Heterogeneity of glycaemic phenotypes in type 1 diabetes.

AIMS/HYPOTHESIS: Our study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes. METHODS: In the Study of the French-speaking Society of Type 1 Diabetes (SFDT1), we characterised glycaemic heterogeneity thanks to a set of complementary metrics: HbA1c, time in range (TIR), time below range (TBR), CV, Gold score and glycaemia risk index (GRI). Applying the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm, we created a phenotypic tree, i.e. a 2D visual mapping. We also carried out a clustering analysis for comparison. RESULTS: We included 618 participants with type 1 diabetes (52.9% men, mean age 40.6 years [SD 14.1]). Our phenotypic tree identified seven glycaemic phenotypes. The 2D phenotypic tree comprised a main branch in the proximal region and glycaemic phenotypes in the distal areas. Dimension 1, the horizontal dimension, was positively associated with GRI (coefficient [95% CI]) (0.54 [0.52, 0.57]), HbA1c (0.39 [0.35, 0.42]), CV (0.24 [0.19, 0.28]) and TBR (0.11 [0.06, 0.15]), and negatively with TIR (-0.52 [-0.54, -0.49]). The vertical dimension was positively associated with TBR (0.41 [0.38, 0.44]), CV (0.40 [0.37, 0.43]), TIR (0.16 [0.12, 0.20]), Gold score (0.10 [0.06, 0.15]) and GRI (0.06 [0.02, 0.11]), and negatively with HbA1c (-0.21 [-0.25, -0.17]). Notably, socioeconomic factors, cardiovascular risk indicators, retinopathy and treatment strategy were significant determinants of glycaemic phenotype diversity. The phenotypic tree enabled more granularity than traditional clustering in revealing clinically relevant subgroups of people with type 1 diabetes. CONCLUSIONS/INTERPRETATION: Our study advances the current understanding of the complex glycaemic profile in people with type 1 diabetes and suggests that strategies based on isolated glycaemic metrics might not capture the complexity of the glycaemic phenotypes in real life. Relying on these phenotypes could improve patient stratification in type 1 diabetes care and personalise disease management.

Insulin Requirements According to Sex and Weight in a Population of 9036 Adult Persons With Type 1 Diabetes Using Closed-Loop Insulin Delivery.

BACKGROUND: The prediction of the individual insulin needs may facilitate the initiation of insulin therapy. Our aim was to explore the relationships between body weight, sex, and daily amounts of insulin delivered by a hybrid closed-loop system. METHODS: We performed a retrospective data collection of all consenting adult patients with type 1 diabetes who were equipped in Europe with the Diabeloop Generation 1 (DBLG1) hybrid closed-loop insulin delivery device between March 1, 2021 and February 28, 2023. RESULTS: A total of 9036 users (59% females, age 45.6 ± 14.3 years) were included, reaching a mean follow-up of 320 ± 143 days, an overall 2 887 188 days of data. We observed a mean insulin-weight ratio of 0.617 ± 0.207 U/kg (0.665 ± 0.217 for males and 0.584 ± 0.193 for females, P < .001). Exploratory analysis of a subset of 4066 patients reaching >70% Time in Range (70-180 mg/dL) showed a mean insulin-weight ratio of 0.55 ± 0.17 U/kg (P < .001) (0.59 ± 0.18 for the 1438 males and 0.53 ± 0.16 for the 2628 females). CONCLUSION: This large real-world analysis provides a quantitative estimation of the daily insulin requirements in adult patients with type 1 diabetes and shows significant differences between sex. These findings have relevant implications in the practical management of insulin therapy.

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.

Combining Glucose Monitoring and Insulin Infusion in an Integrated Device: A Narrative Review of Challenges and Proposed Solutions.

The introduction of automated insulin delivery (AID) systems has enabled increasing numbers of individuals with type 1 diabetes (T1D) to improve their glycemic control largely. However, use of AID systems is limited due to their complexity and costs associated. The user must wear both a continuously monitoring glucose system and an insulin infusion pump. The glucose sensor and the insulin catheter must be inserted at two different body sites using different insertion devices. In addition, the user must pair and manage the different systems. These communicate with the AID software implemented on the pump or on a third device such as a dedicated display device or smart phone application. These components might be developed and commercialized by different manufacturers, which in turn can cause difficulties for patients seeking technical support. A possible solution to these challenges would be to integrate the glucose sensor and insulin catheter into a single device. This would allow the glucose sensor and insulin catheter to be inserted simultaneously, eliminating the need for pairing, and simplifying system management. In recent years, different technologies have been developed and evaluated in clinical investigations that combine the glucose sensor and the insulin catheter in one platform. The consistent finding of all these studies is that integration has no adverse effect on insulin infusion and glucose measurements provided that certain conditions are met. In this review, we discuss the perceived challenges of such an approach and discuss possible solutions that have been proposed.

Islets-on-Chip: A Tool for Real-Time Assessment of Islet Function Prior to Transplantation.

Islet transplantation improves metabolic control in patients with unstable type 1 diabetes. Clinical outcomes have been improving over the last decade, and the widely used beta-score allows the evaluation of transplantation results. However, predictive pre-transplantation criteria of islet quality for clinical outcomes are lacking. In this proof-of-concept study, we examined whether characterization of the electrical activity of donor islets could provide a criterion. Aliquots of 8 human donor islets from the STABILOT study, sampled from islet preparations before transplantation, were characterized for purity and split for glucose-induced insulin secretion and electrical activity using multi-electrode-arrays. The latter tests glucose concentration dependencies, biphasic activity, hormones, and drug effects (adrenalin, GLP-1, glibenclamide) and provides a ranking of CHIP-scores from 1 to 6 (best) based on electrical islet activity. The analysis was performed online in real time using a dedicated board or offline. Grouping of beta-scores and CHIP-scores with high, intermediate, and low values was observed. Further analysis indicated correlation between CHIP-score and beta-score, although significance was not attained (R = 0.51, p = 0.1). This novel approach is easily implantable in islet isolation units and might provide means for the prediction of clinical outcomes. We acknowledge the small cohort size as the limitation of this pilot study.

First Generation of a Modular Interoperable Closed-Loop System for Automated Insulin Delivery in Patients With Type 1 Diabetes: Lessons From Trials and Real-Life Data.

BACKGROUND: DBLG1 (Diabeloop Generation 1) stands as one of the five commercially available closed-loop solution worldwide for patients with type 1 diabetes as of 2023. Our aim was to provide an overview of all data obtained with this system regarding outcomes and populations, with an emphasis on interoperability. METHODS: This report includes all available sources of data (three randomized control trials and five surveys on real-life data). Collection ran from March 3, 2017 to April 30, 2022. RESULTS: We gathered data from 6859 adult patients treated with closed-loop from three to 12 months. Overall, all sources of data showed that time in range (TIR) 70 to 180 mg/dL, starting from 47.4% to 56.6%, improved from 12.2 to 17.3 percentage points. Time in hypoglycemia was reduced by 48% in average (range: 26%-70%) and reached a level of 1.3% in the largest and most recent cohort. In patients with excessive time in hypoglycemia at baseline (≥5%), closed-loop allowed a reduction in time below range (TBR) by 59%. The comparison of days with declared physical activity versus days without physical activity did not show differences in TBR. The improvement in TIR observed with three different pump systems (Vicentra Kaleido, n = 117; Sooil Dana-I, n = 84; and Roche Insight, n = 6684) ranged from 15.4 to 17.3 percentage points. DISCUSSION: These data obtained in different European countries were consistent throughout all reports, showing that this closed-loop system is efficient (high improvement in TIR), safe (remarkably low level of TBR), and interoperable (three pump settings so far).

Insight into continuous glucose monitoring: from medical basics to commercialized devices.

According to the latest statistics, more than 537 million people around the world struggle with diabetes and its adverse consequences. As well as acute risks of hypo- or hyper- glycemia, long-term vascular complications may occur, including coronary heart disease or stroke, as well as diabetic nephropathy leading to end-stage disease, neuropathy or retinopathy. Therefore, there is an urgent need to improve diabetes management to reduce the risk of complications but also to improve patient's quality life. The impact of continuous glucose monitoring (CGM) is well recognized, in this regard. The current review aims at introducing the basic principles of glucose sensing, including electrochemical and optical detection, summarizing CGM technology, its requirements, advantages, and disadvantages. The role of CGM systems in the clinical diagnostics/personal testing, difficulties in their utilization, and recommendations are also discussed. In the end, challenges and prospects in future CGM systems are discussed and non-invasive, wearable glucose biosensors are introduced. Though the scope of this review is CGMs and provides information about medical issues and analytical principles, consideration of broader use will be critical in future if the right systems are to be selected for effective diabetes management.

Prevalence of sleep apnoea in patients with type 1 diabetes and its association with comorbidities and diabetic complications: A French nationwide prospective study.

AIM: To investigate sleep apnoea prevalence, factors influencing severity, and associations between sleep apnoea severity and micro-/macrovascular complications in a large population of patients with type 1 diabetes. MATERIALS AND METHODS: This French multicentre prospective cohort study was conducted between July 2016 and June 2020. Adults with type 1 diabetes using an insulin pump were eligible. Home care provider nurses collected demographic and clinical data and set up oximetry to determine the oxygen desaturation index (ODI). No, mild-moderate and severe sleep apnoea were defined as ODI <15 events/h, 15 to <30 events/h and ≥30 events/h, respectively. Univariate and multivariate analyses were performed to identify factors associated with sleep apnoea, and associations between sleep apnoea severity and micro-/macrovascular complications were determined using logistic regression. RESULTS: Of 769 participants, 12.4% and 3.4% had mild-to-moderate or severe sleep apnoea, respectively. Factors significantly associated with sleep apnoea on multivariate analysis were age, sex, body mass index (BMI) and hypertension. After adjustment for age, sex and BMI, presence of severe sleep apnoea was significantly associated with macrovascular complications (odds ratio vs. no sleep apnoea: 3.96 [95% confidence interval 1.43-11.11]; P < 0.01), while mild-to-moderate sleep apnoea was significantly associated with presence of diabetic retinopathy (odds ratio 2.09 [95% confidence interval 1.10-3.74]; P < 0.01). CONCLUSION: Sleep apnoea is a significant comorbidity in patients with type 1 diabetes, especially with respect to diabetic complications. This highlights the need for sleep apnoea screening and management in these individuals.

One-year real-world performance of the DBLG1 closed-loop system: Data from 3706 adult users with type 1 diabetes in Germany.

AIM: The Diabeloop Generation 1 (DBLG1) system is an interoperable hybrid closed-loop solution that was commercialized in Germany in March 2021. We report the longitudinal glycaemic outcomes among the first 3706 users in a real-world setting. METHODS: We performed a retrospective data collection of all consenting adult patients with type 1 diabetes who were equipped in Germany with the DBLG1 system before 30 April 2022, and with a minimum 14 days of closed-loop usage. RESULTS: In total, 3706 users (41% women, age 45.1 ± 14.5 years) met the inclusion criteria, reaching a mean follow-up of 131.0 ± 85.1 days, an overall 485 600 days of continuous glucose monitoring data, and a median time spent in closed-loop of 95.0% (IQR 89.1-97.4). The median percentage time in range (70-180 mg/dl) was 72.1% (IQR 65.0-78.9); the time below 70 mg/dl was 0.9% (0.5-1.7), the time below 54 mg/dl was 0.1% (0.1-0.3), and the median Glucose Management Index was 7.0% (6.8-7.3). Exploratory analysis of a subset of 2460 patients in whom baseline glycated haemoglobin (HbA1c) was available [7.4% (IQR 6.9-8.0)] showed that the achieved mean time in range was influenced by baseline HbA1c, ranging from 65.8 ± 9.9% (A1c ≥8.5%) to 81.3 ± 6.8% (A1c <6.5%). CONCLUSION: This large real-world analysis confirms the relevance of the DBLG1 automated insulin delivery solution for the achievement of standards of care in adult patients with type 1 diabetes.

Hybrid Closed-Loop Control with Ultrarapid Lispro Compared with Standard Insulin Aspart and Faster Insulin Aspart: An In Silico Study.

OBJECTIVE: There is room for improvement in the performance of closed-loop regulation algorithms during the prandial period. This in silico study evaluated the efficiency and safety of ultrarapid lispro insulin using the Diabeloop DBLG1® algorithm. METHODS: We modeled the insulin profile of URLi according to literature data and integrated it to the model used within a simulation platform built from a 60 patients' virtual cohort. We then ran the DBLG1® algorithm in silico with various meal intakes using modeled URLi, Aspart and Faster Aspart. The primary endpoints were glucose metrics (time in 70-180 mg/dL range and time below range). RESULTS: When insulin time constant values were tuned, time in 70-180 mg/dL range was 69.4 [61.1-75.6] (Aspart) vs 74.7 [65.5-81.5] (URLi). Glucose coefficient of variation was reduced from 34.1 [29.7-37.8] to 28.4 [25.7-34.6]. Time below 70 mg/dL and 54 mg/dL were significantly reduced with URLi, whether or not DBLG1 was specifically tuned to this insulin. Metrics with Faster Aspart were intermediate and did not significantly differ from URLi. CONCLUSIONS: This simulation study performed on a virtual T1D population suggests that the use of URLi within an unmodified closed-loop DBLG1 regulation algorithm is safe and, with DBLG1 being tuned to this specific insulin type, improved the regulation performances as compared with Aspart. This fact supports the use of such an insulin in clinical investigations.

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

BACKGROUND: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Impact of sleep and physical activity habits on real-life glycaemic variability in patients with type 2 diabetes.

The aim of this study was to better characterise whether sleep habits, eating schedule and physical activity in real-life are associated with glycaemic control in patients with type 2 diabetes. A total of 28 patients (aged 60 years [58; 66], 54% female) with type 2 diabetes treated with basal-bolus insulin therapy administered by insulin pumps were analysed. Glycaemic data measured by Flash Glucose Monitor System, physical activity and sleep data measured by accelerometer, and meal schedules were simultaneously collated with insulin pump administration data, for 7 days in real-life. Their respective impact on the time spent in target, in hypoglycaemia, in hyperglycaemia and on glycaemic variability was evaluated. Multiple regressions showed that the total daily dose of meal boluses of insulin was inversely associated with the coefficient of variation (CV; coefficient ß = -0.073; 95% confidence interval: -0.130, -0.015; p = 0.016), as well as sleep duration. The higher the sleep duration, the lower the glycaemic variability (coefficient ß = -0.012; 95% confidence interval: -0.023, -0.002; p = 0.027). The mean 7 days physical activity of the subjects was very low and was not associated with glycaemic control on the 7 days mean values. However, days with at least 1 hr spent in physical activity higher than 1.5 METs were associated with less glycaemic variability that same day. This real-life observation highlights the importance of sufficient sleep duration and regular physical activity to lessen the glycaemic variability of patients with type 2 diabetes.

Efficacy of a Hybrid Closed-Loop Solution in Patients With Excessive Time in Hypoglycaemia: A Post Hoc Analysis of Trials With DBLG1 System.

BACKGROUND: Automated insulin delivery is an efficient treatment for patients with type 1 diabetes. Little is known on its impact on patients with excessive time in hypoglycaemia. METHODS: We performed a post hoc analysis of three randomized control trials that used the DBLG1 (Diabeloop Generation 1) hybrid closed-loop solution. Patients whose time below 70 mg/dL during baseline, open-loop phase exceeded 5% were selected. The outcomes were the differences between the closed-loop and the open-loop phases in time in various ranges and Glycemia Risk Index (GRI). RESULTS: We identified 45 patients exhibiting ≥5% of time below 70 mg/dL during the open-loop phase. Under closed-loop, the time in hypoglycaemia (54 to <70 mg/dL) dropped from 7.9% (SD 2.4) to 3.2% (SD 1.6) (difference -4.7% [-5.3; -4.1], P < 10-4). The time below 54 mg/dL decreased from 1.9% (SD 1.3) to 0.8% (SD 0.7) (difference -0.9% [-1.4; -0.8], P < 10-4). The time in range (TIR 70-180 mg/dL) improved from 63.3 (SD 9.5) to 68.2% (SD 8.2) (difference 5.1% [2.9; 7.0], P < 10-4). The GRI improved from 51.2 (SD 12.4) to 38.0 (SD 10.9) (difference 13.2 [10.4; 16.0], P < 10-4). CONCLUSION: DBLG1 decreased time in hypoglycaemia by more than 50% even in patients with excessive time in hypoglycaemia at baseline, while also improving both TIR and GRI, under real-life conditions. The improvement in GRI (13.2%) exceeded that of the improvement in TIR (5.1%) indicating that in this data set, GRI was more sensitive than TIR to the improvement in glycaemia achieved with closed-loop. These results support the safety and efficacy of this treatment.

An Unsafe/Safe Typology in People with Type 2 Diabetes: Bridging Patients' Expectations, Personality Traits, Medication Adherence, and Clinical Outcomes.

Background: Support programs are provided to people with diabetes to help them manage their disease. However, adherence to and persistence in support programs are often low, making it difficult to demonstrate their effectiveness. Aim: To identify the determinants of patients' perceived interest in diabetes support programs because it may be a powerful determinant of effective participation in such programs. Patients and Methods: An online study conducted in April 2021 in metropolitan France on 600 people with diabetes recruited from a consumer panel. A 64-item psychosocial questionnaire including a question asking to evaluate the helpfulness of a support program was used. Univariate, multivariate, and multiple correspondence analyses were performed. Results: The existence of a typology, known as Unsafe/Safe, was discovered, in which patients with type 2 diabetes respond in two distinct ways. Type U (unsafe) patients, who believe that a support program would be helpful, are more likely to be nonadherent to their treatment, have high hemoglobin A1c levels, have at least one diabetic complication, lack information regarding their disease and treatment, rate the burden of their disease and impairment of their quality of life as high, worry about their future, and are pessimistic. Type S (safe) patients have the opposite characteristics. Type U patients can be dichotomized into two broad classes: one in which they lack information regarding disease and treatment and the other in which alterations in the quality of life and burden of the disease predominate. Insulin-treated patients give more importance to the lack of information, whereas noninsulin-treated patients complain primarily about the burden of the disease and impairment of quality of life. Conclusion: This study describes this new U/S typology, proposes a simple method based on a nine-item questionnaire to identify type U patients by calculating a Program Helpfulness Score described herein, and clarifies the nature of the intervention to be provided to them. This novel approach could be applied to other chronic diseases.

Hybrid closed-loop insulin delivery versus sensor-augmented pump therapy in children aged 6-12 years: a randomised, controlled, cross-over, non-inferiority trial.

BACKGROUND: Time in range (TIR) goals are rarely met in children with type 1 diabetes, except at the cost of increased hypoglycaemia episodes. Our objective was to evaluate the safety and efficiency of the Diabeloop DBL4K (Diabeloop, Grenoble, France) hybrid closed-loop system in prepubescent children. METHODS: We did a multicentre, open-label, randomised, controlled, non-inferiority, two-session crossover study in the paediatric endocrinology departments of three university hospitals in France and Belgium. Eligible participants were aged 6-12 years with type 1 diabetes for at least 1 year, glycated haemoglobin A1C 9% (75 mmol/mol) or less, and insulin pump treatment for at least 3 months. Participants were randomly assigned (1:1) to a closed-loop device or sensor-augmented pump (open loop) therapy. Randomisation was by a permuted block randomisation scheme, using an interactive web-based response system, and was stratified on centre (block size 6). The assessed closed-loop device, the Diabeloop for Kids DBL4K hybrid closed-loop system, is an automated blood glucose regulation system composed of a handset, insulin pump, and continuous glucose monitor. The open-loop system is defined as a sensor-augmented pump therapy composed of the usual insulin pump used by the patient and a continuous glucose monitor. A 72-h in-patient period was followed by a 6-week home phase. After a 1-week washout period, the participants crossed over to the other device. The primary outcome, assessed in the intention-to-treat population, was the mean proportion of time spent in hypoglycaemia (3·9 mmol/L [<70 mg/dL]) during the hospital phase, with a non-inferiority margin of -2·5% (absolute value). Safety was assessed in the intention-to-treat population on a per-protocol basis. This study was registered with ClinicalTrials.gov, NCT03671915. FINDINGS: Between May 6 and Dec 23, 2019, we included 21 participants (closed loop then open loop, n=10; open loop then closed loop, n=11). The proportion of time spent in hypoglycaemia was significantly lower with the closed-loop system than the open-loop system in both groups (2·04% [95% CI 0·44 to 3·64] vs 7·06% [5·46 to 8·66]; non-inferiority one-sided p<0·0001). No severe ketoacidosis, nor severe hyoglycaemic events or fatal adverse events occurred. All 25 adverse events (18 with the closed-loop system, seven with the open-loop system) were related to the treatment. INTERPRETATION: The closed-loop Diabeloop system decreased hypoglycaemic episodes and provided good metabolic control in prepubescent children with type 1 diabetes, under real-life conditions. This finding supports the safe use of closed-loop technology in this paediatric population. FUNDING: Diabeloop. TRANSLATION: For the French translation of the abstract see Supplementary Materials section.