Glucose Outcomes with the In-Home Use of a Hybrid Closed-Loop Insulin Delivery System in Adolescents and Adults with Type 1 Diabetes

Point-Counterpoint: The Need for Do-It-Yourself (DIY) Open Source (OS) AID Systems in Type 1 Diabetes Management

In the last decade, technology developed by people with diabetes and their loved ones has added to the options for diabetes management. One such example is that of automated insulin delivery (AID) algorithms, which were created and shared as open source by people living with type 1 diabetes (T1D) years before commercial systems were first available. Now, numerous options for commercial systems exist in some countries, yet tens of thousands of people with diabetes are still choosing Open-Source AID (OS-AID), previously called "do-it-yourself" (DIY) systems, which are noncommercial versions of these open-source AID systems. In this article, we provide point and counterpoint perspectives regarding (1) safety and efficacy, (2) regulation and support, (3) user choice and flexibility, (4) access and affordability, and (5) patient and provider education, for open source and commercial AID systems. The perspectives reflected here include that of a person living with T1D who uses and has developed OS-AID systems, a physician-researcher based in the United States who conducts clinical trials to support development of commercial AID systems and supports people with diabetes using all types of AID, and an endocrinologist with T1D who uses both systems and treats people with diabetes using all types of AID.

Automated insulin delivery in children with type 1 diabetes during physical activity: a meta-analysis

OBJECTIVES

The aim of this study was to evaluating the performance of the automated insulin delivery (AID) in adolescents, and children with type 1 diabetes (T1D) during physical activity.

METHODS

Relevant studies were searched electronically in the Cochrane Library, PubMed, and Embase utilizing the key words "Child", "Insulin Infusion Systems", and "Diabetes Mellitus" from inception to 17th March 2024 to evaluate the performance of the AID in adolescents, and children with T1D during physical activity.

RESULTS

Twelve studies involving 514 patients were identified. AID did not show a beneficial effect on duration of hypoglycemia<70 mg/dL during study period (p>0.05; I 2=96 %) and during the physical activity (p>0.99). Percentage of sensor glucose values in TIR was higher in AID than the non-AID pumps during study period (p<0.001; I 2=94 %). The duration of hyperglycemic time was significantly decreased in AID group compared to the non-AID pumps group during study period (p<0.05; I 2>50 %).

CONCLUSIONS

AID improved TIR and decreased the duration of hyperglycemic time, but did not appear to have a significant beneficial effect on the already low post-exercise duration of hypoglycemia achievable by open loop or sensor-augmented pumps in adolescents and children with T1D during physical activity; further research is needed to confirm the beneficial effect of AID on duration of hypoglycemia.

Virtual insulin pump initiation is safe effective in children adolescents with type 1 diabetes

Objective

There is no head-to-head comparison of the safety and efficacy of virtual versus in-office insulin pump initiation for youth with type 1 diabetes in the US. The study's aim was to determine the safety and efficacy of virtual versus in-office pump initiation in pediatric type 1 diabetes.

Research design and methods

A longitudinal retrospective study of 112 subjects: 65% (n=73), ages 11.2 ± 3.8 years(y), received in-office training; and 35% (n=39), ages 12.0 ± 4.0y, received virtual training. The number of White subjects was 40 (55%) in the in-office group, and 25 (66%) in the remote group; while Black subjects were 11 (15%) in the in-office group and 4 (10%) in the virtual group. Data were collected at pump initiation, 3 and 6 months.

Results

There were no significant differences in sex, race, height, weight, BMI, and the duration of diabetes between the groups at baseline. There was no significant difference in A1c between the groups at 0, 3, and 6 months. A1c correlated significantly with the glucose management indicator at 0, 3, and 6 months: baseline: r=0.49, p<0.0001; 3 months: r=0.77, p<0.0001; and 6 months: r=0.71, p<0.0001. There was no relationship between A1c or TIR and pubertal status, BMI, sex, or race. A1c was significantly elevated in the non-White individuals at 6 months only: 57.9 mmol/mol (50.8-69.4) versus 51.9 mmol/mol (46.5-59.6)], p=0.007.

Conclusion

Virtual insulin pump initiation is safe and effective in children with type 1 diabetes. This approach could accelerate the adoption of the use of diabetes technology in minority populations in the US.

Comparative Effectiveness of Hybrid Closed-Loop Automated Insulin Delivery Systems Among Patients with Type 1 Diabetes

BACKGROUND

Clinical trials have demonstrated the efficacy and safety of hybrid closed-loop (HCL) systems, yet few studies have compared outcomes in the real-world setting.

METHOD

This retrospective study analyzed patients from an academic endocrinology practice between January 1, 2018, and November 18, 2022. The inclusion criteria were diagnosis code for type I diabetes (T1D), >18 years of age, new to any HCL system [Medtronic 670G/770G (MT), Tandem Control IQ (CIQ), or Omnipod 5 (OP5)], and availability of a pump download within three months. The outcomes included %time in range (TIR) of 70 to 180 mg/dL, %time below range (TBR) <70 mg/dL at 90 days, and HbA1c for 91 to 180 days.

RESULT

Of the 176 participants, 47 were MT, 74 CIQ, and 55 OP5. Median (25%, 75%) change in HbA1c was -0.1 (-0.8, 0.3), -0.6 (-1.1, -0.15), and -0.55 (-0.98, 0)% for MT, CIQ, and OP5, respectively, (P = .04). TIR was 70 (57, 76), 67 (59, 75), and 68 (60, 76)% (P = .95) at 90 days while TBR was 2 (1, 3), 1 (0, 2), and 1 (0, 1)%, respectively, (P = .002). The %time in automated delivery was associated with TIR and change in HbA1c. After controlling other factors including %time in automated delivery, HCL type was not an independent predictor of change in HbA1c nor TIR but remained a significant predictor of TBR.

CONCLUSION

There were significant reductions in HbA1c in CIQ and OP5. TIR was similar across pumps, but TBR was highest with MT. The %time in automated delivery likely explains differences in change in HbA1c but not TBR between HCL systems.

Disordered Eating Behaviors Among Adolescents and Young Adults with Type 1 Diabetes Treated with Insulin Pumps and Hybrid Closed-Loop Systems

Background and Aims: Disordered eating behaviors (DEB) are more common among individuals with type 1 diabetes (T1D) compared to those without, and for insulin pump users may be associated with higher hemoglobin A1c (HbA1c). We investigated DEB risk factors among insulin pump-treated individuals with T1D and clinical characteristics of hybrid closed-loop (HCL) systems' users by DEB level. Methods: An observational, cross-sectional study of 167 insulin pump-treated individuals with T1D, 13-21 years of age. Data were obtained from patients' medical charts with additional self-reported questionnaires, including assessment of DEB. Results: DEB were found in 71 (42.5%) individuals, and positively associated with female sex (β = 2.98 [standard error (SE) = 1.31], P = 0.025), body mass index (BMI)-Z-score (β = 2.12 [SE = 0.64], P = 0.001), HbA1c (β = 1.40 [SE = 0.45], P = 0.02), and higher rate of pump discontinuation (β = 4.48 [SE = 1.99], P = 0.026). The use of HCL systems compared to insulin pumps was associated with higher BMI-Z-score (odds ratio [OR]: 3.46 [95% confidence interval, CI: 1.52-7.87], P = 0.003) and tendency to lower HbA1c level (OR: 0.44 [95% CI: 0.18-1.09], P = 0.078) among individuals without DEB, and with lower HbA1c level (OR: 0.29 [95% CI: 0.10-0.83], P = 0.022) and higher socioeconomic status (OR: 1.73 [95% CI: 1.09-2.74], P = 0.020) among individuals with DEB. Conclusions: DEB are common among individuals with T1D treated with insulin pumps and are associated with higher HbA1c levels. Among T1D individuals with DEB, HCL system use is associated with lower HbA1c compared to insulin pump treatment. Our findings highlight the importance of regular screening for DEB and its risk factors to improve pump treatment and diabetes management. Moreover, individuals with DEB using HCL systems may benefit from reduced HbA1c levels.

Randomized Trial of Assisted Hybrid Closed-Loop Therapy Versus Sensor-Augmented Pump Therapy in Pregnancy

Objective: Examine gestational safety, glycemic and health outcomes, of a hybrid closed-loop (HCL) system without pregnancy-specific glucose targets. Research Design: This was a pilot feasibility investigator-initiated, two-site, single-blind, randomized controlled trial of sensor-augmented pump therapy (SAPT) versus HCL therapy in type 1 diabetes pregnancies. Participants were enrolled in the first trimester and randomized at 14-18 weeks of gestation and used SAPT or HCL until 4-6 weeks postpartum. We compared continuous glucose monitoring (CGM) metrics, severe hypoglycemia (SH), diabetic ketoacidosis (DKA), adverse skin reactions, and pregnancy outcomes between groups. Results: Baseline characteristics were similar between groups (n = 11 HCL and n = 12 SAPT). There was no SH or DKA episode after randomization. Time spent <54 mg/dL did not differ between groups. Time spent <63 mg/dL decreased in both groups, significantly in the HCL group (3.5% [1.3% standard error] second trimester and 2.8% [1.3%] third trimester vs. 7.9% [1.3%] run-in phase, P < 0.05 for both). Mean sensor glucose was lower with SAPT compared to HCL therapy in the third trimester (119 [4] mg/dL SAPT vs. 132 [4] mg/dL HCL, P < 0.05). Third trimester time-in-range (TIR; 63-140 mg/dL) increased with SAPT (68.2% [3.1%] vs. 64.3% [3.1%] run-in phase, P < 0.05). Gestational health outcomes did not differ between groups. The HCL group used assistive techniques, such as fake carbohydrate boluses and exiting HCL overnight. Conclusions: CGM within group differences were seen for time <63 mg/dL favoring HCL therapy and TIR favoring SAPT (third trimester vs. baseline). Safety and adverse pregnancy outcomes were similar between groups.

Evaluation of hybrid closed-loop insulin delivery system in type 1 diabetes in real-world clinical practice: One-year observational study

BACKGROUND

In 2016, the Food and Drug Administration approved the first hybrid closed-loop (HCL) insulin delivery system for adults with type 1 diabetes (T1D). There is limited information on the impact of using HCL systems on patient-reported outcomes (PROs) in patients with T1D in real-world clinical practice. In this independent study, we evaluated glycemic parameters and PROs over one year of continuous use of Medtronic's 670G HCL in real-world clinical practice.

AIM

To assess the effects of hybrid closed loop system on glycemic control and quality of life in adults with T1D.

METHODS

We evaluated 71 patients with T1D (mean age: 45.5 ± 12.1 years; 59% females; body weight: 83.8 ± 18.7 kg, body mass index: 28.7 ± 5.6 kg/m2, A1C: 7.6% ± 0.8%) who were treated with HCL at Joslin Clinic from 2017 to 2019. We measured A1C and percent of glucose time-in-range (%TIR) at baseline and 12 months. We measured percent time in auto mode (%TiAM) for the last two weeks preceding the final visit and assessed PROs through several validated quality-of-life surveys related to general health and diabetes management.

RESULTS

At 12 mo, A1C decreased by 0.3% ± 0.1% (P = 0.001) and %TIR increased by 8.1% ± 2.5% (P = 0.002). The average %TiAM was only 64.3% ± 32.8% and was not associated with A1C, %TIR or PROs. PROs, provided at baseline and at the end of the study, showed that the physical functioning submodule of 36Item Short-Form Health Survey increased significantly by 22.9% (P < 0.001). Hypoglycemia fear survey/worry scale decreased significantly by 24.9% (P < 0.000); Problem Areas In Diabetes reduced significantly by -17.2% (P = 0.002). The emotional burden submodules of dietary diversity score reduced significantly by -44.7% (P = 0.001). Furthermore, analysis of Clarke questionnaire showed no increase in awareness of hypoglycemic episodes. WHO-5 showed no improvements in subject's wellbeing among participants after starting the 670G HCL system. Finally, analysis of Pittsburgh Sleep Quality Index showed no difference in sleep quality, sleep latency, or duration of sleep from baseline to 12 mo.

CONCLUSION

The use of HCL in real-world clinical practice for one year was associated with significant improvements in A1C, %TIR, physical functioning, hypoglycemia fear, emotional distress, and emotional burden related to diabetes management. However, these changes were not associated with time in auto mode.

Acute and Chronic Adverse Outcomes of Type 1 Diabetes

Type 1 diabetes is associated with both acute and chronic complications. Acute complications include diabetic ketoacidosis and severe hypoglycemia. Chronic complications can be microvascular or macrovascular. Microvascular complications include retinopathy, nephropathy, and neuropathy. The pathophysiology of microvascular complications is complex. Hyperglycemia is a common underlying risk factor, underscoring the importance of optimizing glycemic management. Patients with type 1 diabetes are also at increased risk of macrovascular complications including coronary artery disease and vascular disease. The American Diabetes Association provides screening guidelines for chronic complications of diabetes. Adherence to these guidelines is an important aspect of diabetes care.

Reducing Diabetes Burden in Medtronic's Automated Insulin Delivery Systems

Background: The MiniMed™ 780G advanced hybrid closed-loop system (MM780G) builds on the basal automation and low-glucose protection features of the MiniMed™ 670G and 770G systems. While previous publications have focused on glycemic control improvements with MM780G, burden reduction has not been fully described. Methods: Data from two 3-month pivotal trials for the MM670G with Guardian™ Sensor 3 (GS3) (104 adults; 125 children) and MM780G with Guardian™ 4 Sensor (G4S) (67 adults;109 children) were compared. Real-world data (RWD) from United States users (N = 3851) transitioning from MM770G+GS3 to MM780G+G4S were also analyzed. Analyses included a new metric for diabetes management burden (i.e., pentagon composite metric), glycemic outcomes and system burden (e.g., closed-loop exits and fingersticks per day). Results: Diabetes burden metric (-22.8% and -28.5%), time in range (+3.1% [*P = 0.035] and +6.4% [P < 0.001]) and time below range (-1.8% [*P < 0.001] and -0.7% [*P < 0.001]) significantly improved, compared to MM670G for adult and pediatric participants, respectively. The pediatric mean sensor glucose (SG) reduced by -8.6 mg/dL (*P < 0.001), while the adults' saw no change. Closed-loop use significantly increased for both cohorts (+17.1% [*P < 0.001] and +20.5% [*P < 0.001]). Closed-loop exits were significantly reduced to about 1 per week (-0.5 [*P < 0.001] and -0.7 [*P < 0.001]); fingerstick tests were also reduced (-6.2 [*P < 0.001] and -6.9 [*P < 0.001]). Similar outcomes were observed from U.S. RWD. Conclusions: MiniMed™ 780G with G4S use was associated with significant reduction in diabetes management burden with fewer closed-loop exits, fingersticks and other interactions, and improvements in glycemic control when compared to the MiniMed™ 670G with GS3.

Early Real-World Performance of the MiniMed™ 780G Advanced Hybrid Closed-Loop System and Recommended Settings Use in the United States

Background: The MiniMed™ 780G system (MM780G) with Guardian™ 4 sensor includes a 100 mg/dL glucose target (GT) and automated insulin corrections up to every 5 min and was recently approved for use in the United States. In the present study, early real-world MM780G performance and the use of recommended system settings (100 mg/dL GT with an active insulin time of 2 h), by individuals with type 1 diabetes, were evaluated. Methods: CareLink™ personal data uploaded between the launch of the MM780G to August 22, 2023 were aggregated and underwent retrospective analysis (based on user consent) and if users had ≥10 days of continuous glucose monitoring (CGM) data. The 24-h day CGM metrics, including mean glucose, percentage of time spent in (%TIR), above (%TAR), and below (%TBR) target range (70-180 mg/dL), in addition to delivered insulin and closed-loop (CL) exits, were compared between an overall group (n = 7499) and individuals who used recommended settings (each, for >95% of the time). An analysis of the same metrics for MiniMed™ 770G system (MM770G) users (n = 3851) who upgraded to the MM780G was also conducted (paired t-test or Wilcoxon signed-rank test, P < 0.05 considered statistically significant). Results: For MM780G users, CGM use, and time in CL were >90% and all MM780G CGM metrics exceeded consensus-recommended goals. With recommended settings (22% of all users), mean %TIR and %TITR (70-140 mg/dL) were 81.4% and 56.4%, respectively. For individuals who upgraded from the MM770G, %TIR and %TITR increased from 73.2% to 78.3% and 45.8% to 52.6%, respectively, while %TAR reduced from 25.1% to 20.2% (P < 0.001, for all three). CL exits/week averaged <1, for all MM780G users. Conclusions: Early real-world MM780G use in the United States demonstrated a high percentage of time in range with low time above and below range. These outcomes are similar to those observed for real-world MM780G use in other countries.

Real-World Evidence of Automated Insulin Delivery System Use

Objective: Pivotal trials of automated insulin delivery (AID) closed-loop systems have demonstrated a consistent picture of glycemic benefit, supporting approval of multiple systems by the Food and Drug Administration or Conformité Européenne mark receipt. To assess how pivotal trial findings translate to commercial AID use, a systematic review of retrospective real-world studies was conducted. Methods: PubMed and EMBASE were searched for articles published after 2018 with more than five nonpregnant individuals with type 1 diabetes (T1D). Data were screened/extracted in duplicate for sample size, AID system, glycemic outcomes, and time in automation. Results: Of 80 studies identified, 20 met inclusion criteria representing 171,209 individuals. Time in target range 70-180 mg/dL (3.9-10.0 mmol/L) was the primary outcome in 65% of studies, with the majority of reports (71%) demonstrating a >10% change with AID use. Change in hemoglobin A1c (HbA1c) was reported in nine studies (range 0.1%-0.9%), whereas four reported changes in glucose management indicator (GMI) with a 0.1%-0.4% reduction noted. A decrease in HbA1c or GMI of >0.2% was achieved in two-thirds of the studies describing change in HbA1c and 80% of articles where GMI was described. Time below range <70 mg/dL (<3.9 mmol/L) was reported in 16 studies, with all but 1 study showing stable or reduced levels. Most systems had >90% time in automation. Conclusion: With larger and more diverse populations, and follow-up periods of longer duration (∼9 months vs. 3-6 months for pivotal trials), real-world retrospective analyses confirm pivotal trial findings. Given the glycemic benefits demonstrated, AID is rapidly becoming the standard of care for all people living with T1D. Individuals should be informed of these systems and differences between them, have access to and coverage for these technologies, and receive support as they integrate this mode of insulin delivery into their lives.

The Myth of MARD (Mean Absolute Relative Difference): Limitations of MARD in the Clinical Assessment of Continuous Glucose Monitoring Data

The mean absolute relative difference (MARD) is a numerical metric that has been adopted by the diabetes technology community as the main indicator that describes the accuracy of a glucose sensor at a single point in time. The appropriateness of this adoption is questionable because there is limited evidence that MARD has meaningful clinical relevance in the current era of sensor technology. The calculation may be simple, but evaluation of MARD can be very complex because it is substantially impacted by the design of the data collection in an accuracy study. Factors that can influence the overall MARD include participant demographics such as type of diabetes and age, site of sensor wear, and the percentage of collected values in each glycemic range during the study that is, in turn, a function of the study design. MARD is only one of several important statistical metrics such as bias and precision that are relevant to assessing accuracy of a sensor. Furthermore, these analytic metrics convey little information about the safety and effectiveness of sensor use with an automated insulin delivery system or a standalone device. There are no clinical studies in people with diabetes (PWD) proving that MARD can accurately differentiate between a safe and unsafe sensor or between a more and less clinically effective sensor. Moreover, there are alternatives to MARD that can do this in a clinically meaningful way, which include error grid analyses and clinical studies in PWD. This review attempts to demythologize the status of MARD for the diabetes community in an effort to shift the focus from MARD to using clinically relevant assessments.

A Peek Under the Hood: Explaining the MiniMed™ 780G Algorithm with Meal Detection Technology

The MiniMed™ 780G system (780G) received Conformité Européenne mark in June 2020 and was, recently, approved by the U.S. Food and Drug Administration (April 2023). Clinical trials and real-world analyses have demonstrated MiniMed™ 780G system safety and effectiveness and that glycemic outcomes (i.e., time in range) improve with recommended settings use. In this publication, we will explain the iterative development of the 780G algorithm and how this technology has simplified diabetes management.

Retrospective Comparison of Commercially Available Automated Insulin Delivery With Open-Source Automated Insulin Delivery Systems in Type 1 Diabetes

BACKGROUND

Automated insulin delivery (AID) systems have shown to improve glycemic control in a range of populations and settings. At the start of this study, only one commercial AID system had entered the Austrian market (MiniMed 670G, Medtronic). However, there is an ever-growing community of people living with type 1 diabetes (PWT1D) using open-source (OS) AID systems.

MATERIALS AND METHODS

A total of 144 PWT1D who used either the MiniMed 670G (670G) or OS-AID systems routinely for a period of at least three to a maximum of six months, between February 18, 2020 and January 15, 2023, were retrospectively analyzed (116 670G aged from 2.6 to 71.8 years and 28 OS-AID aged from 3.4 to 53.5 years). The goal is to evaluate and compare the quality of glycemic control of commercially available AID and OS-AID systems and to present all data by an in-depth descriptive analysis of the population. No statistical tests were performed.

RESULTS

The PWT1D using OS-AID systems spent more time in range (TIR)70-180 mg/dL (81.7% vs 73.9%), less time above range (TAR)181-250 mg/dL (11.1% vs 19.6%), less TAR>250 mg/dL (2.5% vs 4.3%), and more time below range (TBR)54-69 mg/dL (2.2% vs 1.7%) than PWT1D using the 670G system. The TBR<54 mg/dL was comparable in both groups (0.3% vs 0.4%). In the OS-AID group, median glucose level and glycated hemoglobin (HbA1c) were lower than in the 670G system group (130 vs 150 mg/dL; 6.2% vs 7.0%).

CONCLUSION

In conclusion, both groups were able to achieve satisfactory glycemic outcomes independent of age, gender, and diabetes duration. However, the PWT1D using OS-AID systems attained an even better glycemic control with no clinical safety concerns.

Hypoglycemia Unawareness-A Review on Pathophysiology and Clinical Implications

Hypoglycemia is a particular problem in people with diabetes while it can also occur in other clinical circumstances. Hypoglycemia unawareness describes a condition in which autonomic and neuroglycopenic symptoms of hypoglycemia decrease and hence are hardly perceivable. A failure to recognize hypoglycemia in time can lead to unconsciousness, seizure, and even death. The risk factors include intensive glycemic control, prior episodes of severe hypoglycemia, long duration of diabetes, alcohol consumption, exercise, renal failure, and sepsis. The pathophysiological mechanisms are manifold, but mainly concern altered brain glucose sensing, cerebral adaptations, and an impaired hormonal counterregulation with an attenuated release of glucagon, epinephrine, growth hormone, and other hormones, as well as impaired autonomous and neuroglycopenic symptoms. Physiologically, this counterregulatory response causes blood glucose levels to rise. The impaired hormonal counterregulatory response to recurrent hypoglycemia can lead to a vicious cycle of frequent and poorly recognized hypoglycemic episodes. There is a shift in glycemic threshold to trigger hormonal counterregulation, resulting in hypoglycemia-associated autonomic failure and leading to the clinical syndrome of hypoglycemia unawareness. This clinical syndrome represents a particularly great challenge in diabetes treatment and, thus, prevention of hypoglycemia is crucial in diabetes management. This mini-review provides an overview of hypoglycemia and the associated severe complication of impaired hypoglycemia awareness and its symptoms, pathophysiology, risk factors, consequences, as well as therapeutic strategies.

Addressing disparities in technology use among patients with type 1 diabetes: a review

PURPOSE OF REVIEW

The benefits of continuous glucose monitors (CGMs) and insulin pumps in the management of type 1 diabetes (T1D) are widely recognized. However, glaring disparities in access exist, particularly in marginalized and economically disadvantaged groups that stand to benefit significantly from diabetes technology use. We will review recent data describing drivers of these disparities and approaches to address the disparities.

RECENT FINDINGS

Several qualitative studies were published in recent years that have investigated the drivers of disparities reported over the past decades. These studies report that in addition to typical barriers seen in the diabetes technology, marginalized patients have unique challenges that make insulin pumps and CGMs less accessible.

SUMMARY

Barriers to technology use in these groups include stigmatization, lack of support, financial constraints, provider biases, stringent insurance policies, and clinic infrastructure. To address inequities, multifaceted strategies across community, healthcare, and provider sectors are essential. Key initiatives include enhancing public awareness, refining health policies, ensuring access to high-quality care, and emphasizing patient-centered approaches. The equitable use of technology can narrow the gap in T1D outcomes. The social and economic implications of suboptimal T1D management further underscore the urgency of these efforts for both improved health outcomes and cost-efficient care.

7. Diabetes Technology: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Comparison of the Sensor-Augmented Pump System with the Advanced Hybrid Closed-Loop Delivery System: Quality of Life, Diabetes Distress, and Glycaemic Outcomes in a Real-Life Context

BACKGROUND

Type 1 diabetes mellitus (T1D) is a chronic disease that requires exogenous insulin administration and intensive management to prevent any complications. Recent innovations in T1D management technologies include the Advanced Hybrid Closed-Loop delivery system (AHCL). The pioneer AHCL system provides automated basal and automated bolus corrections when needed.

OBJECTIVE

This study aimed to compare the Advanced Hybrid Closed-Loop (AHCL) system and the Sensor-Augmented Pump (SAP) with Predictive Low Glucose Management (PLGM) system, in relation to glycaemic outcomes, general and diabetes-related Quality of Life (QoL), and diabetes distress.

METHODS

General and diabetes-related QoL were assessed with the Diabetes Quality of Life Brief Clinical Inventory (DQOL-BCI) and the World Health Organization Quality of Life-BREF (WHOQOL-BREF), respectively. Diabetes distress was assessed with the Diabetes Distress Scale for Type 1 diabetes (T1-DDS).

RESULTS

Eighty-nine T1D adults participated in the study, mostly females (65.2%), with a mean age of 39.8 (± 11.5 years). They had on average 23 years of diabetes (± 10.7) and they were on continuous subcutaneous insulin infusion therapy. Significant differences favoring the AHCL over the SAP + PLGM system were demonstrated by lower mean glucose levels, less time above range, lower scores on DQOL-BCI, T1-DDS, and higher scores on WHOQOL-BREF. Finally, the linear regression models revealed the association of time in range in most of the above aspects.

CONCLUSION

This study highlighted the advantages of the AHCL system over the SAP + PLGM system in the real-world setting in relation to general and diabetes-related QoL, diabetes distress, and glycaemic outcomes.

Impact of 6-months of an advanced hybrid closed-loop system on sleep and psychosocial outcomes in youth with type 1 diabetes and their parents

INTRODUCTION

Youth with type 1 diabetes (T1D) and parents experience reduced quality of life and sleep quality due to nocturnal monitoring, hypoglycemia fear, and diabetes-related disruptions. This study examined the sleep and quality of life impact of advanced technology.

METHODS

Thirty-nine youth with T1D, aged 2-17 years, starting an advanced hybrid closed-loop (HCL) system and a parent participated in an observational study. Surveys, actigraphy, sleep diaries, and glycemic data (youth) were captured prior to HCL, at one week, 3 months, and 6 months. Outcomes were modeled using linear mixed effects models with random intercepts to account for within-subject correlation, with least-squares means at each timepoint compared to baseline.

RESULTS

Parents and youth reported improvements in health-related quality of life and fear of hypoglycemia after HCL initiation. Concurrently, nocturnal glycemia improved. Actigraphy-derived sleep outcomes showed improved 6 month adolescent efficiency and 3 and 6 month parent wake after sleep onset. Additionally, parents reported improved subjective sleep quality and child sleep-related impairment at 3 months.

CONCLUSIONS

With nocturnal glycemic improvements in youth using HCL technology, some aspects of parent and youth sleep and quality of life improved. This may reflect decreased parental monitoring and worry and highlights benefits for youth beyond glycemia.

Open-source automated insulin delivery systems and formal healthcare: A qualitative study of challenges in the interaction between service-users with type 1 diabetes and healthcare professionals

OBJECTIVES

To explore the challenges that open-source automated insulin delivery systems pose to the encounter between service-users and healthcare professionals and how these challenges might be addressed in the future.

METHODS

Five adult service-users and six healthcare professionals participated in semi-structured interviews. A six-step thematic analysis was conducted, the focus being on challenges and needs for improvement of the interactions.

RESULTS

The analysis revealed three overarching challenges and wishes: (1) Lack of exchange of experiences. Service-users are reticent about disclosing their use of the systems to healthcare professionals. (2) Unclear ethical responsibility. Service-users are aware of their responsibility and guidelines can help to alleviate healthcare professional's legal responsibility; however, ambiguities around ethical responsibility for healthcare professionals remain an impediment to meaningful clinical interactions. (3) Unresolved individual and mutual expectations. Service-users expect better access to technology and focus on psychosocial aspects. In contrast, healthcare professionals fear the burden associated with the rising technical expectations.

CONCLUSION

Transparency and openness towards open-source systems need to be part of service-user and healthcare professionals interaction. Requirements for future care include improved knowledge about the systems, an openminded approach towards user-driven initiatives among healthcare professionals, and a focus on psychosocial aspects in the interaction.

Glycemic Control, Basal/Bolus Distribution, BMI and Meal Management in Children and Adolescents with Type 1 Diabetes and Advanced Hybrid Closed Loop

Evidence about the impact of advanced hybrid closed loop (AHCL) on body mass index (BMI) and eating habits in children with type 1 diabetes (T1D) is lacking. This real-world study aimed at evaluating glycemic control, BMI, meals and basal/bolus distribution in young subjects with T1D treated by AHCL. Glycemic metrics, HbA1c, basal/bolus distribution, meals/day, BMI, total daily dose (TDD), and carbohydrates/kg (CHO/kg) have been evaluated in 83 subjects, aged 13 ± 4.5 years, in manual mode, 3 and 6 months after auto-mode. Time in range (TIR) increased after 3 months, exceeding the target of 70% and was maintained at 6 months. While coefficient of variation (CV) did not change, the glucose management indicator (GMI) decreased in auto-mode (6.7 ± 0.3 vs. 7.1 ± 0.5%; p < 0.001), as well as HbA1c. Basal proportion decreased in favor of boluses (38.3 ± 7.3 vs. 43.6 ± 10.9%; p < 0.001). Meals increased at 3 and 6 months (4.4 ± 1.2 vs. 5.0 ± 1.5, p 0.002 and 5.1 ± 1.7, p < 0.001), as well as TDD/kg, without changes in BMI and CHO consumed. No differences in meal composition have arisen from food diaries. In conclusion, AHCL ensured the achievement and maintenance of target TIR in young T1D subjects. The number of meals, TDD, and insulin bolus proportion increased over time, but BMI remained stable.

Roadmap to Continuous Glucose Monitoring Adoption and Improved Outcomes in Endocrinology: The 4T (Teamwork, Targets, Technology, and Tight Control) Program

Glucose monitoring is essential for the management of type 1 diabetes and has evolved from urine glucose monitoring in the early 1900s to home blood glucose monitoring in the 1980s to continuous glucose monitoring (CGM) today. Youth with type 1 diabetes struggle to meet A1C goals; however, CGM is associated with improved A1C in these youth and is recommended as a standard of care by diabetes professional organizations. Despite their utility, expanding uptake of CGM systems has been challenging, especially in minoritized communities. The 4T (Teamwork, Targets, Technology, and Tight Control) program was developed using a team-based approach to set consistent glycemic targets and equitably initiate CGM and remote patient monitoring in all youth with new-onset type 1 diabetes. In the pilot 4T study, youth in the 4T cohort had a 0.5% improvement in A1C 12 months after diabetes diagnosis compared with those in the historical cohort. The 4T program can serve as a roadmap for other multidisciplinary pediatric type 1 diabetes clinics to increase CGM adoption and improve glycemic outcomes.

Evaluation of a New Clinical Tool to Enhance Clinical Care of Control-IQ Users

BACKGROUND

The purpose of this study was to develop and test a new Clinic Tool to assist health care professionals with clinical care of persons with diabetes using the Control-IQ system.

METHODS

A Clinic Tool was iteratively developed with input from diabetes clinicians, which outlined a systematic process for assessing data, reviewing insulin settings, providing education, and documenting the encounter. Diabetes clinicians were recruited to trial the Clinical Tool in up to five clinical encounters (in-person, telehealth, or telephone). Quantitative surveys and free-text responses, including a knowledge quiz and the System Usability Scale (SUS), were administered to determine clinician satisfaction, confidence, knowledge, and implications for practice.

RESULTS

Twenty-nine clinicians (43% endocrinologists, mean 10.7 years in practice) enrolled in the study and completed 89 encounters using the Control-IQ Clinic Tool. Participants spent an average of 10 minutes using the Tool and reported excellent SUS scores within the 90%-95% percentile for usability. Knowledge quiz scores increased in 42% of participants. Both familiarity with Control-IQ and confidence providing clinical care to Control-IQ users significantly improved (P = .009 and P < .001 respectively). Ninety percent of participants agreed that the Tool will change their clinical care going forward.

CONCLUSION

The Control-IQ Clinical Tool is highly usable and impacted clinical care delivery to Control-IQ users. Tools that serve to improve clinician confidence in delivery of care to diabetes device users should be expanded, leveraged, and studied to assess the impact on adherence and glycemic control for persons with diabetes.

Safety and Glycemic Outcomes During the MiniMedTM Advanced Hybrid Closed-Loop System Pivotal Trial in Children and Adolescents with Type 1 Diabetes

Background: During MiniMed™ advanced hybrid closed-loop (AHCL) use by adolescents and adults in the pivotal trial, glycated hemoglobin (A1C) was significantly reduced, time spent in range (TIR) was significantly increased, and there were no episodes of severe hypoglycemia or diabetic ketoacidosis (DKA). The present study investigated the same primary safety and effectiveness endpoints during AHCL use by a younger cohort with type 1 diabetes (T1D). Methods: An intention-to-treat population (N = 160, aged 7-17 years) with T1D was enrolled in a single-arm study at 13 investigational centers. There was a run-in period (∼25 days) using HCL or sensor-augmented pump with/without predictive low-glucose management, followed by a 3-month study period with AHCL activated at two glucose targets (GTs; 100 and 120 mg/dL) for ∼45 days each. The mean ± standard deviation values of A1C, TIR, mean sensor glucose (SG), coefficient of variation (CV) of SG, time at SG ranges, and insulin delivered between run-in and study were analyzed (Wilcoxon signed-rank test or t-test). Results: Compared with baseline, AHCL use was associated with reduced A1C from 7.9 ± 0.9% (N = 160) to 7.4 ± 0.7% (N = 136) (P < 0.001) and overall TIR increased from the run-in 59.4 ± 11.8% to 70.3 ± 6.5% by end of study (P < 0.001), without change in CV, time spent below range (TBR) <70 mg/dL, or TBR <54 mg/dL. Relative to longer active insulin time (AIT) settings (N = 52), an AIT of 2 h (N = 19) with the 100 mg/dL GT increased mean TIR to 73.4%, reduced TBR <70 mg/dL from 3.5% to 2.2%, and reduced time spent above range (TAR) >180 mg/dL from 28.7% to 24.4%. During AHCL use, there was no severe hypoglycemia or DKA. Conclusions: In children and adolescents with T1D, MiniMed AHCL system use was safe, A1C was lower, and TIR was increased. The lowest GT and shortest AIT were associated with the highest TIR and lowest TBR and TAR, all of which met consensus-recommended glycemic targets. ClinicalTrials.gov ID: NCT03959423.

Current and future therapies to treat impaired awareness of hypoglycemia

In order to achieve optimal glycemic control, intensive insulin regimes are needed for individuals with Type 1 Diabetes (T1D) and insulin-dependent Type 2 Diabetes (T2D). Unfortunately, intensive glycemic control often results in insulin-induced hypoglycemia. Moreover, recurrent episodes of hypoglycemia result in both the loss of the characteristic warning symptoms associated with hypoglycemia and an attenuated counterregulatory hormone responses. The blunting of warning symptoms is known as impaired awareness of hypoglycemia (IAH). Together, IAH and the loss of the hormonal response is termed hypoglycemia associated autonomic failure (HAAF). IAH is prevalent in up to 25% in people with T1D and up to 10% in people with T2D. IAH and HAAF increase the risk of severe hypoglycemia 6-fold and 25-fold, respectively. To reduce this risk for severe hypoglycemia, multiple different therapeutic approaches are being explored that could improve awareness of hypoglycemia. Current therapies to improve awareness of hypoglycemia include patient education and psychoeducation, the use of novel glycemic control technology, pancreas/islet transplantation, and drug therapy. This review examines both existing therapies and potential therapies that are in pre-clinical testing. Novel treatments that improve awareness of hypoglycemia, via improving the counterregulatory hormone responses or improving hypoglycemic symptom recognition, would also shed light on the possible neurological mechanisms that lead to the development of IAH. To reduce the risk of severe hypoglycemia in people with diabetes, elucidating the mechanism behind IAH, as well as developing targeted therapies is currently an unmet need for those that suffer from IAH.

Glycometabolic outcomes in adult type 1 diabetic patients switching to closed-loop systems

OBJECTIVE

This study aimed to evaluate glycometabolic outcomes in AID technology-naïve T1D patients after switching to Hybrid Closed Loop (HCL) and Advanced Hybrid Closed Loop (AHCL) systems.

RESEARCH DESIGN AND METHODS

This was a 12-month, prospective, observational, two-center study on 54 type 1 diabetes (T1D) patients aged 19-65 years managed with multiple daily injections (MDI) or Continuous Subcutaneous Insulin Infusion (CSII) in open-loop to evaluate the superiority in terms of effectiveness and safety of Automated Insulin Delivery (AID) systems.

RESULTS

HbA1c levels significantly improved at the end of the study. Time spent with glucose levels in target range (TIR70-180 mg/dL, 3.9-10 mmol/L) increased from 50.5 ± 15.6% at baseline to 73.6 ± 8.0% at 12 months (p < 0.001); time spent above range (TAR180-250 mg/dL, 10-13.9 mmol/L and TAR≥250 mg/dL, 13.9 mmol/L) decreased from 30.6 ± 9.0% and 14.2 ± 10.2 at baseline to 19.3 ± 5.3% and 4.8 ± 3.3% at 12 months (p < 0.001 for both), respectively; time spent below range (TBR54-69 mg/dL, 3-3.8 mmol/L and TBR<54 mg/dL, 3.0 mmol/L) decreased from 3.5 ± 2.6% and 1.2 ± 1.4% at baseline to 1.9 ± 1.5% and 0.4 ± 0.7% at the end of the study (p < 0.001 for both); coefficient of variation (CV) decreased from 35.9 ± 7.8% at baseline to 33.0 ± 5.3% (p < 0.05). Satisfaction with the new technology was scored as high.

CONCLUSION

AID-naïve T1D patients switching to HCL/AHCL systems have significantly and safely improved their glycometabolic outcomes with their high satisfaction with the new type of treatment.

Safety Event Outcomes and Glycemic Control with a Hybrid Closed-Loop System Used by Chinese Adolescents and Adults with Type 1 Diabetes Mellitus

Background: While evidence supports glycemic control benefits for individuals with type 1 diabetes mellitus (T1DM) using hybrid closed-loop (HCL) systems, HCL automated insulin delivery therapy in China has not been assessed. This study evaluated safety events and effectiveness during HCL system use by Chinese adolescents and adults with T1DM. Methods: Sixty-two participants (n = 12 adolescents with a mean ± standard deviation [SD] of 15.5 ± 1.1 years and n = 50 adults [mean ± SD of 37.6 ± 11.1 years]) with T1DM and baseline A1C of 7.1% ± 1.0% underwent a run-in period (∼2 weeks) using open-loop Manual Mode (sensor-augmented pump) insulin delivery with the MiniMed™ 770G system with the Guardian™ Sensor (3) glucose sensor, followed by a study period (4 weeks) with HCL Auto Mode enabled. Analyses compared continuous glucose monitoring data and insulin delivered during the run-in versus study period (Wilcoxon signed-rank test or t-test). Safety events included rates of severe hypoglycemia and diabetic ketoacidosis (DKA). Results: Compared to baseline run-in, overall Auto Mode use increased time in range (TIR, 70-180 mg/dL) from 75.3% to 80.9% (P < 0.001) and reduced time below range (TBR, <70 mg/dL) from 4.7% to 2.2% (P < 0.001). Subgroup analysis demonstrated that participants (n = 29) with baseline A1C <7.0% had TBR that reduced from 5.6% to 2.0%, while participants (n = 21) with baseline A1C ≥7.5% had time above range (TAR, >180 mg/dL) that reduced from 31.6% to 20.8%. Auto Mode use also increased the percentage achieving combined recommendations for time at sensor glucose ranges (i.e., TIR of >70%, TBR of <4% and TAR of <25%) from 24.2% at baseline to 77.4% at study end. Total daily insulin dose reduced from 42.8 ± 19.8 to 40.7 ± 18.9 U (P = 0.013). There were no severe hypoglycemic, DKA, or serious adverse events. Conclusions: Chinese adolescents and adults, some of whom met target A1C at baseline, safely achieved significantly improved glycemia with 1 month of MiniMed 770G system use when compared to open-loop insulin delivery. ClinicalTrials.gov ID: NCT04663295.

Communication Protocols Integrating Wearables, Ingestibles, and Implantables for Closed-Loop Therapies

Body-conformal sensors and tissue interfacing robotic therapeutics enable the real-time monitoring and treatment of diabetes, wound healing, and other critical conditions. By integrating sensors and drug delivery devices, scientists and engineers have developed closed-loop drug delivery systems with on-demand therapeutic capabilities to provide just-in-time treatments that correspond to chemical, electrical, and physical signals of a target morbidity. To enable closed-loop functionality in vivo, engineers utilize various low-power means of communication that reduce the size of implants by orders of magnitude, increase device lifetime from hours to months, and ensure the secure high-speed transfer of data. In this review, we highlight how communication protocols used to integrate sensors and drug delivery devices, such as radio frequency communication (e.g., Bluetooth, near-field communication), in-body communication, and ultrasound, enable improved treatment outcomes.

Frequency and Causes of Nocturnal Alarms in Youth and Young Adults With Type 1 Diabetes Using a First-Generation Hybrid Closed-Loop System

Background

Meeting glycemic recommendations is challenging for youth with type 1 diabetes. Diabetes technology, including continuous glucose monitoring (CGM) and hybrid closed-loop (HCL) automated insulin delivery systems, significantly increase achievement of glycemic targets; however, many youth struggle to sustain use of early HCL systems. Nocturnal alarm fatigue contributes to disrupted sleep and device discontinuation.

Methods

We examined the frequency and causes of nocturnal (10:00 p.m. to 6:00 a.m.) alarms in pediatric patients (N = 76, median age 14.5 years [interquartile range 11.8-17.0 years, range 7-24 years]) starting on a first-generation HCL system in a prospective observational study. Device data were analyzed with linear mixed-effects models to examine change across time at 3-month intervals for 12 months.

Results

At baseline (HCL system in nonautomated mode), participants averaged 3.3 ± 0.6 alarms per night. In the 2 weeks after starting HCL (automated) mode, alarm frequency significantly increased to 5.4 ± 0.5 times per night (P <0.001). Alarm frequency decreased through the remainder of the observational period; however, CGM sensor and HCL system use also declined. The types of alarms were evenly distributed among sensor maintenance, sensor threshold, pump, and HCL-specific alarms.

Conclusion

These data show that HCL system nocturnal alarms are frequent and may be barriers to sleep quality and device use. Further research is needed to assess the impact of diabetes technology on sleep and to determine method to improve sleep quality with technology use.

Glucose Rate-of-Change and Insulin-on-Board Jointly Weighted Zone Model Predictive Control

We present design and evaluation of closed-loop insulin delivery using zone model predictive control (MPC) featuring an adaptive weighting scheme to address prolonged hyperglycemia due to changes in insulin sensitivity, underdelivery from profile mismatch, and meal composition. In the MPC cost function, the penalty on predicted glucose deviation from the upper zone boundary is weighted by a joint function of predicted glucose rate-of-change (ROC) and insulin-on-board (IOB). The asymmetric weighting gradually increases when glucose ROC and IOB were jointly low, independent of glucose magnitude, to limit hyperglycemia while aggressively reduces for negative glucose ROC to avoid hypoglycemia. The proposed controller was evaluated using two simulation scenarios: an induced resistance scenario and a nominal scenario to highlight the performance over a reference zone MPC with glucose ROC weighting only. The continuous adaption scheme resulted in consistent improvement for the entire glucose range without incurring additional risk of hypoglycemia. For the induced resistance and no feedforward bolus scenario, the percent time in 70-180 mg/dL was higher (53.5% versus 48.9%, p<0.001) with larger improvement in the overnight percent time in tighter glucose range 70-140 mg/dL (70.9% versus 52.9%, p<0.001). The results from extensive simulations, as well as clinical validation in three different outpatient studies demonstrate the utility and safety of the proposed zone MPC.

Unannounced Meal Challenges Using an Advanced Hybrid Closed-Loop System

Background: The advanced hybrid closed-loop (AHCL) algorithm combines automated basal rates and corrections yet requires meal announcement for optimal outcomes. We aimed to compare the performance of the MiniMed™ 780G AHCL algorithm with and without meal announcement. Methods: In a single-arm study involving 14 adults with type 1 diabetes, we evaluated the safety and efficacy of AHCL when meals were not announced. Participants stayed at a supervised environment for 5 days, during which the outcomes of not announcing meals (≤80 g of carbohydrate) were assessed. Next, participants entered a 90-day at-home "unannounced" phase, during which all meals (≤80 g of carbohydrate) were unannounced, followed by a 90-day at-home phase in which all meals were announced. Results: Time in range (TIR 70-180 mg/dL) was lower in the unannounced versus announced periods (67.5% ± 12.5% vs. 77.7% ± 9.5%; P < 0.01, respectively), with more time spent in hyperglycemia range 180-250 mg/dL (22.7% ± 7.7% vs. 15.7% ± 7.2%) and >250 mg/dL (7.9% ± 6.4% vs. 3.6% ± 2.7%), but less time in hypoglycemia range 54-70 mg/dL (1.6% ± 1% vs. 2.8% ± 1.8%) and <54 mg/dL (0.3% ± 0.4% vs. 0.7% ± 0.9%). Not announcing meals containing up to 60 g of carbohydrate did not lead to increase in postprandial extreme dysglycemia >250 mg/dL, and up to 20 g of unannounced carbohydrates did not significantly change the TIR 70-180 mg/dL compared with full announcement. Conclusion: The AHCL system is optimized for use with meal announcement. While not announcing meals of ≤80 g carbohydrates appears to be safe, it results in suboptimal postprandial glycemic control, especially with high-carbohydrate meals. Not announcing small meals (≤20 g carbohydrate) does not deteriorate glycemic control. Clinical Trial Registration number: NCT04479826.

Glycemic Outcomes During Early Use of the MiniMed™ 780G Advanced Hybrid Closed-Loop System with Guardian™ 4 Sensor

Background: Safety and significant improvement in overall glycated hemoglobin (A1C) and percentage of time spent in (TIR), below (TBR), and above (TAR) glucose range were demonstrated in the pivotal trial of adolescents and adults using the MiniMed™ advanced hybrid closed-loop (AHCL) system with the adjunctive, calibration-required Guardian™ Sensor 3. The present study evaluated early outcomes of continued access study (CAS) participants who transitioned from the pivotal trial investigational system to the approved MiniMed™ 780G system with the non-adjunctive, calibration-free Guardian™ 4 Sensor (MM780G+G4S). Study data were presented alongside those of real-world MM780G+G4S users from Europe, the Middle East, and Africa. Methods: The CAS participants (N = 109, aged 7-17 years and N = 67, aged >17 years) used the MM780G+G4S for 3 months and data of real-world MM780G+G4S system users (N = 10,204 aged ≤15 years and N = 26,099 aged >15 years) were uploaded from September 22, 2021 to December 02, 2022. At least 10 days of real-world continuous glucose monitoring (CGM) data were required for analyses. Glycemic metrics, delivered insulin and system use/interactions underwent descriptive analyses. Results: Time in AHCL and CGM use were >90% for all groups. AHCL exits averaged 0.1/day and there were few blood glucose measurements (BGMs) (0.8/day-1.0/day). Adults in both cohorts met most consensus recommendations for glycemic targets. Pediatric groups met recommendations for %TIR and %TBR, although not those for mean glucose variability and %TAR, possibly due to low use of recommended glucose target (100 mg/dL) and active insulin time (2 h) settings (28.4% in the CAS cohort and 9.4% in the real-world cohort). The CAS pediatric and adult A1C were 7.2% ± 0.7% and 6.8% ± 0.7%, respectively, and there were no serious adverse events. Conclusions: Early clinical use of the MM780G+G4S was safe and involved minimal BGMs and AHCL exits. Consistent with real-world pediatric and adult use, outcomes were associated with achievement of recommended glycemic targets. Clinical Trial Registration number: NCT03959423.

Beneficial Effects of Control-IQ Automated Insulin Delivery in Basal-Bolus and Basal-Only Insulin Users With Type 2 Diabetes

The t:slim X2 insulin pump with Control-IQ technology (Control-IQ) advanced hybrid closed-loop automated insulin delivery system was evaluated in this prospective single-arm trial. Thirty adults with type 2 diabetes using the Control-IQ system showed substantial glycemic improvement with no increase in hypoglycemia. Mean time in range (70-180 mg/dL) improved 15%, representing an increase of 3.6 hours/day, and mean glucose decreased by 22 mg/dL.

Long-Term Benefits of an Integrated Continuous Glucose Monitoring and Insulin Pump System for Emergency Admissions, Hospitalization, and Metabolic Control in a Cohort of People With Diabetes: Retrospective Cohort Study

BACKGROUND

There is evidence in the literature that the use of sensor-augmented insulin pumps in patients with high-complexity diabetes improves metabolic control. However, there is no long-term information on clinical outcomes such as hospitalization or admission to the emergency room. This study describes outcomes for metabolic control, incidence of hospitalizations, and emergency room visits in a specific population using this technology.

OBJECTIVE

We aimed to assess long-term glycemic and clinical outcomes after the use of continuous subcutaneous insulin infusion and continuous glucose monitoring in people with diabetes.

METHODS

A retrospective cohort study was carried out in patients with diabetes previously treated with an intensive insulin regimen at a specialized diabetes treatment center who required a sensor-augmented insulin pump due to nonoptimal glycemic control. Glycated hemoglobin, severe hypoglycemic episodes, nonsevere hypoglycemic episodes, perception of hypoglycemia, and the incidence of emergency room visits and hospitalizations before and after treatment were evaluated.

RESULTS

Between January 2013 and August 2020, 74 patients with a median age of 36 (IQR 27-46) years were included in the study with a median 4 (IQR 2-7) years of follow-up. We found a statistically significant reduction in glycated hemoglobin (8.35% vs 7%; P<.001), nonsevere hypoglycemic episodes (71/74, 96% vs 62/74, 84%; P=.01), emergency room visits (42/73, 58% vs 4/62, 6%; P<.001), and hospitalizations (36/72, 50% vs 10/72, 14%; P<.001) after use of continuous subcutaneous insulin infusion.

CONCLUSIONS

The use of a sensor-augmented insulin pump associated with a strict follow-up program for patients with high-complexity diabetes led to a significant and sustained reduction in glycated hemoglobin and hypoglycemic episodes, as well as in the rate of emergency room visits and hospitalizations. These results encourage the adoption of this technology in patients who do not achieve metabolic control with optimal management of diabetes.

Efficacy and safety of Android artificial pancreas system use at home among adults with type 1 diabetes mellitus in China: protocol of a 26-week, free-living, randomised, open-label, two-arm, two-phase, crossover trial

INTRODUCTION

Do-it-yourself artificial pancreas system (DIY APS) is built using commercially available insulin pump, continuous glucose monitoring (CGM) and an open-source algorithm. Compared with commercial products, DIY systems are affordable, allow personalised settings and provide updated algorithms, making them a more promising therapy for most patients with type 1 diabetes mellitus (T1DM). Many small and self-reported observational studies have found that their real-world use was associated with potential metabolic and psychological benefits. However, rigorous-designed studies are urgently needed to confirm its efficacy and safety.

METHODS AND ANALYSIS

In this 26-week randomised, open-label, two-arm, two-phase, crossover trial, participants aged 18-75 years, with T1DM and glycated haemoglobin (HbA1c) 7-11%, will use AndroidAPS during one 12-week period and sensor-augmented pump during another 12-week period. This study will recruit at least 24 randomised participants. AndroidAPS consists of three components: (1) real-time CGM; (2) insulin pump; (3) AndroidAPS algorithm implemented in Android smartphone. The primary endpoint is time in range (3.9-10.0 mmol/L) derived from CGM. The main secondary endpoints include percentage of sensor glucose values below, within and above target range; mean sensor glucose value; measures of glycaemic variability and centralised HbA1c. Safety endpoints mainly include the frequency of hypoglycaemia events, diabetic ketoacidosis and other serious adverse events.

ETHICS AND DISSEMINATION

This study has been approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University. There will be verbal and written information regarding the trial given to each participant. The study will be disseminated through peer-reviewed publications and conference presentations.

OVERALL STATUS

Recruiting.

STUDY START

11 February 2023.

PRIMARY COMPLETION

31 July 2024.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT05726461).

Racial Disparities in Diabetes Technology Adoption and Their Association with HbA1c and Diabetic Ketoacidosis

Aim

Poorer glycemic control and higher diabetic ketoacidosis (DKA) rates are seen in racial/ethnic minorities with type 1 diabetes (T1D). Use of diabetes technologies such as continuous glucose monitors (CGM), continuous subcutaneous insulin infusion (CSII) and automated insulin delivery (AID) systems has been shown to improve glycemic control and reduce DKA risk. We examined race/ethnicity differences in diabetes technology use and their relationship with HbA1c and DKA.

Methods

Data from patients aged ≥12 years with T1D for ≥1 year, receiving care from a single diabetes center, were examined. Patients were classified as Non-Hispanic White (n=3945), Non-Hispanic Black (Black, n=161), Hispanic (n=719), and Multiracial/Other (n=714). General linear models and logistic regression were used.

Results

Black (OR=0.22, 0.15-0.32) and Hispanic (OR=0.37, 0.30-0.45) patients were less likely to use diabetes technology. This disparity was greater in the pediatric population (p-interaction=0.06). Technology use associated with lower HbA1c in each race/ethnic group. Among technology users, AID use associated with lower HbA1c compared to CGM and/or CSII (HbA1c of 8.4% vs 9.2%, respectively), with the greatest difference observed for Black adult AID users. CSII use associated with a lower odds of DKA in the past year (OR=0.73, 0.54-0.99), a relationship that did not vary by race (p-interaction =0.69); this inverse association with DKA was not observed for CGM or AID.

Conclusion

Disparities in diabetes technology use, DKA, and glycemic control were apparent among Black and Hispanic patients with T1D. Differences in technology use ameliorated but did not fully account for disparities in HbA1c or DKA.

The Growing Role of Technology in the Care of Older Adults With Diabetes

The integration of technologies such as continuous glucose monitors, insulin pumps, and smart pens into diabetes management has the potential to support the transformation of health care services that provide a higher quality of diabetes care, lower costs and administrative burdens, and greater empowerment for people with diabetes and their caregivers. Among people with diabetes, older adults are a distinct subpopulation in terms of their clinical heterogeneity, care priorities, and technology integration. The scientific evidence and clinical experience with these technologies among older adults are growing but are still modest. In this review, we describe the current knowledge regarding the impact of technology in older adults with diabetes, identify major barriers to the use of existing and emerging technologies, describe areas of care that could be optimized by technology, and identify areas for future research to fulfill the potential promise of evidence-based technology integrated into care for this important population.

The changing landscape of automated insulin delivery in the management of type 1 diabetes

Automated insulin delivery systems, also known as closed-loop or 'artificial pancreas' systems, are transforming the management of type 1 diabetes. These systems consist of an algorithm which responds to real-time glucose sensor levels by automatically modulating insulin delivery through an insulin pump. We review the rapidly changing landscape of automated insulin-delivery systems over recent decades, from initial prototypes to the different hybrid closed-loop systems commercially available today. We discuss the growing body of clinical trials and real-world evidence demonstrating their glycaemic and psychosocial benefits. We also address future directions in automated insulin delivery such as dual-hormone systems and adjunct therapy as well as the challenges around ensuring equitable access to closed-loop technology.

The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment

In the last decade, artificial intelligence (AI) and AI-mediated technologies have undergone rapid evolution in healthcare and medicine, from apps to computer software able to analyze medical images, robotic surgery and advanced data storage system. The main aim of the present commentary is to briefly describe the evolution of AI and its applications in healthcare, particularly in nutrition and clinical biochemistry. Indeed, AI is revealing itself to be an important tool in clinical nutrition by using telematic means to self-monitor various health metrics, including blood glucose levels, body weight, heart rate, fat percentage, blood pressure, activity tracking and calorie intake trackers. In particular, the application of the most common digital technologies used in the field of nutrition as well as the employment of AI in the management of diabetes and obesity, two of the most common nutrition-related pathologies worldwide, will be presented.

Beyond A1C: exploring continuous glucose monitoring metrics in managing diabetes

Hemoglobin A1c (HbA1c) has long been considered a cornerstone of diabetes mellitus (DM) management, as both an indicator of average glycemia and a predictor of long-term complications among people with DM. However, HbA1c is subject to non-glycemic influences which confound interpretation and as a measure of average glycemia does not provide information regarding glucose trends or about the occurrence of hypoglycemia and/or hyperglycemia episodes. As such, solitary use of HbA1c, without accompanying glucose data, does not confer actionable information that can be harnessed to guide targeted therapy in many patients with DM. While conventional capillary blood glucose monitoring (BGM) sheds light on momentary glucose levels, in practical use the inherent infrequency of measurement precludes elucidation of glycemic trends or reliable detection of hypoglycemia or hyperglycemia episodes. In contrast, continuous glucose monitoring (CGM) data reveal glucose trends and potentially undetected hypo- and hyperglycemia patterns that can occur between discrete BGM measurements. The use of CGM has grown significantly over the past decades as an ever-expanding body of literature demonstrates a multitude of clinical benefits for people with DM. Continually improving CGM accuracy and ease of use have further fueled the widespread adoption of CGM. Furthermore, percent time in range correlates well with HbA1c, is accepted as a validated indicator of glycemia, and is associated with the risk of several DM complications. We explore the benefits and limitations of CGM use, the use of CGM in clinical practice, and the application of CGM to advanced diabetes technologies.

Outcomes in Pump- and CGM-Baseline Use Subgroups in the International Diabetes Closed-Loop Trial

BACKGROUND

We investigated the potential benefits of automated insulin delivery (AID) among individuals with type 1 diabetes (T1D) in sub-populations of baseline device use determined by continuous glucose monitor (CGM) use status and insulin delivery via multiple daily injections (MDI) or insulin pump.

MATERIALS AND METHODS

In a six-month randomized, multicenter trial, 168 individuals were assigned to closed-loop control (CLC, Control-IQ, Tandem Diabetes Care), or sensor-augmented pump (SAP) therapy. The trial included a two- to eight-week run-in phase to train participants on study devices. The participants were stratified into four subgroups: insulin pump and CGM (pump+CGM), pump-only, MDI and CGM (MDI+CGM), and MDI users without CGM (MDI-only) users. We compared glycemic outcomes among four subgroups.

RESULTS

At baseline, 61% were pump+CGM users, 18% pump-only users, 10% MDI+CGM users, and 11% MDI-only users. Mean time in range 70-180 mg/dL (TIR) improved from baseline in the four subgroups using CLC: pump+CGM, 62% to 73%; pump-only, 61% to 70%; MDI+CGM, 54% to 68%; and MDI-only, 61% to 69%. The reduction in time below 70 mg/dL from baseline was comparable among the four subgroups. No interaction effect was detected with baseline device use for TIR (P = .67) or time below (P = .77). On the System Usability Questionnaire, scores were high at 26 weeks for all subgroups: pump+CGM: 87.2 ± 12.1, pump-only: 89.4 ± 8.2, MDI+CGM 87.2 ± 9.3, MDI: 78.1 ± 15.

CONCLUSIONS

There was a consistent benefit in patients with T1D when using CLC, regardless of baseline insulin delivery modality or CGM use. These data suggest that this CLC system can be considered across a wide range of patients.

Type 1 diabetes

Type 1 diabetes is a chronic disease caused by autoimmune destruction of pancreatic β cells. Individuals with type 1 diabetes are reliant on insulin for survival. Despite enhanced knowledge related to the pathophysiology of the disease, including interactions between genetic, immune, and environmental contributions, and major strides in treatment and management, disease burden remains high. Studies aimed at blocking the immune attack on β cells in people at risk or individuals with very early onset type 1 diabetes show promise in preserving endogenous insulin production. This Seminar will review the field of type 1 diabetes, highlighting recent progress within the past 5 years, challenges to clinical care, and future directions in research, including strategies to prevent, manage, and cure the disease.

Switching from treatment with sensor augmented pump to hybrid closed loop system in type 1 diabetes: impact on glycemic control and neuropsychological tests in the real world

AIMS

The aim of this study is to assess in the real world the impact of initiating hybrid closed loop (HCL) on glycemic control and quality of life in patients using sensor-augmented pump (SAP).

METHODS

In this prospective study, patients using SAP changed to an HCL system in a specialized hospital. HCL devices used were Medtronic 780G®, Tandem Control-IQ® and Diabeloop® system. Glucometric data and hypoglycemia and neuropsychological tests were assessed at baseline and 3 months after initiating HCL.

RESULTS

A total of 66 consecutive patients were included (74% women, mean age 44±11 years, diabetes duration 27.2 ±11 years). Significant improvements were observed in coefficient of variation (from 35.6% to 33.1%), time in range (from 62.2 % to 73.8%), time above 180 mg/dl (from 26.9% to 18%), time below 70 mg/dl (from 3.3% to 2.1%) and time below 55 mg/dl (from 0.7% to 0.3%). In addition, significant improvements were observed in fear of hypoglycemia and grade of distress associated to treatment and to interpersonal sphere.

CONCLUSIONS

Switching from SAP to HCL system improves time in range and reduces time in hypoglycemia and glycemic variability at 3 months. These changes are accompanied by significant reduction of neuropsychological burden related to diabetes.

Insulin detection in diabetes mellitus: challenges and new prospects

Tremendous progress has been made towards achieving tight glycaemic control in individuals with diabetes mellitus through the use of frequent or continuous glucose measurements. However, in patients who require insulin, accurate dosing must consider multiple factors that affect insulin sensitivity and modulate insulin bolus needs. Accordingly, an urgent need exists for frequent and real-time insulin measurements to closely track the dynamic blood concentration of insulin during insulin therapy and guide optimal insulin dosing. Nevertheless, traditional centralized insulin testing cannot offer timely measurements, which are essential to achieving this goal. This Perspective discusses the advances and challenges in moving insulin assays from traditional laboratory-based assays to frequent and continuous measurements in decentralized (point-of-care and home) settings. Technologies that hold promise for insulin testing using disposable test strips, mobile systems and wearable real-time insulin-sensing devices are discussed. We also consider future prospects for continuous insulin monitoring and for fully integrated multisensor-guided closed-loop artificial pancreas systems.

Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes

The widespread adoption of effective hybrid closed loop systems would represent an important milestone of care for people living with type 1 diabetes (T1D). These devices typically utilise simple control algorithms to select the optimal insulin dose for maintaining blood glucose levels within a healthy range. Online reinforcement learning (RL) has been utilised as a method for further enhancing glucose control in these devices. Previous approaches have been shown to reduce patient risk and improve time spent in the target range when compared to classical control algorithms, but are prone to instability in the learning process, often resulting in the selection of unsafe actions. This work presents an evaluation of offline RL for developing effective dosing policies without the need for potentially dangerous patient interaction during training. This paper examines the utility of BCQ, CQL and TD3-BC in managing the blood glucose of the 30 virtual patients available within the FDA-approved UVA/Padova glucose dynamics simulator. When trained on less than a tenth of the total training samples required by online RL to achieve stable performance, this work shows that offline RL can significantly increase time in the healthy blood glucose range from 61.6±0.3% to 65.3±0.5% when compared to the strongest state-of-art baseline (p<0.001). This is achieved without any associated increase in low blood glucose events. Offline RL is also shown to be able to correct for common and challenging control scenarios such as incorrect bolus dosing, irregular meal timings and compression errors. The code for this work is available at: https://github.com/hemerson1/offline-glucose.

A Systematic Review of Commercial Hybrid Closed-Loop Automated Insulin Delivery Systems

INTRODUCTION

Several different forms of automated insulin delivery systems (AID systems) have recently been developed and are now licensed for type 1 diabetes (T1D). We undertook a systematic review of reported trials and real-world studies for commercial hybrid closed-loop (HCL) systems.

METHODS

Pivotal, phase III and real-world studies using commercial HCL systems that are currently approved for use in type 1 diabetes were reviewed with a devised protocol using the Medline database.

RESULTS

Fifty-nine studies were included in the systematic review (19 for 670G; 8 for 780G; 11 for Control-IQ; 14 for CamAPS FX; 4 for Diabeloop; and 3 for Omnipod 5). Twenty were real-world studies, and 39 were trials or sub-analyses. Twenty-three studies, including 17 additional studies, related to psychosocial outcomes and were analysed separately.

CONCLUSIONS

These studies highlighted that HCL systems improve time In range (TIR) and arouse minimal concerns around severe hypoglycaemia. HCL systems are an effective and safe option for improving diabetes care. Real-world comparisons between systems and their effects on psychological outcomes require further study.

A Meta-Analysis of Randomized Trial Outcomes for the t:slim X2 Insulin Pump with Control-IQ Technology in Youth and Adults from Age 2 to 72

Objective: To evaluate the effect of hybrid-closed loop Control-IQ technology (Control-IQ) in randomized controlled trials (RCTs) in subgroups based on baseline characteristics such as race/ethnicity, socioeconomic status (SES), prestudy insulin delivery modality (pump or multiple daily injections), and baseline glycemic control. Methods: Data were pooled and analyzed from 3 RCTs comparing Control-IQ to a Control group using continuous glucose monitoring in 369 participants with type 1 diabetes (T1D) from age 2 to 72 years old. Results: Time in range 70-180 mg/dL (TIR) in the Control-IQ group (n = 256) increased from 57% ± 17% at baseline to 70% ± 11% during follow-up, and in the Control group (n = 113) was 56% ± 15% and 57% ± 14%, respectively (adjusted treatment group difference = 11.5%, 95% confidence interval +9.7% to +13.2%, P < 0.001), an increase of 2.8 h/day on average. Significant reductions in mean glucose, hyperglycemia metrics, hypoglycemic metrics, and HbA1c were also observed. A statistically similar beneficial treatment effect on time in range 70-180 mg/dL was observed across the full age range irrespective of race-ethnicity, household income, prestudy continuous glucose monitor use, or prestudy insulin delivery method. Participants with the highest baseline HbA1c levels showed the greatest improvements in TIR and HbA1c. Conclusion: This pooled analysis of Control-IQ RCTs demonstrates the beneficial effect of Control-IQ in T1D across a broad spectrum of participant characteristics, including racial-ethnic minority, lower SES, lack of prestudy insulin pump experience, and high HbA1c levels. The greatest benefit was observed in participants with the worst baseline glycemic control in whom the auto-bolus feature of the Control-IQ algorithm appears to have substantial impact. Since no subgroups were identified that did not benefit from Control-IQ, hybrid-closed loop technology should be strongly considered for all youth and adults with T1D. Clinical Trials Registry: clinicaltrials.gov; NCT03563313, NCT03844789, and NCT04796779.

Early and sustained increase in time in range 1 year after initiation of hybrid close loop therapy via telemedicine in type 1 diabetes patients

BACKGROUND AND AIMS

Evidence supports the efficacy and safety of the Hybrid Close loop (HCL) system in patients with type 1 diabetes (T1D). However, limited data are available on the long-term outcomes of patients on HCL with telemedicine follow-up.

METHODS

A prospective observational cohort study including T1D patients, who were upgrading to HCL system. Virtual training and follow-up were done through telemedicine. CGM data were analyzed to compare the baseline time in range (TIR), time below range (TBR), glycemic variability and auto mode (AM), with measurements performed at 3, 6 and 12 months.

RESULTS

134 patients were included with baseline A1c 7.6% ± 1.1. 40.5% had a severe hypoglycemia event in the last year. Baseline TIR, measured two weeks after starting AM was 78.6 ± 9.94%. No changes were evident at three (Mean difference - 0.15;CI-2.47,2.17;p = 0.96), six (MD-1.09;CI-3.42,1.24;p = 0.12) and 12 months (MD-1.30;CI-3.64,1.04;p = 0.08). No significant changes were found in TBR or glycemic variability throughout the follow-up. Use of AM was 85.6 ± 17.5% and percentage of use of sensor was 88.75 ± 9.5% at 12 months. No severe hypoglycemic (SH) events were reported.

CONCLUSIONS

HCL systems allow to improve TIR, TBR and glycemic variability safely, early and sustained up to 1 year of follow-up in patients with T1D and high risk of hypoglycemia followed through telemedicine.