Optimal Data Collection Period for Continuous Glucose Monitoring to Assess Long-Term Glycemic Control: Revisited

Background and Objective: It is not clear how the short-term continuous glucose monitoring (CGM) sampling time could influence the bias in estimating long-term glycemic control. A large bias could, in the worst case, lead to incorrect classification of patients achieving glycemic targets, nonoptimal treatment, and false conclusions about the effect of new treatments. This study sought to investigate the relation between sampling time and bias in the estimates. Methods: We included a total of 329 type 1 patients (age 14-86 years) with long-term CGM (90 days) data from three studies. The analysis calculated the bias from estimating long-term glycemic control based on short-term sampling. Time in range (TIR), time above range (TAR), time below range (TBR), correlation, and glycemic target classification accuracy were assessed. Results: A sampling time of ten days is associated with a high bias of 10% to 47%, which can be reduced to 4.9% to 26.4% if a sampling time of 30 days is used ( P < .001). Correct classification of patients archiving glycemic targets can also be improved from 81.5% to 91.9 to 90% to 95.2%. Conclusions: Our results suggest that the proposed 10-14 day CGM sampling time may be associated with a high correlation with three-month CGM. However, these estimates are subject to large intersubject bias, which is clinically relevant. Clinicians and researchers should consider using assessments of longer durations of CGM data if possible, especially when assessing time in hypoglycemia or while testing a new treatment.

6. Glycemic Targets: Standards of Medical Care in Diabetes—2022

The American Diabetes Association (ADA) “Standards of Medical Care in Diabetes” includes the ADA’s current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc22-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA’s clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc22-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Diabetes Device Downloading: Benefits and Barriers Among Youth With Type 1 Diabetes

Background: The majority of youth with type 1 diabetes (T1D) fail to meet glycemic targets despite increasing continuous glucose monitoring (CGM) use. We therefore aimed to determine the proportion of caregivers who review recent glycemic trends (“retrospective review”) and make ensuant insulin adjustments based on this data (“retroactive insulin adjustments”). We additionally considered that fear of hypoglycemia and frequency of severe hypoglycemia would be associated with performing retrospective review. Methods: We conducted a cross-sectional survey of caregivers of youth with T1D, collecting demographics, diabetes technology usage, patterns of glucose data review/insulin dose self-adjustment, and Hypoglycemia Fear Survey (HFS). Results: Nineteen percent of eligible caregivers (191/1003) responded. Performing retrospective review was associated with younger child age (12.2 versus 15.4, P = .0001) and CGM use (92% versus 73%, P = .004), but was not associated with a significant improvement in child’s HbA1c (7.89 versus 8.04, P = .65). Retrospective reviewers had significantly higher HFS-behavior scores (31.9 versus 27.7, P = .0002), which remained significantly higher when adjusted for child’s age and CGM use ( P = .005). Linear regression identified a significant negative association between HbA1c (%) and number of retroactive insulin adjustments (0.24 percent lower mean HbA1c per additional adjustment made, P = .02). Conclusions: Retrospective glucose data review is associated with improved HbA1c when coupled with data-driven retroactive insulin adjustments. Barriers to data downloading existed even in this cohort of predominantly CGM-using T1D families.

Exploring Sleep Health in Young Adults with Type 1 Diabetes

The purpose of this qualitative descriptive study was to describe the sleep health goals in 35 young adults age 18–30 years with type 1 diabetes (T1D). We reviewed clinician sleep reports generated from wrist-worn actigraphs with participants during an in-depth semistructured telephone interview. Interviews were audio-recorded then transcribed verbatim. We performed a constant comparison method for content analysis using NVivoTM. The following two overarching health goals are identified: (a) promoting sleep (quantity, quality, hygiene, bedtime/waketime) and (b) improving diabetes self-management (time in range, sleep and glucose monitoring, and diet). Young adults in the study readily generated goals after visualizing their sleep reports. Sleep data visualization and debriefing is an effective strategy to elicit health goals for young adults with T1D. Supporting young adults with T1D to achieve their health goals for sleep promotion and diabetes self-management is a promising direction for improved sleep and consequently the attainment of glycemic targets.

Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes

Technology advances have made possible improvements such as Continuous Glucose Monitors, giving the patient a glucose reading every few minutes, or insulin pumps, allowing more personalized therapies. With the increasing number of available closed-loop systems, new challenges appear regarding algorithms and functionalities. Several of the analysed systems in this paper try to adapt to changes in some patients’ conditions and, in several of these systems, other variables such as basal needs are considered fixed from day to day to simplify the control problem. Therefore, these systems require a correct adjustment of the basal needs profile which becomes crucial to obtain good results. In this paper a novel approach tries to dynamically determine the insulin basal needs of the patient and use this information within a closed-loop algorithm, allowing the system to dynamically adjust in situations of illness, exercise, high-fat-content meals or even partially blocked infusion sites and avoiding the need for setting a basal profile that approximately matches the basal needs of the patient. The insulin sensitivity factor and the glycemic target are also dynamically modified according to the situation of the patient. Basal insulin needs are dynamically determined through linear regression via the decomposition of previously dosed insulin and its effect on the patient’s glycemia. Using the obtained value as basal insulin needs and other mechanisms such as basal needs modification through its trend, ISF and glycemic targets modification and low-glucose-suspend threshold, the safety of the algorithm is improved. The dynamic basal insulin needs determination was successfully included in a closed-loop control algorithm and was simulated on 30 virtual patients (10 adults, 10 adolescent and 10 children) using an open-source python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator. Simulations showed that the proposed system dynamically determines the basal needs and can adapt to a partial blockage of the insulin infusion, obtaining similar results in terms of time in range to the case in which no blockage was simulated. The proposed algorithm can be incorporated to other current closed-loop control algorithms to directly estimate the patient’s basal insulin needs or as a monitoring channel to detect situations in which basal needs may differ from the expected ones.

Automated Insulin Delivery Systems: Today, Tomorrow and User Requirements

Automated insulin delivery (AID) is the most recent advance in type 1 diabetes (T1D) management. It has the potential to achieve glycemic targets without disabling hypoglycemia, to improve quality of life and reduce diabetes distress and burden associated with self-management. Several AID systems are currently licensed for use by people with T1D in Europe, United States, and the rest of the world. Despite AID becoming a reality in routine clinical practice over the last few years, the commercially hybrid AID and other systems, are still far from a fully optimized automated diabetes management tool. Implementation of AID systems requires education and support of healthcare professionals taking care of people with T1D, as well as users and their families. There is much to do to increase usability, portability, convenience and to reduce the burden associated with the use of the systems. Co-design, involvement of people with lived experience of T1D and robust qualitative assessment is critical to improving the real-world use of AID systems, especially for those who may have greater need. In addition to this, information regarding the psychosocial impact of the use of AID systems in real life is needed. The first commercially available AID systems are not the end of the development journey but are the first step in learning how to optimally automate insulin delivery in a way that is equitably accessible and effective for people living with T1D.

Up and down waves of glycemic control and lower-extremity amputation in diabetes

Lower extremity amputations (LEA) are associated with a high mortality and medical expenditure. Diabetes accounts for 45% to 70% of LEA and is one of the most potent risk factors for peripheral artery diseases (PAD). The existence of a link between the recent relaxation of glycemic targets and the resurgence of LEA is suggested from the analysis of adult participants in the National Health and Nutrition Examination Survey (NHANES) between 2010 and 2015, when diabetes-related LEA increased by more than 25% associated with a decline in glycemic control. Indeed, in “the perfect wave” of NHANES, including the years 2007–2010, there was the highest number of diabetic people with hemoglobin A1c (HbA1c), non-high-density lipoprotein (HDL) cholesterol and blood pressure levels at their respective targets, associated with the lowest number of LEA. Until now, the ACCORD study, testing the role of aggressive vs conventional glucose control, and the LEADER trial, evaluating the effects of liraglutide versus placebo, have shown a reduced incidence of LEA in people with type 2 diabetes. The results of ongoing clinical trials involving glucagon-like peptide-1 receptor agonists (GLP-1RA, liraglutide or semaglutide) hopefully will tell us whether the wider use of these drugs may provide additional vascular benefits for diabetic people affected by PAD to decrease their risk of LEA.

Multicenter Trial of a Tubeless, On-Body Automated Insulin Delivery System With Customizable Glycemic Targets in Pediatric and Adult Participants With Type 1 Diabetes

<b>Objective:</b> Advances in diabetes technology have transformed the treatment paradigm for type 1 diabetes, yet the burden of disease is significant. We report on a pivotal, safety study of the first tubeless, on-body automated insulin delivery system with customizable glycemic targets. <p><b>Research Design and Methods: </b>This single-arm, multicenter, prospective study enrolled 112 children (6-13.9 years) and 129 adults (14-70 years). A two-week standard therapy phase (usual insulin regimen) was followed by 3 months of automated insulin delivery. Primary safety outcomes were incidence of severe hypoglycemia and diabetic ketoacidosis. Primary effectiveness outcomes were change in HbA1c and percent time in sensor glucose range 70-180mg/dL. </p> <p><b>Results: </b>235 participants (98% of enrolled: 111 children, 124 adults) completed the study. HbA1c was significantly reduced in children by 0.71% (7.8mmol/mol) (mean±standard deviation: 7.67±0.95% to 6.99±0.63%, 60±10.4mmol/mol to 53±6.9mmol/mol, <i>p</i><0.0001) and in adults by 0.38% (4.2mmol/mol) (7.16±0.86% to 6.78±0.68%, 55±9.4mmol/mol to 51±7.4mmol/mol, <i>p</i><0.0001). Time in range was improved from standard therapy by 15.6±11.5% or 3.7 hours/day in children and 9.3±11.8% or 2.2 hours/day in adults (both <i>p</i><0.0001). This was accomplished with a reduction in time in hypoglycemia <70mg/dL among adults (median (interquartile range): 2.00% (0.63, 4.06) to 1.09% (0.46, 1.75), <i>p</i><0.0001), while this parameter remained the same in children. There were 3 severe hypoglycemia events not attributable to automated insulin delivery malfunction and 1 diabetic ketoacidosis event from an infusion site failure.<a></a><a></a></p> <p><b>Conclusions: </b>This tubeless automated insulin delivery system was safe, and allowed participants to significantly improve HbA1c levels and time in target glucose range with a very low occurrence of hypoglycemia.<br> </p>