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BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Ketone bodies are an energy substrate produced by the liver and used during states of low carbohydrate availability, such as fasting or prolonged exercise. High ketone concentrations can be present with insulin insufficiency and are a key finding in diabetic ketoacidosis (DKA). During states of insulin deficiency, lipolysis increases and a flood of circulating free fatty acids is converted in the liver into ketone bodies-mainly beta-hydroxybutyrate and acetoacetate. During DKA, beta-hydroxybutyrate is the predominant ketone in blood. As DKA resolves, beta-hydroxybutyrate is oxidized to acetoacetate, which is the predominant ketone in the urine. Because of this lag, a urine ketone test might be increasing even as DKA is resolving. Point-of-care tests are available for self-testing of blood ketones and urine ketones through measurement of beta-hydroxybutyrate and acetoacetate and are cleared by the US Food and Drug Administration (FDA). Acetone forms through spontaneous decarboxylation of acetoacetate and can be measured in exhaled breath, but currently no device is FDA-cleared for this purpose. Recently, technology has been announced for measuring beta-hydroxybutyrate in interstitial fluid. Measurement of ketones can be helpful to assess compliance with low carbohydrate diets; assessment of acidosis associated with alcohol use, in conjunction with SGLT2 inhibitors and immune checkpoint inhibitor therapy, both of which can increase the risk of DKA; and to identify DKA due to insulin deficiency. This article reviews the challenges and shortcomings of ketone testing in diabetes treatment and summarizes emerging trends in the measurement of ketones in the blood, urine, breath, and interstitial fluid.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.

Aim: Automated insulin delivery (AID) systems have demonstrated improved glycaemic outcomes in people with type 1 diabetes (T1D), yet limited data exist on these systems in very young children and their impact on caregivers. We evaluated psychosocial outcomes following use of the tubeless Omnipod® 5 AID System in caregivers of very young children. Materials and Methods: This 3‐month single‐arm, multicentre, pivotal clinical trial enrolled 80 children aged 2.0–5.9 years with T1D to use the Omnipod 5 AID System. Caregivers completed questionnaires assessing psychosocial outcomes—diabetes distress (Problem Areas in Diabetes), hypoglycaemia confidence (Hypoglycemia Confidence Scale), well‐being (World Health Organization 5 Well‐Being Index), sleep quality (Pittsburgh Sleep Quality Index), insulin delivery satisfaction (Insulin Delivery Satisfaction Survey) and system usability (System Usability Scale) at baseline with standard therapy and after 3 months of AID use. Results: Following 3 months of Omnipod 5 use, caregivers experienced significant improvements across all measures, including diabetes‐related psychosocial outcomes (Problem Areas in Diabetes; p < 0.0001, Hypoglycemia Confidence Scale; p < 0.01), well‐being (World Health Organization 5 Well‐Being Index; p < 0.0001) and perceived system usability (System Usability Scale; p < 0.0001). Significant improvements were seen in the Pittsburgh Sleep Quality Index total score and the overall sleep quality, sleep duration and efficiency subscales (all p < 0.05). Insulin Delivery Satisfaction Survey scores improved on all subscales (greater satisfaction, reduced burden and reduced inconvenience; all p < 0.0001). Conclusions: Caregivers face unique challenges when managing T1D in very young children. While glycaemic metrics have unquestioned importance, these results evaluating psychosocial outcomes reveal additional meaningful benefits and suggest that the Omnipod 5 AID System alleviates some of the burdens caregivers face with diabetes management. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To determine how diabetes technologies, including continuous glucose monitoring (CGM) and automated insulin delivery (AID) systems, impact glycemic metrics, prevalence of severe hypoglycemic events (SHEs), and impaired awareness of hypoglycemia (IAH) in people with type 1 diabetes in a real-world setting within the U.S. RESEARCH DESIGN AND METHODS: In this retrospective, observational study with cross-sectional elements, participants aged ≥18 years were enrolled from the T1D Exchange Registry/online community. Participants completed a one-time online survey describing glycemic metrics, SHEs, and IAH. The primary objective was to determine the proportions of participants who reported achieving glycemic targets (assessed according to self-reported hemoglobin A1c) and had SHEs and/or IAH. We performed additional subgroup analyses focusing on the impact of CGM and insulin delivery modality. RESULTS: A total of 2,074 individuals with type 1 diabetes were enrolled (mean ± SD age 43.0 ± 15.6 years and duration of type 1 diabetes 26.3 ± 15.3 years). The majority of participants (91.7%) were using CGM, with one-half (50.8%) incorporating AID. Despite high use of diabetes technologies, only 57.7% reported achieving glycemic targets (hemoglobin A1c <7%). SHEs and IAH still occurred, with ∼20% of respondents experiencing at least one SHE within the prior 12 months and 30.7% (95% CI 28.7, 32.7) reporting IAH, regardless of CGM or AID use. CONCLUSIONS: Despite use of advanced diabetes technologies, a high proportion of people with type 1 diabetes do not achieve glycemic targets and continue to experience SHEs and IAH, suggesting an ongoing need for improved treatment strategies. [ABSTRACT FROM AUTHOR]

BACKGROUND. Residual C-peptide is detected in many people for years following the diagnosis of type 1 diabetes; however, the physiologic significance of low levels of detectable C-peptide is not known. METHODS. We studied 63 adults with type 1 diabetes classified by peak mixed-meal tolerance test (MMTT) C-peptide as negative (0.200-0.400; n = 15), or high (>0.400; n = 17). We compared the groups' glycemia from continuous glucose monitoring (CGM), [beta] cell secretory responses from a glucose- potentiated arginine (GPA) test, insulin sensitivity from a hyperinsulinemic-euglycemic (EU) clamp, and glucose counterregulatory responses from a subsequent hypoglycemic (HYPO) clamp. RESULTS. Low and intermediate MMTT C-peptide groups did not exhibit [beta] cell secretory responses to hyperglycemia, whereas the high C-peptide group showed increases in both C-peptide and proinsulin (P [less than or equal to] 0.01). All groups with detectable MMTT C-peptide demonstrated acute C-peptide and proinsulin responses to arginine that were positively correlated with peak MMTT C-peptide (P < 0.0001 for both analytes). During the EU-HYPO clamp, C-peptide levels were proportionately suppressed in the low, intermediate, and high C-peptide compared with the negative group (P [less than or equal to] 0.0001), whereas glucagon increased from EU to HYPO only in the high C-peptide group compared with negative (P = 0.01). CGM demonstrated lower mean glucose and more time in range for the high C-peptide group. CONCLUSION. These results indicate that in adults with type 1 diabetes, [beta] cell responsiveness to hyperglycemia and a cell responsiveness to hypoglycemia are observed only at high levels of residual C-peptide that likely contribute to glycemic control. FUNDING. Funding for this work was provided by the Leona M. and Harry B. Helmsley Charitable Trust, the National Center for Advancing Translational Sciences, and the National Institute of Diabetes and Digestive and Kidney Diseases., Introduction Type 1 diabetes (T1D) results from autoimmune destruction of the insulin-producing [beta] cells in the endocrine pancreatic islets of Langerhans. After a subclinical period of months to years of [...]

OBJECTIVE: To explore 24-h postexercise glycemia and hypoglycemia risk, data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were analyzed to examine factors that may influence glycemia. RESEARCH DESIGN AND METHODS: This was a real-world observational study with participant self-reported physical activity, food intake, and insulin dosing (multiple daily injection users). Heart rate, continuous glucose data, and available pump data were collected. RESULTS: A total of 251 adolescents (42% females), with a mean ± SD age of 14 ± 2 years, and hemoglobin A1c (HbA1c) of 7.1 ± 1.3% (54 ± 14.2 mmol/mol), recorded 3,319 activities over ∼10 days. Trends for lower mean glucose after exercise were observed in those with shorter disease duration and lower HbA1c; no difference by insulin delivery modality was identified. Larger glucose drops during exercise were associated with lower postexercise mean glucose levels, immediately after activity (P < 0.001) and 12 to <16 h later (P = 0.02). Hypoglycemia occurred on 14% of nights following exercise versus 12% after sedentary days. On nights following exercise, more hypoglycemia occurred when average total activity was ≥60 min/day (17% vs. 8% of nights, P = 0.01) and on days with longer individual exercise sessions. Higher nocturnal hypoglycemia rates were also observed in those with longer disease duration, lower HbA1c, conventional pump use, and if time below range was ≥4% in the previous 24 h. CONCLUSIONS: In this large real-world pediatric exercise study, nocturnal hypoglycemia was higher on nights when average activity duration was higher. Characterizing both participant- and event-level factors that impact glucose in the postexercise recovery period may support development of new guidelines, decision support tools, and refine insulin delivery algorithms to better support exercise in youth with diabetes. [ABSTRACT FROM AUTHOR]

Objective: Pivotal trials of automated insulin delivery (AID) closed-loop systems have demonstrated a consistent picture of glycemic benefit, supporting approval of multiple systems by the Food and Drug Administration or Conformité Européenne mark receipt. To assess how pivotal trial findings translate to commercial AID use, a systematic review of retrospective real-world studies was conducted. Methods: PubMed and EMBASE were searched for articles published after 2018 with more than five nonpregnant individuals with type 1 diabetes (T1D). Data were screened/extracted in duplicate for sample size, AID system, glycemic outcomes, and time in automation. Results: Of 80 studies identified, 20 met inclusion criteria representing 171,209 individuals. Time in target range 70–180 mg/dL (3.9–10.0 mmol/L) was the primary outcome in 65% of studies, with the majority of reports (71%) demonstrating a >10% change with AID use. Change in hemoglobin A1c (HbA1c) was reported in nine studies (range 0.1%–0.9%), whereas four reported changes in glucose management indicator (GMI) with a 0.1%–0.4% reduction noted. A decrease in HbA1c or GMI of >0.2% was achieved in two-thirds of the studies describing change in HbA1c and 80% of articles where GMI was described. Time below range <70 mg/dL (<3.9 mmol/L) was reported in 16 studies, with all but 1 study showing stable or reduced levels. Most systems had >90% time in automation. Conclusion: With larger and more diverse populations, and follow-up periods of longer duration (∼9 months vs. 3–6 months for pivotal trials), real-world retrospective analyses confirm pivotal trial findings. Given the glycemic benefits demonstrated, AID is rapidly becoming the standard of care for all people living with T1D. Individuals should be informed of these systems and differences between them, have access to and coverage for these technologies, and receive support as they integrate this mode of insulin delivery into their lives. [ABSTRACT FROM AUTHOR]

ObjectiveEven under closed-loop (CL) conditions, meal-related blood glucose (BG) excursions frequently exceed target levels as a result of delays in absorption of insulin from the subcutaneous site of infusion. We hypothesized that delaying gastric emptying with preprandial injections of pramlintide would improve postprandial glycemia by allowing a better match between carbohydrate and insulin absorptions.Research design and methodsEight subjects (4 female; age, 15-28 years; A1C, 7.5 ± 0.7%) were studied for 48 h on a CL insulin-delivery system with a proportional integral derivative algorithm with insulin feedback: 24 h on CL control alone (CL) and 24 h on CL control plus 30-μg premeal injections of pramlintide (CLP). Target glucose was set at 120 mg/dL; timing and contents of meals were identical on both study days. No premeal manual boluses were given. Differences in reference BG excursions, defined as the incremental glucose rise from premeal to peak, were compared between conditions for each meal.ResultsCLP was associated with overall delayed time to peak BG (2.5 ± 0.9 vs. 1.5 ± 0.5 h; P < 0.0001) and reduced magnitude of glycemic excursion (88 ± 42 vs. 113 ± 32 mg/dL; P = 0.006) compared with CL alone. Pramlintide effects on glycemic excursions were particularly evident at lunch and dinner, in association with higher premeal insulin concentrations at those mealtimes.ConclusionsPramlintide delayed the time to peak postprandial BG and reduced the magnitude of prandial BG excursions. Beneficial effects of pramlintide on CL may in part be related to higher premeal insulin levels at lunch and dinner compared with breakfast.

OBJECTIVE: Data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were evaluated to understand glucose changes during activity and identify factors that may influence changes. RESEARCH DESIGN AND METHODS: In this real-world observational study, adolescents with type 1 diabetes self-reported physical activity, food intake, and insulin dosing (multiple-daily injection users) using a smartphone application. Heart rate and continuous glucose monitoring data were collected, as well as pump data downloads. RESULTS: Two hundred fifty-one adolescents (age 14 ± 2 years [mean ± SD]; HbA1c 7.1 ± 1.3% [54 ± 14.2 mmol/mol]; 42% female) logged 3,738 activities over ∼10 days of observation. Preactivity glucose was 163 ± 66 mg/dL (9.1 ± 3.7 mmol/L), dropping to 148 ± 66 mg/dL (8.2 ± 3.7 mmol/L) by end of activity; median duration of activity was 40 min (20, 75 [interquartile range]) with a mean and peak heart rate of 109 ± 16 bpm and 130 ± 21 bpm. Drops in glucose were greater in those with lower baseline HbA1c levels (P = 0.002), shorter disease duration (P = 0.02), less hypoglycemia fear (P = 0.04), and a lower BMI (P = 0.05). Event-level predictors of greater drops in glucose included self-classified "noncompetitive" activities, insulin on board >0.05 units/kg body mass, glucose already dropping prior to the activity, preactivity glucose >150 mg/dL (>8.3 mmol/L) and time 70–180 mg/dL >70% in the 24 h before the activity (all P < 0.001). CONCLUSIONS: Participant-level and activity event-level factors can help predict the magnitude of drop in glucose during real-world physical activity in youth with type 1 diabetes. A better appreciation of these factors may improve decision support tools and self-management strategies to reduce activity-induced dysglycemia in active adolescents living with the disease. [ABSTRACT FROM AUTHOR]

Context The value of continuous glucose monitoring (CGM) for monitoring autoantibody (AAB)-positive individuals in clinical trials for progression of type 1 diabetes (T1D) is unknown. Objective Compare CGM with oral glucose tolerance test (OGTT)–based metrics in prediction of T1D. Methods At academic centers, OGTT and CGM data from multiple-AAB relatives were evaluated for associations with T1D diagnosis. Participants were multiple-AAB–positive individuals in a TrialNet Pathway to Prevention (TN01) CGM ancillary study (n = 93). The intervention was CGM for 1 week at baseline, 6 months, and 12 months. Receiver operating characteristic (ROC) curves of CGM and OGTT metrics for prediction of T1D were analyzed. Results Five of 7 OGTT metrics and 29/48 CGM metrics but not HbA1c differed between those who subsequently did or did not develop T1D. ROC area under the curve (AUC) of individual CGM values ranged from 50% to 69% and increased when adjusted for age and AABs. However, the highest-ranking metrics were derived from OGTT: 4/7 with AUC ∼80%. Compared with adjusted multivariable models using CGM data, OGTT-derived variables, Index60 and DPTRS (Diabetes Prevention Trial-Type 1 Risk Score), had higher discriminative ability (higher ROC AUC and positive predictive value with similar negative predictive value). Conclusion Every 6-month CGM measures in multiple-AAB–positive individuals are predictive of subsequent T1D, but less so than OGTT-derived variables. CGM may have feasibility advantages and be useful in some settings. However, our data suggest there is insufficient evidence to replace OGTT measures with CGM in the context of clinical trials. [ABSTRACT FROM AUTHOR]

The authors of this response letter express their gratitude to Dr. van der Heuvel and colleagues for providing additional data on the CareLink Personal database. They clarify that they did not claim a causal link between performance outcomes and selection bias in their previous manuscript on the MiniMed 780G system. However, they emphasize the importance of acknowledging the possibility of bias in sample selection in any peer-reviewed publication. The authors also caution against making direct comparisons between different systems and making causal claims about the impact of the control algorithm alone on outcomes. They believe that automated insulin delivery is the standard of care for individuals with type 1 diabetes and hope that these studies will support greater coverage and access to this technology. [Extracted from the article]