Glycemic Range Research Articles

578-P: Evaluation of Autonomic Function and Its Relation to Glucose Variability, Insulin Resistance, and Oxidative Stress in Prediabetes

Aim: The present study assesses the relation between cardiovascular autonomic function and some indexes of glycose variability, insulin resistance and oxidative stress in prediabetes. Material and Methods: A total of 50 subjects - 12 males, mean age 55.6±9.7 years, mean BMI 28.4±6.4 kg/m2, divided into 2 groups according to glucose tolerance: 32 subjects with prediabetes and 18 subjects with normal glucose tolerance (NG) were included. Glucose tolerance was assessed by OGTT in accordance with WHO 2006 criteria. Plasma glucose was measured by hexokinase method and immunoreactive insulin - by immuno-chemiluminescence method at fasting, 120-minute and 180-minute during the test; and also oxLDL and 5-Nitrotyrosine - by ELISA method at fasting and 120-minute during the test. Indirect indexes of insulin resistance - HOMA-IR and insulin sensitivity - OGIS were calculated. HbA1c was assessed by immuno-turbidimetric, NGSP certified method. Continuous glucose monitoring was performed with blind sensor for professional use - FreeStyle Libre Pro - for a mean period of 13.6 ± 2.3 days. Results: There was a significantly decreased parasympathetic tone during deep breathing test (p=0.009) and sympathetic tone at rest (p=0.021) and during Valsalva maneuver test (p=0.026) in prediabetes. Sympathetic activity was related to the standard deviation of glycemic excursions in prediabetes (F [1,31]=7.91, p=0.018); whilst parasympathetic activity was related to J-index and % time spent in glycemic range >7.8 mmol/l in prediabetes (F [1,31]=7.20, p=0.004) and with CONGA index in NGT (F [1,18]=6.75, p=0.025). No significant correlation between autonomic tone and the assessed markers of insulin resistance and oxidative stress was established. Conclusions: The autonomic tone has been found to be declined in prediabetes. Glucose variability is probably an additional risk factor for autonomic dysfunction at these early stages of glucose intolerance. Disclosure R. Dimova: None. N. Chakarova: None. G. Kirilov: None. G. Grozeva: None. T. Tankova: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Merck Sharp & Dohme Corp., Sanofi. Speaker's Bureau; Self; Lilly Diabetes, Novo Nordisk A/S, Servier.

Flash Continuous Glucose Monitoring: Implications for Use of Continuous Data in Daily Diabetes Management.

Full realization of the benefits of continuous glucose monitoring (CGM) depends on addressing barriers such as cost, accuracy, burdens of daily use, and uncertainty about applying the data. Lack of systematic education has also hampered widespread adoption among patients and health care professionals. This article describes the practical application of an affordable and intuitive category of CGM called "flash" that requires users to scan the sensor with a handheld reader for on-demand access to continuous data. The data may be used for in-the-moment therapy adjustment, retrospective review of glucose patterns, and observation of glucose trending in response to behavior. Higher rates of flash CGM scanning have been associated with increased time in the glycemic target range and reduced time in hyper- and hypoglycemia. Growing interest in this technology suggests new opportunities for helping more patients incorporate CGM into their daily self-care.

A View Beyond HbA1c: Role of Continuous Glucose Monitoring.

Hemoglobin A1C (HbA1c) is used as an index of average blood glucose measurement over a period of months and is a mainstay of blood glucose monitoring. This metric is easy to measure and relatively inexpensive to obtain, and it predicts diabetes-related microvascular complications. However, HbA1c provides only an approximate measure of glucose control; it does not address short-term glycemic variability (GV) or hypoglycemic events. Continuous glucose monitoring (CGM) is a tool which helps clinicians and people with diabetes to overcome the limitations of HbA1c in diabetes management. Time spent in the glycemic target range and time spent in hypoglycemia are the main CGM metrics that provide a more personalized approach to diabetes management. Moreover, the glucose management indicator (GMI), which calculates an approximate HbA1c level based on the average CGM-driven glucose level, facilitates individual decision-making when the laboratory-measured HbA1c and estimated HbA1c are discordant. GV, on the other hand, is a measure of swings in blood glucose levels over hours or days and may contribute to diabetes-related complications. In addition, addressing GV is a major challenge during the optimization of glycemia. The degree of GV is associated with the frequency, duration, and severity of the hypoglycemic events. Many factors affect GV in a patient, including lifestyle, diet, the presence of comorbidities, and diabetes therapy. Recent evidence supports the use of some glucose-lowering agents to improve GV, such as the new ultra-long acting insulin analogs, as these agents have a smoother pharmacodynamic profile and improve glycemic control with fewer fluctuations and fewer nocturnal hypoglycemic events. These newer glucose-lowering agents (such as incretin hormones or sodium–glucose cotransporter 2 inhibitors) can also reduce the degree of GV. However, randomized trials are needed to evaluate the effect of GV on important diabetes outcomes. In this review, we discuss the role of HbA1c as a measure of glycemic control and its limitations. We also explore additional glycemic metrics, with a focus on time (duration) in glucose target range, time (duration) in hypoglycemia, GV, GMI, and their correlation with clinical outcomes.

SAT-LB025 Analysis of Benefit/Risk in the Subgroup of Patients with BMI of >=27 Kg/m2 in the Dapagliflozin DEPICT-1 and -2 Trials in Type 1 Diabetes

Sodium glucose co-transporter-2 inhibitor (SGLT-2i) adjunct therapies have been studied in phase 3 programs for use in people with type 1 diabetes (T1D) and are currently under regulatory review for approval in adults in North America, Europe, and Japan. The DEPICT-1 and -2 programs assessed dapagliflozin (DAPA) 5 and 10 mg in adults with T1D. Treatment with DAPA in people with T1D resulted in important benefits with a manageable risk profile, though there was a higher incidence of events adjudicated as definite DKA in those receiving DAPA. During the 52-week study period, in the total population, the proportion of patients with events adjudicated as definite DKA events was 4.0% and 1.1% in the DAPA 5 mg and PBO groups, with incidence rates of 4.6 and 1.3 per 100 patient years, respectively. However, in the subgroup of patients with BMI ≥27 kg/m², the proportion of patients with events adjudicated as definite DKA was reduced to 1.8% and 1.0%, with incidence rates per 100 patient-years of 1.9 and 1.2 in the DAPA 5 mg and PBO groups, respectively. Efficacy endpoint results for the subgroup with BMI ≥27 kg/m2 were similar to the overall population. After 24 weeks of treatment, HbA1c decreased from a baseline mean of 8.43 to 7.98% in the 5 mg group vs. 8.40 to 8.38% for PBO, with a PBO-corrected difference of –0.43% (95% CI –0.55, –0.31). Mean insulin dose decreased from 72.07 to 64.65 IU in the 5 mg group vs. 70.90 to 70.37 IU for PBO, with a PBO-corrected difference of –10.24 IU (95% CI –13.52, –6.84). Mean body weight decreased from 91.22 to 88.54 kg in the 5 mg group vs. 92.89 to 93.15 kg for PBO, with a PBO-corrected difference of –2.89 kg (95% CI –3.43, –2.36). As assessed by continuous glucose monitoring, mean interstitial glucose decreased from 192.52 to 176.19 mg/dL in the 5 mg group vs. 191.25 to 192.38 mg/dL for PBO, with a PBO-corrected difference of –16.81 mg/dL (95% CI –21.37, –12.25). Mean amplitude of glycemic excursions, a measure of glucose variability, decreased from 169.10 to 148.78 mg/dL in the 5 mg group vs. 164.94 to 163.71 mg/dL for PBO, with a PBO-corrected difference of –17.48 mg/dL (95% CI –22.28, –12.67). Mean time in the target glycemic range (glucose >70 and <180 mg/dL) increased from 44.17% to 53.65% in the 5 mg group vs. 44.67% to 44.44% for PBO, with a PBO-corrected difference of +9.69% (95% CI +7.61, +11.77) without increasing time in a hypoglycemic state (glucose <70 mg/dL). The proportion of patients achieving a reduction in HbA1c ≥0.5% without experiencing severe hypoglycemia was 47.2% in the 5 mg group vs. 21.3% for PBO, with 3.52 (95% CI 2.39, 5.21) greater odds of achieving this endpoint in those receiving 5 mg. In summary, while the benefit/risk profile of DAPA is favorable in the entire T1D study population, in this post-hoc analysis the benefit/risk appeared to be further enhanced in the subgroup of patients with BMI ≥27 kg/m² receiving DAPA 5 mg. Funded by AstraZeneca. Unless otherwise noted, all abstracts presented at ENDO are embargoed until the date and time of presentation. For oral presentations, the abstracts are embargoed until the session begins. s presented at a news conference are embargoed until the date and time of the news conference. The Endocrine Society reserves the right to lift the embargo on specific abstracts that are selected for promotion prior to or during ENDO.

Sodium-glucose co-transporter inhibitors as adjunctive treatment to insulin in type 1 diabetes: A review of randomized controlled trials.

Many patients with type 1 diabetes (T1D) struggle to achieve glycaemic control and experience significant fluctuations in glucose concentrations, despite insulin treatment. Sodium‐glucose co‐transporter (SGLT)‐2 inhibitors and dual SGLT‐1/2 inhibitors increase glucose elimination via the kidneys and reduce hyperglycaemia via insulin‐independent mechanisms. This review examines available efficacy and safety data for these agents under investigation as adjunctive therapy for T1D. Across randomized trials of up to 52 weeks, SGLT‐2 inhibitors or SGLT‐1/2 inhibitors as an adjunct to insulin demonstrated significant reductions in glycated haemoglobin, glucose exposure, and measures of glycaemic variability, as well as increased time in the target glycaemic range, compared with placebo. Non‐glycaemic benefits included reductions in body weight and insulin doses, as well as improvements in some cardiovascular risk factors and treatment satisfaction. SGLT‐2 inhibitors and SGLT‐1/2 inhibitors were associated with similar rates of hypoglycaemia but a higher incidence of genitourinary infections, compared with placebo. Diabetic ketoacidosis occurred more often with SGLT‐2 inhibitors and SGLT‐1/2 inhibitors vs placebo, although the incidence was generally low. Risk mitigation strategies in light of clinical trial data are also discussed. Positive data from randomized controlled trials of the SGLT‐2 inhibitor dapagliflozin have led to the approval of dapagliflozin as an adjunct to insulin in adults with T1D having body mass index ≥27 kg/m2 in whom insulin does not provide adequate glycaemic control in Europe and to approval as an adjunct to insulin for adults with T1D in Japan.

Impact of professional continuous glucose monitoring by clinical pharmacists in an ambulatory care setting

IntroductionUse of continuous glucose monitoring (CGM) in the management of diabetes continues to grow. Despite professional‐use CGM serving as an opportunity for clinical pharmacists to improve care and generate revenue for managing patients with diabetes, there is limited literature describing their involvement with this technology.ObjectivesThe primary objective was to determine if professional CGM improves measures of glycemic control including percentage of time in target glycemic range, change in estimated average interstitial glucose, and change in hemoglobin A1c (HbA1c) from baseline to postintervention. Secondary objectives were to evaluate revenue generation measured by reimbursement rates for Current Procedural Terminology (CPT) codes 95250 and 95251 and utilization of clinical pharmacist services related to professional CGM.MethodsThis was a quasi‐experimental, retrospective, pre‐post intervention analysis. Patients that had a professional CGM placed and more than 24 hours of data interpreted by a clinical pharmacist were included for analysis. All clinical data were extracted from patients' electronic medical records, while reimbursement data were provided by the organization's billing department.ResultsTwenty‐nine patients that received professional CGM were included for analysis. Patients' mean baseline and post‐intervention HbA1c were 9.0% and 8.3%, respectively (P = 0.156). There was no difference in the mean percentage of time in target glycemic range (P = 0.966) or mean estimated average interstitial glucose (P = 0.779) from baseline to post‐intervention. Patients met with clinical pharmacists for a total of 68 follow‐up visits during their individual 14‐day wear periods, 14 of which were unanticipated visits. The mean payment amount for CPT code 95250 was $126.87, while $39.17 was received for 95251.ConclusionsClinical pharmacist‐led professional CGM did not demonstrate a statistically significant difference in measures of glycemic control; however, the 14‐day device wear period provided opportunities for optimization of antihyperglycemic therapy that resulted in a clinically significant reduction in HbA1c and reimbursement for clinical pharmacy services.

Fine-Scale Online Evaluation of Glycemic Control Performance Based on Temporal Feature Analysis

Traditional glycemic control metrics ignore the time axis of continuous glucose monitoring (CGM) data which thus cannot further elaborate control performance of glucose within the normal region. They cannot be implemented in real time either, which may lead to incomplete assessment. A fine-scale assessment method of glycemic control performance is proposed for glucose within the normal range. The glucose control performance within the normal glycemic range is divided into three categories including accurate control, benign control, and suboptimal control by analyzing the temporal changing speed of the CGM data. First, the temporal slowness is extracted based on slow feature analysis (SFA) to capture the temporal variations of CGM data considering the time component. Then a monitoring statistic is developed for the temporal slowness. Finally, the control assessment grid analysis is proposed for both qualitative analysis and quantitative analysis. The proposed method is investigated using data collected from 10 clinical adults with type 1 diabetes. The results show that the proposed method can evaluate the control performance in real time. The proposed metric can be regarded as a good auxiliary metric to improve the control performance assessment for glucose within the normal region, in which the percentages of points falling in accurate control zone A is about 43.80% for the 10 subjects. It is the first time that the concept of online fine-scale assessment for glycemic control performance is proposed which extracts the temporal slow features by quantitative analysis of the temporal distribution of CGM data.

Impact of physical exercise on sensor performance of the FreeStyle Libre intermittently viewed continuous glucose monitoring system in people with Type 1 diabetes: a randomized crossover trial.

To evaluate the sensor performance of the FreeStyle Libre intermittently viewed continuous glucose monitoring system using reference blood glucose levels during moderate-intensity exercise while on either full or reduced basal insulin dose in people with Type 1 diabetes. Ten participants with Type 1 diabetes [four women, mean ± sd age 31.4 ± 9.0 years, BMI 25.5±3.8 kg/m2 , HbA1c 55±7 mmol/mol (7.2±0.6%)] exercised on a cycle ergometer for 55 min at a moderate intensity for 5 consecutive days at the clinical research facility, while receiving either their usual or a 75% basal insulin dose. After a 4-week washout period, participants performed the second exercise period having switched to the alternative basal insulin dose. During exercise, reference capillary blood glucose values were analysed using the fully enzymatic-amperometric method and compared with the interstitial glucose values obtained. Intermittently viewed continuous glucose monitoring accuracy was analysed according to median (interquartile range) absolute relative difference, and Clarke error grid and Bland-Altman analysis for overall glucose levels during exercise, stratified by glycaemic range and basal insulin dosing scheme (P<0.05). A total of 845 glucose values were available during exercise to evaluate intermittently viewed continuous glucose monitoring sensor performance. The median (interquartile range) absolute relative difference between the reference values and those obtained by the sensor across the glycaemic range overall was 22 (13.9-29.7)%, and was 36.3 (24.2-45.2)% during hypoglycaemia, 22.8 (14.6-30.6)% during euglycaemia and 15.4 (9-21)% during hyperglycaemia. Usual basal insulin dose was associated with a worse sensor performance during exercise compared with the reduced (75%) basal insulin dose [median (interquartile range) absolute relative difference: 23.7 (17.2-30.7)% vs 20.5 (12-28.1)%; P<0.001). The intermittently viewed continuous glucose monitoring sensor showed diminished accuracy during exercise. Absolute glucose readings derived from the sensor should be used cautiously and need confirmation by additional finger-prick blood glucose measurements.

Glycemic Control During Enteral Tube Feeding in Patients with Diabetes Mellitus.

IntroductionAchieving good glycemic control during enteral tube feeding in patients with diabetes mellitus can be difficult. National guidelines emphasize the need for the early involvement of the dietitian and diabetes specialist nurse, regular capillary blood glucose (CBG) monitoring, and the avoidance of hypoglycemic events. We aimed to assess glycemic control in patients with diabetes during enteral tube feeding.Materials and methodsA retrospective study, involving patients with diabetes who had enteral tube feeding during their hospital stay from January to December 2016, was performed. Data included feed carbohydrate content, infusion rate and duration, involvement of dietitian and inpatient diabetes specialist nurse, capillary blood glucose monitoring, and documentation of hypoglycemic events.ResultsThere were 40 patient episodes. Mean (range) age: 67 (29-94) years, 60% were male, 97.5% had type 2 diabetes, and 60% were on oral hypoglycemic agents prior to admission. The average feed carbohydrate content was 14.6 g/dL and the average feed rate was 73 ml/hour. Dietitians and diabetes inpatient specialist nurses (DISNs) were involved in 100% and 75% of cases, respectively. During enteral tube feeding, capillary blood glucose was controlled using metformin, subcutaneous insulin, or intravenous insulin in 30%, 42.5%, and 15% of cases, respectively. Capillary blood glucose was checked four to six hourly in 100% of cases. The target capillary blood glucose range (6-12 mmol/L) was achieved in 40% of cases. The minimum capillary blood glucose value (median (interquartile range)) was 4.9 (3.9-6.2) mmol/L and the maximum value was 15.1 (11.9-18.8) mmol/L. Using these lower and upper quartile values for minimum and maximum values, respectively, revealed that 57.5% of patients had a capillary blood glucose range of 3.9-18.8 mmol/L. Two patients had hypoglycemic events requiring treatment.ConclusionsThis study demonstrated that despite adequate adherence to our trust's guidelines for glycemic control during enteral tube feeding in patients with diabetes, the target glycemic range was achieved in 40% of cases. The importance of the early involvement of the diabetes inpatient specialist nurse cannot be overemphasized. Early initiation and proactive (daily) dose up-titration of insulin are required to improve glycemic control during enteral tube feeding. A national audit tool for glycemic control and mortality data during enteral tube feeding is required.

The association between long-term glycaemic control, glycaemic gap and neurological outcome of in-hospital cardiac arrest in diabetics: A retrospective cohort study

AimResuscitation guidelines do not recommend a target blood glucose (BG) level specifically tailored for diabetics experiencing an in-hospital cardiac arrest (IHCA). The glycosylated haemoglobin (HbA1c) level may be associated with neurological prognosis and used to identify the optimal BG level for diabetic IHCA patients. MethodsThis study was a retrospective study in a single medical centre. Patients with an IHCA between 2006 and 2015 were screened. The estimated average glucose (eAG) level was converted from the HbA1c level measured within three months prior to the IHCA. The minimum glycaemic gap was calculated from the post-resuscitation minimum BG level minus the eAG level. ResultsA total of 141 patients were included in this study. The mean HbA1c was 7.2% (corresponding eAG: 160.2 mg/dL [8.9 mmol/L]). Multivariable logistic regression analysis indicated an eAG level of less than 196 mg/dL (10.9 mmol/L; corresponding HbA1c: 8.5%) was positively associated with a favourable neurological outcome at hospital discharge (odds ratio [OR]: 5.12, 95% confidence interval [CI]: 1.11–23.70; p-value = 0.04). An absolute minimum glycaemic gap of less than 70 mg/dL (3.9 mmol/L) was also positively associated with a favourable neurological outcome (OR: 5.41, 95% CI: 1.41–20.78; p-value = 0.01). ConclusionFor diabetic patients, poor long-term glycaemic control correlated with worse neurological recovery following an IHCA. The HbA1c-derived average BG level could be used as a reference point for glycaemic management during the early stage of post-cardiac arrest syndrome. The glycaemic gap could be used to identify the optimal glycaemic range around the reference point.

Open Source Closed-Loop Insulin Delivery Systems: A Clash of Cultures or Merging of Diverse Approaches?

Biomedical outcomes for people with diabetes remain suboptimal for many. Psychosocial care in diabetes does not fare any better. "Artificial pancreas" (also known as "closed-loop" and "automated insulin delivery") systems present a promising therapeutic option for people with diabetes (PWD)-simultaneously improving glycemic outcomes, reducing the burden of self-management, and improving health-related quality of life. In recent years there has emerged a growing movement of PWD innovators rallying behind the mantra #WeAreNotWaiting, developing "do-it-yourself artificial pancreas systems (DIY APS)." Self-reported results by DIY APS users show improved metabolic outcomes such as impressive stability of glucose profiles, significant reduction of A1c, and more time within their glycemic target range. However, the benefits remain unclear for the broader population of PWD beyond these highly engaged, highly tech-savvy users willing and able to engage in the demands of building and maintaining their DIY APS. We discuss the challenges faced by key stakeholder groups in terms of potential collaboration and open debate of these challenges.

Parenting Intervention to Improve Nutrition and Physical Activity for Preschoolers with Type 1 Diabetes: A Feasibility Study

ObjectiveThis study reports the feasibility and acceptability of a healthy eating and physical-activity-focused behavioral intervention for parents of young children with type 1 diabetes (T1D). MethodsTen parents of young children (age 2-5 years) with T1D enrolled. The intervention included six behavioral sessions (five by telephone), diabetes nursing consultation, parent coach contact, text messages, and a study website. Analyses explored feasibility, acceptability, and preliminary findings. ResultsThere was evidence of high acceptability (mean parent satisfaction = 1.11, very satisfied). Although most participants completed all of the assessments, there were some barriers to data collection devices. The number of participants within the American Diabetes Association recommended glycemic range doubled; there was no significant change in hemoglobin A1c, diet, or physical activity. ConclusionThere was evidence of feasibility and acceptability and initial evidence of change in hypothesized directions. Minor changes were made for the larger randomized controlled trial.

Open Artificial Pancreas System Reduced Hypoglycemia and Improved Glycemic Control in Patients with Type 1 Diabetes

Background and Aims: Artificial pancreas combines continuous glucose monitoring (CGM) with insulin pump by using a control algorithm to direct insulin delivery. Although several control algorithms have been developed, the control algorithms are beyond reach of most of diabetic patients in need. The Open Artificial Pancreas System project (openAPS) is an open control algorithm for artificial pancreas, which is available worldwide in type 1 diabetes (T1D). Here, we present several interesting clinical experiences using openAPS. Materials and Methods: Twenty T1D patients using openAPS, CGM (Dexcom G4®), and insulin pump (Sooil, Dana R®) were studied. Normal glycemic range was set as 80∼180 mg/dl. Results: Mean age was 11.9 ± 6.9 years and 10 patients were male. Median openAPS duration was 180 (30-240) days. By the use of openAPS, CGM analysis showed significant decrease in A1C (6.8 ± 1.0% to 6.3 ± 0.7%, p&amp;lt;0.001), increase in percent time in normal glycemic range (70.1 ± 16.4% to 83.3 ± 10.1%, p&amp;lt;0.001), decrease in percent time in high glycemic range (24.7 ± 16.5% to 13.3 ± 9.4%, p&amp;lt;0.001), and decrease in percent time in low glycemic range (5.1 ± 3.3% to 3.4 ± 2.3%, p= 0.004), respectively. There was no significant side effect due to using openAPS. Conclusion: Open artificial pancreas system reduced hypoglycemia and improved glycemic control in patients with T1D. Disclosure S. Choi: Stock/Shareholder; Self; SOOIL Developments Co., Ltd.. E. Hong: None. Y. Noh: None.

Continuous Glucose Monitoring Glycemic Variability Profiles Are More Favorable for Dapagliflozin plus Saxagliptin Compared with Insulin Glargine in Type 2 Diabetes

Glycemic excursions, variability, and hypoglycemia may impact therapeutic effectiveness even when A1C reduction is comparable between therapies. We analyzed masked continuous glucose monitoring (CGM) data from patients with type 2 diabetes (T2D) inadequately controlled on metformin ± sulfonylurea during an international randomized, 24-week, open-label, non-inferiority trial of dapagliflozin plus saxagliptin add-on (DAPA + SAXA, n = 141) compared to titrated insulin glargine add-on (INS, n = 142). Six-day CGM (288 readings/day), A1C and patient-reported outcomes (PRO) were measured at baseline and Weeks 2, 12 and 24. At baseline, patients had mean ± SD age = 55.2 ± 9.4 years; A1C = 9.1 ± 1.1%; diabetes duration = 9.8 ± 6.1 years; 54.1% male. Using linear mixed models, mean ± SE changes in A1C for DAPA + SAXA vs. INS at Week 24 were comparable (−1.75 ± 0.09% vs. −1.63 ± 0.09%, P = 0.33). However, CGM summary analytics were more favorable for DAPA + SAXA: more time in glycemic range, lower glucose variability and less time with nocturnal hypoglycemia (Table 1a). Improvements in CGM at Week 24 correlated with better patient satisfaction and quality of life (n = 225, Table 1b). This analysis of CGM data showed superior diurnal glycemic profiles associated with better PRO for add-on DAPA + SAXA vs. INS in patients with T2D, even though A1C changes were comparable.Table 1a: Treatment Differences in CGM Summary Analytics --- Table 1b: Correlations of CGM with PROTable 1a. CGM Change from Baseline [Mean (SE)]DAPA + SAXA + MetforminINS + MetforminDifferenceP-valueTable 1b. Correlation of Change in CGM with Change in PRO at Week 24 P-valueMean 24-hr glucose mg/dL - Week 2−48.53 (2.51)−28.54 (2.51)−19.99 (3.55)&amp;lt; 0.0001Mean 24-hr glucose mg/dLSatisfaction&amp;lt;0.0001% time glucose ≤ 70 mg/dL - Week 120.52 (0.38)2.29 (0.39)−1.77 (0.54)0.001% time &amp;gt; 70 &amp; ≤ 180 mg/dLSatisfaction&amp;lt;0.0001% time glucose ≤ 70 mg/dL 12 to 6 am - Week 240.60 (0.53)2.66 (0.53)−2.(0.75)0.007Glycemic Risk Assessment Diabetes Equation (GRADE)Satisfaction&amp;lt;0.0001% time glucose &amp;gt; 70 and ≤ 180 mg/dL - Week 2434.28 (1.89)28.50 (1.91)5.78 (2.69)0.033AUC &amp;gt; 180 mg/dLSatisfaction0.001Mean Amplitude Glycemic Excursion (MAGE) mg/dL - Week 24−12.73 (2.26)−5.01 (2.28)−7.72 (3.21)0.017High Blood Glucose Index (HBGI)Satisfaction0.001Within-Day SD (mg/dL) - Week 24−5.93 (0.85)−0.50 (0.86)−5.43 (1.21)&amp;lt; 0.0001Within-Day SD (mg/dL)QOL Weight Interference0.001Low Blood Glucose Index (LBGI) - Week 240.20 (0.05)0.41 (0.05)−0.20 (0.08)0.008Within-Day SD (mg/dL)QOL Composite0.020 Disclosure D.C. Simonson: Advisory Panel; Self; GI Windows, Inc.. Stock/Shareholder; Self; GI Windows, Inc.. Stock/Shareholder; Spouse/Partner; Phase V Technologies, Inc. T. Vilsbøll: None. E. Ekholm: None. E.K. Johnsson: Employee; Self; AstraZeneca. M.A. Testa: Stock/Shareholder; Spouse/Partner; GI Windows, Inc.. Stock/Shareholder; Self; Phase V Technologies, Inc.. S. Jabbour: None. M. Lind: Consultant; Self; AstraZeneca. Research Support; Self; AstraZeneca. Consultant; Self; Novo Nordisk Inc.. Advisory Panel; Self; Novo Nordisk Inc.. Research Support; Self; Novo Nordisk Inc.. Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc.. Consultant; Self; Eli Lilly and Company. Advisory Panel; Self; MSD K.K.. Research Support; Self; Pfizer Inc.. Consultant; Self; Medtronic.

Glucose Variables in T1D Studies with Dapagliflozin—Pooled Analysis of Continuous Glucose Monitoring Data from DEPICT-1 and 2

Improved glycemic control and weight loss, without increased hypoglycemia, were demonstrated in two short-term, 24-week, Phase 3 studies of the SGLT2 inhibitor dapagliflozin (DAPA) as adjunct to adjustable insulin in patients with inadequately controlled T1D (DEPICT-1 and 2). In this post-hoc analysis we pooled continuous glucose monitoring (CGM) data at baseline and week 24 from both studies. In total, 1591 patients were included (DAPA 5 mg N=530; DAPA 10 mg N=529; placebo N=532). Baseline characteristics were comparable between the study groups. DAPA treatment significantly reduced mean interstitial glucose, mean amplitude of glucose excursions (MAGE), and postprandial glucose, while expanding the time in glycemic target range (&amp;gt;70 mg/dL to ≤180 mg/dL)(Table). In addition, DAPA treatment did not increase the percent of glucose readings ≤70 mg/dL or ≤54 mg/dL, or the percent of readings ≤70 mg/dL in the nocturnal period (00:00 to 05:59). These results demonstrate that DAPA as adjunct to insulin in patients with T1D reduced glycemic variability without increasing time in the hypoglycemia range.Table. Pooled CGM parametersCGM parameterTreatment armMean baseline value (SD)Mean Week 24 value (SD)Adjusted mean change from baseline (SE)Difference vs. placebo (95% CI)Mean 24-hour glucose readings (mg/dL)DAPA 5 mg193.00 (28.78)179.93 (32.41)-8.40 (1.30)−15.48 (−18.82, −12.13)DAPA 10 mg191.00 (28.11)174.87 (26.59)–11.82 (1.32)−18.90 (−22.25, − 15.54)Placebo191.64 (29.04)194.34 (32.62)7.07 (1.33)MAGE (mg/dL)DAPA 5 mg170.53 (30.55)154.67 (34.21)–12.48 (1.37)−13.36 (−16.89, −9.83)DAPA 10 mg171.01 (31.59)154.52 (34.52)–13.06 (1.39)−13.94 (−17.48, −10.40)Placebo168.99 (31.46)166.94 (31.62)0.88 (1.40)Percent of 24-hour glucose values within &amp;gt;70 – ≤180 mg/dL (%)DAPA 5 mg43.38 (12.22)51.69 (14.49)6.48 (0.60)9.07 (7.55, 10.59)DAPA 10 mg43.98 (12.32)53.89 (13.26)8.08 (0.60)10.67 (9.15, 12.20)Placebo43.66 (12.64)43.10 (13.97)–2.59 (0.61)Percent of glucose readings ≤70 mg/dL over 24-hours (%)DAPA 5 mg4.96 (4.64)4.85 (4.89)–0.43 (0.21)0.06 (–0.47, 0.59)DAPA 10 mg5.30 (4.90)5.10 (4.44)–0.33 (0.21)0.16 (–0.38, 0.69)Placebo5.14 (5.56)4.83 (4.72)–0.49 (0.21)Percent of glucose readings ≤54 mg/dL (%)DAPA 5 mg2.22 (2.87)2.02 (2.83)–0.32 (0.13)–0.14 (–0.47, 0.19)DAPA 10 mg2.34 (3.13)2.05 (2.63)–0.34 (0.13)–0.16 (–0.48, 0.17)Placebo2.28 (3.82)2.15 (3.15)–0.19 (0.13)Postprandial glucose readings by CGM (mg/dL)DAPA 5 mg199.42 (40.51)192.97 (43.90)–0.85 (1.99)–8.55 (–13.70, –3.41)DAPA 10 mg196.61 (38.57)186.93 (36.21)–5.05 (2.02)–12.76 (–17.93, –7.58)Placebo197.35 (42.32)199.68 (44.36)7.70 (2.03)Percent of nocturnal (00:00–05:59) glucose values ≤70 mg/dL (%)DAPA 5 mg5.94 (7.64)5.95 (8.26)–0.38 (0.36)–0.15 (–1.07, 0.78)DAPA 10 mg5.52 (7.22)6.00 (7.60)–0.18 (0.37)0.06 (–0.87, 0.99)Placebo5.83 (7.74)5.85 (7.64)–0.24 (0.37) Disclosure C. Mathieu: Research Support; Self; Novo Nordisk A/S. Advisory Panel; Self; Novo Nordisk A/S. Speaker's Bureau; Self; Novo Nordisk A/S. Research Support; Self; Sanofi. Speaker's Bureau; Self; Sanofi. Advisory Panel; Self; Sanofi. Research Support; Self; Merck Sharp &amp; Dohme Corp.. Speaker's Bureau; Self; Merck Sharp &amp; Dohme Corp.. Advisory Panel; Self; Merck Sharp &amp; Dohme Corp.. Research Support; Self; Eli Lilly and Company. Speaker's Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Novartis AG. Speaker's Bureau; Self; Novartis AG. Advisory Panel; Self; Novartis AG, Bristol-Myers Squibb Company. Speaker's Bureau; Self; AstraZeneca. Advisory Panel; Self; AstraZeneca, Pfizer Inc., Janssen Pharmaceuticals, Inc., Boehringer Ingelheim GmbH. Speaker's Bureau; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Hanmi Pharmaceutical. Research Support; Self; Roche Diagnostics Corporation. Advisory Panel; Self; Roche Diagnostics Corporation. Research Support; Self; Medtronic. Advisory Panel; Self; Medtronic, MannKind Corporation. Research Support; Self; Intrexon. Advisory Panel; Self; Intrexon, Dianax, UCB, Inc.. Research Support; Self; Abbott. P. Dandona: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca. Research Support; Self; AstraZeneca. M. Phillip: Other Relationship; Self; Sanofi, Medtronic MiniMed, Inc., Novo Nordisk A/S. Speaker's Bureau; Self; Eli Lilly and Company. Research Support; Self; Merck &amp; Co., Inc., Bristol-Myers Squibb Company, Kamada, Lexicon Pharmaceuticals, Inc.. Other Relationship; Self; DreaMed Diabetes, Ltd.. Speaker's Bureau; Self; Abbott. T. Oron: None. L. Hansen: Employee; Self; MedImmune. F.A. Thoren: Employee; Self; AstraZeneca. J. Xu: Employee; Self; AstraZeneca. A. Langkilde: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca.

Continuous Glucose Monitoring Guided Assessment of Concentrated U200 r-DNA Human Premix 30/70 Insulin

Objective: With increasing longevity, obesity and late insulin initiation being common in type 2 diabetes mellitus (T2DM), concentrated insulin formulations could evolve as a choice in a subgroup of these subjects. Globally, 30% of T2DM use more than 60 units of insulin daily. U200 (r-DNA Human Insulin Premix 30/70 200IU/mL) insulin has been developed to provide sustained glucose-lowering effect and enhanced compliance without increasing the risk of hypoglycaemia. This study was conducted to assess the efficacy and safety of U200 insulin in terms of 24-hour glucose profiling using Continuous Glucose Monitoring (CGM) in T2DM. Methods: Prospective, single-arm, open-label, multi-centre study evaluating glycaemic profile in 60 adults with T2DM administered with U200 was undertaken using CGM (iPro2, Medtronic USA) for 7 days. The endpoints assessed were time spent within acceptable glycaemic range, mean amplitude of glucose excursions (MAGE), and the duration and frequency of hypo/hyper-glycaemic periods. Results: Mean age of subjects was 55.47 ± 9.68 (26-70) years. Mean blood glucose level was 151±23.93 (105.73-231.69) mg/dl. Blood glucose levels were within the normal limits (70-150 mg/dL) for 62.35% of time. Average numbers of hyperglycaemic and hypoglycaemic episodes observed were 2.64 and 0.22 with mean duration of 165.16 and 27.35 minutes respectively. MAGE was 77.24±22.50 (37.47-153.00) mg/dl. Medication adherence rate was 100% with no adverse event other than minor hypoglycemia reported. Discussion and Conclusion: CGM provides 24-hour profiling of glycometabolic control and identifies fluctuations. U-200 premixed insulin demonstrated low intraday variability as measured by MAGE of 77.24 mg/dl and patients spending 62.35% of time within normal glucose limits, allowing for a more constant action with less frequent hypoglycaemic or hyperglycaemic excursions. U200 human premix insulin is effective, safe and well tolerated in T2DM patients. Disclosure H. Thacker: Research Support; Self; Wockhardt LTD. J. Kesavadev: Advisory Panel; Self; Novo Nordisk India Private Limited. Speaker's Bureau; Self; Novo Nordisk India Private Limited. Advisory Panel; Self; Medtronic. Speaker's Bureau; Self; Medtronic. Advisory Panel; Self; Sanofi. Speaker's Bureau; Self; Sanofi. Research Support; Self; MSD. Speaker's Bureau; Self; MSD. Advisory Panel; Self; AstraZeneca. Speaker's Bureau; Self; AstraZeneca. G. Singh: Employee; Self; Wockhardt LTD. K. Shukla: Employee; Self; Wockhardt Ltd. G. Puppalwar: Employee; Self; Wockhardt Limited, India. R. Shrimanker: Research Support; Self; Wockhardt LTD.

Efficacy and Safety of Dapagliflozin in Patients with Inadequately Controlled Type 1 Diabetes—DEPICT-2 Study

The DEPICT-2 trial (NCT02460978) evaluated the efficacy and safety of dapagliflozin (DAPA) as add-on to adjustable insulin (INS) in patients (pts) with inadequately controlled T1D (A1c 7.5-10.5%) over 24 weeks. This phase 3 study randomized pts 1:1:1 to DAPA 5 mg (n=271), 10 mg (n=270) or placebo (PBO; n=272) plus INS. INS dose could be adjusted by the investigator according to self-monitored blood glucose readings, local guidance and individual circumstances. At Week 24, DAPA 5 and 10 mg significantly decreased A1c (0.37% and 0.42% reductions in the DAPA 5 mg and 10 mg groups respectively), total daily insulin dose (TDD), and body weight (Table). As measured by masked continuous glucose monitoring (CGM), mean interstitial glucose, mean amplitude of glucose excursion (MAGE) and mean percent of readings within target glycemic range (>70-≤180 mg/dL) vs. PBO were improved. There was an increase in pts who reduced their A1c by ≥0.5% without severe hypoglycemia (odds ratios [95% CI]: 2.71 [1.81, 4.06] and 3.07 [2.05, 4.60] for DAPA 5 and 10 mg respectively). Hypoglycemic events, including severe hypoglycemia were balanced between treatment groups. There were more adjudicated definite diabetic ketoacidosis (DKA) events on DAPA. In conclusion, DAPA vs. PBO as add-on to INS in pts with T1D was well tolerated, improved glycemic control and decreased variability without increasing hypoglycemia but with more DKA events. Disclosure C. Mathieu: Research Support; Self; Novo Nordisk A/S. Advisory Panel; Self; Novo Nordisk A/S. Speaker9s Bureau; Self; Novo Nordisk A/S. Research Support; Self; Sanofi. Speaker9s Bureau; Self; Sanofi. Advisory Panel; Self; Sanofi. Research Support; Self; Merck Sharp & Dohme Corp.. Speaker9s Bureau; Self; Merck Sharp & Dohme Corp.. Advisory Panel; Self; Merck Sharp & Dohme Corp.. Research Support; Self; Eli Lilly and Company. Speaker9s Bureau; Self; Eli Lilly and Company. Advisory Panel; Self; Eli Lilly and Company. Research Support; Self; Novartis AG. Speaker9s Bureau; Self; Novartis AG. Advisory Panel; Self; Novartis AG, Bristol-Myers Squibb Company. Speaker9s Bureau; Self; AstraZeneca. Advisory Panel; Self; AstraZeneca, Pfizer Inc., Janssen Pharmaceuticals, Inc., Boehringer Ingelheim GmbH. Speaker9s Bureau; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Hanmi Pharmaceutical. Research Support; Self; Roche Diagnostics Corporation. Advisory Panel; Self; Roche Diagnostics Corporation. Research Support; Self; Medtronic. Advisory Panel; Self; Medtronic, MannKind Corporation. Research Support; Self; Intrexon. Advisory Panel; Self; Intrexon, Dianax, UCB, Inc.. Research Support; Self; Abbott. P. Dandona: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca. Research Support; Self; AstraZeneca. P. Gillard: None. P.A. Senior: Consultant; Self; Abbott, AstraZeneca. Research Support; Self; AstraZeneca, Prometic Life Sciences Inc., Novo Nordisk Inc., Sanofi. Consultant; Self; Eli Lilly and Company. Speaker9s Bureau; Self; Eli Lilly and Company, Novo Nordisk Inc.. Consultant; Self; Novo Nordisk Inc., Janssen Pharmaceuticals, Inc.. Speaker9s Bureau; Self; Janssen Pharmaceuticals, Inc.. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc.. Speaker9s Bureau; Self; AstraZeneca, Merck & Co., Inc., Abbott. Research Support; Self; Viacyte, Inc.. C. Hasslacher: None. E. Araki: Speaker9s Bureau; Self; Astellas Pharma US, Inc., MSD K.K., Kowa Pharmaceuticals America, Inc., Sanofi, Novo Nordisk Inc.. Research Support; Self; Astellas Pharma US, Inc., MSD K.K., Ono Pharmaceutical Co., Ltd., Shionogi & Co., Ltd., Takeda Pharmaceuticals U.S.A., Inc., Daiichi Sankyo Company, Limited, Nippon Boehringer Ingelheim Co. Ltd., Novartis Pharma K.K., Novo Nordisk Inc., Sanofi, Mitsubishi Tanabe Pharma Corporation, Sumitomo Dainippon Pharma Co., Ltd., Taisho Toyama?Pharmaceutical Co. M. Lind: Consultant; Self; AstraZeneca. Research Support; Self; AstraZeneca. Consultant; Self; Novo Nordisk Inc.. Advisory Panel; Self; Novo Nordisk Inc.. Research Support; Self; Novo Nordisk Inc.. Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc.. Consultant; Self; Eli Lilly and Company. Advisory Panel; Self; MSD K.K.. Research Support; Self; Pfizer Inc.. Consultant; Self; Medtronic. S.C. Bain: Research Support; Self; Novo Nordisk Inc., AstraZeneca. S. Jabbour: None. N. Arya: Employee; Self; AstraZeneca. F.A. Thoren: Employee; Self; AstraZeneca. A. Langkilde: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca.

Development and in silico validation of the PID-algorithm for the artificial pancreas with intraperitoneal insulin delivery

Background: The efficacy of the treatment of type 1 diabetes can be markedly improved using artificial pancreas (AP), which is a technology to automatically control blood glucose levels.&#x0D; Aim: In this paper, we propose the construction of a controller for controlling the automated delivery of insulin in AP based on a proportionalintegralderivative (PID) algorithm using intraperitoneal (IP) insulin delivery.&#x0D; Methods: The project used rapid-acting insulin in the IP space when setting up a PID controller with feedback to ensure the safe and efficient delivery of insulin. The controller was configured to satisfy feedback insulin present in blood. Controller check was performed In Silico using the metabolic simulator UVA|Padova T1DMS on 10 virtual patients.&#x0D; Results: The proposed controller design has time to reach 83% within the glycaemic range of 70140 mg/dl (3.97.8 mmol/l), without time spent in hypoglycaemia.&#x0D; Conclusions: In a future study we plan to test this controller in vivo to evaluate its performance in vivo.

Stepping Up Implementation Project

Introduction (context and problem statement): Most people with type 2 diabetes have their clinical care based in primary care, where evidence-based clinical management can help achieve target levels for glucose levels (glycaemia). However, many people remain out of glycaemic target range, leading to downstream vascular complications. Optimising glycated haemoglobin to minimise risk of long-term complications among people with type 2 diabetes is a global priority. Supporting timely treatment intensification, particularly insulin initiation, through innovation in primary care could have a major impact. Short description of practice change implemented: The project is based on the original Stepping Up randomised trial in general practice which found that with appropriate support and reorientation of roles, General Practitioners and Practice Nurses can manage commencement of insulin as part of routine care of people with Type 2 diabetes in their general practices.(1) For this project, two Registered Nurse Credentialed Diabetes Educators based in community health are trained to undertake mentoring roles supporting Practice Nurses in their areas to play a more active role in the initiation of insulin and other injectable therapies in General Practice. Aim and theory of change: The Stepping Up Implementation project is a community and primary-care led approach to integrated care that is seeking to build the capacity of general practice in the north western Melbourne region to better manage the intensification of diabetes treatment in the community. Our approach to professional behaviour change was informed by Normalisation Process Theory and developed through pilot studies. Targeted population and stakeholders, timeline: The project is a collaboration between North Western Melbourne Primary Health Network, the University of Melbourne Department of General Practice, Merri Health, cohealth, and the Royal Melbourne Hospital. Having commenced in May 2017, the project is seeking to implement the model of care in 20 general practices in inner north western Melbourne by June 2018. Innovation, Impact and outcomes: It is anticipated that enabling primary care practitioners to work to their full scope will make better use of resources and provide patients with more timely local care. Additionally, the role of community health staff will build linkages between general practice and community health. Greater integration driven by this stronger relationship may offer access to a range services offered in community health that is currently elusive to many patients. Conclusion, Sustainability and transferability: There is opportunity to scale-up the number of general practices participating and potential to transfer the project to other community health organisations. As a practice-nurse led model, the enhanced capacity built through mentoring is self-sustaining and utilises existing funding streams available through the Medicare Benefits Scheme. The sustainability of the mentoring element of the project is reliant upon the commitment of funding for the CDE position, which, while not assured, could be justified by the benefits of increased integration. Refrences: 1- Furler J, O’Neal D, Speight J, Manski-Nankervis JA, Gorelik A, Holmes-Truscott E et al. Supporting insulin initiation in type 2 diabetes in primary care: results of the Stepping Up pragmatic cluster randomised controlled clinical trial BMJ 2017; 356 :j783

Personalized Adaptive CBR Bolus Recommender System for Type 1 Diabetes.

Type 1 diabetes mellitus (T1DM) is a chronic disease. Those who have it must administer themselves with insulin to control their blood glucose level. It is difficult to estimate the correct insulin dosage due to the complex glucose metabolism, which can lead to less than optimal blood glucose levels. This paper presents PepperRec, a case-based reasoning (CBR) bolus insulin recommender system capable of dealing with an unrestricted number of situations in which T1DM persons can find themselves. PepperRec considers several factors that affect glucose metabolism, such as data about the physical activity of the user, and can also cope with missing values for these factors. Based on CBR methodology, PepperRec uses new methods to adapt past recommendations to the current state of the user, and retains updated historical patient information to deal with slow and gradual changes in the patient over time (concept drift). The proposed approach is tested using the UVA/PADOVA simulator with 33 virtual subjects and compared with other methods in the literature, and with the default insulin therapy of the simulator. The achieved results demonstrate that PepperRec increases the amount of time the users are in their target glycaemic range, reduces the time spent below it, while maintaining, or even reducing, the time spent above it.