Diabetes Technology & Therapeutics

Second-generation long-acting insulin glargine 300 U/mL (Gla-300) and degludec 100 U/mL (Deg-100) provide novel basal insulin therapies for the treatment of type 1 diabetes (T1D). Both offer a flatter pharmacokinetic (PK) profile than the previous generation of long-acting insulins, thus improving glycemic control while reducing hypoglycemic events. This work describes an in silico head-to-head comparison of the two basal insulins on 24-h glucose profiles and was used to guide the design of a clinical trial.

The Universities of Virginia (UVA)/Padova T1D simulator describes the intra-/interday variability of glucose-insulin dynamics and thus provides a robust bench-test for assessing glucose control for basal insulin therapies. A PK model describing subcutaneous absorption of Deg-100, in addition to the one already available for Gla-300, has been developed based on T1D clinical data and incorporated into the simulator. One hundred in silico T1D subjects received a basal insulin dose (Gla-300 or Deg-100) for 12 weeks (8 weeks uptitration, 4 weeks stable dosing) by morning or evening administration in a basal/bolus regimen. The virtual patients were uptitrated to their individual doses with two different titration rules.

The last 2-week simulated continuous glucose monitoring data were used to calculate various outcome metrics for both basal insulin treatments, with primary outcome being the percent time in glucose target (70–140 mg/dL). The simulations show no statistically significant difference for Gla-300 versus Deg-100 in the main endpoints.

This work suggests comparable glucose control using either Gla-300 or Deg-100 and was used to guide the design of a clinical trial intended to compare second-generation long-acting insulin analogues.

Accurate prediction of glucose levels in patients with type 1 diabetes mellitus (T1DM) is critical both for their glycemic control and for the development of closed-loop systems.

In this study, we utilized real-life, retrospective, continuous glucose monitoring data from 141 T1DM patients (9,083 connection days, 1,592,506 glucose measurements) and in silico data generated by the UVA/Padova T1DM simulator to evaluate different computational methods for glucose prediction. We evaluated the performance of the models using both measures of numerical accuracy, measured by the root mean square error, and clinical accuracy, measured by the percentage of time in each of the Clarke error grid (CEG) zones, and compared the predictions done by autoregressive (AR) models, tree-based methods, artificial neural networks, and a novel model that we devised and optimized for this task.

Our novel model, constructed on real-life data, achieved clinical accuracy of 99.3% and 95.8% in predicting the glucose level 30 and 60 min ahead, respectively, and reduced the percentage of glucose predictions in zones C–E of the CEG by 60.6% and 38.4% in these prediction horizons, compared with a standard AR model. The model was superior to all other models across all age groups and achieved higher clinical accuracy in subgroups of patients with high glucose variability and greater time spent in hypoglycemia. Compared with real-life data, when evaluated on in silico data, the model had a higher clinical and numerical accuracy.

A model that optimizes for CEG zones may significantly improve clinical accuracy and clinical outcomes of treatment decisions in T1DM patients. Results obtained from simulated data may overestimate the performance of models on real-life data.

Continuous glucose monitoring (CGM) systems are widely and increasingly used in diabetes self-management and in the context of clinic visits. However, access to CGM data during visits can be challenging. Clinic inefficiencies can restrict the time available for patient education, and the inability to integrate CGM data into electronic health record (EHR) systems can result in data being lost. In this study, we describe our institution's approach to integrating CGM data directly into the EHR through a partnership with a CGM device manufacturer and without a third-party data aggregation/data visualization platform.

We interviewed key stakeholders with the hospital Information Technology Department, the Division of Pediatric Endocrinology, and a CGM device manufacturer. A collaborative, human-centered design approach was used to define the workflow. Health Level 7 (HL7) standards were used to build all data exchanges.

In collaboration with all parties, we created a simple network architecture design for both account linkage and data acquisition. The system uses the standard, computerized, physician order entry interface available in the EHR for both processes. Data acquisition occurs in real time, and customized reports are displayed within the results section of the EHR. The entire process is Health Insurance Portability and Accountability Act (HIPAA) compliant and meets all security requirements.

Building scalable data integration using HL7 standards is possible and allows real-time access to CGM data within the diabetes provider's existing workflow and can occur with or without the patient present. This may lead to improved clinical outcomes, increased efficiency, and new revenue opportunities by documenting CGM data capture and review.

Clinical remission of type 1 diabetes is not only associated with regeneration of beta cells and preserved insulin secretion but also with increased insulin sensitivity. The aim of the study was to determine the association between presence of remission in the first year of type 1 diabetes and insulin resistance at 7 years from diagnosis of the disease.

A total of 108 consecutive patients with newly diagnosed type 1 diabetes were followed prospectively. During the follow-up time, the onset and duration of clinical remission were registered. Seventy-four patients were included in the final analysis. Insulin sensitivity was assessed by the glucose disposal rate (GDR), determined using the hyperinsulinemic-euglycemic clamp, performed at 7 years from diagnosis of diabetes. Patients were divided into groups with GDR <4.5 mg/(kg·min) (G1-lower insulin sensitivity group) and GDR ≥4.5 mg/(kg·min) (G2-higher insulin sensitivity group).

Higher insulin sensitivity was observed in the remission group [GDR 6.2 interquartile range (IQR) 4.2–7.0 mg/(kg·min) vs. 3.8 (IQR 3.0–4.8) mg/(kg·min); P = 0.01]. Furthermore, in G2 group, the duration of remission was longer than in G1 group: (351 [IQR 206–561] days vs. 70 [IQR 0–289] days; P = 0.002). Also, the GDR value correlated positively with duration of remission (r = 0.42; P = 0.002). In the multivariate logistic regression model, including age, sex, body mass index at diagnosis, and presence of remission, the remission period was independently associated with a higher GDR value (odds ratio 10.88; 95% confidence interval: 1.70–69.50; P = 0.009].

Patients with type 1 diabetes who entered remission at the beginning of the disease, despite its ending, have higher insulin sensitivity at 7 years after diagnosis of diabetes than nonremitters.

Autoantibody screening in type 1 diabetes (T1D) may reduce the chances of potentially life-threatening diabetic ketoacidosis (DKA) at diagnosis by allowing individuals at risk of progression to more actively monitor for and/or manage progression to insulin dependence. We investigated parents' preferences for treatments to delay the onset of insulin dependence in children who are at high risk of developing Stage III T1D.

A web-based survey (n = 1501) was administered to a stratified sample of parents (children <18 years) in the United States from an online panel. Parents were told to hypothetically assume that their youngest child would become insulin dependent within 6 months or 2 years and were offered a series of choices between no treatment and two hypothetical treatments that would delay insulin dependence. Random-parameters logit analysis and maximum acceptable risks were used to evaluate the relative importance of treatment benefits and risks.

Most parents chose at least one active treatment (2% always chose monitoring only). For parents of children without T1D (n = 901), delaying insulin dependence and reducing the risk of long-term health complications and serious infection were the most important treatment attributes. In addition, parents of children with T1D (n = 600) also valued reducing the risk of hospitalizations due to DKA.

When told to assume their child would develop Stage III T1D, most parents considered active treatments to delay progression. For medicines under development to delay insulin dependence in T1D, the preferences expressed in this survey provide guidance on acceptable benefit–risk trade-offs.

To assess the safety and efficacy of a simplified initialization for the Tandem t:slim X2 Control-IQ hybrid closed-loop system, using parameters based on total daily insulin (“MyTDI”) in adolescents with type 1 diabetes under usual activity and during periods of increased exercise.

Adolescents with type 1 diabetes 12–18 years of age used Control-IQ for 5 days at home using their usual parameters. Upon arrival at a 60-h ski camp, participants were randomized to either continue Control-IQ using their home settings or to reinitialize Control-IQ with MyTDI parameters. Control-IQ use continued for 5 days following camp. The effect of MyTDI on continuous glucose monitoring outcomes were analyzed using repeated measures analysis of variance (ANOVA): baseline, camp, and at home.

Twenty participants were enrolled and completed the study; two participants were excluded from the analysis due to absence from ski camp (1) and illness (1). Time in range was similar between both groups at home and camp. A tendency to higher time <70 mg/dL in the MyTDI group was present but only during camp (median 3.8% vs. 1.4%, P = 0.057). MyTDI users with bolus/TDI ratios >40% tended to show greater time in the euglycemic range improvements between baseline and home than users with ratios <40% (+16.3% vs. −9.0%, P = 0.012). All participants maintained an average of 95% time in closed loop (84.1%–100%).

MyTDI is a safe, effective, and easy way to determine insulin parameters for use in the Control-IQ artificial pancreas. Future modifications to account for the influence of carbohydrate intake on MyTDI calculations might further improve time in range.

To analyze key indicators of metabolic control in adults with type 1 diabetes (T1D) using real-time or intermittent scanning continuous glucose monitoring (rtCGM/iscCGM) during real-life care, based on the German/Austrian/Swiss Prospective Diabetes Follow-up (DPV) registry.

Cross-sectional analysis including 233 adults with T1D using CGM. We assessed CGM metrics by gender, age group (18 to <30 years vs. ≥30 years), insulin delivery method (multiple daily injections vs. continuous subcutaneous insulin infusion [CSII]) and sensor type (iscCGM vs. rtCGM), working days versus weekends, and daytime versus night-time using multivariable linear regression models (adjusted for demographic variables) or Wilcoxon signed-rank test.

Overall, 79/21% of T1D patients used iscCGM/rtCGM. Those aged ≥30 years spent more time in range (TIR [70–180 mg/dL] 54% vs. 49%) and hypoglycemic range <70 mg/dL (7% vs. 5%), less time in hyperglycemic range >180 mg/dL (38% vs. 46%) and had a lower glucose variability (coefficient of variation [CV] 36% vs. 37%) compared with adults aged <30 years. We found no significant differences between genders. Multivariable regression models revealed the highest Time In Range (TIR) and lowest time with sensor glucose >250 mg/dL, CV and daytime–night-time differences in those treated with CSII and rtCGM. Glucose profiles were slightly more favorable on working days.

In our real-world data, rtCGM versus iscCGM was associated with a higher percentage of TIR and improved metabolic stability. Differences in ambulatory glucose profiles on working and weekend days may indicate lifestyle habits affecting glycemic stability. Real-life CGM results should be included in benchmarking reports in addition to hemoglobin A1c (HbA1c) and history of hypoglycemia.