To Örebro University

This narrative review assesses the use of automated insulin delivery (AID) systems in managing persons with type 1 diabetes (PWD) in the pediatric population. It outlines current research, the differences between various AID systems currently on the market and the challenges faced, and discusses potential opportunities for further advancements within this field. Furthermore, the narrative review includes various expert opinions on how different AID systems can be used in the event of challenges with rapidly changing insulin requirements. These include examples, such as during illness with increased or decreased insulin requirements and during physical activity of different intensities or durations. Case descriptions give examples of scenarios with added user-initiated actions depending on the type of AID system used. The authors also discuss how another AID system could have been used in these situations.

Purpose: In healthy individuals, high carbohydrate intake is recommended during prolonged exercise for maximum performance. In type 1 diabetes (T1D), this would alter the insulin requirements. The aim of the study was to evaluate the safety of high glucose supplementation during prolonged exercise and the glucose control when a novel strategy of increased carbohydrate supply was implemented during prolonged exercise in T1D.

Methods: Eight subjects with T1D participated in a sports camp including sessions of prolonged exercise and individualized feedback during three consecutive days. This was later followed by a 90 km cross-country skiing race. Large amounts of carbohydrates, 75 g/h, were supplied during exercise and the insulin requirements were registered. Glucose was measured before, during and after exercise aiming at euglycaemia, 4-8 mmol/L (72-144 mg/dL). During the race, continuous glucose monitoring (CGM) was used as an aspect of safety and to allow direct and individual adjustments.

Results: Compared to ordinary carbohydrate supply during exercise, the high carbohydrate supplementation resulted in significantly increased insulin doses to maintain euglycaemia. During the cross-country skiing race, the participants succeeded to reach mean target glucose levels; 6.5 ± 1.9 mmol/L (117 ± 34 mg/dL) and 5.7 ± 1.5 mmol/L (103 ± 27 mg/dL) at the start and finish of the race, respectively. Episodes of documented hypoglycemia (<4 mmol/L/72 mg/dL) were rare. CGM was used for adjustments.

Conclusion: In this study, large carbohydrate supplementation in T1D individuals during prolonged aerobic exercise is safe and allows the subjects to maintain glycaemic control and indicates the feasibility of CGM under these conditions.

Background: There is a need for reliable methods of glucose measurement in different environmental conditions. The objective of this in vitro study was to evaluate the performance of the Enlite® Sensor when connected to either the iPro™ Continuous Glucose Monitor recording device or the Guardian® REAL-Time transmitting device, in hypobaric and hyperbaric conditions.

Methods: Sixteen sensors connected to eight iPro devices and eight Guardian REAL-Time devices were immersed in three beakers containing separate glucose concentrations: 52, 88, and 207 mg/dl (2.9, 4.9, and 11.3 mmol/liter). Two different pressure tests were conducted: a hypobaric test, corresponding to maximum 18000 ft/5500 m height, and a hyperbaric test, corresponding to maximum 100 ft/30 m depth. The linearity of the sensor signals in the different conditions was evaluated.

Results: The sensors worked continuously, and the sensor signals were collected without interruption at all pressures tested. When comparing the input signals for glucose (ISIGs) and the different glucose concentrations during altered pressure, linearity (R(2)) of 0.98 was found. During the hypobaric test, significant differences (p < .005) were seen when comparing the ISIGs during varying pressure at two of the glucose concentrations (52 and 207 mg/dl), whereas no difference was seen at the 88 mg/dl glucose concentration. During the hyperbaric test, no differences were found.

Conclusions: The Enlite Sensors connected to either the iPro or the Guardian REAL-Time device provided values continuously. In hyperbaric conditions, no significant differences were seen during changes in ambient pressure; however, during hypobaric conditions, the ISIG was significantly different in the low and high glucose concentrations.

Background: Physical activity is advocated in all individuals with diabetes. However, good glycemic control can be difficult to achieve due to exercise induced glucose excursions.

Objective: To evaluate the impact on glucose control of a structured diabetes education concerning physical activity, delivered via the web/internet together with telemedical care (individualized feedback by phone).

Methods: Eighty-two individuals with type 1 (T1D) were included in the pre-race intervention and randomized into two groups: intervention (I) (n=48) and control (C) (n=48). Both groups received web-based training of sports and nutrition in relation to diabetes. The intervention group also received structured and individualized feedback on two different occasions. HbA1c was measured at baseline, after 3 and 6 months when a 45 km cross-country skiing race (the HalvVasa) was performed. Only the individuals attending the skiing race were eligible to be included in the study. Level of Physical Activity (LPA), Multidimensional Health Locus of Control (MHLC) and Confidence In Diabetes Self-care (CIDS) were assessed at baseline and after 7 months.

Results: HbA1c at start was 58.5 ± 10.0 (I) respectively 60.7 ± 9.5 (C) mmol/mol. At 3 months 56.7 ± 8.7 (I) respectively 61.0 ± 9.6 (C) mmol/mol and at 6 months 55.7 ± 8.1 (I) respectively 60.3 ± 9.7 (C) mmol/mol. A significant in (I) at 3 months: 2.2 ± 3.8 mmol/mol (0.7-3.7, 95% CI), (p<0.05) and after 6 months: 2.8 ± 5.5 mmol/mol (0.5-5.0, 95% CI), (p<0.05). No reduction was seen in (C). However between the two groups no difference was noted. The LPA was increased in 52% of the participants in (I) respectively 7% in (C), a significant difference, p<0.05. No differences were seen regarding HbA1c or LPA in the control group.

Conclusion: Education and individualized feedback, delivered via telemedicine, to physical active individuals with T1D resulted in improvements in glycemic control within the intervention group and improved level of physical activity and locus of control when compared to the control group(12) (PDF) Education and individualized support regarding exercise and diabetes improves glucose control and level of physical activity in type 1 diabetes individuals.

Background: The performance and accuracy of the Enlite(™) (Medtronic, Inc., Northridge, CA) sensor may be affected by microbubble formation at the electrode surface during hypo- and hyperbaric conditions. The effects of acute pressure changes and of prewetting of sensors were investigated.

Materials and Methods: On Day 1, 24 sensors were inserted on the right side of the abdomen and back in one healthy individual; 12 were prewetted with saline solution, and 12 were inserted dry. On Day 2, this procedure was repeated on the left side. All sensors were attached to an iPro continuous glucose monitoring (CGM) recorder. Hypobaric and hyperbaric tests were conducted in a pressure chamber, with each test lasting 105 min. Plasma glucose values were obtained at 5-min intervals with a HemoCue(®) (Ängelholm, Sweden) model 201 glucose analyzer for comparison with sensor glucose values.

Results: Ninety percent of the CGM systems operated during the tests. The mean absolute relative difference was lower during hyperbaric than hypobaric conditions (6.7% vs. 14.9%, P<0.001). Sensor sensitivity was slightly decreased (P<0.05) during hypobaric but not during hyperbaric conditions. Clarke Error Grid Analysis showed that 100% of the values were found in the A+B region. No differences were found between prewetted and dry sensors.

Conclusions: The Enlite sensor performed adequately during acute pressure changes and was more accurate during hyperbaric than hypobaric conditions. Prewetting the sensors did not improve accuracy. Further studies on type 1 diabetes subjects are needed under various pressure conditions.

BACKGROUND: This study evaluated the accuracy and function of the Continuous Glucose Monitoring System (CGMS, Minneapolis, MN) during recreational scuba diving in individuals with type 1 diabetes.

METHODS: Twenty-four adults, 12 with type 1 diabetes and 12 healthy controls, were studied during five recreational scuba dives performed on three consecutive days. All the participants used the CGMS on all the days and during all the dives. Comparisons were made between plasma glucose at specific time intervals and the CGMS.

RESULTS: The recording by the CGMS was robust, with few sensor problems. The mean sensor survival time was >48 h. Eighty-five percent of the individuals used one sensor during the entire length of the trial. The overall mean absolute difference (MAD) within the group with diabetes was 14.4 +/- 6%, and the corresponding daily figures were 23.2 +/- 19.3% on day 1, 11.6 +/- 4.5% on day 2, and 11.2 +/- 5.7% on day 3. A significant improvement regarding MAD when day 1 was compared with day 2 and 3 (P < 0.05). With a limit set at 70 mg/dL, hypoglycemia pre- and post-dive was detected with a positive predictive value of 0.39, negative predictive value of 0.98, sensitivity of 0.64, and specificity of 0.94.

CONCLUSIONS: We demonstrate that the CGMS was used with accuracy in such difficult conditions as scuba diving and provided robust information on glucose variations.

BACKGROUND: Our objective is to evaluate the Medtronic CGMS continuous glucose monitoring system and plasma glucose (PG) measurement performed in a monitoring schedule as tools to identify individuals with type 1 diabetes at risk when diving.

METHODS: We studied 24 adults, 12 type 1 diabetes subjects and 12 controls, during 5 recreational scuba dives performed on 3 consecutive days. The CGMS was used by all participants on all the days and all the dives. Comparisons were made between PG performed in a monitoring schedule during the days of diving, self-monitored blood glucose (SMBG) performed 2 weeks prior to diving, and the CGMS during the study.

RESULTS: One hundred seventeen dives were performed. Hypoglycemia (<70 mg/dl) was found in six individuals and on nine occasions. However, no symptoms of hypoglycemia were present during or immediately postdiving. In one case, repetitive hypoglycemia prediving gave rise to a decision not to dive. None of the dives were aborted. The number of hypoglycemic episodes, 10 min prediving or immediately postdiving, were related to the duration of diabetes, r = 0.83 and p =0.01, and the percentage of SMBG values below target (<72 mg/dl), r = 0.65 and p =0.02. Moreover, the number of hypoglycemic episodes was also related to the total duration below low limit (<70 mg/dl), measured by the CGMS, r =0.74 and p =0.006.

CONCLUSION: Safe dives are possible to achieve by well-informed, well-controlled individuals with type 1 diabetes. Using downloaded SMBG, CGMS, and repetitive PG in a monitoring schedule, it is possible to identify those subjects who are suitable for diving.

AIMS: The study aims were to determine autoantibodies associated with type 1 diabetes (T1D), celiac disease (CD) and autoimmune thyroid disease (AITD) in individuals living with type 2 diabetes (T2D) compared to T1D and matched controls.

METHODS: Individuals with T1D and T2D were randomly identified in health-care registers. Blood was collected through home-capillary sampling and autoantibodies associated with either T1D against glutamic acid decarboxylase (GADA), insulin (IAA), insulinoma antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A), CD against tissue transglutaminase (tTGA) or AITD against thyroid peroxidase (TPOA) were determined in an automated, multiplex Antibody Detection by Agglutination-PCR (ADAP) assay.

RESULTS: GADA were detected in 46 % (88/191) of T1D and increased to 6.2 % (23/372) in T2D compared to 2.6 % (7/259) of controls (p = 0.0367). tTGA was low (1.1-2.6 %) and not different in between the study cohorts, nonetheless, in T1D tTGA was associated to islet autoantibodies. TPOA was more frequent in T1D, 27.1 % (53/191), compared to either T2D, 14.8 % (55/372; p = 0.0002) or controls, 14.3 % (37/259) (p = 0.0004). Overall, TPOA was more frequent in GADA positive (34.8 %; 8/23) than negative (13.5 %; 47/349; p = 0.0053) T2D individuals.

CONCLUSION: It's suggested that analyzing GADA and TPOA may refine the autoimmune landscape in individuals clinically classified as T2D.

AIMS/HYPOTHESIS: The risk of complications and medical consequences of type 2 diabetes are well known. Hospital costs have been identified as a key driver of total costs in studies of the economic burden of type 2 diabetes. Less evidence has been generated on the impact of individual diabetic complications on the overall societal burden. The objective of this study was to analyse costs of hospital-based healthcare (inpatient and outpatient care) and work absence related to individual macrovascular and microvascular complications of type 2 diabetes in Sweden in 2016.

METHODS: Data for 2016 were retrieved from a Swedish national retrospective observational database cross-linking individual-level data for 1997-2016. The database contained information from population-based health, social insurance and socioeconomic registers for 392,200 people with type 2 diabetes and matched control participants (5:1). Presence of type 2 diabetes and of diabetes complications were derived using all years, 1997-2016. Costs of hospital-based care and of absence from work due to diabetes complications were estimated for the year 2016. Regression analysis was used for comparison with control participants to attribute absence from work to individual complications, and to account for joint presence of complications.

RESULTS: Use of hospital care for complications was higher in type 2 diabetes compared with control participants in 2016: 26% vs 12% had ≥1 hospital contact; there were 86,104 vs 24,608 outpatient visits per 100,000 people; and there were 9894 vs 2546 inpatient admissions per 100,000 people (all p < 0.001). The corresponding total costs of hospital-based care for complications were €919 vs €232 per person (p < 0.001), and 74.7% of costs were then directly attributed to diabetes (€687 per person). Regression analyses distributed the costs of days absent from work across diabetes complications per se, basic type 2 diabetes effect and unattributed causes. Diabetes complications amounted to €1317 per person in 2016, accounting for possible complex interactions (25% of total costs of days absent). Key drivers of costs were the macrovascular complications angina pectoris, heart failure and stroke; and the microvascular complications eye diseases, including retinopathy, kidney disease and neuropathy. Early mortality in working ages cost an additional €579 per person and medications used in risk-factor treatment amounted to €418 per person.

CONCLUSIONS/INTERPRETATION: The economic burden of complications in type 2 diabetes is substantial. Costs of absence from work in this study were found to be greater than of hospital-based care, highlighting the need for considering treatment consequences in a societal perspective in research and policy. Graphical abstract.

Automated insulin delivery (AID) systems mimic an artificial pancreas via a predictive algorithm integrated with continuous glucose monitoring (CGM) and an insulin pump, thereby providing AID. Outside of pregnancy, AID has led to a paradigm shift in the management of people with type 1 diabetes (T1D), leading to improvements in glycemic control with lower risk for hypoglycemia and improved quality of life. As the use of AID in clinical practice is increasing, the number of women of reproductive age becoming pregnant while using AID is also expected to increase. The requirement for lower glucose targets than outside of pregnancy and for frequent adjustments of insulin doses during pregnancy may impact the effectiveness and safety of AID when using algorithms for non-pregnant populations with T1D. Currently, the CamAPS® FX is the only AID approved for use in pregnancy. A recent randomized controlled trial (RCT) with CamAPS® FX demonstrated a 10% increase in time in range in a pregnant population with T1D and a baseline glycated hemoglobin (HbA1c) ≥ 48 mmol/mol (6.5%). Off-label use of AID not approved for pregnancy are currently also being evaluated in ongoing RCTs. More evidence is needed on the impact of AID on maternal and neonatal outcomes. We review the current evidence on the use of AID in pregnancy and provide an overview of the completed and ongoing RCTs evaluating AID in pregnancy. In addition, we discuss the advantages and challenges of the use of current AID in pregnancy and future directions for research.

OBJECTIVE: Insulin degludec (IDeg) is a basal insulin that forms soluble multihexamers after subcutaneous injection, resulting in an ultra-long action profile. We assessed the efficacy and safety of IDeg formulations administered once daily in combination with mealtime insulin aspart in people with type 1 diabetes.

RESEARCH DESIGN AND METHODS: In this 16-week, randomized, open-label trial, participants (mean: 45.8 years old, A1C 8.4%, fasting plasma glucose [FPG] 9.9 mmol/L, BMI 26.9 kg/m(2)) received subcutaneous injections of IDeg(A) (600 mu mol/L; n = 59), IDeg(B) (900 mu mol/L; n = 60), or insulin glargine (IGlar; n = 59), all given once daily in the evening. Insulin aspart was administered at mealtimes.

RESULTS: At 16 weeks, mean A1C was comparable for IDeg(A) (7.8 +/- 0.8%), IDeg(B) (8.0 +/- 1.0%), and IGlar (7.6 +/- 0.8%), as was FPG (8.3 +/- 4.0, 8.3 +/- 2.8, and 8.9 +/- 3.5 mmol/L, respectively). Estimated mean rates of confirmed hypoglycemia were 28% lower for IDeg(A) compared with IGlar (rate ratio [RR]: 0.72 [95% CI 0.52-1.00]) and 10% lower for IDeg(B) compared with IGlar (RR: 0.90 [0.65-1.24]); rates of nocturnal hypoglycemia were 58% lower for IDeg(A) (RR: 0.42 [0.25-0.69]) and 29% lower for IDeg(B) (RR: 0.71 [0.44-1.16]). Mean total daily insulin dose was similar to baseline. The frequency and pattern of adverse events was similar between insulin treatments.

CONCLUSIONS: In this clinical exploratory phase 2 trial in people with type 1 diabetes, IDeg is safe and well tolerated and provides comparable glycemic control to IGlar at similar doses, with reduced rates of hypoglycemia.

Background: For patients with type 2 diabetes who do not achieve target glycaemic control with conventional insulin treatment, advancing to a basal-bolus insulin regimen is often recommended. We aimed to compare the efficacy and safety of long-acting glucagon-like peptide-1 receptor agonist dulaglutide with that of insulin glargine, both combined with prandial insulin lispro, in patients with type 2 diabetes.

Methods: We did this 52 week, randomised, open-label, phase 3, non-inferiority trial at 105 study sites in 15 countries. Patients (aged ≥18 years) with type 2 diabetes inadequately controlled with conventional insulin treatment were randomly assigned (1:1:1), via a computer-generated randomisation sequence with an interactive voice-response system, to receive once-weekly dulaglutide 1·5 mg, dulaglutide 0·75 mg, or daily bedtime glargine. Randomisation was stratified by country and metformin use. Participants and study investigators were not masked to treatment allocation, but were unaware of dulaglutide dose assignment. The primary outcome was a change in glycated haemoglobin A1c (HbA1c) from baseline to week 26, with a 0·4% non-inferiority margin. Analysis was by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT01191268.

Findings: Between Dec 9, 2010, and Sept 21, 2012, we randomly assigned 884 patients to receive dulaglutide 1·5 mg (n=295), dulaglutide 0·75 mg (n=293), or glargine (n=296). At 26 weeks, the adjusted mean change in HbA1c was greater in patients receiving dulaglutide 1·5 mg (-1·64% [95% CI -1·78 to -1·50], -17·93 mmol/mol [-19·44 to -16·42]) and dulaglutide 0·75 mg (-1·59% [-1·73 to -1·45], -17·38 mmol/mol [-18·89 to -15·87]) than in those receiving glargine (-1·41% [-1·55 to -1·27], -15·41 mmol/mol [-16·92 to -13·90]). The adjusted mean difference versus glargine was -0·22% (95% CI -0·38 to -0·07, -2·40 mmol/mol [-4·15 to -0·77]; p=0·005) for dulaglutide 1·5 mg and -0·17% (-0·33 to -0·02, -1·86 mmol/mol [-3·61 to -0·22]; p=0·015) for dulaglutide 0·75 mg. Five (<1%) patients died after randomisation because of septicaemia (n=1 in the dulaglutide 1·5 mg group); pneumonia (n=1 in the dulaglutide 0·75 mg group); cardiogenic shock; ventricular fibrillation; and an unknown cause (n=3 in the glargine group). We recorded serious adverse events in 27 (9%) patients in the dulaglutide 1·5 mg group, 44 (15%) patients in the dulaglutide 0·75 mg group, and 54 (18%) patients in the glargine group. The most frequent adverse events, arising more often with dulaglutide than glargine, were nausea, diarrhoea, and vomiting.

Iinterpretation: Dulaglutide in combination with lispro resulted in a significantly greater improvement in glycaemic control than did glargine and represents a new treatment option for patients unable to achieve glycaemic targets with conventional insulin treatment.

FUNDING: Eli Lilly and Company.

AIMS/HYPOTHESIS: The AERx insulin Diabetes Management system (AERx iDMS) is a liquid aerosol device that enables insulin to be administered to the peripheral parts of the lung. This study aimed to compare the pharmacokinetic and pharmacodynamic properties of insulin which is inhaled using AERx iDMS with insulin which is subcutaneously administered.

METHODS: In total, 18 C-peptide negative patients with Type I (insulin-dependent) diabetes mellitus participated in this randomised, open-label, 5-period crossover trial. Human regular insulin was administered subcutaneously (0.12 U/kg body weight) or inhaled by means of the AERx iDMS (dosages 0.3, 0.6, 1.2, and 1.8 U/kg body weight). Thereafter plasma glucose was kept constant at 7.2 mmol/l for a 10-h period (glucose clamp technique).

RESULTS: Inhaled insulin provided a dose-response relation that was close to linear for both pharmacokinetic (AUC-Ins(0-10 h); Cmax-Ins) and pharmacodynamic (AUC-GIR(0-10 h); GIRmax) parameters. Time to maximum insulin concentration (Tmax-Ins) and time to maximum glucose infusion rate (TGIRmax) were shorter with inhaled insulin than with subcutaneous administration. The pharmacodynamic system efficiency of inhaled insulin (AUC-GIR(0-6 h) was 12.7% (95% C.I.: 10.2-15.6).

CONCLUSION/INTERPRETATION: The inhalation of soluble human insulin using the AERx iDMS is feasible and provides a clear dose response. Further long-term studies are required to investigate safety aspects, HbA1c values, incidence of hypoglycaemic events and the quality of life.

Background and aims: The prevalence of diabetes has increased rapidly over the last decades worldwide. The risk of complications and medical consequences is well known and identified as key driver of costs. Less evidence on the impact of individual diabetic complications on the societal burden is available. The objective was to analyse costs of hospital-based health care and work absence related to individual macrovascular and microvascular complications of type 2 diabetes in Sweden in 2016.

Materials and methods: The study used data from a Swedish retrospective observational database cross-linking 20 years of individual-level data (1997-2016) from national population-based health, social insurance and socio-economic registers for 392,200 people with type 2 diabetes and matched controls (5:1). Diabetes status and presence of 19 types of complications were derived from years 1997-2016 while the costs of hospital-based care and of production loss due to diabetes complications were estimated for 2016. Regression analysis was used for comparison to controls, to attribute production loss to individual complications, and to account for joint presence of complications.

Results: Complications are prevalent and patterns complex in type 2 diabetes (Fig. 1). Use of hospital care for complications was higher compared to controls: 86,104 vs 24,608 outpatient visits per 100,000 persons and 9,894 vs 2,546 inpatient admissions per 100,000 persons (p<0.001) in 2016. 26% vs 12% had ≥1 hospital contact. The corresponding total costs of hospital-based care fo rcomplications were EUR 91,875 vs EUR 23,222 per 100 persons (p<0.001) and 75% were directly attributed to diabetes (EUR 689/person). Regression analyses distributed the costs of days absent from work across diabetes complications, basic type 2 diabetes effect and unattributed causes: diabetes complications amounted to EUR 2,165/person in 2016. Key drivers of costs of production loss were macrovascular complications angina pectoris, heart failure and stroke, and microvascular complications eye disease including retinopathy, kidney disease and neuropathy. Early mortality in working ages cost additional EUR 579/person and medications used in risk-factor treatment amounted to EUR 418/person.

Conclusion: The economic burden of complications in type 2 diabetes is substantial. Costs of productivity loss in this study were found to be greater than those of hospital-based care highlighting the need for considering treatment consequences in a societal perspective in research and policy.

Background and aims: Multiple daily insulin injections are an integral part of life for patients with type 1 diabetes (T1D). Moreover, patients’ schedules can change from day-to-day, further complicating T1D management. This study aimed to analyze daily injection patterns for patients with T1D, thereby providing insights into patients’ behaviour.

Materials and methods: This post hoc observational study included children and adults with T1D from Sweden using NovoPen®6with bolus insulin (primarily insulin aspart) and/or basal insulin (primarily insulin degludec). Injection data were uploaded via the Glooko® cloud system. Daily bolus and basal injection timing profiles were documented, weighting each dose relative to the total daily dose. The time difference between two consecutive basal insulin injections was used to evaluate basal timing variation.

Results: Overall, 159 adults and 47 children were included in the analysis, with a total of 38678 days of bolus injections and 17869 days of basal injections. The mean bolus daily injection profile displayed peaks at typical mealtimes, but substantial variation was observed both across patients and between adults and children (Figure 1). In those receiving insulin degludec, the timing of consecutive basal injections differed by more than 6 hr in approximately 9% of the injections for adults and 7% of those for children.

Conclusion: These real-world data provide a unique insight into the everyday bolus and basal adherence of paediatric and adult patients with T1D. Furthermore, the results illustrate the flexibility needed for basal injections to enable optimal management of T1D.

Objectives: For health care decision-makers, the use of computer simulation modelsr equires transparency, precision and accuracy. Systematic comparisons of diabetes models, per Mount Hood Challenges, have shown significant variability in results between models. We developed and validated a new cost-effectiveness model (the DEtermination of Diabetes Utilities, Costs, and Effects [DEDUCE] model) in both type 1 and 2 diabetes mellitus (T1DM, T2DM) to evaluate sensor-based glucose monitoring.

Methods: This Excel-based patient-level microsimulation model used a cost-utility approach to compare sensor-based glucose monitoring systems to self-monitoring of blood glucose (SMBG) testing over a specified time horizon (1 to 100 years) with yearly cycles. The model used the Sheffield risk engine for T1DM and the Risk Equations for Complications Of type 2 Diabetes (RECODe) risk engine for T2DM to predict macro- and microvascular events. Inputs, model architecture, and subse-quent validation analyses were reviewed and informed by an advisory board of health economists, endocrinologists and diabetologists.

Results: Internal validation (comparing model predictions to observed outcomes from studies from which the risk equations were derived) and external validation (predictions compared to external datasets) demonstrated high precision (R2 $ 0.98) and reasonable accuracy (mean absolute percentage error [MAPE] ranging from 7.64-68%) with regards to macrovascular outcomes for T1DM, and high precision (R2 = 0.94) and high accuracy (MAPE = 19.8%) with regards to all-cause mortality in T2DM. Cross validation (comparing model outcomes between DEDUCE and published results from models participating in previous Mount Hood Challenges) indicated that DEDUCE had the best accuracy (MAPE = 36%) and non-inferior precision (R2 = 0.16) relative to other T1DM models, and second-to-best accuracy (MAPE = 25.03%) and high precision (R2 = 0.95) relative to other T2DM models.

Conclusions: In both T1DM & T2DM, DEDUCE suitably predicted key outcomes and performed favorably compared with existing models that participated in the Mount Hood Challenges, including the Core Diabetes Model.

Background and Aims: Type 1 diabetes (T1D) therapy is continually evolving and Advanced Hybrid Closed Loop (AHCL) insulin pump systems and intermittently scanned continuous glucose monitoring (IS‐CGM) are emerging as the standard of care for many individuals with T1D. The objective of this analysis was to assess the cost‐effectiveness of the MiniMedTM 780G AHCL system versus IS‐CGM plus multiple daily injections of insulin (MDI) or continuous subcutaneous insulin infusion (CSII) in adults with T1D in Sweden.

Methods: The analysis was performed using the IQVIA CORE Diabetes Model and clinical input data were sourced from observational studies. Simulated patients were assumed to have a baseline HbA1c of 7.8% (62 mmol/mol)[1] and use of the MiniMed™ 780G system was assumed to reduce HbA1c by 0.5%.[2] The analysis was performed from a societal perspective over a lifetime time horizon. Future costs and clinical outcomes were discounted at 3% per annum.

Results: The MiniMedTM 780G system was associated with a quality‐adjusted life‐year (QALY) gain of 1.946 but generated higher overall costs versus MDI/CSII+IS‐CGM, leading to an incremental cost‐effectiveness ratio of SEK 373,700 (€ 36,857.80) per QALY‐gained. MiniMedTM 780G system use resulted in a lower cumulative incidence of diabetes‐related complications. Higher acquisition costs were partially offset by reduced complications costs. Extensive sensitivity analysis on key drivers confirmed the robustness of results.

Conclusions: For the lifetime of adults with long‐standing T1D based in Sweden, use of the MiniMed™ 780G system is projected to be cost‐effective when compared with IS‐CGM plus MDI/CSII.

AIMS: The MiniMed™ 670G system has been available in Europe since October 2018. Herein, the system's real-world performance in individuals with diabetes is evaluated.

MATERIALS AND METHODS: Data uploaded October 2018 to July 2020 by individuals living in Europe were aggregated and retrospectively analyzed. The mean Glucose Management Indicator (GMI), percentage of time spent within (TIR), below (TBR) and above (TAR) glycemic ranges, system use and insulin consumed in users with ≥10 days of SG data after initial Auto Mode start were determined. Another analysis based on suboptimally- (GMI >8.0%) and well-controlled (GMI <7.0%) glycemia pre-Auto Mode initiation was also performed.

RESULTS: Users (N=14,899) spent a mean of 81.4% of the time in Auto Mode and achieved a mean GMI of 7.0±0.4%, TIR of 72.0±9.7%, TBR <3.9 mmol/L of 2.4±2.1% and TAR >10 mmol/L of 25.7±10%, after initiating Auto Mode. When compared to pre-Auto Mode initiation, GMI reduced by 0.3±0.4% and TIR increased by 9.6±9.9% (p<0.0001 for both). Significantly improved glycemic control was observed irrespectively of pre-Auto Mode GMI level <7.0% or >8.0%. While total daily dose of insulin increased for both groups, a greater increase was observed in the latter: an increase due primarily to increased basal insulin delivery. In contrast, basal insulin decreased slightly in well-controlled users.

CONCLUSIONS: Most MiniMed™ 670G system users in Europe achieved TIR >70% and GMI <7% while minimizing hypoglycemia, in a real-world environment. These international consensus-met outcomes were enabled by automated insulin delivery meeting real-time insulin requirements adapted to each individual user.

Introduction: Hypoglycaemia is a common side effect of insulin therapy; low or high glycated haemoglobin (HbA(1c)) levels, history of hypoglycaemia or long diabetes duration are known modifiers of hypoglycaemia risk. In randomised clinical trials, lower rates of hypoglycaemia have been observed with the new-generation insulin analogue, long-acting insulin degludec, compared with other basal insulins.

Methods: The ReFLeCT study was a prospective observational study over 12 months. Patient-reported diary data on hypoglycaemia were collected from patients with type 1 diabetes (T1D) or type 2 diabetes (T2D) who were switching from other basal insulins to insulin degludec (degludec) at their physician's discretion in routine clinical care. Two secondary analyses were undertaken to investigate the change in number of hypoglycaemic events: a post hoc analysis using the updated American Diabetes Association (ADA) level 1, 2 and 3 hypoglycaemia definitions, and a pre-specified analysis using patient characteristics (baseline HbA(1c), diabetes duration, and physician's rationale for initiating degludec).

Results: Switching to degludec was associated with significantly fewer hypoglycaemic events for all definitions in T1D, and level 1 and 2 in T2D (too few level 3 events for statistical comparison). Moreover, patient characteristics did not influence the observed reduction in hypoglycaemia in T1D and T2D.

Conclusion: These results demonstrate that switching to degludec from other basal insulins was associated with reduced rates of hypoglycaemia, irrespective of the definition used or baseline patient characteristics.

Plain Language Summary: Low blood sugar levels (hypoglycaemia) are a common, and sometimes serious, side effect of treatment with insulin in people with diabetes. In the ReFleCT study, adults with type 1 (T1D) and type 2 diabetes (T2D) were asked to complete a diary for 12 months when their doctor changed their previous long-acting insulin treatment to insulin degludec (degludec). The key outcome of the study was whether the frequency of hypoglycaemia changed when a patient's insulin treatment was switched. Here, we used the diary information from the ReFLeCT study to investigate whether the change in the rate of hypoglycaemia was related to the way hypoglycaemia was defined, or to patients' characteristics at the time their insulin was switched. These characteristics included the length of time that patients had had diabetes, their blood sugar control, and their doctor's reason for changing their medication. Our findings showed that the way hypoglycaemia was defined, and patients' characteristics, did not generally influence the frequency of hypoglycaemia for patients with T1D or T2D. However, the most severe hypoglycaemia in patients with T2D occurred too infrequently to be assessed. Patients in all groups had less hypoglycaemia overall after switching compared with their previous treatment, suggesting that degludec may be a treatment option for a broad range of patients with diabetes.

ReFLeCT, a multicenter, prospective, observational study evaluated the safety and effectiveness of switching from other basal insulins to insulin degludec (degludec) in patients with type 1 (T1D) or type 2 diabetes (T2D) in routine clinical practice. ReFLeCT comprised a 4-week baseline period (pre-switch basal insulin) and 12-month follow-up period (degludec). The primary endpoint of overall hypoglycemia reported in patient diaries was reduced during the 12-month follow-up period vs. baseline, without compromising glycemic control. In pre-specified subgroup analyses of the primary endpoint, we assessed if the overall result was robust in different subgroups, characterized according to baseline A1C (<7.5, ≥7.5-<8.5, ≥8.5-<9.5, ≥9.5%), diabetes duration (quartiles) and physician’s reason for initiating degludec (hypoglycemia [Yes/No]). The estimated rate ratios of hypoglycemia were similar within subgroups (no significant interactions), and demonstrated overall lower rates (the majority significantly lower) during the 12-month follow-up periods vs. baseline in patients with T1D or T2D (Figure). Irrespective of baseline characteristics or physician’s reason for initiating degludec, switching to degludec from other basal insulins reduced rates of overall hypoglycemia in patients with T1D or T2D, in routine clinical practice.

Background: Available prediction models ofcardiovascular diseases (CVDs) may not accuratelypredict outcomes among individuals initiatingpharmacological treatment for attention-deficit/hyperactivity disorder (ADHD).

Objective: To improve the predictive accuracyof traditional CVD risk factors for adults initiatingpharmacological treatment of ADHD, by consideringnovel CVD risk factors associated with ADHD (comorbidpsychiatric disorders, sociodemographic factors andpsychotropic medication).

Methods: The cohort composed of 24 186 adultsresiding in Sweden without previous CVDs, born between1932 and 1990, who started pharmacological treatmentof ADHD between 2008 and 2011, and were followedfor up to 2 years. CVDs were identified using diagnosesaccording to the International Classification of Diseases,and dispended medication prescriptions from Swedishnational registers. Cox proportional hazards regressionwas employed to derive the prediction model.

Findings: The developed model included eighttraditional and four novel CVD risk factors. Themodel showed acceptable overall discrimination (Cindex=0.72, 95% CI 0.70 to 0.74) and calibration(Brier score=0.008). The Integrated DiscriminationImprovement index showed a significant improvementafter adding novel risk factors (0.003 (95% CI 0.001 to0.007), p<0.001).

Conclusions: The inclusion of the novel CVD riskfactors may provide a better prediction of CVDs in thispopulation compared with traditional CVD predictorsonly, when the model is used with a continuous riskscore. External validation studies and studies assessingclinical impact of the model are warranted.

Clinical implications: Individuals initiatingpharmacological treatment of ADHD at higher risk ofdeveloping CVDs should be more closely monitored.

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC), that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels is suggested that enables the collection of comparator data with these characteristics. This work is an important step towards establishing a future standard for the performance evaluation of CGM systems.

BACKGROUND: The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for "integrated" CGM systems.

METHODS: The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA.

RESULTS: The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment).

CONCLUSIONS: We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements.

Aims: To evaluate the safety and effectiveness of switching to once‐daily insulin degludec (degludec) from other basal insulins in patients with Type 1 diabetes in routine clinical practice.

Methods: ReFLeCT was a multicentre, prospective, observational study in seven European countries in patients (≥18 years) with Type 1 or Type 2 diabetes, whose physician planned to switch their basal insulin to degludec (ClinicalTrials.gov: NCT02392117). ReFLeCT comprised a four week baseline period (pre‐switch basal insulin) and a 12 month follow‐up period (degludec). For the Type 1 diabetes cohort presented here, primary endpoint was changed from baseline in the rate of overall hypoglycaemia recorded in patient diaries.

Results: Baseline characteristics (mean [SD]) for patients with Type 1 diabetes (n = 556) were: age 47.4 (15.7) years, diabetes duration 21.4 (13.5) years, HbA1c 8.1 (1.3)% (65.0 [14.2]mmol/mol), fasting plasma glucose (FPG) 8.8 (3.9)mmol/l, pre‐switch basal insulin dose 25.0 (14.1)u/day, body mass index (BMI) 26.1 (4.7)kg/m² and body weight 76.4 (15.6)kg. Estimated rate ratios of overall (0.80 [0.74; 0.88]95%CI), non‐severe (0.81 [0.74; 0.88]95%CI), severe (American Diabetes Association definition; 0.28 [0.14; 0.56]95%CI) and nocturnal (00:01−05:59am; 0.61 [0.50; 0.73]95%CI) hypoglycaemia illustrated significantly lower rates during 12 month follow‐up vs baseline. HbA1c, FPG and basal insulin dose decreased significantly by –0.15% [–0.23; –0.07]95%CI (–1.64mmol/mol [–2.51; –0.77]95%CI), –0.54mmol/l [–0.95; –0.14]95%CI and –2.21u/day [–2.90; –1.53]95%CI, respectively, and body weight was 0.79kg [0.38; 1.20]95%CI higher, at 12 month follow‐up vs baseline.

Conclusion: Switching from other basal insulins to degludec significantly reduced the rates of hypoglycaemia and improved glycaemic control at lower basal insulin doses in patients with Type 1 diabetes in routine clinical practice.

CONTEXT: Observational studies of insulin degludec (degludec) with hypoglycemia events prospectively recorded are lacking.

OBJECTIVE: To evaluate the safety and effectiveness of degludec in patients with type 1 (T1D) or type 2 diabetes (T2D) switching from other basal insulins in routine care.

DESIGN: ReFLeCT was a multinational, multicenter, prospective, observational, single-arm study comprising a 4-week baseline period (pre-switch basal insulin) and 12-month follow-up (degludec).

SETTING: Routine clinical practice. Patients or Other Participants: Insulin-treated patients (≥18 years) with T1D (n=556) or T2D (n=611) with treatment plans to initiate degludec.

INTERVENTIONS: Switching to degludec from other basal insulins.

MAIN OUTCOME MEASURE(S): Change from baseline in number of overall hypoglycemic events recorded in patient diaries.

RESULTS: In T1D, the 12-month follow-up/baseline rate ratios [95% CI] of overall (0.80 [0.74;0.88]), non-severe (0.83 [0.76;0.91]), severe (0.28 [0.14;0.56]) and nocturnal (0.61 [0.50;0.73]) hypoglycemia suggested significantly reduced hypoglycemia rates with degludec (all P<0.001). At 12 months, HbA1c, fasting plasma glucose (FPG) and basal insulin dose decreased significantly. Body weight increased and treatment satisfaction improved significantly. In T2D, the hypoglycemia rate ratios were: overall (0.46 [0.38;0.56]), non-severe (0.53 [0.44;0.64]) and nocturnal (0.35 [0.20;0.62]) (all P<0.001; too few events for analysis of severe). At 12 months, HbA1c and FPG decreased significantly. Body weight and insulin doses remained unchanged, and treatment satisfaction was significantly improved.

CONCLUSIONS: In a routine clinical care setting, switching to degludec from other basal insulins was associated with significantly reduced rates of hypoglycemia, improved glycemic control, and treatment satisfaction in patients with T1D or T2D.

OBJECTIVE: To establish the clinical phenotype and genetic background in a family with diabetes insipidus.

MATERIAL AND METHODS: The subjects were a sister and brother, aged 34 and 27 years, respectively, with a history of polyuria since infancy. Clinical testing confirmed a diagnosis of congenital nephrogenic diabetes insipidus (CNDI) in both. Samples of purified genomic DNA were analysed.

RESULTS: The sequence of the entire coding region of the AQP2 gene as well as the AVPR2 gene was determined. Sequence analysis revealed no variations in the AQP2 gene. A missense variation in exon 2 of the AVPR2 gene (g.685G>A), predicting a p.Asp85Asn substitution, was identified in the X-chromosome of the affected male and one allele in the sister and the asymptomatic mother. The p.Asp85Asn variation in AVPR2 is known to cause CNDI, and has previously been described as inducing a partial phenotype treatable with dDAVP. However, in this family dDAVP had no influence on urine osmolality, whereas combination therapy with indomethacin and hydrochlorothiazide increased urine osmolality to 299 mosm/l in the proband. A skewed X-inactivation pattern (93%) occurring in the normal X allele was recognized in the sister.

CONCLUSIONS: This study demonstrates the effect of skewed X-chromosome inactivation associated with X-linked CNDI. Polydipsia in early childhood could be due to X-linked CNDI despite affecting both genders. The significant heterogeneity in the clinical phenotype in CNDI carries a risk of diagnostic misinterpretation and emphasizes the need for genetic characterization. Treatment combining indomethacin and hydrochlorothiazide results in a marked response on both urine output and urine osmolality.

Background and aims: ReFLeCT was a multicentre, prospective, observational study designed to investigate the safety and effectiveness of switching to insulin degludec (degludec) from other basal insulins in patients with type 1 (T1D) or type 2 diabetes (T2D). Few studies had prospectively collected hypoglycaemia data from patient diaries following a switch to degludec in everyday clinical practice. These additional analyses from the ReFLeCT study aimed to assess the effects of switching to degludec according to different hypoglycaemia definitions.

Materials and methods: ReFLeCT comprised a 4-week baseline period (pre-switch basal insulin) and a 12-month follow-up period (degludec treatment). The primary endpoint of overall hypoglycaemia reported in patient diaries was reduced during follow-up vs baseline in T1D and T2D with improvement of glycaemic control, as previously reported. Here, hypoglycaemia data from ReFLeCT were analysed using pre-specified and updated (post hoc) American Diabetes Association (ADA) hypoglycaemia definitions. Definitions consisted of: documented asymptomatic and symptomatic, pseudo, probable symptomatic, and Level 1, 2 and 3 (severe) hypoglycaemia (Fig). Hypoglycaemic events were analysed using fully adjusted, negative binomial regression models.

Results: In T1D (n=556) and T2D (n=611), estimated rate ratios across the previous and the updated ADA hypoglycaemia definitions were significantly lower during the 12-month follow-up vs the baseline period, except for asymptomatic hypoglycaemia in T1D and Level 3 hypoglycaemia in T2D (due to a low number of severe hypoglycaemic events, no comparable statistics were performed) (Fig). Event rates per patient year were also lower for all definitions during the 12-month follow-up vs the baseline period, except for Level 3 hypoglycaemia in T2D, which marginally increased, although this was likely due to the low number of events in this group.

Conclusion: In patients with T1D and T2D, switching to degludec from other basal insulins in routine clinical care is associated with lower rates of hypoglycaemia across a broad range of hypoglycaemia definitions, in combination with improved glycaemic control.

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

AIM: To compare the effect of insulin degludec and insulin glargine on health-related quality of life in patients with Type 2 diabetes starting on insulin therapy.

METHODS: Patient-level data from three open-label, randomized, treat-to-target trials of 26 or 52 weeks' duration were pooled using a weighted analysis in conjunction with a fixed-effects model. Insulin-naive patients received either insulin degludec (n = 1290) or insulin glargine (n = 632) once daily, in combination with oral anti-diabetic drugs. Glycaemic control was assessed via HbA(1c) and fasting plasma glucose concentrations. Rates of hypoglycaemia, defined as plasma glucose < 3.1 mmol/l (< 56 mg/dl), were recorded. Health-related quality of life was evaluated using the 36-item Short Form (SF-36(®) ) version 2 questionnaire. Statistical analysis was performed using a generalized linear model with treatment, trial, anti-diabetic therapy at baseline, gender, region and age as explanatory variables.

RESULTS: Insulin degludec was confirmed as non-inferior to insulin glargine based on HbA(1c) concentrations. In each trial comprising the meta-analysis, fasting plasma glucose and confirmed overall and nocturnal (00.01-05.59 h) hypoglycaemia were all numerically or significantly lower with insulin degludec vs. insulin glargine. At endpoint, the overall physical health component score was significantly higher (better) with insulin degludec vs. insulin glargine [+0.66 (95% CI 0.04-1.28)], largely attributable to a difference [+1.10 (95% CI 0.22-1.98)] in the bodily pain domain score. In the mental domains, vitality was significantly higher with insulin degludec vs. insulin glargine [+0.81 (95% CI 0.01-1.59)].

CONCLUSIONS: Compared with insulin glargine, insulin degludec leads to improvements in both mental and physical health status for patients with Type 2 diabetes initiating insulin therapy.

We conducted a systematic review and a meta-analysis to quantitatively summarize evidence on the association between attention-deficit/hyperactivity disorder (ADHD) and type 2 diabetes (T2D). Moreover, a register-based sibling study was conducted to simultaneously control for confounding factors. A systematic search identified four eligible observational studies (N=5,738,287). The meta-analysis showed that individuals with ADHD have a more than doubled risk of T2D when considering adjusted estimates (OR=2.29 [1.48-3.55], d=0.46). Results from the register-based Swedish data showed a significant association between ADHD and T2D (HR=2.35 [2.14-2.58]), with substance use disorder, depression, and anxiety being the main drivers of the association, and cardiovascular and familiar risk playing a smaller role. While results from the meta-analysis provide evidence for an increased risk of T2D in individuals with ADHD, the register-based analyses show that the association between ADHD and T2D is largely explained by psychiatric comorbidities. Pending further evidence of causal association, our findings suggest that early identification and treatment of ADHD comorbidities might greatly reduce the risk of developing T2D in individuals with ADHD.

OBJECTIVES: To evaluate the long-term clinical and economic outcomes of biphasic insulin aspart 70/30 (BIAsp 70/30) treatment vs. insulin glargine in insulin naïve, type 2 diabetes patients failing oral antidiabetic drugs in a Swedish setting.

METHODS: A published and validated computer simulation model (the CORE Diabetes Model) was used to project life expectancy, quality-adjusted life expectancy (QALE) and costs over patient lifetimes. Cohort characteristics [54.5% male, mean age 52.4 years, 9 years mean diabetes duration, mean glycosylated haemoglobin (HbA1c) 9.77%] and treatment effects were based on results from the Initiate Insulin by Aggressive Titration and Education (INITIATE) clinical trial. Direct medical costs were accounted in 2006 Swedish Kronor (SEK) and economic and clinical benefits were discounted at 3% per annum.

RESULTS: Biphasic insulin aspart 70/30 treatment when compared with insulin glargine treatment was associated with improvements in discounted life expectancy of 0.21 years (13.10 vs. 12.89 years) and QALE of 0.21 quality-adjusted life years (QALYs) (9.16 vs. 8.96 QALYs). Reductions in the incidence of diabetes-related complications in the BIAsp 70/30 treatment arm led to reduced total costs of SEK 10,367 when compared with insulin glargine (SEK 396,475 vs. SEK 406,842) over patient lifetimes. BIAsp 70/30 treatment was projected to be dominant (cost and lifesaving) when compared with insulin glargine in the base case analysis.

CONCLUSIONS: Biphasic insulin aspart 70/30 treatment was associated with improved clinical outcomes and reduced costs compared with insulin glargine treatment over patient lifetimes. These results were driven by improved HbA1c levels associated with BIAsp 70/30 compared with insulin glargine and the accompanying reduction in diabetes-related complications despite increases in body mass index.

Background: The DEPICT-1 and DEPICT-2 studies showed that dapagliflozin as an adjunct to insulin in individuals with inadequately controlled type 1 diabetes improved glycaemic control and bodyweight, without increase in risk of hypoglycaemia. We aimed to determine the effect of dapagliflozin on urinary albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) using pooled data from the DEPICT studies.

Methods: In this post-hoc analysis, we used data pooled from both DEPICT studies (DEPICT-1 ran from Nov 11, 2014, to Aug 25, 2017; DEPICT-2 ran from July 8, 2015, to April 18, 2018), in which participants were aged 18-75 years, with inadequately controlled type 1 diabetes and with a baseline UACR of at least 30 mg/g. In the DEPICT studies, participants were randomly assigned (1:1:1) to receive dapagliflozin (5 mg or 10 mg) or placebo all plus insulin, for 24 weeks, with a 28-week long-term extension (ie, 52 weeks in total). In this post-hoc analysis, we assessed the percentage change from baseline in UACR and in eGFR, up to 52 weeks. UACR, eGFR, and safety were assessed in all eligible participants who had received at least one dose of study drug. HbA 1c, bodyweight, and systolic blood pressure were assessed in all participants who received at least one dose of study drug during the first 24-week period, and who had a baseline and any post-baseline assessment for that parameter. The DEPICT trials were registered with ClinicalTrials.gov, NCT02268214 (DEPICT-1), NCT02460978 (DEPICT-2), and are now complete.

Results: 251 participants with albuminuria at baseline were included in this post-hoc analysis; of whom 80 (32%) had been randomly assigned to dapagliflozin 5 mg, 84 (33%) to dapagliflozin 10 mg, and 87 (35%) to placebo. Compared with placebo, treatment with both dapagliflozin doses improved UACR over 52 weeks. At week 52, mean difference in change from baseline versus placebo in UACR was -13.3% (95% CI -37.2 to 19.8) for dapagliflozin 5 mg and -31.1% (-49.9 to -5.2) for dapagliflozin 10 mg. No notable change from baseline was seen in eGFR, with a mean difference in change from baseline versus placebo of 3.27 mL/min per 1.73 m(2) (95% CI -0.92 to 7.45) for dapagliflozin 5 mg and 2.12 mL/min per 1.73 m(2) (-2.03 to 6.27) for dapagliflozin 10 mg. Similar proportions of participants in each treatment group had adverse events and serious adverse events, including hypoglycaemia and diabetic ketoacidosis; no new safety signals were identified in this population.

Interpretation: Treatment with dapagliflozin resulted in UACR reduction, which might provide renoprotective benefits in individuals with type 1 diabetes and albuminuria. Dedicated prospective studies are needed to confirm these findings as prespecified endpoints.

Background and Aims: Smart pen injection data can provide unique insight into routine diabetes treatment. NovoPen 6 users may consent to share their injection and continuous glucose monitoring (CGM) data anonymously for research purposes. Here, based on real-world data from people with type 1 diabetes (T1D), we explored the association between the number of daily bolus doses, and smart pen engagement, with time-in-range (TIR).

Methods: We included adults with T1D in Sweden administering Fiasp with a NovoPen 6 device for days with bolus injections and CGM data (‡70% coverage). CGM parameters were used as glycaemic control measures. Smart pen engagement was characterised by the number of injection data uploads over the previous 14 days. A linear mixed model was used to determine relationship between TIR and covariates, adjusted for known confounders.

Results: Overall, data from 224 patients were analysed. The number of daily bolus doses was significantly associated with improved TIR (p <0.0001). Patients with an average£3 daily bolus had <10% chance of reaching the target TIR >70% (Fig-ure). The number of uploads was also significantly associated with improved TIR (p <0.0001). Days where uploads were conducted daily over the previous 14 days had 5% greater TIR than days without any uploads during the previous 14 days.

Conclusions: Daily bolus dose number and smart pen engagement are strong predictors of TIR. Most patients take too few bolus doses to reach treatment targets.

AIM: To evaluate whether both bolus insulin injection frequency and smart pen engagement were associated with changes in glycaemic control, using real-world data from adults with type 1 diabetes (T1D).

MATERIALS AND METHODS: Adults using a smart pen (NovoPen 6) to administer bolus insulin (fast-acting insulin aspart or insulin aspart) alongside continuous glucose monitoring were eligible for inclusion. Smart pen engagement was characterized by number of days with pen data uploads over the previous 14 days. Glycaemic control was evaluated by analysing glucose metrics.

RESULTS: Overall, data from 1194 individuals were analysed. The number of daily bolus injections was significantly associated with time in range (TIR; 3.9-10.0 mmol/L [70-180 mg/dL]; P < 0.0001). Individuals administering, on average, three daily bolus insulin injections had an estimated 11% chance of achieving >70% TIR. The probability of achieving >70% TIR increased with the mean number of daily bolus injections. However, the percentage of TIR was lower on days when individuals administered higher-than-average numbers of injections. The observed mean number of daily bolus injections administered across the study population was lower than the optimal number required to reach glycaemic targets (4.8 injections vs. 6-8 injections). Smart pen engagement was significantly associated with improved TIR.

CONCLUSIONS: Glycaemic control was associated with daily bolus insulin injection frequency and smart pen engagement. A treatment regimen combining an optimal bolus injection strategy, and effective smart pen engagement, may improve glycaemic control among adults with T1D.

OBJECTIVE: This study, one of the first to address issues of pulmonary insulin delivery in smokers, compared pharmacokinetics of inhaled insulin delivered via the AERx insulin Diabetes Management System (iDMS) in nondiabetic cigarette smokers and nonsmokers.

RESEARCH DESIGN AND METHODS: In this randomized two-period crossover efficacy and safety trial in 27 nondiabetic smokers and 16 nonsmokers (18 men/25 women, mean age 28 years, mean BMI 23.0 kg/m(2)), subjects received single doses of inhaled insulin (33.8 IU) following overnight fasting on consecutive dosing days. On one dosing day, smokers smoked three cigarettes immediately before insulin administration ("acute smoking"); on the other dosing day, smokers had not smoked since midnight ("nonacute smoking"). After inhalation, 6-h serum insulin and serum glucose profiles were determined.

RESULTS: Pharmacokinetic results for evaluable subjects were derived from serum insulin profiles. The amount of insulin absorbed during the first 6 h after dosing (area under the exogenous serum insulin curve from 0 to 6 h [AUC((0-6 h))]) was significantly greater in smokers (63.2 vs. 40.0 mU l(-1) x h(-1), P = 0.0017); peak concentration was both higher and earlier in the smokers (maximal serum concentration of insulin [C(max)] 42.0 vs. 13.9 mU/l, P < 0.0001; time to maximal serum concentration of insulin [t(max)] 31.5 vs. 53.9 min, P = 0.0003). The estimated intrasubject variability of AUC((0-6 h)) was 13.7 and 16.5% for nonsmokers and smokers, respectively. No safety issues arose.

CONCLUSIONS: Absorption of inhaled insulin via the AERx iDMS was significantly greater in smokers, with a higher AUC((0-6 h)) and C(max) and a shorter t(max). Intrasubject variability of AUC((0-6 h)) was low and similar in nonsmokers and smokers. These data prompt more extensive investigation of inhaled insulin in diabetic smokers.

Aims: The efficacy and safety of insulin degludec (degludec), a new-generation ultra-long-acting basal insulin, was compared with insulin glargine (glargine) in people with Type 1 diabetes mellitus in a 16-week, open-label, randomized trial. Health status, an important aspect of effective diabetes management, was also assessed.

Methods: Degludec (n = 59) or glargine (n = 59) were injected once daily, with insulin aspart at mealtimes. Health status assessment utilized the validated Short Form 36 Health Survey, version 2, which has two summary component scores for mental and physical well-being, each comprising four domains.

Results: At study end, HbA1c reductions were comparable between groups, but confirmed nocturnal hypoglycaemia was significantly less frequent with degludec [relative rate 0.42 (95% CI 0.250.69)], and overall hypoglycaemia numerically less frequent [relative rate 0.72 (95% CI 0.521.00)]. After 16 weeks, a significant improvement in Short Form 36 Health Survey mental component score of +3.01 (95% CI 0.325.70) was obtained for degludec against glargine, attributable to significant differences in the social functioning [+8.04 (95% CI 1.8914.18)] and mental health domains [+2.46 (95% CI 0.104.82)]. For mental component score, Cohens effect size was 0.42, indicating a small-to-medium clinically meaningful difference. The physical component score [+0.66 (95% CI 2.30 to 3.62)] and remaining domains were not significantly different between degludec and glargine.

Conclusions: In the context of comparable overall glycaemic control with glargine, degludec improved mental well-being as measured using the mental component score of the Short Form 36 Health Survey. The improvements in overall mental component score and the underlying social functioning and mental health domains with degludec compared with glargine may relate to the observed reduction in hypoglycaemic events.

INTRODUCTION: Real-time continuous glucose monitoring (rt-CGM) informs users about current interstitial glucose levels and allows early detection of glycaemic excursions and timely adaptation by behavioural change or pharmacological intervention. Randomised controlled studies adequately powered to evaluate the impact of long-term application of rt-CGM systems on the reduction of adverse obstetric outcomes in women with gestational diabetes (GDM) are missing. We aim to assess differences in the proportion of large for gestational age newborns in women using rt-CGM as compared with women with self-monitored blood glucose (primary outcome). Rates of neonatal hypoglycaemia, caesarean section and shoulder dystocia are secondary outcomes. A comparison of glucose metabolism and quality of life during and after pregnancy completes the scope of this study.

METHODS AND ANALYSIS: Open-label multicentre randomised controlled trial with two parallel groups including 372 female patients with a recent diagnosis of GDM (between 24+0 until 31+6 weeks of gestation): 186 with rt-CGM (Dexcom G6) and 186 with self-monitored blood glucose (SMBG). Women with GDM will be consecutively recruited and randomised to rt-CGM or control (SMBG) group after a run-in period of 6-8 days. The third visit will be scheduled 8-10 days later and then every 2 weeks. At every visit, glucose measurements will be evaluated and all patients will be treated according to the standard care. The control group will receive a blinded CGM for 10 days between the second and third visit and between week 36+0 and 38+6. Cord blood will be sampled immediately after delivery. 48 hours after delivery neonatal biometry and maternal glycosylated haemoglobin A1c (HbA1c) will be assessed, and between weeks 8 and 16 after delivery all patients receive a re-examination of glucose metabolism including blinded CGM for 8-10 days.

ETHICS AND DISSEMINATION: This study received ethical approval from the main ethic committee in Vienna. Data will be presented at international conferences and published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER: NCT03981328; Pre-results.

Background and aims: The development and application of digital technologies to healthcare is a key component in meeting the increasing demand from patients for chronic disease management. Healthcare payers need evidence to support value-based decisions on new technologies. Smart insulin pens record the timing and dose of insulin, and data can integrate with continuous glucose monitoring (CGM) to improve diabetes self-management. The present analysis assessed the cost-effectiveness of introducing a smart insulin pen from a Swedish public healthcare payer perspective.

Materials andmethods: The IQVIA CORE DiabetesModel was used to project clinical outcomes and healthcare costs (2018 Swedish krona [SEK]) over patients’ lifetimes in a Swedish type 1 diabetes (T1D) population. The model projected the development of complications, mortality, HbA1c, hypoglycaemia and insulin dosing to estimate cost effectiveness. Clinical model inputs were informed by an observational study of the introduction of an NFC-enabled smart insulin pen (NovoPen® 6) in 94 adults with T1D receiving basal-bolus insulin and using CGM. Smar tinsulin pen use (median follow up 7 months) was associated with an additional 1.89 h/day time in range (TIR 3.9-10.0 mmol/L [70-180 mg/dL]) compared with baseline. Change in TIR was converted to change in HbA1c using a published regression equation to allow long-term outcomes to be modelled based on published risk equations. Additional TIR with the smart insulin pen translated to a 0.62% (6.8 mmol/mol) HbA1c reduction and there were 33 fewer CGM-documented non-severe hypoglycaemic events/patient/year (≥15 min <3.0 mmol/L [54 mg/dL]) relative to baseline. Baseline characteristics were taken from the study cohort or, if unavailable, adults with T1D from the Swedish National Diabetes Register. Future costs and clinical benefits were discounted at 3% annually. Costs were converted to Euros (EUR) using a 0.091 SEK exchange rate.

Results: Over patients’ lifetimes, smart insulin pen use was associated with improved mean discounted quality-adjusted life expectancy (1.13 quality-adjusted life years) and cost savings (EUR11,091) vs standard care. Improvements in quality-adjusted life expectancy were driven by a lower frequency and delayed onset of complications predicted with the smart insulin pen relative to standard care. Higher treatment costs (due to the higher bolus insulin dose) with the smart insulin pen were offset by the lower cost of complications compared with standard care (Fig).

Conclusion: In this long-term modelling analysis, lifelong use of a smart insulin pen improved clinical outcomes at a lower cost relative to standard care in a T1D population, suggesting that the smart insulin pen represents an efficient use of Swedish public healthcare resources in this patient population.

Background and Aims: The development of diabetes technology is rapid and needs both education and resources to be successfully implemented in diabetes care management. The aims were to identify factors associated with glucose control andcosts.

Methods: In an observational study we evaluated the use of advanced diabetes technology, resource utilization, glycemic control, and costs. The study population was all T1D individuals in the Region Halland in Sweden. The study cohort was followed for 7 years (2013-2019).

Results: Children aged 0-17 years have significantly better glucose control than young adults aged 18-25 years. The mean HbA1c difference between children and young adults was 8 mmol/mol. Significant difference was noted from 6 months after diabetes diagnoses and onwards. Co-morbidities such as ADHD, anxiety, depression, and eating disorders were associated with higher HbA1c. All groups, irrelevant of age and co-morbidity, had positive effect on glucose control after a visit to a dietitian or psychologist. Differences were found between the age groups in terms of more advanced diabetes technology and more frequent visits to a physician in children.

Conclusions: More frequent visits to physicians, dietitians, and psychologists are linked to improved glucose control. Increased resources including access to more advanced technology are required in young adults. A young adult diabetes team could be implemented to mitigate this inequality between age groups found in our study. Increased resources and strategies to improve glucose control, will likely lead to numerous positive effects, reduction of the burden of disease, and reduction of long-term costs in T1D.

Background and Aims: The standard of care for people with type 1 diabetes (T1D) is continually evolving and Automated Insulin Delivery system (AID) systems and continuous glucose monitoring (CGM) are emerging as the standard of care for many individuals with T1D. This study aimed to compare the long-term cost-effec-tiveness of the MiniMedTM 780G system versus MDI+ intermittently scanning CGM in people with T1D across European countries.

Methods: Long-term costs and clinical outcomes were estimated using the CORE Diabetes Model. Clinical data were derived from ADAPT, prospective, multicentre,   open-label, randomized control trial [1]. MiniMedTM 780G system was associated with a reduction in HbA1c of 1.54%, from 9.04% (75 mmol/mol) at baseline to 7.5% (58 mmol/mol) at the end oft he study; isCGM was associated with a reduction in HbA1c of 0.2%1. Quality of life benefits associated with a reduced fear of hypoglycaemia were also applied. Analyses were conducted in Sweden and other countries across Europe.

Results: The MiniMedTM 780G system was associated with aquality-adjusted life-year (QALY) gain of 2.24 with higher overall costs versus MDI+isCGM, leading to an incremental cost-effectiveness ratio of SEK 366,919 (33,757 Euro) per QALY-gained. MiniMedTM 780G system resulted in a lower cumulative incidence of diabetes-related complications. Higher acquisition costs were partially offset by reduced complications costs. Extensive analysis of key drivers and analysis conducted across different countries confirmed the robustness of the results.

Conclusions: Over patient lifetimes, for adults with T1D, the use of the AID system is projected to be cost-effective when compared with MDI+isCGM.

1. Choudhary P, et al. Lancet Diabetes Endocrinol 2022 https://doi.org/10.1016/S2213-8587(22)00212-1.

In an article in the Journal of Diabetes Science and Technology, Arunachalum et al retrospectively analyzed glycemic outcomes, regarding the use of hybrid closed-loop (HCL) systems in individuals with type-1 diabetes (T1D) in the United States in a real-world evidence (RWE) setting. In clinical trials, diabetes technology has shown to improve time in range (TIR) as well as other glucose metrics. In the light of increasing the use of diabetes technology in the T1D population, why do we not see improvement in clinical outcomes? Is it cost-effective to increase the use diabetes technology? Does access to diabetes technology vary in the United States? There is a need for additional clinical studies evaluating the effectiveness of diabetes technology in T1D including health economic aspects.