Six-Month Randomized, Multicenter Trial of Closed-Loop Control in Type 1 Diabetes.

BACKGROUND: Closed-loop systems that automate insulin delivery may improve glycemic outcomes in patients with type 1 diabetes. METHODS: In this 6-month randomized, multicenter trial, patients with type 1 diabetes were assigned in a 2:1 ratio to receive treatment with a closed-loop system (closed-loop group) or a sensor-augmented pump (control group). The primary outcome was the percentage of time that the blood glucose level was within the target range of 70 to 180 mg per deciliter (3.9 to 10.0 mmol per liter), as measured by continuous glucose monitoring. RESULTS: A total of 168 patients underwent randomization; 112 were assigned to the closed-loop group, and 56 were assigned to the control group. The age range of the patients was 14 to 71 years, and the glycated hemoglobin level ranged from 5.4 to 10.6%. All 168 patients completed the trial. The mean (±SD) percentage of time that the glucose level was within the target range increased in the closed-loop group from 61±17% at baseline to 71±12% during the 6 months and remained unchanged at 59±14% in the control group (mean adjusted difference, 11 percentage points; 95% confidence interval [CI], 9 to 14; P<0.001). The results with regard to the main secondary outcomes (percentage of time that the glucose level was >180 mg per deciliter, mean glucose level, glycated hemoglobin level, and percentage of time that the glucose level was <70 mg per deciliter or <54 mg per deciliter [3.0 mmol per liter]) all met the prespecified hierarchical criterion for significance, favoring the closed-loop system. The mean difference (closed loop minus control) in the percentage of time that the blood glucose level was lower than 70 mg per deciliter was -0.88 percentage points (95% CI, -1.19 to -0.57; P<0.001). The mean adjusted difference in glycated hemoglobin level after 6 months was -0.33 percentage points (95% CI, -0.53 to -0.13; P = 0.001). In the closed-loop group, the median percentage of time that the system was in closed-loop mode was 90% over 6 months. No serious hypoglycemic events occurred in either group; one episode of diabetic ketoacidosis occurred in the closed-loop group. CONCLUSIONS: In this 6-month trial involving patients with type 1 diabetes, the use of a closed-loop system was associated with a greater percentage of time spent in a target glycemic range than the use of a sensor-augmented insulin pump. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases; iDCL ClinicalTrials.gov number, NCT03563313.).

Insulin glargine/lixisenatide fixed-ratio combination improves glycaemic variability and control without increasing hypoglycaemia.

Maintaining optimal glycaemic control reduces the risk of micro- and macrovascular complications in patients with type 2 diabetes. Typically, glycaemic control is based on glycated haemoglobin (HbA1c) as a measure of mean glucose concentration; however, this marker does not accurately reflect glycaemic variability (GV), which is characterized by the amplitude, frequency and duration of hypo- and hyperglycaemic fluctuations. In the present study, we analysed data from the LixiLan-O trial, which compared iGlarLixi, a titratable fixed-ratio combination of the glucagon-like peptide-1 receptor agonist lixisenatide (Lixi) and long-acting basal insulin glargine 100 units/mL (iGlar), with its individual components, and the LixiLan-L trial, which compared iGlarLixi with iGlar. The GV features that were measured were mean and SD of self-measured plasma glucose (SMPG), high blood glucose index (HBGI) and low blood glucose index, area under the SMPG curve for each patient (AUCn), mean absolute glucose (MAG) and mean amplitude of glycaemic excursions (MAGE). By week 30, iGlarLixi improved all GV markers from baseline, with no increased hypoglycaemia risk. Significant improvements were observed in SMPG, SD of SMPG, HBGI, AUCn, MAG and MAGE compared with iGlar, and in SMPG, HBGI and AUCn, compared with Lixi.

Closed loop control in adolescents and children during winter sports: Use of the Tandem Control-IQ AP system.

OBJECTIVE: Artificial pancreas (AP) systems have been shown to improve glycemic control throughout the day and night in adults, adolescents, and children. However, AP testing remains limited during intense and prolonged exercise in adolescents and children. We present the performance of the Tandem Control-IQ AP system in adolescents and children during a winter ski camp study, where high altitude, low temperature, prolonged intense activity, and stress challenged glycemic control. METHODS: In a randomized controlled trial, 24 adolescents (ages 13-18 years) and 24 school-aged children (6-12 years) with Type 1 diabetes (T1D) participated in a 48 hours ski camp (∼5 hours skiing/day) at three sites: Wintergreen, VA; Kirkwood, and Breckenridge, CO. Study participants were randomized 1:1 at each site. The control group used remote monitored sensor-augmented pump (RM-SAP), and the experimental group used the t: slim X2 with Control-IQ Technology AP system. All subjects were remotely monitored 24 hours per day by study staff. RESULTS: The Control-IQ system improved percent time within range (70-180 mg/dL) over the entire camp duration: 66.4 ± 16.4 vs 53.9 ± 24.8%; P = .01 in both children and adolescents. The AP system was associated with a significantly lower average glucose based on continuous glucose monitor data: 161 ± 29.9 vs 176.8 ± 36.5 mg/dL; P = .023. There were no differences between groups for hypoglycemia exposure or carbohydrate interventions. There were no adverse events. CONCLUSIONS: The use of the Control-IQ AP improved glycemic control and safely reduced exposure to hyperglycemia relative to RM-SAP in pediatric patients with T1D during prolonged intensive winter sport activities.

Lixisenatide reduces glycaemic variability in insulin-treated patients with type 2 diabetes.

Chronic hyperglycaemia and glucose variability are associated with the development of chronic diabetes-related complications. We conducted a pooled analysis of patient-level data from three 24-week, randomized, phase III clinical trials to evaluate the impact of lixisenatide (LIXI) on glycaemic variability (GV) vs placebo as add-on to basal insulin. The main outcome GV measures were standard deviation (s.d.), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG) level, area under the curve for fasting glucose (AUC-F), and high (HBGI) and low blood glucose index (LBGI). The change in GV metrics over 24 weeks and relationships among baseline GV, patient characteristics and outcomes were assessed. Data were pooled from 1198 patients (665 LIXI, 533 placebo). Values for s.d., MAG level, MAGE, HBGI, and AUC-F significantly decreased with LIXI vs placebo, while LBGI values were unchanged. Higher baseline GV measures correlated with older age, longer type 2 diabetes duration, lower body mass index, higher baseline glycated/haemogobin, greater reduction in postprandial glucose (PPG) level, and higher rates of symptomatic hypoglycaemia. These data show that LIXI added to basal insulin significantly reduced GV and PPG excursions vs placebo, without increasing the risk of hypoglycaemia (LBGI).

Heart rate informed artificial pancreas system enhances glycemic control during exercise in adolescents with T1D.

OBJECTIVE: To evaluate the safety and performance of using a heart rate (HR) monitor to inform an artificial pancreas (AP) system during exercise among adolescents with type 1 diabetes (T1D). MATERIALS AND METHODS: In a randomized, cross-over trial, adolescents with T1D age 13 - 18 years were enrolled to receive on separate days either the unmodified UVa AP (stdAP) or an AP system connected to a portable HR monitor (AP-HR) that triggered an exercise algorithm for blood glucose (BG) control. During admissions participants underwent a structured exercise regimen. Hypoglycemic events and CGM tracings were compared between the two admissions, during exercise and for the full 24-hour period. RESULTS: Eighteen participants completed the trial. While number of hypoglycemic events during exercise and rest was not different between visits (0.39 AP-HR vs 0.50 stdAP), time below 70 mg dL -1 was lower on AP-HR compared to stdAP, 0.5±2.1% vs 7.4±12.5% (P = 0.028). Time with BG within 70-180 mg dL -1 was higher for the AP-HR admission vs stdAP during the exercise portion and overall (96% vs 87%, and 77% vs 74%), but these did not reach statistical significance (P = 0.075 and P = 0.366). CONCLUSIONS: Heart rate signals can safely and efficaciously be integrated in a wireless AP system to inform of physical activity. While exercise contributes to hypoglycemia among adolescents, even when using an AP system, informing the system of exercise via a HR monitor improved time <70 mg dL -1 . Nonetheless, it did not significantly reduce the total number of hypoglycemic events, which were low in both groups.

Use of an artificial pancreas among adolescents for a missed snack bolus and an underestimated meal bolus.

OBJECTIVE: The objective of this study was to evaluate the safety and performance of the artificial pancreas (AP) in adolescents with type 1 diabetes (T1D) following insulin omission for food. RESEARCH DESIGN AND METHODS: In a randomized, cross-over trial, adolescents with T1D aged 13-18 yr were enrolled in a randomized, cross-over trial. On separate days, received either usual care (UC) through their home insulin pump or used an AP system (Diabetes Assistant platform, continuous glucose monitor, and insulin pump). Approximately 1 h after admission, participants in both groups received an unannounced snack of 30 g carbohydrate, and 4 h later they received an 80 g lunch, for which both groups only received 75% of the calculated insulin dose to cover carbohydrates. On the UC day (but not the AP day), they received their full high blood glucose (BG) correction factor at lunch. Each admission lasted approximately 8 h. RESULTS: A total of 16 participants completed the trial. On the AP day (compared to UC), mean BG was lower (197 ± 10 vs. 235 ± 14 mg/dL) and time in range 70-180 mg/dL was higher (43% ± 7 vs. 19% ± 7) (both p < 0.05) overall; these results held in the time following the snack and meal (also p < 0.05). During the trial, there were no differences between groups in the rate of hypoglycemia <70 mg/dL. CONCLUSIONS: The AP provided improvements in short-term glycemic control without increases in hypoglycemia following missed insulin for food in adolescents. Thus, the AP partly compensates for missed insulin boluses for food, a common occurrence in adolescent diabetes care. Further testing is needed in longer-term settings.

Novel Detection and Progression Markers for Diabetes Based on Continuous Glucose Monitoring Data Dynamics.

PURPOSE: Develop a diabetes diagnostic tool based on two markers of continuous glucose monitoring (CGM) dynamics: CGM entropy rate (ER) and Poincaré plot (PP) ellipse area (S). METHODS: 5,754 daily CGM profiles from 843 individuals with type 1, type 2 diabetes, or healthy individuals with or without islet autoantibody status were used to compute two individual dynamic markers: ER (in bits per transition; BPT) of daily probability matrices describing CGM transitions between eight glycemic states, and the area S (mg2/dL2) of individual CGM PP ellipses using standard PP descriptors. The Youden's index was used to determine "optimal" cut-points for ER and S for health vs. diabetes (case 1); type 1 vs. type 2 (case 2); and low vs. high type 1 immunological risk (case 3). The markers' discriminative power was assessed through the area under the receiver operating characteristics curves (AUC). RESULTS: Optimal cut-off points were determined for ER and S for each of the three cases. ER and S discriminated case 1 with AUC = 0.98 (95% CI: 0.97-0.99) and AUC = 0.99 (95% CI: 0.99-1.00), respectively, (cut-offs ERcase1 = 0.76 BPT, Scase1 = 1993.91 mg2/dL2), case 2 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase2 = 1.00 BPT, Scase2 = 5112.98 mg2/dL2), and case 3 with AUC = 0.81 (95% CI, 0.77-0.84) and AUC = 0.76 (95% CI, 0.72-0.81), respectively (ERcase3 = 0.52 BPT, Scase3 = 923.65 mg2/dL2). CONCLUSIONS: CGM dynamics markers can be an alternative to fasting plasma glucose or glucose tolerance testing and identifying individuals at higher immunological risk of progressing to type 1 diabetes.

Neural-Net Artificial Pancreas: A Randomized Crossover Trial of a First-in-Class Automated Insulin Delivery Algorithm.

Background: Automated insulin delivery (AID) is now integral to the clinical practice of type 1 diabetes (T1D). The objective of this pilot-feasibility study was to introduce a new regulatory and clinical paradigm-a Neural-Net Artificial Pancreas (NAP)-an encoding of an AID algorithm into a neural network that approximates its action and assess NAP versus the original AID algorithm. Methods: The University of Virginia Model-Predictive Control (UMPC) algorithm was encoded into a neural network, creating its NAP approximation. Seventeen AID users with T1D were recruited and 15 participated in two consecutive 20-h hotel sessions, receiving in random order either NAP or UMPC. Their demographic characteristics were ages 22-68 years old, duration of diabetes 7-58 years, gender 10/5 female/male, White Non-Hispanic/Black 13/2, and baseline glycated hemoglobin 5.4%-8.1%. Results: The time-in-range (TIR) difference between NAP and UMPC, adjusted for entry glucose level, was 1 percentage point, with absolute TIR values of 86% (NAP) and 87% (UMPC). The two algorithms achieved similar times <70 mg/dL of 2.0% versus 1.8% and coefficients of variation of 29.3% (NAP) versus 29.1 (UMPC)%. Under identical inputs, the average absolute insulin-recommendation difference was 0.031 U/h. There were no serious adverse events on either controller. NAP had sixfold lower computational demands than UMPC. Conclusion: In a randomized crossover study, a neural-network encoding of a complex model-predictive control algorithm demonstrated similar performance, at a fraction of the computational demands. Regulatory and clinical doors are therefore open for contemporary machine-learning methods to enter the AID field. Clinical Trial Registration number: NCT05876273.

Adaptive Biobehavioral Control: A Pilot Analysis of Human-Machine Coadaptation in Type 1 Diabetes.

Background: While it is well recognized that an automated insulin delivery (AID) algorithm should adapt to changes in physiology, it is less understood that the individual would also have to adapt to the AID system. The adaptive biobehavioral control (ABC) method presented here attempts to compensate for this deficiency by including AID into an information cloud-based ecosystem. Methods: The Web Information Tool (WIT) implements the ABC concept via the following: (1) a Physiological Adaptation Module (PAM) that tracks metabolic changes and adapts AID parameters accordingly and (2) a Behavioral Adaptation Module (BAM) that provides information feedback. The safety of WIT (primary outcome) was assessed in an 8-week randomized, two-arm parallel pilot study. All participants used the Control-IQ® AID system enhanced with PAM, but only those in the Experimental group had access to BAM. Secondary glycemic outcomes were computed using the 2-week baseline period and the last 2 weeks of treatment. Results: Thirty participants with type 1 diabetes (T1D) completed all study procedures (17 female/13 male; age: 40 ± 14 years; HbA1c: 6.6% ± 0.5%). No severe hypoglycemia, DKA, or other serious adverse events were reported. Comparing the Experimental and Control groups, no significant difference was observed in time in range (70-180 mg/dL): 74.6% vs 73.8%, adjusted mean difference: 2.65%, 95% CI (-1.12%,6.41%), P = 0.161. Time in 70-140 mg/dL was significantly higher in the Experimental group: 50.7% vs 49.2%, 5.71% (0.44%,10.97%), P = 0.035, without increased time below range: 0.54% (-0.09%,1.17%), P = 0.089. Conclusion: The results demonstrate that it is safe to integrate an AID system into the WIT ecosystem. Validation in a full-scale study is ongoing.

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities.

OBJECTIVE: Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. METHODS: Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. SIGNIFICANCE: These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

Clinically Similar Clusters of Daily Continuous Glucose Monitoring Profiles: Tracking the Progression of Glycemic Control Over Time.

Background: The adoption of continuous glucose monitoring (CGM) results in vast amounts of data, but their interpretation is still more art than exact science. The International Consensus on Time in Range (TIR) proposed the widely accepted TIR system of metrics, which we now take forward by introducing a finite and fixed set of clinically similar clusters (CSCs), such that the TIR metrics of daily CGM profiles within a cluster are homogeneous. Methods: CSC definition and validation used 204,710 daily CGM profiles in health, and types 1 and 2 diabetes (T1D and T2D) on different treatments. The CSCs were defined using 23,916 daily CGM profiles (Training set), and the final fixed set of CSCs was obtained using another 37,758 profiles (Validation set). The Testing set (143,036 profiles) was used to establish the robustness and generalizability of CSCs. Results: The final set of CSCs contains 32 clusters. Any daily CGM profile was classifiable to a single CSC, which approximated common glycemic metrics of the daily CGM profile, as evidenced by regression analyses with 0 intercept (R-squares ≥0.83, e.g., correlation ≥0.91), for all TIR and several other metrics. The CSCs distinguished CGM profiles in health, T2D, and T1D on different treatments, and allowed tracking of daily changes in a person's glycemic control over time. Conclusion: Daily CGM profiles can be classified into one of 32 prefixed CSCs, which enables a host of applications, for example, tabulated data interpretation and algorithmic approaches to treatment, database indexing, pattern recognition, and tracking disease progression. Clinical Trial Registration: N/A-not a clinical trial.

Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility.

Arguably, diabetes mellitus is one of the best quantified human conditions. In the past 50 years, the metabolic monitoring technologies progressed from occasional assessment of average glycemia via HbA1c, through episodic blood glucose readings, to continuous glucose monitoring (CGM) producing data points every few minutes. The high-temporal resolution of CGM data enabled increasingly intensive treatments, from decision support assisting insulin injection or oral medication, to automated closed-loop control, known as the "artificial pancreas." Throughout this progress, mathematical models and computer simulation of the human metabolic system became indispensable for the technological progress of diabetes treatment, enabling every step, from assessment of insulin sensitivity via the now classic Minimal Model of Glucose Kinetics, to in silico trials replacing animal experiments, to automated insulin delivery algorithms. In this review, we follow these developments, beginning with the Minimal Model, which evolved through the years to become large and comprehensive and trigger a paradigm change in the design of diabetes optimization strategies: in 2007, we introduced a sophisticated model of glucose-insulin dynamics and a computer simulator equipped with a "population" of N = 300 in silico "subjects" with type 1 diabetes. In January 2008, in an unprecedented decision, the Food and Drug Administration (FDA) accepted this simulator as a substitute to animal trials for the pre-clinical testing of insulin treatment strategies. This opened the field for rapid and cost-effective development and pre-clinical testing of new treatment approaches, which continues today. Meanwhile, animal experiments for the purpose of designing new insulin treatment algorithms have been abandoned.

Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning.

Background: Predicting the risk for type 1 diabetes (T1D) is a significant challenge. We use a 1-week continuous glucose monitoring (CGM) home test to characterize differences in glycemia in at-risk healthy individuals based on autoantibody presence and develop a machine-learning technology for CGM-based islet autoantibody classification. Methods: Sixty healthy relatives of people with T1D with mean ± standard deviation age of 23.7 ± 10.7 years, HbA1c of 5.3% ± 0.3%, and body mass index of 23.8 ± 5.6 kg/m2 with zero (n = 21), one (n = 18), and ≥2 (n = 21) autoantibodies were enrolled in an National Institutes of Health TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic outcomes were computed from weekly, overnight (12:00-06:00), and post-SLMM CGM traces, compared across groups, and used in four supervised machine-learning autoantibody status classifiers. Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. Results: Among all computed glycemia metrics, only three were different across the autoantibodies groups: percent time >180 mg/dL (T180) weekly (P = 0.04), overnight CGM incremental AUC (P = 0.005), and T180 for 75 min post-SLMM CGM traces (P = 0.004). Once overnight and post-SLMM features are incorporated in machine-learning classifiers, a linear support vector machine model achieved the best performance of classifying autoantibody positive versus autoantibody negative participants with AUC-ROC ≥0.81. Conclusion: A new technology combining machine learning with a potentially self-administered 1-week CGM home test can help improve T1D risk detection without the need to visit a hospital or use a medical laboratory. Trial registration: ClinicalTrials.gov registration no. NCT02663661.

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.

Safety and Efficacy of Sustained Automated Insulin Delivery Compared With Sensor and Pump Therapy in Adults With Type 1 Diabetes at High Risk for Hypoglycemia: A Randomized Controlled Trial.

OBJECTIVE: Assess the safety and efficacy of automated insulin delivery (AID) in adults with type 1 diabetes (T1D) at high risk for hypoglycemia. RESEARCH DESIGN AND METHODS: Participants were 72 adults with T1D who used an insulin pump with Clarke Hypoglycemia Perception Awareness scale score >3 and/or had severe hypoglycemia during the previous 6 months confirmed by time below range (TBR; defined as sensor glucose [SG] reading <70 mg/dL) of at least 5% during 2 weeks of blinded continuous glucose monitoring (CGM). Parallel-arm, randomized trial (2:1) of AID (Tandem t:slim ×2 with Control-IQ technology) versus CGM and pump therapy for 12 weeks. The primary outcome was TBR change from baseline. Secondary outcomes included time in target range (TIR; 70-180 mg/dL), time above range (TAR), mean SG reading, and time with glucose level <54 mg/dL. An optional 12-week extension with AID was offered to all participants. RESULTS: Compared with the sensor and pump (S&P), AID resulted in significant reduction of TBR by -3.7% (95% CI -4.8, -2.6), P < 0.001; an 8.6% increase in TIR (95% CI 5.2, 12.1), P < 0.001; and a -5.3% decrease in TAR (95% CI -87.7, -1.8), P = 0.004. Mean SG reading remained similar in the AID and S&P groups. During the 12-week extension, the effects of AID were sustained in the AID group and reproduced in the S&P group. Two severe hypoglycemic episodes occurred using AID. CONCLUSIONS: In adults with T1D at high risk for hypoglycemia, AID reduced the risk for hypoglycemia more than twofold, as quantified by TBR, while improving TIR and reducing hyperglycemia. Hence, AID is strongly recommended for this specific population.

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.

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.

Resolving the conundrum of islet transplantation by linking metabolic dysregulation, inflammation, and immune regulation.

Although type 1 diabetes cannot be prevented or reversed, replacement of insulin production by transplantation of the pancreas or pancreatic islets represents a definitive solution. At present, transplantation can restore euglycemia, but this restoration is short-lived, requires islets from multiple donors, and necessitates lifelong immunosuppression. An emerging paradigm in transplantation and autoimmunity indicates that systemic inflammation contributes to tissue injury while disrupting immune tolerance. We identify multiple barriers to successful islet transplantation, each of which either contributes to the inflammatory state or is augmented by it. To optimize islet transplantation for diabetes reversal, we suggest that targeting these interacting barriers and the accompanying inflammation may represent an improved approach to achieve successful clinical islet transplantation by enhancing islet survival, regeneration or neogenesis potential, and tolerance induction. Overall, we consider the proinflammatory effects of important technical, immunological, and metabolic barriers including: 1) islet isolation and transplantation, including selection of implantation site; 2) recurrent autoimmunity, alloimmune rejection, and unique features of the autoimmune-prone immune system; and 3) the deranged metabolism of the islet transplant recipient. Consideration of these themes reveals that each is interrelated to and exacerbated by the other and that this connection is mediated by a systemic inflammatory state. This inflammatory state may form the central barrier to successful islet transplantation. Overall, there remains substantial promise in islet transplantation with several avenues of ongoing promising research. This review focuses on interactions between the technical, immunological, and metabolic barriers that must be overcome to optimize the success of this important therapeutic approach.