Optimal Insulin Therapy Research Articles

Challenges in Insulin Therapy: Perspectives of Malaysian Diabetes Educators

Background Individuals with type 2 Diabetes Mellitus (T2DM) commonly need insulin therapy as their condition advances. Healthcare providers often face difficulties in managing patients who require insulin treatment. Gaining insight into the views of healthcare professionals on the obstacles encountered by patients using insulin can aid in improving care and developing effective treatment plans. Objective This study explores the views of Malaysian diabetes educators on the barriers faced by patients in using insulin. Methods Semi-structured qualitative interviews were conducted with diabetes educators (n=8) from the northern, central, and southern regions of Malaysia who were involved in diabetes care. We used an interview guide to facilitate the in-depth interviews, which were digitally recorded, transcribed verbatim, and analyzed using a thematic approach. Results Five themes were identified: the occurrence of side effects, the need for better patient education, negative perceptions towards insulin, the financial burden, and the complexity of adherence to treatment. There is a lack of patient education on proper glucose monitoring and how to optimize insulin therapy. The cost of treatment and patient ignorance are highlighted when discussing patient self-monitoring of blood glucose. Diabetes educators recognize that the absence of an effective follow-up mechanism is a major issue, particularly for those patients who fail to maintain consistent appointment schedules. Conclusion This research highlights five significant obstacles to maximizing the effectiveness of insulin therapy. Diabetes educators who identify and address these challenges can empower patients to manage their condition effectively. To overcome systemic barriers, government involvement is needed in creating insulin follow-up initiatives, establishing multidisciplinary teams for diabetes care, empowering education strategies, and providing financial support for self-monitoring.

Clinical decision support system based on artificial intelligence for adjusting insulin pump parameters in children with type 1 diabetes mellitus

BACKGROUND: Widely available diabetes devices (continuous glucose monitoring, insulin pump etc.) generate large amount of data and development of an advanced clinical decision support system (CDSS), able to automatically evaluate and optimize insulin therapy, is relevant.AIM: Development of a mathematical model and an CDSS based on it to optimize insulin therapy in children with type 1 diabetes (T1D) and assessment of the agreement between the recommendations of the CDSS and the physician on insulin pump (IP) parameters: basal profile (BP), carbohydrate ratio (CR), correction factor (СF).MATERIALS AND METHODS: Data from 504 children with T1DM were analyzed over the period of 7875 days. The data included glucose, insulin, food, sex, age, height, weight, diabetes duration and HbA1c. We constructed recurrent neural network (RNN) to predict glucose concentration for 30-120 minutes, an algorithm for optimizing IP settings using prediction results. Next, a software product was developed — a CDSS. To assess the agreement of the recommendations of the CDSS and physicians, retrospective data from 40 remote telemedicine consultations of 40 patients with T1D (median age 11.6 years [7; 15]) were used and 960 points of possible adjustments were analyzed. Three degrees of agreement have been introduced: complete agreement, partial agreement, and complete disagreement. The magnitude of the adjustments was also analyzed.RESULTS: The accuracy of glycemic predictions was better or comparable with other similar models. The assessment of agreement for BP, CR and CF, according to the Kappa index, showed slight and weak agreement. The frequency of complete agreement between recommendations for adjusting the ongoing IP therapy between the CDSS and physicians is 37.5–53.8%, and complete inconsistency is 4.5–17.4%. From a clinical point of view, consistency in the frequency of occurrence of the indicator is more important. There were no differences in median IP settings between the CDSS and physicians.CONCLUSION: The CDSS has an acceptable accuracy of glycemic predictions. The CDSS and physicians provide comparable recommendations regarding CSII parameters.

The Rate of Insulin use and Suboptimal Glycemic Control among Egyptian Patients with T2DM: Cohort Analysis of Eighth Wave of the International Diabetes Management Practices Study (IDMPS).

The International Diabetes Management Practices Study (IDMPS) is an international annual survey aiming to study and characterize the current standards of care for managing DM in developing countries. In Egypt, DM represents a substantial burden on the healthcare system, with an estimated 10.9 million patients, ranking it 10th amongst countries with the highest prevalence of DM. Previous studies showed that to maintain safety and achieve treatment goals among diabetic patients, optimal insulin therapy should be selected individually based on the patient's needs. We reported the proportion of Egyptian T2DM patients on insulin therapy who participated in the eighth wave of the IDMPS. The 2018 IDMPS wave consisted of cross-sectional and longitudinal phases and aimed to evaluate the proportion of T2DM who were on insulin therapy in 13 countries from four regions. In Egypt, 17 physicians agreed to participate in the present study and were required to include at least one patient. A total of 180 T2DM patients were included in the cross-section phase. At the end of the ninth month of follow-up, data from 170 T2DM patients were available. A total of 39 T2DM patients (21.7%) were on insulin therapy, with a mean duration of 32.4 ± 36.6 months. More than half of the patients (n = 22; 56.4%) were on basal insulin, mainly long-acting (n = 20; 90.9%). The mean basal insulin daily dose was 0.3 ± 0.1 IU/Kg. Notably, 28.2% of the patients received insulin via vials, and 46.2% stated that they were adjusting the insulin dose by themselves. On the other hand, 60.2% of the study population was on oral antidiabetic drugs at the cross-sectional phase. Nearly 17.4% and 27% of the patients in the cross-sectional phase achieved the glycemic target per recommendations of international guidelines and the treating physicians, respectively. At the end of the longitudinal phase, the percentage of T2DM patients who achieved glycemic targets increased to 38.4% and 77.4% as per recommendations of international guidelines and the treating physicians, respectively. Overall, 38.3% of T2DM patients received diabetes education, and 28.9% were involved in an educational program provided by the physician or their clinical staff. Besides, 85.5% of T2DM patients followed their diabetes medication dosage and frequency strictly as prescribed. The proportion of insulin use in patients with T2DM aligned with the previous studies from different countries; however, it is still inadequate to achieve the targeted glycemic control. Nearly one-third of Egyptian patients received diabetes education, highlighting the need for adopting a national educational program. Nonetheless, the level of adherence among T2DM from Egypt appears to be high.

AWD-stacking: An enhanced ensemble learning model for predicting glucose levels.

Accurate prediction of blood glucose levels is essential for type 1 diabetes optimizing insulin therapy and minimizing complications in patients with type 1 diabetes. Using ensemble learning algorithms is a promising approach. In this regard, this study proposes an improved stacking ensemble learning algorithm for predicting blood glucose level, in which three improved long short-term memory network models are used as the base model, and an improved nearest neighbor propagation clustering algorithm is adaptively weighted to this ensemble model. The OhioT1DM dataset is used to train and evaluate the performance of the proposed model. This study evaluated the performance of the proposed model using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Matthews Correlation Coefficient (MCC) as the evaluation metrics. The experimental results demonstrate that the proposed model achieves an RMSE of 1.425 mg/dL, MAE of 0.721 mg/dL, and MCC of 0.982 mg/dL for a 30-minute prediction horizon(PH), RMSE of 3.212 mg/dL, MAE of 1.605 mg/dL, and MCC of 0.950 mg/dL for a 45-minute PH; and RMSE of 6.346 mg/dL, MAE of 3.232 mg/dL, and MCC of 0.930 mg/dL for a 60-minute PH. Compared with the best non-ensemble model StackLSTM, the RMSE and MAE were improved by up to 27.92% and 65.32%, respectively. Clarke Error Grid Analysis and critical difference diagram revealed that the model errors were within 10%. The model proposed in this study exhibits state-of-the-art predictive performance, making it suitable for clinical decision-making and of significant importance for the effective treatment of diabetes in patients.

Use of Voice-Based Conversational Artificial Intelligence for Basal Insulin Prescription Management Among Patients With Type 2 Diabetes

Optimizing insulin therapy for patients with type 2 diabetes can be challenging given the need for frequent dose adjustments. Most patients receive suboptimal doses and do not achieve glycemic control. To examine whether a voice-based conversational artificial intelligence (AI) application can help patients with type 2 diabetes titrate basal insulin at home to achieve rapid glycemic control. In this randomized clinical trial conducted at 4 primary care clinics at an academic medical center from March 1, 2021, to December 31, 2022, 32 adults with type 2 diabetes requiring initiation or adjustment of once-daily basal insulin were followed up for 8 weeks. Statistical analysis was performed from January to February 2023. Participants were randomized in a 1:1 ratio to receive basal insulin management with a voice-based conversational AI application or standard of care. Primary outcomes were time to optimal insulin dose (number of days needed to achieve glycemic control), insulin adherence, and change in composite survey scores measuring diabetes-related emotional distress and attitudes toward health technology and medication adherence. Secondary outcomes were glycemic control and glycemic improvement. Analysis was performed on an intent-to-treat basis. The study population included 32 patients (mean [SD] age, 55.1 [12.7] years; 19 women [59.4%]). Participants in the voice-based conversational AI group more quickly achieved optimal insulin dosing compared with the standard of care group (median, 15 days [IQR, 6-27 days] vs >56 days [IQR, >29.5 to >56 days]; a significant difference in time-to-event curves; P = .006) and had better insulin adherence (mean [SD], 82.9% [20.6%] vs 50.2% [43.0%]; difference, 32.7% [95% CI, 8.0%-57.4%]; P = .01). Participants in the voice-based conversational AI group were also more likely than those in the standard of care group to achieve glycemic control (13 of 16 [81.3%; 95% CI, 53.7%-95.0%] vs 4 of 16 [25.0%; 95% CI, 8.3%-52.6%]; difference, 56.3% [95% CI, 21.4%-91.1%]; P = .005) and glycemic improvement, as measured by change in mean (SD) fasting blood glucose level (-45.9 [45.9] mg/dL [95% CI, -70.4 to -21.5 mg/dL] vs 23.0 [54.7] mg/dL [95% CI, -8.6 to 54.6 mg/dL]; difference, -68.9 mg/dL [95% CI, -107.1 to -30.7 mg/dL]; P = .001). There was a significant difference between the voice-based conversational AI group and the standard of care group in change in composite survey scores measuring diabetes-related emotional distress (-1.9 points vs 1.7 points; difference, -3.6 points [95% CI, -6.8 to -0.4 points]; P = .03). In this randomized clinical trial of a voice-based conversational AI application that provided autonomous basal insulin management for adults with type 2 diabetes, participants in the AI group had significantly improved time to optimal insulin dose, insulin adherence, glycemic control, and diabetes-related emotional distress compared with those in the standard of care group. These findings suggest that voice-based digital health solutions can be useful for medication titration. ClinicalTrials.gov Identifier: NCT05081011.

Simulation-driven optimization of insulin therapy profiles in a commercial hybrid closed-loop system

BackgroundAutomated insulin delivery (AID) has represented a breakthrough in managing type 1 diabetes (T1D), showing safe and effective glucose control extensively across the board. However, metabolic variability still poses a challenge to commercial hybrid closed-loop (HCL) solutions, whose performance depends on customizable insulin therapy profiles. In this work, we propose an Identification-Replay-Optimization (IRO) approach to optimize gradually and safely such profiles for the Control-IQ AID algorithm. MethodsClosed-loop data are generated using the full adult cohort of the UVA/Padova T1D simulation platform in diverse glycemic scenarios. For each subject, daily records are processed and used to estimate a personalized model of the underlying insulin-glucose dynamics. Every two weeks, all identified models are integrated into an optimization procedure where daily basal and bolus profiles are adjusted so as to minimize the risks for hypo- and hyperglycemia. The proposed strategy is tested under different scenarios of metabolic and behavioral variability in order to evaluate the efficacy and convergence of the proposed strategy. Finally, glycemic metrics between cycles are compared using paired t-tests with p<0.05 as the significance threshold. ResultsSimulations reveal that the proposed IRO approach was able to improve glucose control over time by safely mitigating the risks for both hypo- and hyperglycemia. Furthermore, smaller changes were recommended at each cycle, indicating convergence when simulation conditions were maintained. ConclusionsThe use of reliable simulation-driven tools capable of accurately reproducing field-collected data and predicting changes can substantially shorten the process of optimizing insulin therapy, adjusting it to metabolic changes and leading to improved glucose control.

#5876 ISLET TRANSPLANTATION VERSUS INSULIN ALONE IN TYPE 1 DIABETIC KIDNEY TRANSPLANT RECIPIENTS: A FRENCH NATIONWIDE STUDY ON BEHALF OF THE TREPID GROUP

Abstract Background and Aims Islet transplantation is associated with a benefit on glycaemic control compared to optimized insulin therapy in recent clinical trials. However, there is a lack of evidence concerning the long-term impact of islet transplantation on type 1 diabetic kidney transplant recipients’ prognosis. Method Every type 1 diabetic recipient transplanted with a kidney in France between 2000 and 2017 was included. Patients transplanted with pancreatic islets were compared to controls treated with insulin alone according to a matching method based on time-dependent propensity scores (using the following variables : yera of transplantation, donor age, and recipient age, serum creatinine, HBA1c, BMI, cardiovascular background) which allow to ensure patients comparability at the time of islet transplantation. The primary outcome was graft failure, defined by death or return to dialysis. Results Among 2393 type 1 diabetic patients transplanted with a kidney during the study period, 381 were eligible to islet transplantation, including 47 that were actually transplanted with islets. Median time for islet transplantation was 34.8 months [21.8-48.4]. Probabilities of insulino-independence and islet graft survival at 1, 5 and 10 years were respectively 63.8% [51.5-79.2], 46.3% [33.9-63.2], 38.7% [25.9-57.8] and 89.4% [81.0-98.6], 87.2% [78.2-97.3], 78.2% [66.2-92.4]. After matching, we observed a significant benefit of islet transplantation compared to insulin alone on graft failure, with a HR of 0.48 [0.20-0.94], mainly explained by a protective effect on the risk of death (HR = 0.38 [0.11-0.95]). We finally estimated the life-expectancy for a 10-year follow-up and found 9.61 years [9.02-10.00] in the islet transplantation group versus 8.85 years [7.97-9.56], with a difference of 8.88 months [-2.16-20.44] Conclusion We observe a significant benefit of islet transplantation on the risk of graft failure and death in type 1 diabetic kidney transplant recipients. These results provide incentives to promote islet transplantation in this population.

Telemonitoring With a Connected Glucose Meter Improves Glycemia Among People With Insulin-Treated Type 2 Diabetes.

Delayed initiation and inadequate titration remain critical challenges to optimizing insulin therapy in type 2 diabetes (T2D). We aimed to study whether hemoglobin A1c (HbA1c) can be lowered in people with insulin-treated T2D using telemonitoring. This single-center study recruited adults with greater than or equal to six months of diabetes, greater than or equal to three months of insulin therapy, HbA1c ≥8.5% and ≤12.5%, and body mass index (BMI) ≤40 kg/m2. All participants received a connected glucose meter and the accompanying smartphone application. Participants sent weekly blood glucose (BG) diary to their primary endocrinologist via email. Adjustments in insulin doses were communicated to the participants. HbA1c, proportion of BG readings in range (70-180 mg/dL, PIR), below range (<70 mg/dL, PBR) and above range (>180 mg/dL, PAR), and glycemic variability as the coefficient of variation (% CV) were measured at baseline, week 12, and week 24 and compared using repeated-measures analysis of variance (ANOVA) or Friedman's ANOVA. We recruited 40 people (55% women). Mean age was 57.9 years, BMI 27.8 kg/m2, and baseline HbA1c 9.8% (83.7 mmol/mol). Mean HbA1c improved by 1.7%, % CV reduced from 32.9% to 30.7%, PIR increased from 58.8% to 67.1% (all P <.01) by week 24, without any change in PBR. This was achieved with a 0.04 U/kg/d median increase in total daily dose of insulin and 0.9 kg weight gain over 24 weeks. Telemonitoring and titration of insulin using a connected glucose meter resulted in significant improvements in glycemia, characterized by a reduction in HbA1c, increase in PIR, and reduction in glycemic variability without any increase in hypoglycemia.

The efficacy of insulin degludec and insulin glargine over NPH insulin among toddlers and preschoolers with type 1 diabetes using glycemic variability and time in range

Optimizing glycemic control without risking hypoglycemia is crucial in toddlers and preschoolers with type 1 diabetes (T1D) to avoid cognitive impairment later in life. Hence, this study aims to compare glycemic parameters among toddlers and preschoolers with T1D in relation to different basal insulins. Sixty toddlers and preschoolers with T1D with mean age of 3.53 ± 1.17 years (range, 2–6) and mean diabetes duration of 9.37 ± 1.85 months were randomly assigned into three equal groups; group A received insulin degludec, group B received insulin glargine, and group C were on NPH. At baseline, the three groups were matched regarding clinical and laboratory parameters (p > 0.05). They were followed up at 3 and 6 months for insulin daily dose (IDD), hypoglycemia and severe-hypoglycemia frequency, and glycated hemoglobin (HbA1c). At the study endpoint, continuous glucose monitoring (CGM) was assessed in a random sample of 10 patients from each group. The mean time in range (TIR) of the studied cohort was 55.07 ± 24.05%, and their mean coefficient of variation (CV) was 42.82 ± 11.69%. The TIR was significantly higher in the degludec group (69.36 ± 18.54) and the glargine group (55.43 ± 26.51) than the NPH group (32.56 ± 9.11), p < 0.001. Meanwhile, the CV was significantly lower in the degludec group (35.12 ± 6.47) than the gargine (44.1 ± 13.13) and the NPH (53.8 ± 7.54) groups, p < 0.001. The insulin degludec and glargine groups had significantly lower HbA1c (p = 0.002), hypoglycemia (p = 0.006), severe hypoglycemia (p = 0.029), and IDD (p = 0.015) than the NPH group.Conclusion: Insulin degludec and glargine resulted in better HbA1c and TIR with reduced hypoglycemia and IDD than NPH among toddlers and preschoolers with T1D. Moreover, CV was lowest in the insulin degludec group.What is Known:• Insulin therapy is the mainstay of T1D management.• Optimal insulin therapy for young children with T1D should provideeffective glycemic.What is New:• Insulin degludec and insulin glargine have better efficacy than NPH insulin among toddlers and preschoolers with T1D in the term of significantly lowercoefficient of variation, HbA1c and IDD and significantly higher time in range.• Insulin degludec and insulin glargine have better safety in the term ofless hypoglycemia and severe hypoglycemia episodes than NPH insulin among toddlers andpreschoolers with T1D.

A New Index of Insulin Sensitivity from Glucose Sensor and Insulin Pump Data: In Silico and In Vivo Validation in Youths with Type 1 Diabetes.

Background: Estimation of insulin sensitivity (SI) and its daily variation are key for optimizing insulin therapy in patients with type 1 diabetes (T1D). We recently developed a method for SI estimation from continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) data in adults with T1D (SISP) and validated it under restrained experimental conditions. Herein, we validate in vivo a new version of SISP performing well in daily life unrestrained conditions. Methods: The new SISP was tested in both simulated and real data. The simulated dataset consists of 100 virtual adults of the UVa/Padova T1D Simulator monitored during an open-loop experiment, whereas the real dataset consists of 10 youths with T1D monitored during a hybrid closed-loop meal study. In both datasets, participants underwent two consecutive meals (breakfast and lunch, at 7 and 11 am) with the same carbohydrate content (70 g). Plasma glucose and insulin were measured during each meal to estimate the oral glucose minimal model SI (SIMM). CGM and CSII data were used for SISP calculation, which was then validated against the gold standard SIMM. Results: SISP was estimated with good precision (median coefficient of variation <20%) in 100% of the real and 91% of the simulated meals. SISP and SIMM were highly correlated, both in the simulated and real datasets (R = 0.82 and R = 0.83, P < 0.001), and exhibited a similar intraday pattern. Conclusions: SISP is suitable for estimating SI in both closed- and open-loop settings, provided that the subject wears a CGM sensor and a subcutaneous insulin pump.

1359-P: Effect of Fat Content on Postprandial Gastric Retention and Glucose Absorption in Subjects with Type 1 Diabetes during Daily Life Conditions: Assessment through a Computational Model

Understanding the impact of dietary nutrients on postprandial gastric retention (GR) and glucose absorption (GA) is crucial for optimizing both open- and closed-loop therapy in type 1 diabetes (T1D) . Here, we aim to investigate the effect of fat (F) content of a meal on both GR and GA in real life conditions, using a recently developed minimally invasive oral minimal model (MI-OMM) . Data was collected during an outpatient 5-days clinical trial involving 1patients (age = 3-61 y; M/F = 60/50) wearing a sensor augmented insulin pump. At each meal, the medical staff recorded carbohydrate (C) and F intake. A total of 159 meals were analyzed that included &amp;gt;75% sensor data, C and F contents, no exercise sessions, or other meals/snacks within 2h before and 3h after the meal. Meals were grouped based on F content: high fat (HF, F≥30%) , and Low Fat (LF, F≤15%) . The model well fitted glucose sensor data and provided estimated GR and GA curves. Fig. 1 shows that model-derived GR was significantly lower from 0.5h onward, and GA was significantly faster from 0.5h to 2.3h after a LF meal compared to a HF. These findings proved that MI-OMM can be used to estimate GR and GA, using sensor data, in real life meals with different F content and, therefore, it is potentially applicable to optimize insulin therapy in T1D. Disclosure E.Faggionato: None. M.Schiavon: None. B.A.Buckingham: Advisory Panel; Arecor, Lilly Diabetes, Medtronic, Other Relationship; Insulet Corporation, Research Support; Insulet Corporation, Lilly Diabetes, Medtronic. L.Ekhlaspour: Consultant; Tandem Diabetes Care, Inc., Ypsomed AG. C.Dalla man: Research Support; Becton, Dickinson and Company, Sanofi-Aventis Deutschland GmbH. Funding This work was supported by MIUR (Italian Minister for Education) under the initiative “Departments of Excellence” (Law 232/2016) . Laya Ekhlaspour is funded by NIH (National Institutes of Health) 1K23DK121942.

Race/ethnicity and challenges for optimal insulin therapy

AimsWe aimed to review insulin dosing recommendations, insulin regulation and its determinants, glycaemic response to carbohydrates, and the efficacy and safety of insulin therapy in different races/ethnicities. MethodsWe searched for articles in PubMed and Google Scholar databases up to 31 March 2021, with the following keywords: “ethnicity”, “diabetes”, “insulin”, “history of insulin”, “insulin therapy”, “food/rice”, “carbohydrate intake”, “insulin resistance”, “BMI”, “insulin dosing”, “insulin sensitivity”, “insulin response”, “glycaemic index”, “glycaemic response”, “efficacy and safety”, with interposition of the Boolean operator “AND”.In addition, we reviewed the reference lists of the articles found. ResultsThe differential effect of race/ethnicity has not yet been considered in current insulin therapy guidelines. Nevertheless, body size and composition, body mass index, fat distribution, diet, storage, and energy expenditure vary significantly across populations. Further, insulin sensitivity, insulin response, and glycaemicresponse to carbohydrates differ by ethnicity. These disparities may lead to different insulin requirements, adversely impacting the efficacy and safety of insulin therapy among ethnic groups. ConclusionsRace/ethnicity affects glucose metabolism and insulin regulation.Until now, international guidelines addressing racial/ethnic-specific clinical recommendations are limited. Comprehensive updated insulin therapy guidelines by ethnicity are urgently needed.

Stem Cell-Based Clinical Trials for Diabetes Mellitus.

Since its introduction more than twenty years ago, intraportal allogeneic cadaveric islet transplantation has been shown to be a promising therapy for patients with Type I Diabetes (T1D). Despite its positive outcome, the impact of islet transplantation has been limited due to a number of confounding issues, including the limited availability of cadaveric islets, the typically lifelong dependence of immunosuppressive drugs, and the lack of coverage of transplant costs by health insurance companies in some countries. Despite improvements in the immunosuppressive regimen, the number of required islets remains high, with two or more donors per patient often needed. Insulin independence is typically achieved upon islet transplantation, but on average just 25% of patients do not require exogenous insulin injections five years after. For these reasons, implementation of islet transplantation has been restricted almost exclusively to patients with brittle T1D who cannot avoid hypoglycemic events despite optimized insulin therapy. To improve C-peptide levels in patients with both T1 and T2 Diabetes, numerous clinical trials have explored the efficacy of mesenchymal stem cells (MSCs), both as supporting cells to protect existing β cells, and as source for newly generated β cells. Transplantation of MSCs is found to be effective for T2D patients, but its efficacy in T1D is controversial, as the ability of MSCs to differentiate into functional β cells in vitro is poor, and transdifferentiation in vivo does not seem to occur. Instead, to address limitations related to supply, human embryonic stem cell (hESC)-derived β cells are being explored as surrogates for cadaveric islets. Transplantation of allogeneic hESC-derived insulin-producing organoids has recently entered Phase I and Phase II clinical trials. Stem cell replacement therapies overcome the barrier of finite availability, but they still face immune rejection. Immune protective strategies, including coupling hESC-derived insulin-producing organoids with macroencapsulation devices and microencapsulation technologies, are being tested to balance the necessity of immune protection with the need for vascularization. Here, we compare the diverse human stem cell approaches and outcomes of recently completed and ongoing clinical trials, and discuss innovative strategies developed to overcome the most significant challenges remaining for transplanting stem cell-derived β cells.

Efficacy and safety of adding sotagliflozin, a dual sodium-glucose co-transporter (SGLT)1 and SGLT2 inhibitor, to optimized insulin therapy in adults with type 1 diabetes and baseline body mass index ≥ 27 kg/m2.

Sotagliflozin, a dual sodium-glucose co-transporter (SGLT)1/SGLT2 inhibitor, is currently approved in Europe as an adjunct to optimal insulin therapy in adults with type 1 diabetes (T1D) and a body mass index (BMI) ≥ 27 kg/m2 . In this post hoc analysis, efficacy at 24 weeks and safety at 52 weeks from pooled phase 3 clinical trials were evaluated in patients with baseline BMI ≥ 27 kg/m2 . Sotagliflozin 200 mg and 400 mg added to insulin reduced glycated haemoglobin level and increased time in range assessed by continuous glucose monitoring versus placebo and also reduced body weight and systolic blood pressure. Differences in efficacy endpoints between sotagliflozin and placebo tended to be greater among patients with BMI ≥ 27 kg/m2 compared to those with baseline BMI < 27 kg/m2 . Consistent with published results for the entire population, fewer severe hypoglycaemia and documented hypoglycaemia ≤3.1 mmol/L events and a higher incidence of diabetic ketoacidosis occurred with sotagliflozin versus placebo in patients with BMI ≥ 27 kg/m2 . Sotagliflozin as an adjunct to optimized insulin therapy in overweight/obese patients with T1D addressed some unmet needs and may help achieve optimal glycaemic control, mitigating weight gain without increasing hypoglycaemia risk in this high-risk population.

Insulinization in T2DM with Basal Analogues During COVID-19 Pandemic: Expert Opinionfrom an Indian Panel.

The ongoing global pandemic of the coronavirus disease 2019 (COVID-19) has placed a severe strain on the management of chronic conditions like diabetes. Optimal glycemic control is always important, but more so in the existing environment of COVID-19. In this context, timely insulinization to achieve optimal glycemic control assumes major significance. However, given the challenges associated with the pandemic like restrictions of movement and access to healthcare resources, a simple and easy way to initiate and optimize insulin therapy in people with uncontrolled diabetes is required. With this premise, a group of clinical experts comprising diabetologists and endocrinologists from India discussed the challenges and potential solutions for insulin initiation, titration, and optimization in type 2 diabetes mellitus (T2DM) during the COVID-19 pandemic and how basal insulin can be a good option in this situation owing to its unique set of advantages like lower risk of hypoglycemia, ease of training, need for less monitoring, better adherence, flexibility of using oral antidiabetic drugs, and improved quality of life compared to other insulin regimens. The panel agreed that the existing challenges should not be a reason to delay insulin initiation in people with uncontrolled T2DM and provided recommendations, which included potential solutions for initiating insulin in the absence or restriction of in-person consultations; the dose of insulin at initiation; the type of insulin preferred for simplified regimen and best practices for optimal titration to achieve glycemic targets during the pandemic. Practical and easily implementable tips for patients and involvement of stakeholders (caregivers and healthcare providers) to facilitate insulin acceptance were also outlined by the expert panel. Simplified and convenient insulin regimens like basal insulin analogues are advised during and following the pandemic in order to achieve glycemic control in people with uncontrolled T2DM.

Beta cell replacement therapy

Beta cell replacement therapy is currently the only treatment method that allows restoration of physiological endogenous insulin secretion in the amounts corresponding to the current body requirements. Beta cell replacement options available for highly selected patients with brittle type 1 diabetes include solid- -organ pancreas and islet transplantation. Beta cell replacement therapy may be offered to patients with both good kidney function and renal failure. In progressive renal failure, beta cell transplantation may be performed simultaneously with kidney transplantation or afterwards. Islet autotransplantation is offered to patients submitted to total pancreatectomy. In patients with brittle type 1 diabetes who continue to experience life threatening severe hypoglycaemia episodes despite optimized insulin therapy, beta cell replacement helps improve hypoglycaemia awareness, thus reducing the risk of severe hypoglycaemia episodes, facilitates blood glucose control with normalization of haemoglobin A1c (HbA1c) level, and reduces microvascular disease progression. In patients undergoing total pancreatectomy, infusion of the patient’s own islets isolated from the removed pancreas prevents blood glucose level excursions and reduces the risk of surgically- -induced diabetes. In this article, we review the current indications and contraindications to beta cell replacement, expected benefits, and possible complications of beta cell transplantation.

An up-to-date evaluation of sotagliflozin for the treatment of type 1 diabetes

ABSTRACT Introduction The majority of patients with type 1 diabetes mellitus (T1DM) do not achieve glycemic targets. In addition, treatment with insulin is associated with increased risk for hypoglycemia and weight gain. Accordingly, there is an unmet need for new safe and effective glucose-lowering agents in this population. Sotagliflozin, a dual inhibitor of sodium-glucose co-transporters 1 and 2, has been recently approved for use in patients with T1DM. Areas covered The authors review the major trials that have evaluated the safety and efficacy of sotagliflozin and provide their expert opinion. Expert opinion Even though sotagliflozin reduces HbA1 c levels and does not appear to increase the risk for hypoglycemia in most patients, the substantially increased risk for diabetic ketoacidosis limits the use of this agent to a carefully selected subgroup of patients with T1DM. Based on the existing evidence, sotagliflozin should be considered only in patients who have failed to achieve adequate glycemic control despite optimal insulin therapy, are at low risk for diabetic ketoacidosis, have been adequately trained to recognize this complication and are able to be in close contact with their physician.

Sotagliflozin added to optimized insulin therapy leads to HbA1c reduction without weight gain in adults with type 1 diabetes: A pooled analysis of inTandem1 and inTandem2.

To evaluate whether the addition of sotagliflozin to optimized insulin significantly increases the proportion of adults with type 1 diabetes who achieve HbA1c goals without weight gain. In a patient-level pooled analysis (n = 1575) of data from two phase 3, 52-week clinical trials (inTandem1 and inTandem2), the change from baseline in HbA1c and weight as well as the proportion of participants achieving an HbA1c of less than 7% without weight gain were compared between groups treated with placebo, sotagliflozin 200 mg and sotagliflozin 400 mg. From a mean baseline HbA1c of 7.7%, mean HbA1c changes at week 24 were -0.36% (95% CI -0.44% to -0.29%) and -0.38% (-0.45% to -0.31%) with sotagliflozin 200 and 400 mg versus placebo (P = .001 for both), respectively, with sustained effects through week 52. Weight significantly decreased at weeks 24 and 52 in both sotagliflozin groups compared with placebo. At week 52, the proportion of patients who achieved an HbA1c of less than 7% without weight gain was 21.8% with sotagliflozin 200 mg, 26.1% with sotagliflozin 400 mg and 9.1% with placebo (P < .001). Other HbA1c, weight and safety composite variables showed similar significant trends. When added to optimized insulin therapy, sotagliflozin improved glycaemic control and body weight and enabled more adults with type 1 diabetes to achieve HbA1c goals without weight gain over 52 weeks, although there was more diabetic ketoacidosis relative to placebo.

Improvement in Patient-Reported Outcomes in Adults with Type 1 Diabetes Treated with Sotagliflozin plus Insulin Versus Insulin Alone

Background: Diabetes-related distress is common among persons affected by diabetes and is associated with suboptimal glycemic control and complications, thus constituting a relevant patient-report outcome (PRO). Improving glycemic control may reduce diabetes distress and improve treatment satisfaction. This post hoc analysis evaluated PRO data for a pooled cohort of adults with type 1 diabetes (T1D) receiving sotagliflozin as adjunct to optimized insulin in the inTandem1 and inTandem2 studies. Methods: Clinically meaningful changes in the Diabetes Treatment Satisfaction Questionnaire status version (DTSQs) and the two-item Diabetes Distress Scale (DDS2) total and individual scores were examined in the pooled data from the first 24 weeks of the studies. Results: In the cohort of patients with a baseline DTSQs total score ≤32 (∼76% of entire cohort), nearly twice as many patients treated with sotagliflozin 200 (45.9%) or 400 mg (42.3%) experienced a >3-point improvement from baseline versus those treated with placebo (24%). Treatment with sotagliflozin led to statistically significant (P < 0.05) improvements across all DTSQs items. Approximately 42% of all patients were considered to have a high risk of diabetes distress (total DDS2 score ≥6) at baseline following insulin optimization. More patients shifted from high to low risk with sotagliflozin compared with placebo (∼40% vs. 23%; P ≤ 0.0002). The baseline-adjusted difference in DDS2 from placebo was significantly (P < 0.001) reduced by -0.5 and -0.6 for sotagliflozin 200 and 400 mg, respectively. Conclusions: Patients with T1D treated with sotagliflozin in addition to optimized insulin therapy reported meaningful improvements in treatment satisfaction and diabetes distress. NCT02384941 and NCT02421510.

1129-P: Effect of Sotagliflozin Added to Insulin Therapy on Beta-Hydroxybutyrate in Patients with Type 1 Diabetes

Objective: To evaluate the effect of sotagliflozin (SOTA), a dual SGLT 1 and 2 inhibitor, on beta-hydroxybutyrate (BHB) over 52 weeks in adults with type 1 diabetes (T1D) on insulin therapy. Methods: Analyses were performed on pooled data from two, 52-week, placebo-controlled studies in 1575 adult patients with T1D on optimized insulin therapy, who were randomly assigned 1:1:1 to placebo (PBO), SOTA 200 mg, and SOTA 400 mg. Descriptive statistics were performed on serum BHB using results from scheduled visits at baseline and Weeks 4, 12, 24, and 52. A 2-sample Wilcoxon rank sum test was performed to test the distribution of change in BHB from baseline between SOTA and placebo. The proportion of patients experiencing ≥1 measurement meeting predefined limits of DKA risk based on BHB levels (≥0.6 mmol/L “ketonemia” and ≥1.5 mmol/L “impending DKA”) was assessed. Results: Median baseline BHB was 0.13 (IQR 0.10, 0.20), 0.13 (IQR 0.10, 0.22), and 0.14 mmol/L (IQR 0.10, 0.23) in the PBO, SOTA 200-mg, and SOTA 400-mg groups, respectively. While the median change in BHB was 0 mmol/L with PBO, median BHB increased from baseline by 0.03 to 0.05 mmol/L with SOTA, and generally remained stable over 52 weeks (p&amp;lt;0.001 vs. PBO at each time point). Per the predefined limits of DKA risk based on BHB levels, a BHB level ≥0.6 mmol/L was experienced by 19.8, 46.9, and 45.5% and a BHB level ≥1.5 mmol/L in 2.1, 6.3, and 8.2% of patients on PBO, SOTA 200 mg, and SOTA 400 mg, respectively. Approximately 2/3 of the BHB ≥0.6 mmol/L events were observed by Week 16 and ∼2/3 of the BHB ≥1.5 mmol/L events were observed by Week 24. Conclusion: Treatment with SOTA was associated with small increases in BHB over time, which on average remained well below the clinically important level of 0.6 mmol/L. Although the DKA incidence was &amp;lt;4% overall on SOTA, the proportions of patients with ≥1 BHB measurement ≥0.6 mmol/L (∼45-47%) or ≥1.5 mmol/L (∼6-8%) suggest many BHB elevations were recognized and managed without progression to DKA. Disclosure A.L. Peters: Advisory Panel; Self; Abbott, Bigfoot Biomedical, Boehringer Ingelheim Pharmaceuticals, Inc., Eli Lilly and Company, MannKind Corporation, Medscape, Novo Nordisk Inc., Sanofi US. Consultant; Self; Livongo Health. Research Support; Self; Dexcom, Inc., vTv Therapeutics. Other Relationship; Self; Livongo Health, Mellitus Health, Omada Health, Stability Healthcare, Whole Biome Inc. T. Danne: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Medtronic, Novo Nordisk A/S, Sanofi. S. Sawhney: None. P.L. Banks: Employee; Self; Lexicon Pharmaceuticals, Inc. M.J. Davies: Employee; Self; Lexicon Pharmaceuticals, Inc. P. Lapuerta: Employee; Self; Lexicon Pharmaceuticals, Inc. Stock/Shareholder; Self; Lexicon Pharmaceuticals, Inc.