Combined intake of caffeine and low-dose glucose to reduce exercise-related hypoglycaemia in individuals with type 1 diabetes on ultra-long-acting insulin degludec: A randomized, controlled, double-blind, cross-over trial.

AIM: To evaluate whether caffeine combined with a moderate amount of glucose reduces the risk for exercise-related hypoglycaemia compared with glucose alone or control in adult people with type 1 diabetes using ultra-long-acting insulin degludec. MATERIALS AND METHODS: Sixteen participants conducted three aerobic exercise sessions (maximum 75 min) in a randomized, double-blind, cross-over design. Thirty minutes before exercise, participants ingested a drink containing either 250 mg of caffeine + 10 g of glucose + aspartame (CAF), 10 g of glucose + aspartame (GLU), or aspartame alone (ASP). The primary outcome was time to hypoglycaemia. RESULTS: There was a significant effect of the condition on time to hypoglycaemia (χ2 = 7.674, p = .0216). Pairwise comparisons revealed an 85.7% risk reduction of hypoglycaemia for CAF compared with ASP (p = .044). No difference was observed between GLU and ASP (p = .104) or between CAF and GLU (p = .77). While CAF increased glucose levels during exercise compared with GLU and ASP (8.3 ± 1.9 mmol/L vs. 7.7 ± 2.2 mmol/L vs. 5.8 ± 1.4 mmol/L; p < .001), peak plasma glucose levels during exercise did not differ between CAF and GLU (9.3 ± 1.4 mmol/L and 9.1 ± 1.6 mmol/L, p = .80), but were higher than in ASP (6.6 ± 1.1 mmol/L; p < .001). The difference in glucose levels between CAF and GLU was largest during the last 15 min of exercise (p = .002). Compared with GLU, CAF lowered perceived exertion (p = .023). CONCLUSIONS: Pre-exercise caffeine ingestion combined with a low dose of glucose reduced exercise-related hypoglycaemia compared with control while avoiding hyperglycaemia.

Clinical evidence for high-risk CE-marked medical devices for glucose management: A systematic review and meta-analysis.

AIMS: To conduct a systematic review and meta-analysis, within the Coordinating Research and Evidence for Medical Devices (CORE-MD) project, evaluating CE-marked high-risk devices for glucose management. MATERIALS AND METHODS: We identified interventional and observational studies evaluating the efficacy and safety of eight automated insulin delivery (AID) systems, two implantable insulin pumps, and three implantable continuous glucose monitoring (CGM) devices. We meta-analysed randomized controlled trials (RCTs) comparing AID systems with other treatments. RESULTS: A total of 182 studies published between 2009 and 2024 were included, comprising 166 studies on AID systems, six on insulin pumps, and 10 on CGM devices; 26% reported industry funding; 18% were pre-market; 37% had a comparator group. Of the studies identified, 29% were RCTs, 24% were non-randomized trials, and 47% were observational studies. The median (interquartile range) sample size was 48 (28-102), age 34.8 (14-44.2) years, and study duration 17.5 (12-26) weeks. AID systems lowered glycated haemoglobin by 0.5 percentage points (absolute mean difference [MD] = -0.5; 21 RCTs; I2 = 86%) and increased time in target range for sensor glucose level by 13.4 percentage points (MD = 13.4; 14 RCTs; I2 = 90%). At least one safety outcome was assessed in 71% of studies. CONCLUSIONS: High-risk devices for glucose monitoring or insulin dosing, in particular AID systems, improve glucose control safely, but evidence on diabetes-related end-organ damage is lacking due to short study durations. Methodological heterogeneity highlights the need for developing standards for future pre- and post-market investigations of diabetes-specific high-risk medical devices.

Effects of home-based exergaming on cardio-metabolic and cognitive health in physically inactive individuals.

AIMS: To examine the effects of a home-based exergame training over 6 weeks on cardio-metabolic and cognitive health, as well as training adherence, in physically inactive individuals. MATERIALS AND METHODS: Twenty participants were equipped with an exergame system specifically designed for use at home. Each participant performed at least three weekly exercise sessions at ≥80% of their individual maximum heart rate, over 6 weeks. Exercise duration increased biweekly until 75 min of vigorous exercise were performed in Weeks 5 and 6. Maximum oxygen uptake (VO2max), cardio-metabolic profiling, and neuro-cognitive tests were performed at baseline and study end. Additionally, training adherence was assessed via training diaries. RESULTS: After 6 weeks of home-based exergaming, VO2max increased significantly, while there was a significant decrease in heart rate (resting and maximum), blood pressure (systolic, diastolic and mean), and low-density lipoprotein cholesterol. Dynamic balance and reaction time improved after 6 weeks of exergaming. Training adherence was 88.4%. CONCLUSIONS: Home-based exergaming induced a clinically relevant increase in VO2max, a determinant of cardiovascular health, accompanied by further improvements in cardiovascular, metabolic and neuro-cognitive parameters. Exergaming may, therefore, offer an innovative approach to increasing regular physical activity, improving metabolic risk profile, and preventing chronic diseases.

Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes.

AIM: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data. MATERIALS AND METHODS: We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced hypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L-1 ). In study 2 (n = 9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L-1 ). RESULTS: Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively). CONCLUSIONS: Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia.

Glycaemic patterns of male professional athletes with type 1 diabetes during exercise, recovery and sleep: Retrospective, observational study over an entire competitive season.

AIMS: To analyse glycaemic patterns of professional athletes with type 1 diabetes during a competitive season. MATERIALS AND METHODS: We analysed continuous glucose monitoring data of 12 professional male cyclists with type 1 diabetes during exercise, recovery and sleep on days with competitive exercise (CE) and non-competitive exercise (NCE). We assessed whether differences exist between CE and NCE days and analysed associations between exercise and dysglycaemia. RESULTS: The mean glycated haemoglobin was 50 ± 5 mmol/mol (6.7 ± 0.5%). The athletes cycled on 280.8 ± 28.1 days (entire season 332.6 ± 18.8 days). Overall, time in range (3.9-10 mmol/L) was 70.0 ± 13.7%, time in hypoglycaemia (<3.9 mmol/L) was 6.4 ± 4.7% and time in hyperglycaemia (>10 mmol/L) was 23.6 ± 12.5%. During the nights of NCE days, athletes spent 10.1 ± 7.4% of time in hypoglycaemia, particularly after exercise in the endurance zones. The CE days were characterized by a higher time in hyperglycaemia compared with NCE days (25.2 ± 12.5% vs. 22.2 ± 12.1%, p = .012). This was driven by the CE phase, where time in range dropped to 60.4 ± 13.0% and time in hyperglycaemia was elevated (38.5 ± 12.9%). Mean glucose was higher during CE compared with NCE sessions (9.6 ± 0.9 mmol/L vs. 7.8 ± 1.1 mmol/L, p < .001). The probability of hyperglycaemia during exercise was particularly increased with longer duration, higher intensity and higher variability of exercise. CONCLUSIONS: The analysis of glycaemic patterns of professional endurance athletes revealed that overall glycaemia was generally within targets. For further improvement, athletes, team staff and caregivers may focus on hyperglycaemia during competitions and nocturnal hypoglycaemia after NCE.

First-trimester glycosylated hemoglobin (HbA1c) and maternal characteristics in the prediction of gestational diabetes: An observational cohort study.

INTRODUCTION: This study aimed to investigate the extent to which gestational diabetes mellitus (GDM) can be predicted in the first trimester by combining a marker of growing interest, glycosylated hemoglobin A1c (HbA1c), and maternal characteristics. MATERIAL AND METHODS: This observational study was conducted in the outpatient obstetric department of our institution. The values of HbA1c and venous random plasma glucose were prospectively assessed in the first trimester of pregnancy. We determined maternal characteristics that were independent predictors from the regression analysis and calculated areas under the receiver-operating curves by combining the maternal age, body mass index, previous history of GDM, and first-degree family history for diabetes mellitus. Moreover we investigated the predictive capability of HbA1c to exclude GDM. Patients with a first-trimester HbA1c level of 6.5% (48 mmol/mol) or more were excluded. The study was registered at ClinicalTrials.gov ID: NCT02139254. RESULTS: We included 785 cases with complete dataset. The prevalence of GDM was 14.7% (115/785). Those who developed GDM had significantly higher HbA1c and random plasma glucose values (p < 0.0001 and p = 0.0002, respectively). In addition, they had a higher body mass index, were more likely to have a history of GDM and/or a first-degree family history of diabetes. When these maternal characteristics were combined with the first-trimester HbA1c and random plasma glucose the combined area under the receiver operating characteristics curve was 0.76 (95% CI 0.70-0.81). CONCLUSIONS: Our results indicate that HbA1c and random plasma glucose values combined with age, body mass index, and personal and family history, allow the identification of women in the first trimester who are at increased risk of developing GDM.

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study.

BACKGROUND: Micro- and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential. OBJECTIVE: This study aimed to build machine learning (ML) models that predict the risk of developing a micro- or macrovascular complication in individuals with prediabetes or diabetes. METHODS: In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and disease codes; span from 2003 to 2013; and were queried to identify individuals with prediabetes or diabetes in 2008. Subsequently, we aimed to predict which of these individuals developed a micro- or macrovascular complication within the next 5 years. We included 3 microvascular complications: retinopathy, nephropathy, and neuropathy. In addition, we considered 3 macrovascular complications: peripheral vascular disease (PVD), cerebrovascular disease (CeVD), and cardiovascular disease (CVD). Complications were identified via disease codes, and, for nephropathy, the estimated glomerular filtration rate and albuminuria were considered additionally. Inclusion criteria were complete information on age and sex and on disease codes (or measurements of estimated glomerular filtration rate and albuminuria for nephropathy) until 2013 to account for patient dropout. Exclusion criteria for predicting a complication were diagnosis of this specific complication before or in 2008. In total, 105 predictors from demographics, biomarkers, medications, and disease codes were used to build the ML models. We compared 2 ML models: logistic regression and gradient-boosted decision trees (GBDTs). To explain the predictions of the GBDTs, we calculated Shapley additive explanations values. RESULTS: Overall, 13,904 and 4259 individuals with prediabetes and diabetes, respectively, were identified in our underlying data set. For individuals with prediabetes, the areas under the receiver operating characteristic curve for logistic regression and GBDTs were, respectively, 0.657 and 0.681 (retinopathy), 0.807 and 0.815 (nephropathy), 0.727 and 0.706 (neuropathy), 0.730 and 0.727 (PVD), 0.687 and 0.693 (CeVD), and 0.707 and 0.705 (CVD); for individuals with diabetes, the areas under the receiver operating characteristic curve were, respectively, 0.673 and 0.726 (retinopathy), 0.763 and 0.775 (nephropathy), 0.745 and 0.771 (neuropathy), 0.698 and 0.715 (PVD), 0.651 and 0.646 (CeVD), and 0.686 and 0.680 (CVD). Overall, the prediction performance is comparable for logistic regression and GBDTs. The Shapley additive explanations values showed that increased levels of blood glucose, glycated hemoglobin, and serum creatinine are risk factors for microvascular complications. Age and hypertension were associated with an elevated risk for macrovascular complications. CONCLUSIONS: Our ML models allow for an identification of individuals with prediabetes or diabetes who are at increased risk of developing micro- or macrovascular complications. The prediction performance varied across complications and target populations but was in an acceptable range for most prediction tasks.

Closed-Loop Insulin Delivery for Glycemic Control in Noncritical Care.

BACKGROUND: In patients with diabetes, hospitalization can complicate the achievement of recommended glycemic targets. There is increasing evidence that a closed-loop delivery system (artificial pancreas) can improve glucose control in patients with type 1 diabetes. We wanted to investigate whether a closed-loop system could also improve glycemic control in patients with type 2 diabetes who were receiving noncritical care. METHODS: In this randomized, open-label trial conducted on general wards in two tertiary hospitals located in the United Kingdom and Switzerland, we assigned 136 adults with type 2 diabetes who required subcutaneous insulin therapy to receive either closed-loop insulin delivery (70 patients) or conventional subcutaneous insulin therapy, according to local clinical practice (66 patients). The primary end point was the percentage of time that the sensor glucose measurement was within the target range of 100 to 180 mg per deciliter (5.6 to 10.0 mmol per liter) for up to 15 days or until hospital discharge. RESULTS: The mean (±SD) percentage of time that the sensor glucose measurement was in the target range was 65.8±16.8% in the closed-loop group and 41.5±16.9% in the control group, a difference of 24.3±2.9 percentage points (95% confidence interval [CI], 18.6 to 30.0; P<0.001); values above the target range were found in 23.6±16.6% and 49.5±22.8% of the patients, respectively, a difference of 25.9±3.4 percentage points (95% CI, 19.2 to 32.7; P<0.001). The mean glucose level was 154 mg per deciliter (8.5 mmol per liter) in the closed-loop group and 188 mg per deciliter (10.4 mmol per liter) in the control group (P<0.001). There was no significant between-group difference in the duration of hypoglycemia (as defined by a sensor glucose measurement of <54 mg per deciliter; P=0.80) or in the amount of insulin that was delivered (median dose, 44.4 U and 40.2 U, respectively; P=0.50). No episode of severe hypoglycemia or clinically significant hyperglycemia with ketonemia occurred in either trial group. CONCLUSIONS: Among inpatients with type 2 diabetes receiving noncritical care, the use of an automated, closed-loop insulin-delivery system resulted in significantly better glycemic control than conventional subcutaneous insulin therapy, without a higher risk of hypoglycemia. (Funded by Diabetes UK and others; ClinicalTrials.gov number, NCT01774565 .).

The competitive athlete with type 1 diabetes.

Regular exercise is important for health, fitness and longevity in people living with type 1 diabetes, and many individuals seek to train and compete while living with the condition. Muscle, liver and glycogen metabolism can be normal in athletes with diabetes with good overall glucose management, and exercise performance can be facilitated by modifications to insulin dose and nutrition. However, maintaining normal glucose levels during training, travel and competition can be a major challenge for athletes living with type 1 diabetes. Some athletes have low-to-moderate levels of carbohydrate intake during training and rest days but tend to benefit, from both a glucose and performance perspective, from high rates of carbohydrate feeding during long-distance events. This review highlights the unique metabolic responses to various types of exercise in athletes living 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 (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.

Is There a Role for Surgery in Patients with Neuroendocrine Tumors of the Esophagus? A Contemporary View from the NCDB.

BACKGROUND: Esophageal neuroendocrine tumors (eNETs) are exceedingly rare, aggressive and have a poor prognosis. Treatment guidelines are ill-defined and mainly based on evidence from case reports and analogous experiences drawn from similar disease sites. METHODS: The NCDB was reviewed for histologically confirmed stage I-III, primary eNETs from 2006 to 2014. Patients were grouped into whether or not they underwent primary tumor resection. Univariate, multivariable, and full bipartite propensity score (PS) adjusted Cox regression analyses were used to assess overall and relative survival differences. RESULTS: A total of 250 patients were identified. Mean age was 65.0 (standard deviation [SD] 11.9) years, and 174 (69.6%) patients were male. Most patients had stage III disease (n = 136, 54.4%), and the most common type of NET was small cell eNET (n = 111, 44.4%). Chemotherapy was used in 186 (74.4%), radiation therapy in 178 (71.2%), and oncological resection was performed in 69 (27.6%) patients. Crude 2-year survival rates were higher in the operated (57.3%) compared with the nonoperated group (35.2%; p < 0.001). The survival benefit held true after multivariable adjustment (hazard ratio [HR] 0.47, 95% confidence interval [CI] 0.32-0.69, p < 0.001). After full bipartite PS adjustment analysis, survival was longer for patients who received a surgical resection compared with those who did not (HR 0.48, 95% CI 0.31-0.75, p = 0.003) with a corresponding 2-year overall survival rate of 63.3% (95% CI 52.0-77.2) versus 38.8% (95% CI 30.9-48.8), respectively. CONCLUSIONS: Multimodal treatment that includes surgery is associated with better overall survival for eNETs. Additional research is needed to more definitively identify patients who benefit from esophagectomy and to establish an appropriate treatment algorithm.

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.

[Closed-loop systems - Update 2020]. / Closed-loop-Systeme ­ Update 2020.

Closed-loop systems - Update 2020 Abstract. The artificial pancreas (also referred to as a Closed-loop system) represents the latest development in diabetes therapy. Closed-loop systems autonomously direct subcutaneous insulin infusion via a control algorithm based on real-time continuous glucose monitoring. Closed-loop systems have been tested in clinical studies since 2011, and were shown to improve glucose control by reducing hyper- and hypoglycaemia as well as glucose variability when compared with conventional insulin pump therapy. In 2016, the US regulatory authority approved the first hybrid-closed-loop system for the treatment of type 1 diabetes. Hybrid-closed loop systems are not yet fully automated and still require the user to be actively involved for insulin dosing at mealtimes. In spite of the remarkable progress, the delayed action profile of subcutaneously administered insulin and the need to wear multiple devices on the body remain challenging. The present review article summarizes the current state of the art of closed-loop systems in diabetes care and will address technical aspects, evidence from clinical studies as well as future developments in the field.

Fully closed-loop insulin delivery improves glucose control of inpatients with type 2 diabetes receiving hemodialysis.

Inpatient diabetes management of those on hemodialysis poses a major challenge. In a post hoc analysis of a randomized controlled clinical trial, we compared the efficacy of fully automated closed-loop insulin delivery vs. usual care in patients undergoing hemodialysis while in hospital. Compared to control patients receiving conventional subcutaneous insulin therapy, those patients receiving closed-loop insulin delivery significantly increased the proportion of time when a continuous glucose monitor was in the target range of 5.6-10.0 mmol/l by 37.6 percent without increasing the risk of hypoglycemia. Thus, closed-loop insulin delivery offers a novel way to achieve effective and safe glucose control in this vulnerable patient population.

Metabolomics by UHPLC-MS: benefits provided by complementary use of Q-TOF and QQQ for pathway profiling.

INTRODUCTION: Non-targeted metabolic profiling using high-resolution mass spectrometry (HRMS) is a standard approach for pathway identification despite technical limitations. OBJECTIVES: To assess the performance of combining targeted quadrupole (QQQ) analysis with HRMS for in-depth pathway profiling. METHODS: Serum of exercising patients with type 1 diabetes (T1D) was profiled using targeted and non-targeted assays. RESULTS: Non-targeted analysis yielded a broad unbiased metabolic profile, targeted analysis increased coverage of purine metabolism (twofold) and TCA cycle (three metabolites). CONCLUSION: Our screening strategy combined the benefits of the unbiased full-scan HRMS acquisition with the deeper insight into specific pathways by large-scale QQQ analysis.

Glycaemic control in individuals with type 1 diabetes using an open source artificial pancreas system (OpenAPS).

Open source artificial pancreas systems (OpenAPS) have gained considerable interest in the diabetes community. We analyzed continuous glucose monitoring (CGM) records of 80 OpenAPS users with type 1 diabetes (T1D). A total of 19 495 days (53.4 years) of CGM records were available. Mean glucose was 7.6 ± 1.1 mmol/L, time in range 3.9-10 mmol/L was 77.5 ± 10.5%, <3.9 mmol/L was 4.3 ± 3.6%, <3.0 mmol/L was 1.3 ± 1.9%, >10 mmol/L was 18.2 ± 11.0% and > 13.9 mmol/L was 4.1 ± 4.0%, respectively. In 34 OpenAPS users, additional CGM records were obtained while using sensor-augmented pump therapy (SAP). After changing from SAP to OpenAPS, lower mean glucose (-0.6 ± 0.7; P < 0.0001), lower estimated HbA1c (-0.4 ± 0.5%; P < 0.0001), higher time in range 3.9-10 mmol/L (+9.3 ± 9.5%; P < 0.0001), less time < 3.0 mmol/L (-0.7 ± 2.2%; P = 0.0171), lower coefficient of variation (-2.4 ± 5.8; P = 0.0198) and lower mean of daily differences (-0.6 ± 0.9 mmol/L; P = 0.0005) was observed. Glycaemic control using OpenAPS was comparable with results of more rigorously developed and tested AP systems. However, OpenAPS was used by a highly selective, motivated and technology-adept cohort, despite not being approved for the treatment of individuals with T1D.

Short-term fully closed-loop insulin delivery using faster insulin aspart compared with standard insulin aspart in type 2 diabetes.

We evaluated the efficacy and safety of short-term fully closed-loop insulin delivery using faster versus standard insulin aspart in type 2 diabetes. Fifteen adults with insulin-treated type 2 diabetes underwent 22 hours of closed-loop insulin delivery with either faster or standard insulin aspart in a double-blind randomized crossover design. Basal-bolus regimen was replaced by model predictive control algorithm-directed insulin delivery based on sensor glucose levels. The primary outcome was time with plasma glucose in target range (5.6-10.0 mmol/L) and did not differ between treatments (mean difference [95% CI] 3.3% [-8.2; 1.7], P = 0.17). Mean glucose and glucose variability were comparable, as was time spent below and above target range. Hypoglycaemia (<3.5 mmol/L) occurred once with faster insulin aspart and twice with standard insulin aspart. Mean total insulin dose was higher with faster insulin aspart (mean difference [95% CI] 3.7 U [0.7; 6.8], P = 0.021). No episodes of severe hypoglycaemia or other serious adverse events occurred. In conclusion, short-term fully closed-loop in type 2 diabetes may require higher dose of faster insulin aspart compared with standard insulin aspart to achieve comparable glucose control.