12-Month Real-Life Efficacy of the MiniMed 780G Advanced Closed-Loop System in Patients Living with Type 1 Diabetes: A French Observational, Retrospective, Multicentric Study.

Aim: To evaluate the evolution of glycemic outcomes in patients living with type 1 diabetes (T1D) after 1 year of use of the MiniMed 780G advanced hybrid closed-loop (AHCL) system. Methods: We conducted an observational, retrospective, multicentric study in 20 centers in France. The primary objective was to evaluate the improvement in glycemic control after 1-year use of AHCL. The primary endpoint was the variation of time in range (TIR) between pre-AHCL and after 1-year use of AHCL. Secondary objectives were to analyze the glycemic outcomes after 3, 6, and 12 months of AHCL use, the safety, and the long-term observance of AHCL. Results: Two hundred twenty patients were included, and 200 were analyzed for the primary endpoint. 92.7% of patients continued to use AHCL. After 1 year of use of AHCL, TIR was 72.5% ± 10.6% (+9.1%; 95% confidence interval [CI] [7.6-10.5] compared to pre-AHCL initiation, P < 0.001), HbA1c 7.1% ± 0.7% (-0.5%; 95% CI [-0.6 to -0.4]; P < 0.001), time below range 2.0% [1.0; 3.0] (0.0% [-2.0; 0.0], P < 0.001), and time above range 24.8% ± 10.9% (-7.3%; 95% CI [-8.8 to -5.7]; P < 0.001). More patients achieved the glycemic treatment goals of HbA1c <7.0% (45.1% vs. 18.1%, P < 0.001) and TIR >70% (59.0% vs. 29.5% P < 0.001) when compared with pre-AHCL. Five patients experienced severe hypoglycemia events and two patients experienced ketoacidosis. Conclusion: After 1 year of use of AHCL, people living with T1D safely improved their glucose control and a higher proportion of them achieved optimal glycemic control.

Glycemic variability indices can be used to diagnose islet transplantation success in type 1 diabetic patients.

AIMS: High glycemic variability (GV) is the major indication for islet transplantation (IT) in patients with type 1 diabetes (T1D). The actual criteria used to assess graft function do not consider GV improvement. Our study aimed to describe GV indices' evolution in T1D patients who benefited from IT during the TRIMECO trial and to evaluate if thresholds might be defined to diagnose IT success. METHODS: We collected data from 29 patients of the TRIMECO trial, a clinical trial (NCT01148680) comparing the metabolic efficacy of IT with intensive insulin therapy. Based on CGM data, we analyzed mean glucose level and four GV indices (standard deviation, coefficient of variation, MAGE and GVP) before (M0) and 6 months (M6) after IT. RESULTS: Each GV index decreased significantly between M0 and M6: SD 53.9 mg/dL [44.6-61.5] versus 20.1 mg/dL [13.5-24.3]; CV 35.2% [30.6-37.7] versus 17.3% [12.0-20.5]; MAGE 134.9 mg/dl [111.2-155.8] versus 51.9 mg/dL [32.4-62.4]; GVP 35.3% [24.9-47.2] versus 12.2% [6.2-18.8] (p ≤ 0.0001). Thresholds diagnosing IT success at 6 months post-transplant were an SD at 22.76 mg/dL (sensibility 88.89%, specificity 80.00%), a CV at 17.47% (sensibility 88.89%, specificity 70.00%), a MAGE at 54.81 mg/dL (sensibility 88.89%, specificity 80.00%) and a GVP at 12.27% (sensibility 88.89%, specificity 70.00%). CONCLUSIONS: This study confirms a positive impact of IT on GV. The proposed thresholds allow an easy evaluation of IT success using only CGM data and may be a clinical tool for the follow-up of transplanted patients.

Characterization of Daily Glycemic Variability in Subjects with Type 1 Diabetes Using a Mixture of Metrics.

Background: Glycemic variability (GV) is an important component of glycemic control for patients with type 1 diabetes (T1D). The inadequacy of existing measurements lies in the fact that they view the variability from different aspects, so that no consensus has been reached among physicians as to which metrics to use in practice. Moreover, although GV, from 1 day to another, can show very different patterns, few metrics have been dedicated to daily evaluations. Materials and Methods: A reference (stable glycemia) statistical model is built based on a combination of daily computed canonical glycemic control metrics including variability. The metrics are computed for subjects from the TRIMECO islet transplantation trial, selected when their ß-score (composite score for grading success) is ≥6 after a transplantation. Then, for any new daily glycemia recording, its likelihood with respect to this reference model provides a multimetric score of daily GV severity. In addition, determining the likelihood value that best separates the daily glycemia with ß-score = 0 from that with ß-score ≥6, we propose an objective decision rule to classify daily glycemia into "stable" or "unstable." Results: The proposed characterization framework integrates multiple standard metrics and provides a comprehensive daily GV index, based on which, long-term variability evaluations and investigations on the implicit link between variability and ß-score can be carried out. Evaluation, in a daily GV classification task, shows that the proposed method is highly concordant to the experience of diabetologists. Conclusion: A multivariate statistical model is proposed to characterize the daily GV of subjects with T1D. The model has the advantage to provide a single variability score that gathers the information power of a number of canonical scores, too partial to be used individually. A reliable decision rule to classify daily variability measurements into stable or unstable is also provided.