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Background: As a result of the COVID-19 pandemic first wave, reductions in ST-elevation myocardial infarction (STEMI) invasive care, ranging from 23% to 76%, have been reported from various countries. Whether this change had any impact on coronary angiography (CA) volume or on mechanical support device use for STEMI and post-STEMI mechanical complications in Canada is unknown. Methods: We administered a Canada-wide survey to all cardiac catheterization laboratory directors, seeking the volume of CA use for STEMI performed during the period from March 1 2020 to May 31, 2020 (pandemic period), and during 2 control periods (March 1, 2019 to May 31, 2019 and March 1, 2018 to May 31, 2018). The number of left ventricular support devices used, as well as the number of ventricular septal defects and papillary muscle rupture cases diagnosed, was also recorded. We also assessed whether the number of COVID-19 cases recorded in each province was associated with STEMI-related CA volume. Results: A total of 41 of 42 Canadian catheterization laboratories (98%) provided data. There was a modest but statistically significant 16% reduction (incidence rate ratio [IRR] 0.84; 95% confidence interval 0.80-0.87) in CA for STEMI during the first wave of the pandemic, compared to control periods. IRR was not associated with provincial COVID-19 caseload. We observed a 26% reduction (IRR 0.74; 95% confidence interval 0.61-0.89) in the use of intra-aortic balloon pump use for STEMI. Use of an Impella pump and mechanical complications from STEMI were exceedingly rare. Conclusions: We observed a modest 16% decrease in use of CA for STEMI during the pandemic first wave in Canada, lower than the level reported in other countries. Provincial COVID-19 caseload did not influence this reduction. Résumé: Introduction: Après la première vague de la pandémie de COVID-19, de nombreux pays ont déclaré une réduction de 23 % à 76 % des soins invasifs de l'infarctus du myocarde avec élévation du segment ST (STEMI). On ignore si ce changement a entraîné des répercussions sur le volume d'angiographies coronariennes (AC) ou sur l'utilisation des dispositifs d'assistance mécanique lors de STEMI et des complications mécaniques post-STEMI au Canada. Méthodes: Nous avons réalisé un sondage pancanadien auprès de tous les directeurs de laboratoire de cathétérisme cardiaque pour obtenir le volume d'utilisation des AC lors des STEMI réalisées durant la période du 1er mars 2020 au 31 mai 2020 (période de pandémie) et durant 2 périodes témoins (1er mars 2019 au 31 mai 2019 et 1er mars 2018 au 31 mai 2018). Le nombre de dispositifs d'assistance ventriculaire gauche utilisés et le nombre de cas de communications interventriculaires et de ruptures du muscle papillaire diagnostiqués ont également été enregistrés. Nous avons aussi évalué si le nombre de cas de COVID-19 enregistrés dans chaque province était associé au volume d'AC liées aux STEMI. Résultats: Au total, 41 des 42 laboratoires canadiens de cathétérisme (98 %) ont fourni des données. Lors de la comparaison de la première vague de la pandémie aux périodes témoins, nous avons noté une réduction modeste, mais significative, sur le plan statistique de 16 % (ratio du taux d'incidence [RTI] 0,84; intervalle de confiance à 95 % 0,80-0,87) des AC lors de STEMI. Le RTI n’était pas associé au nombre provincial de cas de COVID-19. Nous avons observé une réduction de 26 % (RTI 0,74; intervalle de confiance à 95 % 0,61-0,89) de l'utilisation de pompes à ballonnet intra-aortique lors de STEMI. L'utilisation d'une pompe Impella et les complications mécaniques après les STEMI étaient extrêmement rares. Conclusions: Nous avons observé une diminution modeste de 16 % de l'utilisation des AC lors de STEMI durant la première vague de la pandémie au Canada, soit une diminution plus faible que ce que les autres pays ont signalé. Le nombre provincial de cas de COVID-19 n'a pas influencé cette réduction.

Background: Automated insulin delivery (AID) is widely available to people with type 1 diabetes (T1D), providing superior glycemic control versus traditional methods. The next generation of AID devices focus on minimizing user/device interactions, especially around meals ("full closed loop," [FCL]). Our goal was to assess the postprandial glycemic impact of the bolus priming system (BPS), an algorithm delivering fixed insulin doses based on the likelihood of a meal having occurred, in conjunction with UVA's latest AID. Method: Eleven adults with T1D participated in a supervised randomized-crossover trial assessing glycemic control during two 24-h sessions with identical meals and activity-with and without BPS. On the day in-between study sessions, participants underwent food and activity challenges to test BPS safety and robustness. Continuous glucose monitor (CGM) outcomes and total insulin doses were assessed overall and following meals with potential for BPS to dose additional insulin (CGM >90 mg/dL for 1 h prior). Results: Daytime CGM outcomes were similar with and without BPS: time-in-range (TIR) 70-180 mg/dL 70.6% [62.2-76.5] versus 65.7% [58.6%-80.6%]; time-below-range <70 mg/dL 0% [0-2.1] versus 0% [0-1.3]; respectively. Insulin delivery during 3 h postprandial was indistinguishable 33.5 U [26.4-47.0] versus 35.7 U [28.7-44.9]. Among 43 out of 66 meals with potential to trigger BPS (24/19 BPS/no-BPS), postprandial incremental area-under-the-curve (iAUC) was lower for BPS versus no-BPS (2530 ± 1934 versus 3228 ± 2029, P = 0.047), but CGM outcomes were inconclusive: 4-h-TIR 51.2% [19.8-83.3] versus 40.2% [20.8-56.3] ( P = 0.24). There were no severe adverse events. Conclusion: While there was no difference in TIR, when BPS was active an improved postprandial AUC in FCL was obtained via earlier insulin injection.

Children with type 1 diabetes and their caregivers face numerous challenges navigating the unpredictability of this complex disease. Although the burden of managing diabetes remains significant, new technology has eased some of the load and allowed children with type 1 diabetes to achieve tighter glycaemic management without fear of excess hypoglycaemia. Continuous glucose monitor use alone improves outcomes and is considered standard of care for paediatric type 1 diabetes management. Similarly, automated insulin delivery (AID) systems have proven to be safe and effective for children as young as 2 years of age. AID use improves not only blood glucose levels but also quality of life for children with type 1 diabetes and their caregivers and should be strongly considered for all youth with type 1 diabetes if available and affordable. Here, we review key data on the use of diabetes technology in the paediatric population and discuss management issues unique to children and adolescents., (© 2024. The Author(s).)

Objective: To evaluate the impact of prolonged hybrid closed loop (HCL) use in children with type 1 diabetes (T1D) on glucose control and BMI throughout pubertal progression., Research Design and Methods: We used a prospective multicenter extension study following the Free-Life Kid AP (FLKAP) HCL trial. The 9-month previously reported FLKAP trial included 119 prepubertal children (aged 6-12 years). During the extension study, participants could continue to use HCL for 30 months (M9 to M39). HbA1c values were collected every 3 months up to M39, while continuous glucose monitoring metrics, BMI z scores, and Tanner stages were collected up to M24. Noninferiority tests were performed to assess parameter sustainability over time., Results: One hundred seventeen children completed the extension study, with mean age 10.1 years (minimum to maximum, 6.8-14.0) at the beginning. Improvement of HbA1c obtained in the FLKAP trial was significantly sustained during extension (median [interquartile range], M9 7.0% [6.8-7.4], and M39 7.0% [6.6-7.4], P < 0.0001 for noninferiority test) and did not differ between children who entered puberty at M24 (Tanner stage ≥2; 54% of the patients) and patients who remained prepubertal. BMI z score also remained stable (M9 0.41 [-0.29 to 1.13] and M24 0.48 [-0.11 to 1.13], P < 0.0001, for noninferiority test). No severe hypoglycemia and one ketoacidosis episode not related to the HCL system occurred., Conclusions: Prolonged use of HCL can safely and effectively mitigate impairment of glucose control usually associated with pubertal progression without impact on BMI in children with T1D., (© 2024 by the American Diabetes Association.)

Context: Insulin sensitivity (SI) varies with age in Type 1 diabetes (T1D)., Objective: To compare postprandial glucose turnover and insulin sensitivity between adolescents and adults with T1D., Design: Cross-sectional comparison., Setting: Clinical Research Unit., Patients: 21 early adolescents with T1D (T1D-adol) [12F; age: 11.5 ± 0.5yrs.; BMI: 19 ± 2 kg/m2], 13 adults with T1D (T1D-adult) [5F; 37.8 ± 9.1yrs.; BMI: 27 ± 2 kg/m2] and 14 anthropometrically matched adults without diabetes (ND) [7F; 26.9 ± 7.0yrs.; BMI: 25 ± 2.5 kg/m2]., Procedure: Triple-tracer mixed meal study and oral glucose models., Main Outcome Measure: SI between T1D-adol and T1D-adult., Results: Post-prandial glucose excursions were not different in T1D-adol vs T1D-adult (p = 0.111) but higher than in ND (p < 0.01). Insulin excursions were also similar in T1D-adol vs. T1D-adult (p = 0.600) and they were both lower (p < 0.05) compared to ND, while glucagon excursions were lower (p < 0.01) in T1D-adol than in T1D-adult and ND. Integrated rates of endogenous glucose production and glucose disappearance were lower in T1D-adol than in T1D-adult and in ND vs. T1D-adult but did not differ between T1D-adol and ND. Meal glucose appearance did not differ between groups. Insulin sensitivity (SI) in T1D-adol vs ND was similar (p = 0.299). However, SI was higher in T1D-adol and ND vs. T1D-adult (p < 0.01)., Conclusions: We report differences in parameters of postprandial glucose turnover and insulin sensitivity between adults and early adolescents with T1D that could, at least in part, be due to the shorter duration of diabetes among T1D-adol. These data support the concept that over time with T1D endogenous glucose production increases and SI deteriorates., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. See the journal About page for additional terms.)

Background: It has been shown that insulin acceleration by itself might not be sufficient to see clear improvements in glycemic metrics, and insulin therapy may need to be adjusted to fully leverage the extra safety margin provided by faster pharmacokinetic (PK) and pharmacodynamic (PD) profiles. The objective of this work is to explore how to perform such adjustments on a commercially available automated insulin delivery (AID) system., Methods: Ultra-rapid lispro (URLi) is modeled within the UVA/Padova simulation platform using data from previously published clamp studies. The Control-IQ AID algorithm is selected as it leverages carbohydrate-to-insulin ratio (CR in g/U), correction factor (CF in mg/dL/U), and basal rate (BR in U/h) daily profiles that are fully customizable. An experiment roadmap is proposed to understand how to safely modify these profiles when switching from lispro to URLi., Results: Simulations show that a 7% decrease in CR (approximately an 8% increase in prandial insulin) and a 7.5% increase in BR lead to cumulative improvements in glucose control with URLi. Comparing with baseline metrics using lispro, a clinically significant increase in time in the range of 70 to 180 mg/dL (overall: 70.2%-75.2%, P < .001; 6 am-12 am: 62.4%-68.5%, P < .001) and a reduction in time below 70 mg/dL (overall: 1.8%-1.2%, P < .001; 6 am-12 am: 1.8%-1.3%, P < .001) were observed., Conclusion: Properly adjusting therapy parameters allows to fully leverage glucose control benefits provided by faster insulin analogues, opening opportunities to take another step forward into a next generation of more effective AID solutions., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: PC, JD, and JG receive research support and royalties from Dexcom handled by the University of Virginia’s Licensing and Ventures Group. MDD has received research support from Tandem, Dexcom, and Medtronic. MDB consults for Roche Diagnostics, Dexcom, Adocia, and Arecor; receives research support from Dexcom, Tandem, and Novo Nordisk.

Background: With automated insulin delivery (AID) systems becoming widely adopted in the management of type 1 diabetes, we have seen an increase in occurrences of rebound hypoglycemia or generated hypoglycemia induced by the controller's response to rapid glucose rises following rescue carbohydrates. Furthermore, as AID systems aim to enable complete automation of prandial control, algorithms are designed to react even more strongly to glycemic rises. This work introduces a rebound hypoglycemia prevention layer (HypoSafe) that can be easily integrated into any AID system., Methods: HypoSafe constrains the maximum permissible insulin delivery dose based on the minimum glucose reading from the previous hour and the current glucose level. To demonstrate its efficacy, we integrated HypoSafe into the latest University of Virginia (UVA) AID system and simulated two scenarios using the 100-adult cohort of the UVA/Padova T1D simulator: a nominal case including three unannounced meals, and another case where hypoglycemia was purposely induced by an overestimated manual bolus., Results: In both simulation scenarios, rebound hypoglycemia events were reduced with HypoSafe (nominal: from 39 to 0, hypo-induced: from 55 to 7) by attenuating the commanded basal (nominal: 0.27U vs. 0.04U, hypo-induced: 0.27U vs. 0.03U) and bolus (nominal: 1.02U vs. 0.05U, hypo-induced: 0.43U vs. 0.02U) within the 30-minute interval after treating a hypoglycemia event. No clinically significant changes resulted for time in the range of 70 to 180 mg/dL or above 180 mg/dL., Conclusion: HypoSafe was shown to be effective in reducing rebound hypoglycemia events without affecting achieved time in range when combined with an advanced AID system., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: M.F.V.-T., P.C., and M.D.B. receive research support and royalties from Dexcom handled by the University of Virginia’s Licensing and Ventures Group. M.D.B. reports consulting and speaker engagement from Dexcom, Tandem Diabetes Care, Sanofi, Roche, and Portal Insulin LLC.

Objective: Examine patient-reported outcomes (PROs) after the use of t:slim X2 insulin pump with Control-IQ technology (CIQ) in young children with type 1 diabetes., Methods: Children with type 1 diabetes, ages 2 to < 6 years (n = 102), were randomly assigned 2:1 to either CIQ or standard care (SC) with pump or multiple daily injections (MDI) plus continuous glucose monitoring (CGM) for 13 weeks. Both groups were offered to use CIQ for an additional 13 weeks after the randomized control trial's (RCT) completion. Guardians completed PRO questionnaires at baseline, 13-, and 26-weeks examining hypoglycemia concerns, quality of life, parenting stress, and sleep. At 26 weeks, 28 families participated in user-experience interviews. Repeated measures analyses compared PRO scores between systems used., Result: Comparing CIQ vs SC, responses on all 5 PRO surveys favored the CIQ group, showing that CIQ was superior to SC at 26 weeks (p values < 0.05). User-experience interviews indicated significant benefits in optimized glycemic control overall and nighttime control (28 of 28 families endorsed). All but 2/28 families noted substantial reduction in management burden resulting in less mental burden and all but 4 stated that they wanted their children to continue using CIQ., Conclusions: Families utilizing CIQ experienced glycemic benefits coupled with substantial benefits in PROs, documented in surveys and interviews. Families utilizing CIQ had reduced hypoglycemia concerns and parenting stress, and improved quality of life and sleep. These findings demonstrate the benefit of CIQ in young children with type 1 diabetes that goes beyond documented glycemic benefit., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid., Methods: Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources., Significance: These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

Objective: To determine insulin dosing parameters that are associated with and predict optimal outcomes for people using t:slim X2 with Control-IQ technology (CIQ). Methods: Retrospective deidentified data from CIQ users were analyzed to determine the effect of Correction Factor, Carbohydrate-to-Insulin (C:I) Ratio, and basal rate settings (standardized by total daily insulin [TDI]) on glycemic control. We performed an associative analysis followed by linear regressions to determine the relative importance of the settings and confounding variables (e.g., age or number of user-initiated boluses) in predicting consensus glycemic outcomes. Results: Data from 20,764 individuals were analyzed (median age 39 years [interquartile range 19, 59], 55% female, TDI 46.4 U [33-65.2]). More aggressive Correction Factor settings, C:I ratio settings, and basal programs were all strongly associated with higher time in range (TIR, 70-180 mg/dL) and to a lesser degree to higher time <70 mg/dL. In linear regression, more aggressive Correction Factor predicted higher TIR, lower coefficient of variation, and importantly had only negligible impact on time below range. Higher basal rate settings and lower C:I ratio predicted increased TIR as well as increased hypoglycemia. The most important predictor in all glycemic outcomes was the average number of user-given boluses per day. Conclusion: Basal rates, C:I ratios, and Correction Factor settings all impact glycemic outcomes in CIQ users in usual clinical care. The correction Factor setting may be the most impactful "lever to pull" for clinicians and CIQ users to optimize TIR while not increasing hypoglycemia.

Objective: Assess the safety and efficacy of automated insulin delivery (AID) in adults with type 1 diabetes (T1D) at high risk for hypoglycemia., Research Design and Methods: Participants were 72 adults with T1D who used an insulin pump with Clarke Hypoglycemia Perception Awareness scale score >3 and/or had severe hypoglycemia during the previous 6 months confirmed by time below range (TBR; defined as sensor glucose [SG] reading <70 mg/dL) of at least 5% during 2 weeks of blinded continuous glucose monitoring (CGM). Parallel-arm, randomized trial (2:1) of AID (Tandem t:slim ×2 with Control-IQ technology) versus CGM and pump therapy for 12 weeks. The primary outcome was TBR change from baseline. Secondary outcomes included time in target range (TIR; 70-180 mg/dL), time above range (TAR), mean SG reading, and time with glucose level <54 mg/dL. An optional 12-week extension with AID was offered to all participants., Results: Compared with the sensor and pump (S&P), AID resulted in significant reduction of TBR by -3.7% (95% CI -4.8, -2.6), P < 0.001; an 8.6% increase in TIR (95% CI 5.2, 12.1), P < 0.001; and a -5.3% decrease in TAR (95% CI -87.7, -1.8), P = 0.004. Mean SG reading remained similar in the AID and S&P groups. During the 12-week extension, the effects of AID were sustained in the AID group and reproduced in the S&P group. Two severe hypoglycemic episodes occurred using AID., Conclusions: In adults with T1D at high risk for hypoglycemia, AID reduced the risk for hypoglycemia more than twofold, as quantified by TBR, while improving TIR and reducing hyperglycemia. Hence, AID is strongly recommended for this specific population., (© 2023 by the American Diabetes Association.)

Background: Model predictive control (MPC) has become one of the most popular control strategies for automated insulin delivery (AID) in type 1 diabetes (T1D). These algorithms rely on a prediction model to determine the best insulin dosing every sampling time. Although these algorithms have been shown to be safe and effective for glucose management through clinical trials, managing the ever-fluctuating relationship between insulin delivery and resulting glucose uptake (aka insulin sensitivity, IS) remains a challenge. We aim to evaluate the effect of informing an AID system with IS on the performance of the system., Method: The University of Virginia (UVA) MPC control-based hybrid closed-loop (HCL) and fully closed-loop (FCL) system was used. One-day simulations at varying levels of IS were run with the UVA/Padova T1D Simulator. The AID system was informed with an estimated value of IS obtained through a mixed meal glucose tolerance test. Relevant controller parameters are updated to inform insulin dosing of IS. Performance of the HCL/FCL system with and without information of the changing IS was assessed using a novel performance metric penalizing the time outside the target glucose range., Results: Feedback in AID systems provides a certain degree tolerance to changes in IS. However, IS-informed bolus and basal dosing improve glycemic outcomes, providing increased protection against hyperglycemia and hypoglycemia according to the individual's physiological state., Conclusions: The proof-of-concept analysis presented here shows the potentially beneficial effects on system performance of informing the AID system with accurate estimates of IS. In particular, when considering reduced IS, the informed controller provides increased protection against hyperglycemia compared with the naïve controller. Similarly, reduced hypoglycemia is obtained for situations with increased IS. Further tailoring of the adaptation schemes proposed in this work is needed to overcome the increased hypoglycemia observed in the more resistant cases and to optimize the performance of the adaptation method., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: M.M-.V. receives research support and royalties through her institution from Dexcom. C.F. receives royalties from Dexcom and Novo Nordisk managed through her institution. M.D.B. receives research support through his institution from Tandem, Dexcom, and Novo Nordisk; M.D.B. received honorarium and travel compensation from Sanofi and Tandem; M.D.B. consults for Dexcom and Sanofi.

Objective: Meals are a consistent challenge to glycemic control in type 1 diabetes (T1D). Our objective was to assess the glycemic impact of meal anticipation within a fully automated insulin delivery (AID) system among adults with T1D., Research Design and Methods: We report the results of a randomized crossover clinical trial comparing three modalities of AID systems: hybrid closed loop (HCL), full closed loop (FCL), and full closed loop with meal anticipation (FCL+). Modalities were tested during three supervised 24-h admissions, where breakfast, lunch, and dinner were consumed per participant's home schedule, at a fixed time, and with a 1.5-h delay, respectively. Primary outcome was the percent time in range 70-180 mg/dL (TIR) during the breakfast postprandial period for FCL+ versus FCL., Results: Thirty-five adults with T1D (age 44.5 ± 15.4 years; HbA1c 6.7 ± 0.9%; n = 23 women and n = 12 men) were randomly assigned. TIR for the 5-h period after breakfast was 75 ± 23%, 58 ± 21%, and 63 ± 19% for HCL, FCL, and FCL+, respectively, with no significant difference between FCL+ and FCL. For the 2 h before dinner, time below range (TBR) was similar for FCL and FCL+. For the 5-h period after dinner, TIR was similar for FCL+ and FCL (71 ± 34% vs. 72 ± 29%; P = 1.0), whereas TBR was reduced in FCL+ (median 0% [0-0%] vs. 0% [0-0.8%]; P = 0.03). Overall, 24-h control for HCL, FCL, and FCL+ was 86 ± 10%, 77 ± 11%, and 77 ± 12%, respectively., Conclusions: Although postprandial control remained optimal with hybrid AID, both fully AID solutions offered overall TIR >70% with similar or lower exposure to hypoglycemia. Anticipation did not significantly improve postprandial control in AID systems but also did not increase hypoglycemic risk when meals were delayed., (© 2023 by the American Diabetes Association.)

Background: Predicting the risk for type 1 diabetes (T1D) is a significant challenge. We use a 1-week continuous glucose monitoring (CGM) home test to characterize differences in glycemia in at-risk healthy individuals based on autoantibody presence and develop a machine-learning technology for CGM-based islet autoantibody classification. Methods: Sixty healthy relatives of people with T1D with mean ± standard deviation age of 23.7 ± 10.7 years, HbA1c of 5.3% ± 0.3%, and body mass index of 23.8 ± 5.6 kg/m 2 with zero ( n  = 21), one ( n  = 18), and ≥2 ( n  = 21) autoantibodies were enrolled in an National Institutes of Health TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic outcomes were computed from weekly, overnight (12:00-06:00), and post-SLMM CGM traces, compared across groups, and used in four supervised machine-learning autoantibody status classifiers. Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. Results: Among all computed glycemia metrics, only three were different across the autoantibodies groups: percent time >180 mg/dL (T180) weekly ( P  = 0.04), overnight CGM incremental AUC ( P  = 0.005), and T180 for 75 min post-SLMM CGM traces ( P  = 0.004). Once overnight and post-SLMM features are incorporated in machine-learning classifiers, a linear support vector machine model achieved the best performance of classifying autoantibody positive versus autoantibody negative participants with AUC-ROC ≥0.81. Conclusion: A new technology combining machine learning with a potentially self-administered 1-week CGM home test can help improve T1D risk detection without the need to visit a hospital or use a medical laboratory. Trial registration: ClinicalTrials.gov registration no. NCT02663661.

Background: A 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., Methods: We 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., Results: The 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., Conclusion: The 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.

Background: It is unclear whether hybrid closed-loop (HCL) therapy attenuates the metabolic impact of missed or suboptimal meal insulin bolus compared with sensor-augmented pump (SAP) therapy in children with type 1 diabetes in free-living conditions. Methods: This is an ancillary study from a multicenter randomized controlled trial that compared 24/7 HCL with evening and night (E/N) HCL for 36 weeks in children between 6 and 12 years old. In the present study, the 60 children from the E/N arm underwent a SAP phase, an E/N HCL for 18 weeks, then a 24/7 phase for 18 weeks, extended for 36 more weeks. The last 28-30 days of each of the four phases were analyzed according to meal bolus management (cumulated 6817 days). The primary endpoint was the percentage of time that the sensor glucose was in the target range (TIR, 70-180 mg/dL) according to the number of missed boluses per day. Findings: TIR was 54% ± 10% with SAP, 63% ± 7% with E/N HCL, and steadily 67% ± 7% with 24/7 HCL. From the SAP phase to 72 weeks of HCL, the percentage of days with at least one missed meal bolus increased from 12% to 22%. Estimated marginal (EM) mean TIR when no bolus was missed was 54% (95% confidence intervals [CI] 53-56) in SAP and it was 13% higher (95% CI 11-15) in the 24/7 HCL phase. EM mean TIR with 1 and ≥2 missed boluses/day was 49.5% (95% CI 46-52) and 45% (95% CI 39-51) in SAP, and it was 15% (95% CI 14-16) and 17% higher (95% CI 6-28), respectively, in the 24/7 HCL phase ( P  < 0.05 for all comparisons vs. SAP). Interpretation: HCL persistently improves glycemic control compared with SAP, even in case of meal bolus omission. ClinicalTrials.gov (NCT03739099).

Objective: To evaluate the effect of hybrid-closed loop Control-IQ technology (Control-IQ) in randomized controlled trials (RCTs) in subgroups based on baseline characteristics such as race/ethnicity, socioeconomic status (SES), prestudy insulin delivery modality (pump or multiple daily injections), and baseline glycemic control. Methods: Data were pooled and analyzed from 3 RCTs comparing Control-IQ to a Control group using continuous glucose monitoring in 369 participants with type 1 diabetes (T1D) from age 2 to 72 years old. Results: Time in range 70-180 mg/dL (TIR) in the Control-IQ group ( n  = 256) increased from 57% ± 17% at baseline to 70% ± 11% during follow-up, and in the Control group ( n  = 113) was 56% ± 15% and 57% ± 14%, respectively (adjusted treatment group difference = 11.5%, 95% confidence interval +9.7% to +13.2%, P  < 0.001), an increase of 2.8 h/day on average. Significant reductions in mean glucose, hyperglycemia metrics, hypoglycemic metrics, and HbA1c were also observed. A statistically similar beneficial treatment effect on time in range 70-180 mg/dL was observed across the full age range irrespective of race-ethnicity, household income, prestudy continuous glucose monitor use, or prestudy insulin delivery method. Participants with the highest baseline HbA1c levels showed the greatest improvements in TIR and HbA1c. Conclusion: This pooled analysis of Control-IQ RCTs demonstrates the beneficial effect of Control-IQ in T1D across a broad spectrum of participant characteristics, including racial-ethnic minority, lower SES, lack of prestudy insulin pump experience, and high HbA1c levels. The greatest benefit was observed in participants with the worst baseline glycemic control in whom the auto-bolus feature of the Control-IQ algorithm appears to have substantial impact. Since no subgroups were identified that did not benefit from Control-IQ, hybrid-closed loop technology should be strongly considered for all youth and adults with T1D. Clinical Trials Registry: clinicaltrials.gov; NCT03563313, NCT03844789, and NCT04796779.

Background: Ultrarapid-acting insulin analogs that could improve or even prevent postprandial hyperglycemia are now available for both research and clinical care. However, clear glycemic benefits remain elusive, especially when combined with automated insulin delivery (AID) systems. In this work, we study two insulin formulations in silico and highlight adjustments of both open-loop and closed-loop insulin delivery therapies as a critical step to achieve clinically meaningful improvements. Methods: Subcutaneous insulin transport models for two faster analogs, Fiasp (Novo Nordisk, Bagsværd, Denmark) and AT247 (Arecor, Saffron Walden, United Kingdom), were identified using data collected from prior clamp experiments, and integrated into the UVA/Padova type 1 diabetes simulator (adult cohort, N  = 100). Pump therapy parameters and the aggressiveness of our full closed-loop algorithm were adapted to the new insulin pharmacokinetic and pharmacodynamic profiles through a sequence of in silico studies. Finally, we assessed these analogs' glycemic impact with and without modified therapy parameters in simulated conditions designed to match clinical trial data. Results: Simply switching to faster insulin analogs shows limited improvements in glycemic outcomes. However, when insulin acceleration is accompanied by therapy adaptation, clinical significance is found comparing time-in-range (70-180 mg/dL) with Aspart versus AT247 in open-loop (+5.1%); and Aspart versus Fiasp (+5.4%) or AT247 (+10.6%) in full closed-loop with no clinically significant differences in the exposure to hypoglycemia. Conclusion: In silico results suggest that properly adjusting intensive insulin therapy profiles, or AID tuning, to faster insulin analogs is necessary to obtain clinically significant improvements in glucose control.

Background: Closed-loop control systems of insulin delivery may improve glycemic outcomes in young children with type 1 diabetes. The efficacy and safety of initiating a closed-loop system virtually are unclear., Methods: In this 13-week, multicenter trial, we randomly assigned, in a 2:1 ratio, children who were at least 2 years of age but younger than 6 years of age who had type 1 diabetes to receive treatment with a closed-loop system of insulin delivery or standard care that included either an insulin pump or multiple daily injections of insulin plus a continuous glucose monitor. The primary outcome was the percentage of time that the glucose level was in the target range of 70 to 180 mg per deciliter, as measured by continuous glucose monitoring. Secondary outcomes included the percentage of time that the glucose level was above 250 mg per deciliter or below 70 mg per deciliter, the mean glucose level, the glycated hemoglobin level, and safety outcomes., Results: A total of 102 children underwent randomization (68 to the closed-loop group and 34 to the standard-care group); the glycated hemoglobin levels at baseline ranged from 5.2 to 11.5%. Initiation of the closed-loop system was virtual in 55 patients (81%). The mean (±SD) percentage of time that the glucose level was within the target range increased from 56.7±18.0% at baseline to 69.3±11.1% during the 13-week follow-up period in the closed-loop group and from 54.9±14.7% to 55.9±12.6% in the standard-care group (mean adjusted difference, 12.4 percentage points [equivalent to approximately 3 hours per day]; 95% confidence interval, 9.5 to 15.3; P<0.001). We observed similar treatment effects (favoring the closed-loop system) on the percentage of time that the glucose level was above 250 mg per deciliter, on the mean glucose level, and on the glycated hemoglobin level, with no significant between-group difference in the percentage of time that the glucose level was below 70 mg per deciliter. There were two cases of severe hypoglycemia in the closed-loop group and one case in the standard-care group. One case of diabetic ketoacidosis occurred in the closed-loop group., Conclusions: In this trial involving young children with type 1 diabetes, the glucose level was in the target range for a greater percentage of time with a closed-loop system than with standard care. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases; PEDAP ClinicalTrials.gov number, NCT04796779.)., (Copyright © 2023 Massachusetts Medical Society.)

The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society.)

Background: Women with type 1 diabetes (T1D) of fertile age may experience fluctuations in insulin needs across the menstrual cycle. When present, these fluctuations complicate glucose management and oftentimes worsen glycemic control. In this work, an in silico analysis was conducted to assess whether current technology is sufficient to handle changes in insulin needs due to the menstrual cycle in women with T1D. Methods: Euglycemic clamp studies were performed in 16 women with T1D in the follicular phase (FP) and luteal phase (LP) of the menstrual cycle. Interphase insulin sensitivity (IS) variability observed in the data was modeled and introduced in the University of Virginia/Padova T1D Simulator. Open-loop and closed-loop insulin delivery was tested in two in silico studies, without ( nominal study ) and with ( informed study ) a priori knowledge on cycle-related IS variability informing insulin therapy. Glycemic metrics were computed on the obtained glucose traces. Results: In the pool of studied women, the glucose infusion rate area under the curve significantly decreased from FP to LP ( P  = 0.0107), indicating an average decrease of IS in LP. When introduced in the simulator, this pattern led to increased time spent >180 and >250 mg/dL during LP versus FP in the nominal studies, irrespective of the insulin delivery strategy. In the informed studies, glycemic metrics stabilized across the cycle. Conclusion: This work suggests that current insulin delivery technology may benefit from informing the dosing algorithm with knowledge on menstrual cycle related IS changes. Clinical validation of these results is warranted. ClinicalTrials.gov identifier: NCT02693938.

Objective: Continuous glucose monitoring (CGM) improves diabetes management, but its reliability in individuals on hemodialysis is poorly understood and potentially affected by interstitial and intravascular volume variations., Research Design and Methods: We assessed the accuracy of a factory-calibrated CGM by using venous blood glucose measurements (vBGM) during hemodialysis sessions and self-monitoring blood glucose (SMBG) at home., Results: Twenty participants completed the protocol. The mean absolute relative difference of the CGM was 13.8% and 14.4%, when calculated on SMBG (n = 684) and on vBGM (n = 624), and 98.7% and 100% of values in the Parkes error grid A/B zones, respectively. Throughout 181 days of CGM monitoring, the median time in range (70-180 mg/dL) was 38.5% (interquartile range 29.3-57.9), with 28.7% (7.8-40.6) of the time >250 mg/dL., Conclusions: The overall performance of a factory-calibrated CGM appears reasonably accurate and clinically relevant for use in practice by individuals on hemodialysis and health professionals to improve diabetes management., (© 2022 by the American Diabetes Association.)

Objective: Parental sleep quality may contribute to glycemic control in youth with type 1 diabetes. In this article we present sleep analysis from a multicenter, randomized trial of children ages 6-13 years with type 1 diabetes evaluating the Tandem Control-IQ (CIQ) hybrid closed-loop (HCL) system., Research Design and Methods: Pittsburgh Sleep Quality Index (PSQI) scores were assessed at baseline to identify parents as "poor" sleepers (PSQI >5). Glycemic and psycho-behavioral outcomes before and after CIQ use were analyzed in poor sleepers (n = 49) and their children., Results: Nocturnal time in range (P < 0.001) and time hyperglycemic (P < 0.001), Hypoglycemia Fear Survey for Parents score (P < 0.001), Problem Areas in Diabetes scale score (P < 0.001), PSQI score (P < 0.001), and Hypoglycemia Fear Survey for Children score (P = 0.025) significantly improved. Of poor sleepers, 27 became good sleepers (PSQI score <5)., Conclusions: Use of CIQ in youth with type 1 diabetes ages 6-13 years significantly improved sleep and psychosocial measures in parent poor sleepers, coinciding with improvements in child nocturnal glycemia, highlighting the relationship between HCL systems and parent sleep quality., (© 2022 by the American Diabetes Association.)

Background: Physical activity can cause glucose fluctuations both during and after it is performed, leading to hurdles in optimal insulin dosing in people with type 1 diabetes (T1D). We conducted a pilot clinical trial assessing the safety and feasibility of a physical activity-informed mealtime insulin bolus advisor that adjusts the meal bolus according to previous physical activity, based on step count data collected through an off-the-shelf physical activity tracker., Methods: Fifteen adults with T1D, each using a continuous glucose monitor (CGM) and an insulin pump with carbohydrate counting, completed two randomized crossover daily visits. Participants performed a 30 to 45-minute brisk walk before lunch and lunchtime insulin boluses were calculated based on either their standard therapy (ST) or the physical activity-informed bolus method. Post-lunch glycemic excursions were assessed using CGM readings., Results: There was no significant difference between visits in the time spent in hypoglycemia in the post-lunch period (median [IQR] standard: 0 [0]% vs physical activity-informed: 0 [0]%, P  = NS). Standard therapy bolus yielded a higher time spent in 70 to 180 mg/dL target range (mean ± standard: 77% ± 27% vs physical activity-informed: 59% ± 31%, P  = .03) yet, it was associated with a steeper negative slope in the early postprandial phase ( P  = .032)., Conclusions: Use of step count to adjust mealtime insulin following a walking bout has proved to be safe and feasible in a cohort of 15 T1D subjects. Physical activity-informed insulin dosing of meals eaten soon after a walking bout has a potential of mitigating physical activity related glucose reduction in the early postprandial phase.

We assessed predictors of rising hemoglobin A1c (HbA1c) during long-term use of closed-loop control (CLC) in children aged 6-13 years with type 1 diabetes. Participants used a CLC system during a 16-week randomization phase followed by a 12-week extension phase. We compared an "Increased-HbA1c" group ( n  = 17, ≥0.5% rise in HbA1c between randomization and extension phases) to a "Maintained-Improvement" group ( n  = 18, had stable or improved HbA1c). The Increased-HbA1c group had higher pre-CLC HbA1c (8.42% ± 0.80 vs. 7.45% ± 0.93, P  = 0.002). Contrary to a-priori hypotheses, there were no differences in Δ-height-for-age z -score, a surrogate for a pubertal growth spurt (+0.16 vs. -0.15, P  = 0.113), or number of carbohydrate boluses per day, a surrogate for missed boluses (4.4 ± 2.2 vs. 5.2 ± 2.1, P  = 0.263). Both groups maintained high rates in closed-loop. Thus, some children exhibit meaningful rise in HbA1c after initial CLC use, likely from multiple contributing factors, and may benefit from added encouragement during ongoing use.

Background: As type 2 diabetes (T2D) progresses, intensification to combination therapies, such as iGlarLixi (a fixed-ratio GLP-1 RA and basal insulin combination), may be required. Here a simulation study was used to assess the effect of iGlarLixi administration timing (am vs pm) on blood sugar profiles., Methods: Models of lixisenatide were built with a selection procedure, optimizing measurement fits and model complexity, and were included in a pre-existing T2D simulation platform containing glargine models. With the resulting tool, a simulated trial was conducted with 100 in-silico participants with T2D. Individuals were given iGLarLixi either before breakfast or before an evening meal for 2 weeks and daily glycemic profiles were analyzed. In the model, breakfast was considered the largest meal of the day., Results: A similar percentage of time within 24 hours was spent with blood sugar levels between 70 to 180 mg/dL when iGlarLixi was administered pre-breakfast or pre-evening meal (73% vs 71%, respectively). Overall percent of time with blood glucose levels above 180 mg/dL within a 24-hour period was similar when iGlarLixi was administered pre-breakfast or pre-evening meal (26% vs 28%, respectively). Rates of hypoglycemia were low in both regimens, with a blood glucose concentration of below 70 mg/dL only observed for 1% of the 24-hour time period for either timing of administration., Conclusions: Good efficacy was observed when iGlarlixi was administered pre-breakfast; however, administration of iGlarlixi pre-evening meal was also deemed to be effective, even though in the model the size of the evening meal was smaller than that of the breakfast.

Aim: To assess the safety and efficacy of hybrid closed-loop (HCL) insulin delivery 24/7 versus only evening and night (E/N), and on extended 24/7 use, in free-living children with type 1 diabetes., Materials and Methods: Prepubertal children (n = 122; 49 females/73 males; age, 8.6 ± 1.6 years; diabetes duration, 5.2 ± 2.3 years; insulin pump use, 4.6 ± 2.5 years; HbA1c 7.7% ± 0.7%/61 ± 5 mmol/mol) from four centres were randomized for 24/7 versus E/N activation of the Tandem Control-IQ system for 18 weeks. Afterwards, all children used the activated system 24/7 for 18 more weeks. The primary outcome was the percentage of time spent in the 70-180 mg/dL glucose range (TIR)., Results: HCL was active 94.1% and 51.1% of the time in the 24/7 and E/N modes, respectively. TIR from baseline increased more in the 24/7 versus the E/N mode (52.9% ± 9.5% to 67.3% ± 5.6% [+14.4%, 95% CI 12.4%-16.7%] vs. 55.1% ± 10.8% to 64.7% ± 7.0% [+9.6%, 95% CI 7.4%-11.6%]; P = .001). Mean percentage time below range was similarly reduced, from 4.2% and 4.6% to 2.7%, and the mean percentage time above range decreased more in the 24/7 mode (41.9% to 30.0% [-11.9%, 95% CI 9.7%-14.6%] vs. 39.8% to 32.6% [-7.2%, 95% CI 5.0%-9.9%]; P = .007). TIR increased through the whole range of baseline levels and always more with 24/7 use. The results were maintained during the extension phase in those initially on 24/7 use and improved in those with initial E/N use up to those with 24/7 use. Neither ketoacidosis nor severe hypoglycaemia occurred., Conclusions: The current study shows the safety and efficacy of the Tandem Control-IQ system in free-living children with type 1 diabetes for both E/N and 24/7 use; 24/7 use shows better outcomes, sustained for up to 36 weeks with no safety issues., (© 2021 John Wiley & Sons Ltd.)

Objective: Multiple daily injections (MDI) therapy is the most common treatment for type 1 diabetes (T1D), consisting of long-acting insulin to cover fasting conditions and rapid-acting insulin to cover meals. Titration of long-acting insulin is needed to achieve satisfactory glycemia but is challenging due to inter-and intra-individual metabolic variability. In this work, a novel titration algorithm for long-acting insulin leveraging continuous glucose monitoring (CGM) and smart insulin pens (SIP) data is proposed., Methods: The algorithm is based on a glucoregulatory model that describes insulin and meal effects on blood glucose fluctuations. The model is individualized on patient's data and used to extract the theoretical glucose curve in fasting conditions; the individualization step does not require any carbohydrate records. A cost function is employed to search for the optimal long-acting insulin dose to achieve the desired glycemic target in the fasting state. The algorithm was tested in two virtual studies performed within a validated T1D simulation platform, deploying different levels of metabolic variability (nominal and variance). The performance of the method was compared to that achieved with two published titration algorithms based on self-measured blood glucose (SMBG) records. The sensitivity of the algorithm to carbohydrate records was also analyzed., Results: The proposed method outperformed SMBG-based methods in terms of reduction of exposure to hypoglycemia, especially during the night period (0 am-6 am). In the variance scenario, during the night, an improvement in the time in the target glycemic range (70-180 mg/dL) from 69.0% to 86.4% and a decrease in the time in hypoglycemia (<70 mg/dL) from 10.7% to 2.6% was observed. Robustness analysis showed that the method performance is non-sensitive to carbohydrate records., Conclusion: The use of CGM and SIP in people with T1D using MDI therapy has the potential to inform smart insulin titration algorithms that improve glycemic control. Clinical studies in real-world settings are warranted to further test the proposed titration algorithm., Significance: This algorithm is a step towards a decision support system that improves glycemic control and potentially the quality of life, in a population of individuals with T1D who cannot benefit from the artificial pancreas system., Competing Interests: MBD receives research support from Tandem Diabetes, Dexcom, Novo Nordisk, and Arecor paid to his institution. MDB serves as a consultant for Tandem, Dexcom, Adocia, Air Liquide, and Roche. MBD received speaker fees from Tandem and Arecor. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 El Fathi, Fabris and Breton.)

Introduction: Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control., Methods: A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient's total daily insulin (TDI) modulated by the disturbance's likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module., Results: Postprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL <0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 ± 25.32%, MPC + BPS: 42.99 ± 25.81%, MS-MPC: 45.09 ± 25.96%, MPC: 48.18 ± 26.09%)., Conclusions: The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.

Objective: Achieving optimal glycemic control for many individuals with type 1 diabetes (T1D) remains challenging, even with the advent of newer management tools, including continuous glucose monitoring (CGM). Modern management of T1D generates a wealth of data; however, use of these data to optimize glycemic control remains limited. We evaluated the impact of a CGM-based decision support system (DSS) in patients with T1D using multiple daily injections (MDI)., Research Design and Methods: The studied DSS included real-time dosing advice and retrospective therapy optimization. Adults and adolescents (age >15 years) with T1D using MDI were enrolled at three sites in a 14-week randomized controlled trial of MDI + CGM + DSS versus MDI + CGM. All participants (N = 80) used degludec basal insulin and Dexcom G5 CGM. CGM-based and patient-reported outcomes were analyzed. Within the DSS group, ad hoc analysis further contrasted active versus nonactive DSS users., Results: No significant differences were detected between experimental and control groups (e.g., time in range [TIR] +3.3% with CGM vs. +4.4% with DSS). Participants in both groups reported lower HbA1c (-0.3%; P = 0.001) with respect to baseline. While TIR may have improved in both groups, it was statistically significant only for DSS; the same was apparent for time spent <60 mg/dL. Active versus nonactive DSS users showed lower risk of and exposure to hypoglycemia with system use., Conclusions: Our DSS seems to be a feasible option for individuals using MDI, although the glycemic benefits associated with use need to be further investigated. System design, therapy requirements, and target population should be further refined prior to use in clinical care., (© 2021 by the American Diabetes Association.)

Women with type 1 diabetes (T1D) typically experience a decrease in insulin sensitivity (SI) during the second half of their menstrual cycle (or the luteal phase (LP)), which oftentimes is not properly addressed by insulin therapy, therefore leading to increased exposure to hyperglycemia. This study proposes a suitable way to model SI variability due to the menstrual cycle in the FDA-accepted University of Virginia (UVA)/Padova T1D Simulator, to determine to what extent the inclusion of menstrual cycle information to fine-tune insulin therapy could help improve glycemic control in the LP of the menstrual cycle. In-silico tests were performed considering different simulation scenarios, and the obtained results show that hyperglycemic excursions can be largely reduced when SI variability is taken into account for planning insulin therapy, without a relevant increase in hypoglycemic events.

Background and Objective: Glycemic control, especially meal-related disturbance rejection, has proven to be a major challenge for people with type 1 diabetes. In this manuscript, we introduce a novel, personalized, advanced hybrid insulin infusion system (a.k.a. artificial pancreas) based on the Model Predictive Control (MPC) methodology to adjust insulin infusion while automatically rejecting uninformed meals., Methods: The proposed advanced hybrid closed-loop system relies on the integration of three key elements: (i) an adaptive personalized MPC control law that modulates the control strength depending on recent past control actions, glucose measurements, and its derivative, (ii) an automatic Bolus Priming System (BPS) that commands additional insulin injections safely upon the detection of enabling metabolic conditions (e.g., an unacknowledged meal), and (iii) a new hyperglycemia mitigation system to avoid prevailing hyperglycemia. The benefits of the proposed system are demonstrated through simulations and tests using the most up-to-date Type 1 UVA/Padova simulator as preclinical stage prior to in vivo clinical tests. We used a legacy algorithm (USS Virginia), currently used in clinical care, as a benchmark controller., Results: Overall, the proposed control strategy enhanced by an automatic BPS improves glycemic control when compared with an available system. When a large meal is not announced (80g CHO), the proposed controller outperformed the legacy controller in time-in-target-range TIR (postprandial and overnight) and time-in-tight-range TTR (overall, postprandial, and overnight)., Conclusion: The integration of a novel BPS into an advanced control system allowed to automatically reject unannounced meals. Exhaustive simulation studies indicated the safety and feasibility of the proposed controller to be deployed in human clinical trials., Competing Interests: Declaration of Competing Interest M.D.D. reports receiving grant or material support from Tandem, Medtronic, and Dexcom. The remaining authors have nothing to declare. This manuscript is entirely original, has not been copyrighted, published, submitted, or accepted for publication elsewhere., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: The capacity to replay data collected in real life by people with type 1 diabetes mellitus (T1DM) would lead to individualized (vs population) assessment of treatment strategies to control blood glucose and possibly true personalization. Patek et al introduced such a technique, relying on regularized deconvolution of a population glucose homeostasis model to estimate a residual additive signal and reproduce the experimental data; therefore, allowing the subject-specific replay of what-if scenarios by altering the model inputs (eg, insulin). This early method was shown to have a limited domain of validity. We propose and test in silico a similar approach and extend the method applicability., Methods: A subject-specific model personalization of insulin sensitivity and meal-absorption parameters is performed. The University of Virginia (UVa)/Padova T1DM simulator is used to generate experimental scenarios and test the ability of the methodology to accurately reproduce changes in glucose concentration to alteration in meal and insulin inputs. Method performance is assessed by comparing true (UVa/Padova simulator) and replayed glucose traces, using the mean absolute relative difference (MARD) and the Clarke error grid analysis (CEGA)., Results: Model personalization led to a 9.08 and 6.07 decrease in MARD over a prior published method of replaying altered insulin scenarios for basal and bolus changes, respectively. Replay simulations achieved high accuracy, with MARD <10% and more than 95% of readings falling in the CEGA A-B zones for a wide range of interventions., Conclusions: In silico studies demonstrate that the proposed method for replay simulation is numerically and clinically valid over broad changes in scenario inputs, indicating possible use in treatment optimization.

Background: The t:slim X2™ insulin pump with Control-IQ ® technology from Tandem Diabetes Care is an advanced hybrid closed-loop system that was first commercialized in the United States in January 2020. Longitudinal glycemic outcomes associated with real-world use of this system have yet to be reported. Methods: A retrospective analysis of Control-IQ technology users who uploaded data to Tandem's t:connect ® web application as of February 11, 2021 was performed. Users age ≥6 years, with >2 weeks of continuous glucose monitoring (CGM) data pre- and >12 months post-Control-IQ technology initiation were included in the analysis. Results: In total 9451 users met the inclusion criteria, 83% had type 1 diabetes, and the rest had type 2 or other forms of diabetes. The mean age was 42.6 ± 20.8 years, and 52% were female. Median percent time in automation was 94.2% [interquartile range, IQR: 90.1%-96.4%] for the entire 12-month duration of observation, with no significant changes over time. Of these users, 9010 (96.8%) had ≥75% of their CGM data available, that is, sufficient data for reliable computation of CGM-based glycemic outcomes. At baseline, median percent time in range (70-180 mg/dL) was 63.6 (IQR: 49.9%-75.6%) and increased to 73.6% (IQR: 64.4%-81.8%) for the 12 months of Control-IQ technology use with no significant changes over time. Median percent time <70 mg/dL remained consistent at ∼1% (IQR: 0.5%-1.9%). Conclusion: In this real-world use analysis, Control-IQ technology retained, and to some extent exceeded, the results obtained in randomized controlled trials, showing glycemic improvements in a broad age range of people with different types of diabetes.

Objective: Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement., Research Design and Methods: Eighteen adolescents with T1D (age 15.6 ± 1.7 years; HbA 1c 7.4 ± 1.5%; 9 females/9 males) participated in a randomized crossover clinical trial comparing our legacy hybrid CLC system (Unified Safety System Virginia [USS]-Virginia) with a novel fully automated CLC system (RocketAP) during two 46-h supervised admissions (each with one announced and one unannounced dinner), following 2 weeks of data collection. Primary outcome was the percentage time-in-range 70-180 mg/dL (TIR) following the unannounced meal, with secondary outcomes related to additional continuous glucose monitoring-based metrics., Results: Both TIR and time-in-tight-range 70-140 mg/dL (TTR) were significantly higher using RocketAP than using USS-Virginia during the 6 h following the unannounced meal (83% [interquartile range 64-93] vs. 53% [40-71]; P = 0.004 and 49% [41-59] vs. 27% [22-36]; P = 0.002, respectively), primarily driven by reduced time-above-range (TAR >180 mg/dL: 17% [1.3-34] vs. 47% [28-60]), with no increase in time-below-range (TBR <70 mg/dL: 0% median for both). RocketAP also improved control following the announced meal (mean difference TBR: -0.7%, TIR: +7%, TTR: +6%), overall (TIR: +5%, TAR: -5%, TTR: +8%), and overnight (TIR: +7%, TTR: +19%, TAR: -5%). RocketAP delivered less insulin overall (78 ± 23 units vs. 85 ± 20 units, P = 0.01)., Conclusions: A new fully automated CLC system with automatic prandial dosing was proven to be safe and feasible and outperformed our legacy USS-Virginia in an adolescent population with and without meal announcement., (© 2021 by the American Diabetes Association.)

Background: Controlling postprandial blood glucose without the benefit of an appropriately sized premeal insulin bolus has been challenging given the delays in absorption and action of subcutaneously injected insulin during conventional and artificial pancreas (AP) system diabetes treatment. We aim to understand the impact of accelerating insulin and increasing aggressiveness of the AP controller as potential solutions to address the postprandial hyperglycemia challenge posed by unannounced meals through a simulation study., Methods: Accelerated rapid-acting insulin analogue is modeled within the UVA/Padova simulation platform by uniformly reducing its pharmacokinetic time constants (α multiplier) and used with a model predictive control, where the controller's aggressiveness depends on α. Two sets of single-meal simulations were performed: (1) where we only tune the controller's aggressiveness and (2) where we also accelerate insulin absorption and action to assess postprandial glycemic control during each intervention., Results: Mean percent of time spent within the 70 to 180 mg/dL postprandial glycemic range is significantly higher in set (2) than in set (1): 79.9, 95% confidence interval [77.0, 82.7] vs 88.8 [86.8, 90.9] ([Note to typesetter: Set all unnecessary math in text format and insert appropriate spaces between operators.] P < .05) for α = 2, and 81.4 [78.6, 84.3] vs 94.1 [92.6, 95.6] ( P < .05) for α = 3. A decrease in percent of time below 70 mg/dL is also detected: 0.9 [0.4, 2.2] vs 0.6 [0.2, 1.4] ( P = .23) for α = 2 and 1.4 [0.7, 2.8] vs 0.4 [0.1, 1.4] ( P < .05) for α = 3., Conclusion: These proof-of-concept simulations suggest that an AP without prandial insulin boluses combined with significantly faster insulin analogues could match the glycemic performance obtained with an optimal hybrid AP.

Background: Studies of closed-loop control (CLC) in patients with type 1 diabetes (T1D) consistently demonstrate improvements in glycemic control as measured by increased time-in-range (TIR) 70-180 mg/dL. However, clinical predictors of TIR in users of CLC systems are needed. Materials and Methods: We analyzed data from 100 children aged 6-13 years with T1D using the Tandem Control-IQ CLC system during a randomized trial or subsequent extension phase. Continuous glucose monitor data were collected at baseline and during 12-16 weeks of CLC use. Participants were stratified into quartiles of TIR on CLC to compare clinical characteristics. Results: TIR for those in the first, second, third, and fourth quartiles was 54%, 65%, 71%, and 78%, respectively. Lower baseline TIR was associated with lower TIR on CLC ( r  = 0.69, P  < 0.001). However, lower baseline TIR was also associated with greater improvement in TIR on CLC ( r  = -0.81, P  < 0.001). During CLC, participants in the highest versus lowest TIR-quartile administered more user-initiated boluses daily (8.5 ± 2.8 vs. 5.8 ± 2.6, P  < 0.001) and received fewer automated boluses (3.5 ± 1.0 vs. 6.0 ± 1.6, P  < 0.001). Participants in the lowest (vs. the highest) TIR-quartile received more insulin per body weight (1.13 ± 0.27 vs. 0.87 ± 0.20 U/kg/d, P  = 0.008). However, in a multivariate model adjusting for baseline TIR, user-initiated boluses and insulin-per-body-weight were no longer significant. Conclusions: Higher baseline TIR is the strongest predictor of TIR on CLC in children with T1D. However, lower baseline TIR is associated with the greatest improvement in TIR. As with open-loop systems, user engagement is important for optimal glycemic control.

Background: Glycemic control is particularly challenging for toddlers and preschoolers with type 1 diabetes (T1D), and data on the use of closed-loop systems in this age range are limited. Materials and Methods: We studied use of a modified investigational version of the Tandem t:slim X2 Control-IQ system in children aged 2 to 5 years during 48 h in an outpatient supervised hotel (SH) setting followed by 3 days of home use to examine the safety of this system in young children. Meals and snacks were not restricted and boluses were estimated per parents' usual routine. At least 30 min of daily exercise was required during the SH phase. All participants were remotely monitored by study staff while on closed-loop in addition to monitoring by at least one parent throughout the study. Results: Twelve participants diagnosed with T1D for at least 3 months with mean age 4.7 ± 1.0 years (range 2.0-5.8 years) and hemoglobin A1c of 7.3% ± 0.8% were enrolled at three sites. With use of Control-IQ, the percentage of participants meeting our prespecified goals of less than 6% time below 70 mg/dL and less than 40% time above 180 mg/dL increased from 33% to 83%. Control-IQ use significantly improved percent time in range (70-180 mg/dL) compared to baseline (71.3 ± 12.5 vs. 63.7 ± 15.1, P  = 0.016). All participants completed the study with no adverse events. Conclusions: In this brief pilot study, use of the modified Control-IQ system was safe in 2-5-year-old children with T1D and improved glycemic control.

Objective: Physical activity is a major challenge to glycemic control for people with type 1 diabetes. Moderate-intensity exercise often leads to steep decreases in blood glucose and hypoglycemia that closed-loop control systems have so far failed to protect against, despite improving glycemic control overall. Research Design and Methods: Fifteen adults with type 1 diabetes (42 ± 13.5 years old; hemoglobin A 1c 6.6% ± 1.0%; 10F/5M) participated in a randomized crossover clinical trial comparing two hybrid closed-loop (HCL) systems, a state-of-the-art hybrid model predictive controller and a modified system designed to anticipate and detect unannounced exercise (APEX), during two 32-h supervised admissions with 45 min of planned moderate activity, following 4 weeks of data collection. Primary outcome was the number of hypoglycemic episodes during exercise. Continuous glucose monitor (CGM)-based metrics and hypoglycemia are also reported across the entire admissions. Results: The APEX system reduced hypoglycemic episodes overall (9 vs. 33; P  = 0.02), during exercise (5 vs. 13; P  = 0.04), and in the 4 h following (2 vs. 11; P  = 0.02). Overall CGM median percent time <70 mg/dL decreased as well (0.3% vs. 1.6%; P  = 0.004). This protection was obtained with no significant increase in time >180 mg/dL (18.5% vs. 16.6%, P  = 0.15). Overnight control was notable for both systems with no hypoglycemia, median percent in time 70-180 mg/dL at 100% and median percent time 70-140 mg/dL at ∼96% for both. Conclusions: A new closed-loop system capable of anticipating and detecting exercise was proven to be safe and feasible and outperformed a state-of-the-art HCL, reducing participants' exposure to hypoglycemia during and after moderate-intensity physical activity. ClinicalTrials.gov NCT03859401.