3% other or mixed race compared with 52%, 16%, 22%, and 9%, respectively, in the T2D cohort. The relationship between CGM-measured mean glucose and laboratory-measured HbA1c significantly differed between T1D and T2D cohorts, with HbA1c on average being higher with T2D than T1D for the same mean glucose (P = 0.002). However, this difference was largely attributable to the difference in the proportion of African Americans between T1D and T2D; and after stratifying by race, the mean glucose-HbA1c relationship showed only a small difference between T1D non-African Americans and T2D non-African Americans. The mean glucose-HbA1c relationship appeared similar for White non-Hispanic and Hispanic individuals. Conclusion: HbA1c on average was higher in T2D than T1D for a given mean glucose, but after accounting for race, there was no meaningful difference in the mean glucose-HbA1c relationship comparing T1D and T2D. The mean glucose-HbA1c relationship differs in African American compared with White individuals, but does not appear to differ comparing White non-Hispanic to Hispanic individuals. The published glucose management indicator formula appears to be suitable for both T1D and T2D.

Update on Measuring Ketones

Ketone bodies are an energy substrate produced by the liver and used during states of low carbohydrate availability, such as fasting or prolonged exercise. High ketone concentrations can be present with insulin insufficiency and are a key finding in diabetic ketoacidosis (DKA). During states of insulin deficiency, lipolysis increases and a flood of circulating free fatty acids is converted in the liver into ketone bodies-mainly beta-hydroxybutyrate and acetoacetate. During DKA, beta-hydroxybutyrate is the predominant ketone in blood. As DKA resolves, beta-hydroxybutyrate is oxidized to acetoacetate, which is the predominant ketone in the urine. Because of this lag, a urine ketone test might be increasing even as DKA is resolving. Point-of-care tests are available for self-testing of blood ketones and urine ketones through measurement of beta-hydroxybutyrate and acetoacetate and are cleared by the US Food and Drug Administration (FDA). Acetone forms through spontaneous decarboxylation of acetoacetate and can be measured in exhaled breath, but currently no device is FDA-cleared for this purpose. Recently, technology has been announced for measuring beta-hydroxybutyrate in interstitial fluid. Measurement of ketones can be helpful to assess compliance with low carbohydrate diets; assessment of acidosis associated with alcohol use, in conjunction with SGLT2 inhibitors and immune checkpoint inhibitor therapy, both of which can increase the risk of DKA; and to identify DKA due to insulin deficiency. This article reviews the challenges and shortcomings of ketone testing in diabetes treatment and summarizes emerging trends in the measurement of ketones in the blood, urine, breath, and interstitial fluid.

Continuous glucose monitoring-based time-in-range using insulin glargine 300 units/ml versus insulin degludec 100 units/ml in type 1 diabetes: The head-to-head randomized controlled InRange trial

AIM

To use continuous glucose monitoring (CGM)-based time-in-range (TIR) as a primary efficacy endpoint to compare the second-generation basal insulin (BI) analogues insulin glargine 300 U/ml (Gla-300) and insulin degludec 100 U/ml (IDeg-100) in adults with type 1 diabetes (T1D).

MATERIALS AND METHODS

InRange was a 12-week, multicentre, randomized, active-controlled, parallel-group, open-label study comparing glucose TIR and variability between Gla-300 and IDeg-100 using blinded 20-day CGM profiles. The inclusion criteria consisted of adults with T1D treated with multiple daily injections, using BI once daily and rapid-acting insulin analogues for at least 1 year, with an HbA1c of 7% or higher and of 10% or less at screening.

RESULTS

Overall, 343 participants were randomized: 172 received Gla-300 and 171 IDeg-100. Non-inferiority (10% relative margin) of Gla-300 versus IDeg-100 was shown for the primary endpoint (percentage TIR ≥ 70 to ≤ 180 mg/dl): least squares (LS) mean (95% confidence interval) 52.74% (51.06%, 54.42%) for Gla-300 and 55.09% (53.34%, 56.84%) for IDeg-100; LS mean difference (non-inferiority): 3.16% (0.88%, 5.44%) (non-inferiority P = .0067). Non-inferiority was shown on glucose total coefficient of variation (main secondary endpoint): LS mean 39.91% (39.20%, 40.61%) and 41.22% (40.49%, 41.95%), respectively; LS mean difference (non-inferiority) -5.44% (-6.50%, -4.38%) (non-inferiority P < .0001). Superiority of Gla-300 over IDeg-100 was not shown on TIR. Occurrences of self-measured and CGM-derived hypoglycaemia were comparable between treatment groups. Safety profiles were consistent with known profiles, with no unexpected findings.

CONCLUSIONS

Using clinically relevant CGM metrics, InRange shows that Gla-300 is non-inferior to IDeg-100 in people with T1D, with comparable hypoglycaemia and safety profiles.

Assessment of the Glucose Management Indicator Using Different Sampling Durations

We compared the glucose management indicator (GMI) calculated using 14 days of continuous glucose monitor (CGM) data with GMI calculated using <14 days. Analysis included 581 individuals with type 1 diabetes or type 2 diabetes from five clinical trials. The correlation between the 14- and 7-day GMI was 0.95 and the correlation between 14 days versus 10, 5, and 3 days GMI was 0.98, 0.91, and 0.86, respectively. The percentages of GMI values within 0.3% of the 14-day GMI were 98% with 10-day GMI, 87% with 7-day GMI, 77% with 5-day GMI, and 60% with 3-day GMI. Minimal differences were observed between GMI computed using 14 days of data compared with GMI computed with 7 days. Although 10-14 days of CGM data are preferred for computing GMI, for most patients a satisfactory estimate of HbA1c can be obtained with 7 days of data.

Automated insulin delivery: benefits, challenges, and recommendations. A Consensus Report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association

A technological solution for the management of diabetes in people who require intensive insulin therapy has been sought for decades. The last 10 years have seen substantial growth in devices that can be integrated into clinical care. Driven by the availability of reliable systems for continuous glucose monitoring, we have entered an era in which insulin delivery through insulin pumps can be modulated based on sensor glucose data. Over the past few years, regulatory approval of the first automated insulin delivery (AID) systems has been granted, and these systems have been adopted into clinical care. Additionally, a community of people living with type 1 diabetes has created its own systems using a do-it-yourself approach by using products commercialised for independent use. With several AID systems in development, some of which are anticipated to be granted regulatory approval in the near future, the joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association has created this consensus report. We provide a review of the current landscape of AID systems, with a particular focus on their safety. We conclude with a series of recommended targeted actions. This is the fourth in a series of reports issued by this working group. The working group was jointly commissioned by the executives of both organisations to write the first statement on insulin pumps, which was published in 2015. The original authoring group was comprised by three nominated members of the American Diabetes Association and three nominated members of the European Association for the Study of Diabetes. Additional authors have been added to the group to increase diversity and range of expertise. Each organisation has provided a similar internal review process for each manuscript prior to submission for editorial review by the two journals. Harmonisation of editorial and substantial modifications has occurred at both levels. The members of the group have selected the subject of each statement and submitted the selection to both organisations for confirmation.

Continuous glucose monitoring and metrics for clinical trials: an international consensus statement

Randomised controlled trials and other prospective clinical studies for novel medical interventions in people with diabetes have traditionally reported HbA1c as the measure of average blood glucose levels for the 3 months preceding the HbA1c test date. The use of this measure highlights the long-established correlation between HbA1c and relative risk of diabetes complications; the change in the measure, before and after the therapeutic intervention, is used by regulators for the approval of medications for diabetes. However, with the increasing use of continuous glucose monitoring (CGM) in clinical practice, prospective clinical studies are also increasingly using CGM devices to collect data and evaluate glucose profiles among study participants, complementing HbA1c findings, and further assess the effects of therapeutic interventions on HbA1c. Data is collected by CGM devices at 1-5 min intervals, which obtains data on glycaemic excursions and periods of asymptomatic hypoglycaemia or hyperglycaemia (ie, details of glycaemic control that are not provided by HbA1c concentrations alone that are measured continuously and can be analysed in daily, weekly, or monthly timeframes). These CGM-derived metrics are the subject of standardised, internationally agreed reporting formats and should, therefore, be considered for use in all clinical studies in diabetes. The purpose of this consensus statement is to recommend the ways CGM data might be used in prospective clinical studies, either as a specified study endpoint or as supportive complementary glucose metrics, to provide clinical information that can be considered by investigators, regulators, companies, clinicians, and individuals with diabetes who are stakeholders in trial outcomes. In this consensus statement, we provide recommendations on how to optimise CGM-derived glucose data collection in clinical studies, including the specific glucose metrics and specific glucose metrics that should be evaluated. These recommendations have been endorsed by the American Association of Clinical Endocrinologists, the American Diabetes Association, the Association of Diabetes Care and Education Specialists, DiabetesIndia, the European Association for the Study of Diabetes, the International Society for Pediatric and Adolescent Diabetes, the Japanese Diabetes Society, and the Juvenile Diabetes Research Foundation. A standardised approach to CGM data collection and reporting in clinical trials will encourage the use of these metrics and enhance the interpretability of CGM data, which could provide useful information other than HbA1c for informing therapeutic and treatment decisions, particularly related to hypoglycaemia, postprandial hyperglycaemia, and glucose variability.

Automated Insulin Delivery: Benefits, Challenges, and Recommendations. A Consensus Report of the Joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association

A technological solution for the management of diabetes in people who require intensive insulin therapy has been sought for decades. The last 10 years have seen substantial growth in devices that can be integrated into clinical care. Driven by the availability of reliable systems for continuous glucose monitoring, we have entered an era in which insulin delivery through insulin pumps can be modulated based on sensor glucose data. Over the past few years, regulatory approval of the first automated insulin delivery (AID) systems has been granted, and these systems have been adopted into clinical care. Additionally, a community of people living with type 1 diabetes has created its own systems using a do-it-yourself approach by using products commercialized for independent use. With several AID systems in development, some of which are anticipated to be granted regulatory approval in the near future, the joint Diabetes Technology Working Group of the European Association for the Study of Diabetes and the American Diabetes Association has created this consensus report. We provide a review of the current landscape of AID systems, with a particular focus on their safety. We conclude with a series of recommended targeted actions. This is the fourth in a series of reports issued by this working group. The working group was jointly commissioned by the executives of both organizations to write the first statement on insulin pumps, which was published in 2015. The original authoring group was comprised by three nominated members of the American Diabetes Association and three nominated members of the European Association for the Study of Diabetes. Additional authors have been added to the group to increase diversity and range of expertise. Each organization has provided a similar internal review process for each manuscript prior to submission for editorial review by the two journals. Harmonization of editorial and substantial modifications has occurred at both levels. The members of the group have selected the subject of each statement and submitted the selection to both organizations for confirmation.

The Optimal Duration of a Run-In Period to Initiate Continuous Glucose Monitoring for a Randomized Trial

Objective: To determine the optimal duration of a run-in period for initiation of real-time continuous glucose monitoring (CGM) before the start of a randomized controlled trial (RCT) in type 1 diabetes (T1D) or type 2 diabetes (T2D). Methods: Data sets were pooled from 8 RCTs, which had a blinded CGM wear period followed by at least 3 months of unblinded CGM use. Across all participants, mean time in range 70-180 mg/dL (TIR) and mean time <54 mg/dL (T < 54) as well as other key CGM metrics were computed for the initial period of blinded CGM wear and from the subsequent 13 weeks of unblinded CGM use. Results: The analysis cohort included data from 485 participants: 348 with T1D and 137 with T2D, ranging in age from 2 to 82 years. Mean TIR was 49% with blinded CGM before initiation of unblinded CGM use, increased to 55% by the end of the first week of unblinded CGM use, and then showed little change through 13 weeks. Mean T < 54 decreased from 1.4% with blinded CGM to 0.8% 1 week and 0.6% 2 weeks after initiating unblinded CGM use, which matched the value in month 3. Similar results were obtained for mean glucose, time >180 mg/dL, time >250 mg/dL, and time <70 mg/dL, with the mean improvement in hyperglycemia metrics plateauing slightly faster than hypoglycemia metrics. Findings were largely similar for T1D and T2D. Conclusion: When initiating unblinded real-time CGM, improvement in key CGM metrics occurs rapidly, with maximal effect on the mean of each metric achieved within 1-2 weeks. For a randomized trial in which all participants will use real-time unblinded CGM for glucose monitoring, a run-in period should be implemented before collecting baseline data for participants who are not CGM users. For such CGM-naive individuals, a 7- to 14-day acclimation period is sufficient followed by a 14-day period for collection of baseline unblinded CGM data.

Impact of Temporary Glycemic Target Use in the Hybrid and Advanced Hybrid Closed-Loop Systems

The Medtronic advanced hybrid closed-loop (AHCL) and MiniMed™ 670G hybrid closed-loop (HCL) systems provide the option to temporarily increase the glucose target to 150 mg/dL (8.3 mmol/L). This analysis investigated the efficacy of the AHCL compared with that of the HCL after the use of this setting. Data from 60 participants in the Fuzzy Logic Automated Insulin Regulation (FLAIR) study were used to compare the AHCL and HCL systems after the use of the temporary target (TT), and during analogous periods where this setting was not used. Differences in time in range 70-180 mg/dL between the systems were similar after the use of the TT setting and during analogous non-TT periods (interaction P = 0.87). Similar trends were observed for mean glucose, percentage time >180 mg/dL, and percentage time >250 mg/dL. Differences between AHCL and HCL systems were similar after the use of the TT setting compared with those of non-TT periods. ClinicalTrials.gov NCT03040414.

Comparing Patch vs Pen Bolus Insulin Delivery in Type 2 Diabetes Using Continuous Glucose Monitoring Metrics and Profiles

OBJECTIVE

CeQur Simplicity™ (CeQur, Marlborough, MA) is a 3-day insulin delivery patch designed to meet mealtime insulin requirements. A recently reported 48-week, randomized, multicenter, interventional trial compared efficacy, safety and self-reported outcomes in 278 adults with type 2 diabetes (T2D) on basal insulin therapy who initiated and managed mealtime insulin therapy with a patch pump versus insulin pen. We assessed changes in key glycemic metrics among a subset of patients who wore a continuous glucose monitoring (CGM) device.

METHODS

Study participants (patch, n = 49; pen, n = 48) wore a CGM device in masked setting during the baseline period and prior to week 24. Glycemic control was assessed using international consensus guidelines for percentage of Time In Range (%TIR: >70% at 70-180 mg/dL), Time Below Range (%TBR: <4% at <70 mg/dL; <1% at <54 mg/dL), and Time Above Range (%TAR: <25% at >180 mg/dL; <5% at >250 mg/dL).

RESULTS

Both the patch and pen groups achieved recommended targets in %TIR (74.1% ± 18.7%, 75.2 ± 16.1%, respectively) and marked reductions in %TAR >180 mg/dL (21.1% ± 19.9%, 19.7% ± 17.5%, respectively) but with increased %TBR <70 mg/dL (4.7% ± 5.2%, 5.1 ± 5.8, respectively), all P < .0001. No significant between-group differences in glycemic improvements or adverse events were observed.

CONCLUSIONS

CGM confirmed that the patch or pen can be used to safely initiate and optimize basal-bolus therapy using a simple insulin adjustment algorithm with SMBG. Preference data suggest that use of the patch vs pen may enhance treatment adherence.

Interindividual variability in average glucose-glycated haemoglobin relationship in type 1 diabetes and implications for clinical practice

AIM

Glycated haemoglobin (HbA1c) can fail to reflect average glucose levels, potentially compromising management decisions. We analysed variability in the relationship between mean glucose and HbA1c in individuals with diabetes.

MATERIALS AND METHODS

Three months of continuous glucose monitoring and HbA1c data were obtained from 216 individuals with type 1 diabetes. Universal red blood cell glucose transporter-1 Michaelis constant K_M and individualized apparent glycation ratio (AGR) were calculated and compared across age, racial and gender groups.

RESULTS

The mean age (range) was 30 years (8-72) with 94 younger than 19 years, 78 between 19 and 50 years, and 44 were >50 years. The group contained 120 women and 96 men with 106 white and 110 black individuals. The determined K_M value was 464 mg/dl and AGR was (mean ± SD) 72.1 ± 7 ml/g. AGR, which correlated with red blood cell lifespan marker, was highest in those aged >50 years at 75.4 ± 6.9 ml/g, decreasing to 73.2 ± 7.8 ml/g in 19-50 years, with a further drop to 71.0 ± 5.8 ml/g in the youngest group (p <0 .05). AGR differed between white and black groups (69.9 ± 5.8 and 74.2 ± 7.1 ml/g, respectively; p < .001). In contrast, AGR values were similar in men and women (71.5 ± 7.5 and 72.5 ± 6.6 ml/g, respectively; p = .27). Interestingly, interindividual AGR variation within each group was at least four-fold higher than average for between-group variation.

CONCLUSIONS

In this type 1 diabetes cohort, ethnicity and age, but not gender, alter the HbA1c-glucose relationship with even larger interindividual variations found within each group than between groups. Clinical application of personalized HbA1c-glucose relationships has the potential to optimize glycaemic care in the population with diabetes.

A Comparison of Postprandial Glucose Control in the Medtronic Advanced Hybrid Closed-Loop System Versus 670G

Background: We recently reported that use of an "advanced" hybrid closed-loop system reduced hyperglycemia without increasing hypoglycemia compared to a first-generation system. The aim of this analysis was to evaluate whether this improved performance was specifically related to better mealtime glycemic control. Methods: We conducted a secondary analysis of postprandial glycemic control in an open-label, multinational, randomized crossover trial of 112 participants with type 1 diabetes, aged 14-29, of the Medtronic MiniMed™ 670G hybrid closed-loop system (670G) versus the Medtronic advanced hybrid closed-loop (AHCL) system, for 12 weeks each. We compared glycemic and insulin delivery metrics over a 3 h horizon across all meals to assess system performance and outcomes. Results: Overall meal size and premeal insulin on board were similar during run-in and between 670G and AHCL arms. Compared with 670G arm, premeal, peak, and mean glucose levels were numerically lower in the AHCL arm (167 ± 23, 231 ± 23, and 177 ± 20 mg/dL vs. 175 ± 23, 235 ± 23, and 180 ± 19 mg/dL, respectively), with a trend to lower hyperglycemia level 2 in AHCL arm. Adjusting for premeal glucose level, all postmeal outcomes between 670G and AHCL were statistically similar. Prandial insulin delivery also was similar in both treatment arms (21 ± 9 vs. 23 ± 10 U), with a shift in basal/bolus ratio from 28%/71% in 670G arm to 20%/80% in AHCL arm. Conclusions: Reduced hyperglycemia with AHCL compared to 670G was not related to early postprandial glycemic excursions after adjusting for premeal glucose level (<3 h after meal), but likely to later (>3 h) postprandial or overnight improvements. Further refinements to mealtime bolus algorithms and strategies may more optimally control prandial glycemic excursions.

Efficacy of once-weekly tirzepatide versus once-daily insulin degludec on glycaemic control measured by continuous glucose monitoring in adults with type 2 diabetes (SURPASS-3 CGM): a substudy of the randomised, open-label, parallel-group, phase 3 SURPASS-3 trial

BACKGROUND

Tirzepatide is a novel dual glucose-dependent insulinotropic polypeptide (GIP) and GLP-1 receptor agonist under development for the treatment of type 2 diabetes. In this study, we used continuous glucose monitoring (CGM) to compare the 24 h glucose profile for participants given tirzepatide compared with those given insulin degludec.

METHODS

This substudy of the open-label, parallel-group, phase 3 SURPASS-3 trial, was done at 45 sites across six countries (Hungary, Poland, Romania, Spain, Ukraine, and the USA). Eligible participants in the main study were adults with type 2 diabetes, a baseline HbA_1c of 7·0-10·5% (53-91 mmol/mol), and a BMI of 25 kg/m² or more, who were insulin-naive, and treated with metformin alone or in combination with a SGLT2 inhibitor for at least 3 months before screening. Participants in the main study were randomly assigned (1:1:1:1) to receive once-weekly subcutaneous injection of tirzepatide 5 mg, 10 mg, or 15 mg, or once-daily subcutaneous injection of titrated insulin degludec (100 U/mL), using an interactive web-response system. Participants were stratified by country, HbA_1c concentration, and concomitant oral antihyperglycaemic medication. A subset of these patients with a normal wake-sleep cycle were enrolled into this substudy, and interstitial glucose values were collected by CGM for approximately 7 days at baseline, 24 weeks, and 52 weeks. The primary outcome was to compare pooled participants assigned to 10 mg and 15 mg tirzepatide versus insulin degludec for the proportion of time that CGM values were in the tight target range (71-140 mg/dL) at 52 weeks, assessed in all randomly assigned participants who received at least one dose of study drug and had an evaluable CGM session at either baseline or after baseline. The secondary outcomes were to compare tirzepatide (5 mg, 10 mg, and 15 mg) versus insulin degludec for the proportion and duration of time in tight target range at 24 and 52 weeks. This was a substudy of the trial registered with ClinicalTrials.gov, NCT03882970, and is complete.

FINDINGS

From April 1 to Nov 27, 2019, 313 participants were screened for eligibility, 243 of whom were enrolled in CGM substudy (tirzepatide 5 mg, n=64; tirzepatide 10 mg, n=51; tirzepatide 15 mg, n=73; and insulin degludec, n=55). Patients given once-weekly tirzepatide (pooled 10 mg and 15 mg groups) had a greater proportion of time in tight target range compared with patients given insulin degludec (estimated treatment difference 25% [95% CI 16-33]; p<0·0001). Participants assigned to tirzepatide spent significantly more time in tight target range at 52 weeks compared with those assigned to insulin degludec (5 mg 12% [1-22], p=0·031; 10 mg 24% [13-35], p<0·0001; and 15 mg 25% [14-35], p<0·0001). Participants assigned to tirzepatide 10 mg and 15 mg, but not to tirzepatide 5 mg, spent significantly more time in tight target range at 24 weeks compared with insulin degludec (10 mg 19% [8-30], p=0·0008; 15 mg 21% [11-31], p<0·0001).

INTERPRETATION

Once-weekly treatment with tirzepatide showed superior glycaemic control measured using CGM compared with insulin degludec in participants with type 2 diabetes on metformin, with or without a SGLT2 inhibitor. These new data provide additional evidence to the effect of tirzepatide and potential for achieving glycaemic targets without increase of hypoglycaemic risk compared with a basal insulin.

FUNDING

Eli Lilly and Company.

Continuous Ketone Monitoring Consensus Report 2021

This article is the work product of the Continuous Ketone Monitoring Consensus Panel, which was organized by Diabetes Technology Society and met virtually on April 20, 2021. The panel consisted of 20 US-based experts in the use of diabetes technology, representing adult endocrinology, pediatric endocrinology, advanced practice nursing, diabetes care and education, clinical chemistry, and bioengineering. The panelists were from universities, hospitals, freestanding research institutes, government, and private practice. Panelists reviewed the medical literature pertaining to ten topics: (1) physiology of ketone production, (2) measurement of ketones, (3) performance of the first continuous ketone monitor (CKM) reported to be used in human trials, (4) demographics and epidemiology of diabetic ketoacidosis (DKA), (5) atypical hyperketonemia, (6) prevention of DKA, (7) non-DKA states of fasting ketonemia and ketonuria, (8) potential integration of CKMs with pumps and automated insulin delivery systems to prevent DKA, (9) clinical trials of CKMs, and (10) the future of CKMs. The panelists summarized the medical literature for each of the ten topics in this report. They also developed 30 conclusions (amounting to three conclusions for each topic) about CKMs and voted unanimously to adopt the 30 conclusions. This report is intended to support the development of safe and effective continuous ketone monitoring and to apply this technology in ways that will benefit people with diabetes.

Randomized comparison of self-monitored blood glucose (BGM) versus continuous glucose monitoring (CGM) data to optimize glucose control in type 2 diabetes

AIMS

Evaluate whether structured BGM testing (BGM) or real-time CGM (CGM) lead to improved glucose control (A1c). Determine which approach optimized glucose control more effectively. METHODS-MULTI-ARM PARALLEL: trial of three type 2 diabetes (T2D) therapies ± metformin: (1) sulfonylurea (SU), (2) incretin (DPP4 inhibitor or GLP-1 agonist), or (3) insulin. After a baseline CGM, 114 adult subjects were randomized to either BGM (4 times daily) or CGM (24/7) for 16 weeks with therapies adjusted every 4 weeks.

RESULTS

A1c means decreased from 8.19 to 7.07 (1.12% difference) with CGM (n = 59) and 7.85 to 7.03 (0.82% difference) with BGM (n = 55) (p < 0.001). BGM and CGM groups showed significant improvements in time in range and glucose variability-with no significant difference between the two groups. Clinically important hypoglycemia (<50 mg/dL) was significantly reduced for the CGM group compared with BGM (p < 0.01), particularly in subjects taking insulin or therapies with higher hypoglycemic risk (SU).

CONCLUSION

In T2D, structured, consistent use of glucose data regardless of device (structured BGM or CGM) leads to improvements in A1c control. CGM is more effective than BGM in minimizing hypoglycemia particularly for those using higher hypoglycemic risk therapies.

Continuous Glucose Monitoring in Adults With Type 1 Diabetes With 35 Years Duration From the DCCT/EDIC Study

OBJECTIVE

We evaluated blinded continuous glucose monitoring (CGM) profiles in a subset of adults with type 1 diabetes from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study to characterize the frequency of glycemic excursions and contributing factors.

RESEARCH DESIGN AND METHODS

CGM-derived metrics were compared for daytime and nighttime periods using blinded CGM for a minimum of 6.5 days (average 11.9 days) and correlated with HbA1c levels, routine use of diabetes devices, and other characteristics in 765 participants.

RESULTS

Participants were 58.9 ± 6.5 years of age with diabetes duration 36.8 ± 4.9 years and HbA1c 7.8 ± 1.2%; 58% used insulin pumps, and 27% used personal, unblinded CGM. Compared with daytime, nighttime mean sensor glucose was lower, percent time in range 70-180 mg/dL (TIR) was similar, and hypoglycemia was more common. Over the entire recording period, only 9% of the 765 participants achieved >70% TIR and only 28% achieved <1% of observations of <54 mg/dL. Indeed, participants with the highest percentage of hypoglycemia had the lowest HbA1c levels. However, use of insulin pumps and CGM decreased the percent time at <54 mg/dL.

CONCLUSIONS

In adults with long-standing type 1 diabetes, short-term blinded CGM profiles revealed frequent clinically significant hypoglycemia (<54 mg/dL) during the night and more time in hyperglycemia during the day. The small subset of participants using routine CGM and insulin pumps had fewer hypoglycemic and hyperglycemic excursions and lower HbA1c levels. Thus, strategies to lower meal-stimulated hyperglycemia during the day and prevent hypoglycemia at night are relevant clinical goals in older patients with type 1 diabetes.

Real-World, Patient-Reported and Clinic Data from Individuals with Type 1 Diabetes Using the MiniMed 670G Hybrid Closed-Loop System

Background: The purpose of this study was to collect 1 year of real-world data from individuals with type 1 diabetes (T1D) initiating the Medtronic 670G hybrid closed-loop insulin delivery system as part of usual care. We sought to expand current knowledge to understand how use of the system impacts patient-reported outcomes, in addition to clinical outcomes, for children and adults with T1D. Methods: Questionnaires were completed by the participant (and/or parent) before initiation of the 670G system (baseline) and at 6 weeks, 6 months, and 12 months from enrollment. Clinical data were obtained at routine clinical visits. Results: Of 132 participants who initiated Auto Mode, 80 completed the 12-month questionnaires while persisting with the system. Nearly all reported receiving adequate training on the 670G. Participant and parent-reported fear of hypoglycemia decreased by 6 and 11 points, respectively, from baseline to 12 months. More than half reported issues with sleep interruption at night due to alarms and 40% did not like frequent exits from Auto Mode. For the subset who had complete continuous glucose monitor data (n = 27), mean percent time in target range (70-180 mg/dL) was 66% at baseline, and 74% and 68% at 6 and 12 months, respectively. Conclusions: With this study, we have captured real-time feedback from patients with T1D who initiated the 670G system and continued to use it over 12 months regarding their experience with the system. This has helped to illuminate both benefits and burdens associated with the first commercially available hybrid closed-loop system.

Lived Experience of Advanced Hybrid Closed-Loop Versus Hybrid Closed-Loop: Patient-Reported Outcomes and Perspectives

This article reports on the lived experience of Medtronic advanced hybrid closed-loop (AHCL) in comparison to first generation hybrid closed-loop (HCL) in a randomized, open-label, two-period crossover trial. Patient-reported outcome (PROs) measures were administered before randomization and at the end of each study period in 113 adolescents and young adults with type 1 diabetes. Glucose monitoring satisfaction subscales for emotional burden and behavioral burden improved significantly (P < 0.01) over time with use of AHCL versus HCL and co-occurred with glycemic improvements (reduced percent time above 180 mg/dL during the day and no change in % time less than 54 mg/dL across 24 h) and greater time in Auto Mode. PROs, including distress, technology attitudes, and hypoglycemia confidence, were not different. AHCL use was associated with improved glucose monitoring satisfaction. Satisfaction was greater in those participants who had more appreciable glycemic benefit and stayed in Auto Mode more often. Clinical Trial Registration number: NCT03040414.

The Effect of Discontinuing Continuous Glucose Monitoring in Adults With Type 2 Diabetes Treated With Basal Insulin

OBJECTIVE

To explore the effect of discontinuing continuous glucose monitoring (CGM) after 8 months of CGM use in adults with type 2 diabetes treated with basal without bolus insulin.

RESEARCH DESIGN AND METHODS

This multicenter trial had an initial randomization to either real-time CGM or blood glucose monitoring (BGM) for 8 months followed by 6 months in which the BGM group continued to use BGM (n = 57) and the CGM group was randomly reassigned either to continue CGM (n = 53) or discontinue CGM with resumption of BGM for glucose monitoring (n = 53).

RESULTS

In the group that discontinued CGM, mean time in range (TIR) 70-180 mg/dL, which improved from 38% before initiating CGM to 62% after 8 months of CGM, decreased after discontinuing CGM to 50% at 14 months (mean change from 8 to 14 months -12% [95% CI -21% to -3%], P = 0.01). In the group that continued CGM use, little change was found in TIR from 8 to 14 months (baseline 44%, 8 months 56%, 14 months 57%, mean change from 8 to 14 months 1% [95% CI -11% to 12%], P = 0.89). Comparing the two groups at 14 months, the adjusted treatment group difference in mean TIR was -6% (95% CI -16% to 4%, P = 0.20).

CONCLUSIONS

In adults with type 2 diabetes treated with basal insulin who had been using real-time CGM for 8 months, discontinuing CGM resulted in a loss of about one-half of the initial gain in TIR that had been achieved during CGM use.

Effect of insulin degludec versus insulin glargine U100 on time in range: SWITCH PRO, a crossover study of basal insulin-treated adults with type 2 diabetes and risk factors for hypoglycaemia

AIMS

To compare time in range (TIR) with use of insulin degludec U100 (degludec) versus insulin glargine U100 (glargine U100) in people with type 2 diabetes.

MATERIALS AND METHODS

We conducted a randomized, crossover, multicentre trial comparing degludec and glargine U100 in basal insulin-treated adults with type 2 diabetes and ≥1 hypoglycaemia risk factor. There were two treatment periods, each with 16-week titration and 2-week maintenance phases (with evaluation of glucose using blinded professional continuous glucose monitoring). The once-weekly titration (target: 3.9-5.0 mmol/L) was based on pre-breakfast self-measured blood glucose. The primary endpoint was percentage of TIR (3.9─10.0 mmol/L). Secondary endpoints included overall and nocturnal percentage of time in tight glycaemic range (3.9-7.8 mmol/L), and mean glycated haemoglobin (HbA1c) and glucose levels.

RESULTS

At baseline, participants (n = 498) had a mean (SD) age of 62.8 (9.8) years, a diabetes duration of 15.1 (7.7) years and an HbA1c level of 59.6 (11.0) mmol/mol (7.6 [1.0]%). Noninferiority and superiority were confirmed for degludec versus glargine U100 for the primary endpoint, with a mean TIR of 72.1% for degludec versus 70.7% for glargine U100 (estimated treatment difference [ETD] 1.43% [95% confidence interval (CI): 0.12, 2.74; P = 0.03] or 20.6 min/d). Overall time in tight glycaemic range favoured degludec versus glargine U100 (ETD 1.5% [95% CI: 0.15, 2.89] or 21.9 min/d). Degludec also reduced nocturnal time below range (TBR; <3.9 mmol/L) compared with glargine U100 (ETD -0.88% [95% CI: -1.34, -0.42] or 12.7 min/night; post hoc) and significantly fewer nocturnal hypoglycaemic episodes of <3.0 mmol/L were observed.

CONCLUSIONS

Degludec, compared with glargine U100, provided more TIR and time in tight glycaemic range, and reduced nocturnal TBR in insulin-treated people with type 2 diabetes.