Diabetes Technology Meeting 2023.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Update on Measuring Ketones.

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

A New Index of Insulin Sensitivity from Glucose Sensor and Insulin Pump Data: In Silico and In Vivo Validation in Youths with Type 1 Diabetes.

Background: Estimation of insulin sensitivity (SI) and its daily variation are key for optimizing insulin therapy in patients with type 1 diabetes (T1D). We recently developed a method for SI estimation from continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) data in adults with T1D (SISP) and validated it under restrained experimental conditions. Herein, we validate in vivo a new version of SISP performing well in daily life unrestrained conditions. Methods: The new SISP was tested in both simulated and real data. The simulated dataset consists of 100 virtual adults of the UVa/Padova T1D Simulator monitored during an open-loop experiment, whereas the real dataset consists of 10 youths with T1D monitored during a hybrid closed-loop meal study. In both datasets, participants underwent two consecutive meals (breakfast and lunch, at 7 and 11 am) with the same carbohydrate content (70 g). Plasma glucose and insulin were measured during each meal to estimate the oral glucose minimal model SI (SIMM). CGM and CSII data were used for SISP calculation, which was then validated against the gold standard SIMM. Results: SISP was estimated with good precision (median coefficient of variation <20%) in 100% of the real and 91% of the simulated meals. SISP and SIMM were highly correlated, both in the simulated and real datasets (R = 0.82 and R = 0.83, P < 0.001), and exhibited a similar intraday pattern. Conclusions: SISP is suitable for estimating SI in both closed- and open-loop settings, provided that the subject wears a CGM sensor and a subcutaneous insulin pump.

Increase Access, Reduce Disparities: Recommendations for Modifying Medicaid CGM Coverage Eligibility Criteria.

Numerous studies have demonstrated the clinical value of continuous glucose monitoring (CGM) in type 1 diabetes (T1D) and type 2 diabetes (T2D) populations. However, the eligibility criteria for CGM coverage required by the Centers for Medicare & Medicaid Services (CMS) ignore the conclusive evidence that supports CGM use in various diabetes populations that are currently deemed ineligible. In an earlier article, we discussed the limitations and inconsistencies of the agency's CGM eligibility criteria relative to current scientific evidence and proposed practice solutions to address this issue and improve the safety and care of Medicare beneficiaries with diabetes. Although Medicaid is administered through CMS, there is no consistent Medicaid policy for CGM coverage in the United States. This article presents a rationale for modifying and standardizing Medicaid CGM coverage eligibility across the United States.

Insulin Replacement Across the Menstrual Cycle in Women with Type 1 Diabetes: An In Silico Assessment of the Need for Ad Hoc Technology.

Background: Women with type 1 diabetes (T1D) of fertile age may experience fluctuations in insulin needs across the menstrual cycle. When present, these fluctuations complicate glucose management and oftentimes worsen glycemic control. In this work, an in silico analysis was conducted to assess whether current technology is sufficient to handle changes in insulin needs due to the menstrual cycle in women with T1D. Methods: Euglycemic clamp studies were performed in 16 women with T1D in the follicular phase (FP) and luteal phase (LP) of the menstrual cycle. Interphase insulin sensitivity (IS) variability observed in the data was modeled and introduced in the University of Virginia/Padova T1D Simulator. Open-loop and closed-loop insulin delivery was tested in two in silico studies, without (nominal study) and with (informed study) a priori knowledge on cycle-related IS variability informing insulin therapy. Glycemic metrics were computed on the obtained glucose traces. Results: In the pool of studied women, the glucose infusion rate area under the curve significantly decreased from FP to LP (P = 0.0107), indicating an average decrease of IS in LP. When introduced in the simulator, this pattern led to increased time spent >180 and >250 mg/dL during LP versus FP in the nominal studies, irrespective of the insulin delivery strategy. In the informed studies, glycemic metrics stabilized across the cycle. Conclusion: This work suggests that current insulin delivery technology may benefit from informing the dosing algorithm with knowledge on menstrual cycle related IS changes. Clinical validation of these results is warranted. ClinicalTrials.gov identifier: NCT02693938.

Diabetes Technology Meeting 2021.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.

Digital Connectivity: The Sixth Vital Sign.

Digital health and telehealth connectivity have become important aspects of clinical care. Connected devices, including continuous glucose monitors and automated insulin delivery systems for diabetes, are being used increasingly to support personalized clinical decisions based on automatically collected data. Furthermore, the development, demand, and coverage for telehealth have all recently expanded, as a result of the COVID-19 pandemic. Medical care, and especially diabetes care, are therefore becoming more digital through the use of both connected digital health devices and telehealth communication. It has therefore become necessary to integrate digital data into the electronic health record and maintain personal data confidentiality, integrity, and availability. Connected digital monitoring combined with telehealth communication is known as virtual health. For this virtual care paradigm to be successful, patients must have proper skills, training, and equipment. We propose that along with the five current vital signs of blood pressure, pulse, respiratory rate, temperature, and pain, at this time, digital connectivity should be considered as the sixth vital sign. In this article, we present a scale to assess digital connectivity.

Modeling the variability of insulin sensitivity during the menstrual cycle in women with type 1 diabetes to adjust open-loop insulin therapy.

Women with type 1 diabetes (T1D) typically experience a decrease in insulin sensitivity (SI) during the second half of their menstrual cycle (or the luteal phase (LP)), which oftentimes is not properly addressed by insulin therapy, therefore leading to increased exposure to hyperglycemia. This study proposes a suitable way to model SI variability due to the menstrual cycle in the FDA-accepted University of Virginia (UVA)/Padova T1D Simulator, to determine to what extent the inclusion of menstrual cycle information to fine-tune insulin therapy could help improve glycemic control in the LP of the menstrual cycle. In-silico tests were performed considering different simulation scenarios, and the obtained results show that hyperglycemic excursions can be largely reduced when SI variability is taken into account for planning insulin therapy, without a relevant increase in hypoglycemic events.

Adjunctive Therapies to Optimize Closed-loop Glucose Control.

Closed-loop insulin delivery systems are fast becoming the standard of care in the management of type 1 diabetes and have led to significant improvements in diabetes management. Nevertheless, there is still room for improvement for the closed-loop systems to optimize treatment and meet target glycemic control. Adjunct treatments have been introduced as an alternative method to insulin-only treatment methods to overcome diabetes treatment challenges and improve clinical and patient reported outcomes during closed-loop treatment. The adjunct treatment agents mostly consist of medications that are already approved for type 2 diabetes treatment and aim to complete the missing physiologic factors, such as the entero-endocrine system, that regulate glycemia in addition to insulin. This paper will review many of these adjunct therapies, including the basic mechanisms of action, potential benefits, side effects, and the evidence supporting their use during closed-loop treatment.

Predictors of Time-in-Range (70-180 mg/dL) Achieved Using a Closed-Loop Control System.

Background: Studies of closed-loop control (CLC) in patients with type 1 diabetes (T1D) consistently demonstrate improvements in glycemic control as measured by increased time-in-range (TIR) 70-180 mg/dL. However, clinical predictors of TIR in users of CLC systems are needed. Materials and Methods: We analyzed data from 100 children aged 6-13 years with T1D using the Tandem Control-IQ CLC system during a randomized trial or subsequent extension phase. Continuous glucose monitor data were collected at baseline and during 12-16 weeks of CLC use. Participants were stratified into quartiles of TIR on CLC to compare clinical characteristics. Results: TIR for those in the first, second, third, and fourth quartiles was 54%, 65%, 71%, and 78%, respectively. Lower baseline TIR was associated with lower TIR on CLC (r = 0.69, P < 0.001). However, lower baseline TIR was also associated with greater improvement in TIR on CLC (r = -0.81, P < 0.001). During CLC, participants in the highest versus lowest TIR-quartile administered more user-initiated boluses daily (8.5 ± 2.8 vs. 5.8 ± 2.6, P < 0.001) and received fewer automated boluses (3.5 ± 1.0 vs. 6.0 ± 1.6, P < 0.001). Participants in the lowest (vs. the highest) TIR-quartile received more insulin per body weight (1.13 ± 0.27 vs. 0.87 ± 0.20 U/kg/d, P = 0.008). However, in a multivariate model adjusting for baseline TIR, user-initiated boluses and insulin-per-body-weight were no longer significant. Conclusions: Higher baseline TIR is the strongest predictor of TIR on CLC in children with T1D. However, lower baseline TIR is associated with the greatest improvement in TIR. As with open-loop systems, user engagement is important for optimal glycemic control.

Health-Related Quality of Life and Treatment Satisfaction in Parents and Children with Type 1 Diabetes Using Closed-Loop Control.

Introduction: Hybrid closed-loop systems increase time-in-range (TIR) and reduce glycemic variability. Person-reported outcomes (PROs) are essential to assess the utility of new devices and their impact on quality of life. This article focuses on the PROs for pediatric participants (ages 6-13 years) with type 1 diabetes (T1D) and their parents during a trial using the Tandem Control-IQ system, which was shown to increase TIR and improve other glycemic metrics. Research Design and Methods: One hundred and one children 6 to 13 years old with T1D were randomly assigned to closed-loop control (CLC) or sensor-augmented pump (SAP) in a 16-week randomized clinical trial with extension to 28 weeks during which the SAP group crossed over to CLC. Health-related quality of life and treatment satisfaction measures were obtained from children and their parents at baseline, 16 weeks, and 28 weeks. Results: Neither the children in the CLC group nor their parents had statistically significant changes in PRO outcomes compared with the SAP group at the end of the 16-week randomized controlled trial and the 28-week extension. Parents in the CLC group reported nonsignificant improvements in some PRO scores when compared with the SAP group at 16 weeks, which were sustained at 28 weeks. Sleep scores for parents improved from "poor sleep quality" to "adequate sleep quality" between baseline and 16 weeks, however, the change in scores was not statistically different between groups. Conclusions: Children with T1D who used the Control-IQ system did not experience increased burden compared with those using SAP based on person-reported outcomes from the children and their parents. Clinical Trials Registration: NCT03844789.

Effects of Sotagliflozin Combined with Intensive Insulin Therapy in Young Adults with Poorly Controlled Type 1 Diabetes: The JDRF Sotagliflozin Study.

Background: Young adults with type 1 diabetes (T1D) tend to have higher A1C than older adults and are at increased risk for diabetic ketoacidosis (DKA). Oral adjuncts to insulin have not been previously studied in this population. Methods: In this phase 2, multicenter, randomized, double-blind, placebo-controlled, parallel-group study, adults aged 18-30 years with T1D and A1C ≥9.0% were randomly assigned to placebo (n = 42) or sotagliflozin 400 mg (n = 43), in addition to insulin for 12 weeks. Insulin doses were adjusted to meet glucose targets (preprandial 80-130 mg/dL, postprandial <180 mg/dL). The primary endpoint was change from baseline in A1C at week 12. Results: From a baseline of 9.8%, mean A1C decreased by 1.0% with placebo and 1.3% with sotagliflozin (-0.4% [95% confidence interval (CI): -0.8 to 0.1]; P = 0.10 vs. placebo). In the prespecified A1C ≤10.0% subgroup, the treatment difference was -0.8% (-1.3 to -0.2; P = 0.006), favoring sotagliflozin. Overall, relative to placebo, postprandial glucose (PPG) decreased by 56.6 mg/dL (-89.7 to -23.6; P < 0.001) and weight decreased by 2.37 kg (-3.5 to -1.2; P < 0.001). More patients achieved an A1C <7.0% with sotagliflozin (16.3%) than placebo (2.4%; P = 0.026). Rates of documented hypoglycemia and severe hypoglycemia were similar between groups. One DKA event occurred with placebo, and none occurred with sotagliflozin. Conclusions: In young adults with T1D and suboptimal glycemic control, sotagliflozin plus insulin for 12 weeks numerically improved A1C and significantly improved A1C goal attainment, PPG, and body weight. Sotagliflozin plus insulin was generally well tolerated without any episodes of DKA (NCT02383940).

Impact of Accelerating Insulin on an Artificial Pancreas System Without Meal Announcement: An In Silico Examination.

BACKGROUND: Controlling postprandial blood glucose without the benefit of an appropriately sized premeal insulin bolus has been challenging given the delays in absorption and action of subcutaneously injected insulin during conventional and artificial pancreas (AP) system diabetes treatment. We aim to understand the impact of accelerating insulin and increasing aggressiveness of the AP controller as potential solutions to address the postprandial hyperglycemia challenge posed by unannounced meals through a simulation study. METHODS: Accelerated rapid-acting insulin analogue is modeled within the UVA/Padova simulation platform by uniformly reducing its pharmacokinetic time constants (α multiplier) and used with a model predictive control, where the controller's aggressiveness depends on α. Two sets of single-meal simulations were performed: (1) where we only tune the controller's aggressiveness and (2) where we also accelerate insulin absorption and action to assess postprandial glycemic control during each intervention. RESULTS: Mean percent of time spent within the 70 to 180 mg/dL postprandial glycemic range is significantly higher in set (2) than in set (1): 79.9, 95% confidence interval [77.0, 82.7] vs 88.8 [86.8, 90.9] ([Note to typesetter: Set all unnecessary math in text format and insert appropriate spaces between operators.] P < .05) for α = 2, and 81.4 [78.6, 84.3] vs 94.1 [92.6, 95.6] (P < .05) for α = 3. A decrease in percent of time below 70 mg/dL is also detected: 0.9 [0.4, 2.2] vs 0.6 [0.2, 1.4] (P = .23) for α = 2 and 1.4 [0.7, 2.8] vs 0.4 [0.1, 1.4] (P < .05) for α = 3. CONCLUSION: These proof-of-concept simulations suggest that an AP without prandial insulin boluses combined with significantly faster insulin analogues could match the glycemic performance obtained with an optimal hybrid AP.

Extended Use of the Control-IQ Closed-Loop Control System in Children With Type 1 Diabetes.

OBJECTIVE: To further evaluate the safety and efficacy of the Control-IQ closed-loop control (CLC) system in children with type 1 diabetes. RESEARCH DESIGN AND METHODS: After a 16-week randomized clinical trial (RCT) comparing CLC with sensor-augmented pump (SAP) therapy in 101 children 6-13 years old with type 1 diabetes, 22 participants in the SAP group initiated use of the CLC system (referred to as SAP-CLC cohort), and 78 participants in the CLC group continued use of CLC (CLC-CLC cohort) for 12 weeks. RESULTS: In the SAP-CLC cohort, mean percentage of time in range 70-180 mg/dL (TIR) increased from 55 ± 13% using SAP during the RCT to 65 ± 10% using CLC (P < 0.001), with 36% of the cohort achieving TIR >70% plus time <54 mg/dL <1% compared with 14% when using SAP (P = 0.03). Substantial improvement in TIR was seen after the 1st day of CLC. Time <70 mg/dL decreased from 1.80% to 1.34% (P < 0.001). In the CLC-CLC cohort, mean TIR increased from 53 ± 17% prerandomization to 67 ± 10% during the RCT and remained reasonably stable at 66 ± 10% through the 12 weeks post-RCT. No episodes of diabetic ketoacidosis or severe hypoglycemia occurred in either cohort. CONCLUSIONS: This further evaluation of the Control-IQ CLC system supports the findings of the preceding RCT that use of a closed-loop system can safely improve glycemic control in children 6-13 years old with type 1 diabetes from the 1st day of use and demonstrates that these improvements can be sustained through 28 weeks of use.

Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA).

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (i.e. before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes. Graphical abstract.

Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA).

Physical exercise is an important component in the management of type 1 diabetes across the lifespan. Yet, acute exercise increases the risk of dysglycaemia, and the direction of glycaemic excursions depends, to some extent, on the intensity and duration of the type of exercise. Understandably, fear of hypoglycaemia is one of the strongest barriers to incorporating exercise into daily life. Risk of hypoglycaemia during and after exercise can be lowered when insulin-dose adjustments are made and/or additional carbohydrates are consumed. Glycaemic management during exercise has been made easier with continuous glucose monitoring (CGM) and intermittently scanned continuous glucose monitoring (isCGM) systems; however, because of the complexity of CGM and isCGM systems, both individuals with type 1 diabetes and their healthcare professionals may struggle with the interpretation of given information to maximise the technological potential for effective use around exercise (ie, before, during and after). This position statement highlights the recent advancements in CGM and isCGM technology, with a focus on the evidence base for their efficacy to sense glucose around exercise and adaptations in the use of these emerging tools, and updates the guidance for exercise in adults, children and adolescents with type 1 diabetes.

A Randomized Trial of Closed-Loop Control in Children with Type 1 Diabetes.

BACKGROUND: A closed-loop system of insulin delivery (also called an artificial pancreas) may improve glycemic outcomes in children with type 1 diabetes. METHODS: In a 16-week, multicenter, randomized, open-label, parallel-group trial, we assigned, in a 3:1 ratio, children 6 to 13 years of age who had type 1 diabetes to receive treatment with the use of either a closed-loop system of insulin delivery (closed-loop group) or a sensor-augmented insulin pump (control group). The primary outcome was the percentage of time that the glucose level was in the target range of 70 to 180 mg per deciliter, as measured by continuous glucose monitoring. RESULTS: A total of 101 children underwent randomization (78 to the closed-loop group and 23 to the control group); the glycated hemoglobin levels at baseline ranged from 5.7 to 10.1%. The mean (±SD) percentage of time that the glucose level was in the target range of 70 to 180 mg per deciliter increased from 53±17% at baseline to 67±10% (the mean over 16 weeks of treatment) in the closed-loop group and from 51±16% to 55±13% in the control group (mean adjusted difference, 11 percentage points [equivalent to 2.6 hours per day]; 95% confidence interval, 7 to 14; P<0.001). In both groups, the median percentage of time that the glucose level was below 70 mg per deciliter was low (1.6% in the closed-loop group and 1.8% in the control group). In the closed-loop group, the median percentage of time that the system was in the closed-loop mode was 93% (interquartile range, 91 to 95). No episodes of diabetic ketoacidosis or severe hypoglycemia occurred in either group. CONCLUSIONS: In this 16-week trial involving children with type 1 diabetes, the glucose level was in the target range for a greater percentage of time with the use of a closed-loop system than with the use of a sensor-augmented insulin pump. (Funded by Tandem Diabetes Care and the National Institute of Diabetes and Digestive and Kidney Diseases; ClinicalTrials.gov number, NCT03844789.).