Adaptive Biobehavioral Control: A Pilot Analysis of Human-Machine Coadaptation in Type 1 Diabetes.

Background: While it is well recognized that an automated insulin delivery (AID) algorithm should adapt to changes in physiology, it is less understood that the individual would also have to adapt to the AID system. The adaptive biobehavioral control (ABC) method presented here attempts to compensate for this deficiency by including AID into an information cloud-based ecosystem. Methods: The Web Information Tool (WIT) implements the ABC concept via the following: (1) a Physiological Adaptation Module (PAM) that tracks metabolic changes and adapts AID parameters accordingly and (2) a Behavioral Adaptation Module (BAM) that provides information feedback. The safety of WIT (primary outcome) was assessed in an 8-week randomized, two-arm parallel pilot study. All participants used the Control-IQ® AID system enhanced with PAM, but only those in the Experimental group had access to BAM. Secondary glycemic outcomes were computed using the 2-week baseline period and the last 2 weeks of treatment. Results: Thirty participants with type 1 diabetes (T1D) completed all study procedures (17 female/13 male; age: 40 ± 14 years; HbA1c: 6.6% ± 0.5%). No severe hypoglycemia, DKA, or other serious adverse events were reported. Comparing the Experimental and Control groups, no significant difference was observed in time in range (70-180 mg/dL): 74.6% vs 73.8%, adjusted mean difference: 2.65%, 95% CI (-1.12%,6.41%), P = 0.161. Time in 70-140 mg/dL was significantly higher in the Experimental group: 50.7% vs 49.2%, 5.71% (0.44%,10.97%), P = 0.035, without increased time below range: 0.54% (-0.09%,1.17%), P = 0.089. Conclusion: The results demonstrate that it is safe to integrate an AID system into the WIT ecosystem. Validation in a full-scale study is ongoing.

Performance Effect of Adjusting Insulin Sensitivity for Model-Based Automated Insulin Delivery Systems.

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.

Scientific and technical assistance on welfare aspects related to housing and health of cats and dogs in commercial breeding establishments.

This Scientific Report addresses a mandate from the European Commission according to Article 31 of Regulation (EC) No 178/2002 on the welfare of cats and dogs in commercial breeding establishments kept for sport, hunting and companion purposes. The aim was to scrutinise recent recommendations made by the EU Platform on Animal Welfare Voluntary Initiative on measures to assist the preparation of policy options for the legal framework of commercial breeding of cats and dogs. Specifically, the main question addressed was if there is scientific evidence to support the measures for protection of cats and dogs in commercial breeding related to housing, health considerations and painful procedures. Three judgements were carried out based on scientific literature reviews and, where possible a review of national regulations. The first judgement addressed housing and included: type of accommodation, outdoor access, exercise, social behaviour, housing temperature and light requirements. The second judgement addressed health and included: age at first and last breeding, and breeding frequency. Judgement 3 addressed painful procedures (mutilations or convenience surgeries) and included: ear cropping, tail docking and vocal cord resections in dogs and declawing in cats. For each of these judgements, considerations were provided indicating where scientific literature is available to support recommendations on providing or avoiding specific housing, health or painful surgical interventions. Areas where evidence is lacking are indicated.

Welfare of ducks, geese and quail on farm.

This Scientific Opinion concerns the welfare of Domestic ducks (Anas platyrhynchos domesticus), Muscovy ducks (Cairina moschata domesticus) and their hybrids (Mule ducks), Domestic geese (Anser anser f. domesticus) and Japanese quail (Coturnix japonica) in relation to the rearing of breeders, birds for meat, Muscovy and Mule ducks and Domestic geese for foie gras and layer Japanese quail for egg production. The most common husbandry systems (HSs) in the European Union are described for each animal species and category. The following welfare consequences are described and assessed for each species: restriction of movement, injuries (bone lesions including fractures and dislocations, soft tissue lesions and integument damage and locomotory disorders including lameness), group stress, inability to perform comfort behaviour, inability to perform exploratory or foraging behaviour and inability to express maternal behaviour (related to prelaying and nesting behaviours). Animal-based measures relevant for the assessment of these welfare consequences were identified and described. The relevant hazards leading to the welfare consequences in the different HSs were identified. Specific factors such as space allowance (including minimum enclosure area and height) per bird, group size, floor quality, characteristics of nesting facilities and enrichment provided (including access to water to fulfil biological needs) were assessed in relation to the welfare consequences and, recommendations on how to prevent the welfare consequences were provided in a quantitative or qualitative way.

Welfare of pigs on farm.

This scientific opinion focuses on the welfare of pigs on farm, and is based on literature and expert opinion. All pig categories were assessed: gilts and dry sows, farrowing and lactating sows, suckling piglets, weaners, rearing pigs and boars. The most relevant husbandry systems used in Europe are described. For each system, highly relevant welfare consequences were identified, as well as related animal-based measures (ABMs), and hazards leading to the welfare consequences. Moreover, measures to prevent or correct the hazards and/or mitigate the welfare consequences are recommended. Recommendations are also provided on quantitative or qualitative criteria to answer specific questions on the welfare of pigs related to tail biting and related to the European Citizen's Initiative 'End the Cage Age'. For example, the AHAW Panel recommends how to mitigate group stress when dry sows and gilts are grouped immediately after weaning or in early pregnancy. Results of a comparative qualitative assessment suggested that long-stemmed or long-cut straw, hay or haylage is the most suitable material for nest-building. A period of time will be needed for staff and animals to adapt to housing lactating sows and their piglets in farrowing pens (as opposed to crates) before achieving stable welfare outcomes. The panel recommends a minimum available space to the lactating sow to ensure piglet welfare (measured by live-born piglet mortality). Among the main risk factors for tail biting are space allowance, types of flooring, air quality, health status and diet composition, while weaning age was not associated directly with tail biting in later life. The relationship between the availability of space and growth rate, lying behaviour and tail biting in rearing pigs is quantified and presented. Finally, the panel suggests a set of ABMs to use at slaughter for monitoring on-farm welfare of cull sows and rearing pigs.

Methodological guidance for the development of animal welfare mandates in the context of the Farm to Fork Strategy.

This document provides methodological guidance developed by the EFSA Panel on Animal Health and Welfare to produce Scientific Opinions in response to mandates received from the European Commission in the context of the Farm to Fork Strategy. The mandates relate to the welfare of (i) animals during transport, (ii) calves, (iii) laying hens, (iv) broilers, (v) pigs, (vi) ducks, geese and quails, and (vii) dairy cows. This guidance was developed in order to define the methods and strategy to be applied for responding to the Terms of Reference (ToRs) of the mandates. The mandates each consist of a set of General ToRs which refer to the husbandry systems used in the production of each animal species or the current transport practices for free moving animals and animals transported in cages, and a set of specific ToRs for which difficulties in ensuring animal welfare have been identified and where specific scenarios are envisaged. Part I of the guidance includes a description of welfare consequences for the animals. Part II includes a new methodology for providing quantitative recommendations regarding animal welfare. The proposed methodology follows the assumption that the effect of an exposure variable (e.g. space allowance) on animal welfare can be quantified by comparing the expression of an animal-based measure (ABM) under 'unexposed conditions' (e.g. unlimited space) and under high exposure (e.g. restrictive conditions). The level of welfare as assessed through this ABM can be quantified for different levels of the exposure variable (e.g. at increasing space allowances) and quantitative recommendations can thus be provided. The final version of the methodological guidance was endorsed for public consultation, which took place between 14 February 2022 and 31 March 2022. The comments received are integrated in this document.

Insulin Replacement Across the Menstrual Cycle in Women with Type 1 Diabetes: An In Silico Assessment of the Need for Ad Hoc Technology.

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.

Essential Continuous Glucose Monitoring Metrics: The Principal Dimensions of Glycemic Control in Diabetes.

Background: With the proliferation of continuous glucose monitoring (CGM), a number of metrics were developed to assess quality of glycemic control. Many of them are highly correlated. Thus, we aim to identify the principal dimensions of glycemic control-a minimal set of metrics, necessary and sufficient for comprehensive assessment of diabetes management. Methods: Seventy-five thousand five hundred sixty-three 2-week CGM profiles recorded in six studies by 790 individuals with type 1 or type 2 diabetes were used to compute mean glucose (MG), percentage time >180 mg/dL (TAR), >250 mg/dL (TAR2), <70 mg/dL (TBR), <54 mg/dL (TBR2), and coefficient of variation (CV). The true dimensionality of the glycemic-metric space was identified in a training set (53,380 profiles) and validated in an independent test set (22,183 profiles). Results: Correlation analysis identified two blocks of metrics-(MG, TAR, TAR2) and (TBR, TBR2, CV)-each with high internal correlation, but insignificant between-block correlation, suggesting that the true dimensionality of the glycemic-metric space is 2. Principal component analysis confirmed two essential metrics quantifying exposure to hyperglycemia (i.e., treatment efficacy) and risk for hypoglycemia (i.e., treatment safety), and explaining ∼90% of the variance in the training and test data. Conclusion: Two essential metrics, treatment efficacy and treatment safety, are necessary and sufficient to characterize glycemic control in diabetes. Thus, quantitatively, diabetes treatment optimization is reduced to a two-dimensional problem, meaning that minimizing both exposure to hyperglycemia and risk for hypoglycemia will lead to improvement in any other metric of glycemic control.

Modeling the variability of insulin sensitivity during the menstrual cycle in women with type 1 diabetes to adjust open-loop insulin therapy.

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.

Automated Adaptation of Insulin Treatment in Type 1 Diabetes.

Individuals with type 1 diabetes (T1D) need life-long insulin therapy to compensate for the lack of endogenous insulin due to the autoimmune damage to pancreatic beta-cells. Treatment is based on basal and bolus insulin, to cover fasting and postprandial periods, respectively, according to three insulin dosing parameters: basal rate (BR), carbohydrate-to-insulin ratio (CR), and correction factor (CF). Suboptimal BR, CR, and CF profiles leading to incorrect insulin dosing may be the cause of undesired glycemic events, which carry dangerous short-term and long-term effects. Therefore, correct tuning of these parameters is of the utmost importance. In this work, we propose a new algorithm to optimize insulin dosing parameters in individuals with T1D who use a continuous glucose monitor and an insulin pump. The algorithm was tested using the University of Virginia/Padova T1D Simulator and led to an improvement in the quality of glycemic control. Future efforts will be devoted to test the algorithm in human clinical trials.

Titration of Long-Acting Insulin Using Continuous Glucose Monitoring and Smart Insulin Pens in Type 1 Diabetes: A Model-Based Carbohydrate-Free Approach.

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.

Replay Simulations with Personalized Metabolic Model for Treatment Design and Evaluation in Type 1 Diabetes.

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.

Automatically accounting for physical activity in insulin dosing for type 1 diabetes.

BACKGROUND AND OBJECTIVE: Type 1 diabetes is a disease characterized by lifelong insulin administration to compensate for the autoimmune destruction of insulin-producing pancreatic beta-cells. Optimal insulin dosing presents a challenge for individuals with type 1 diabetes, as the amount of insulin needed for optimal blood glucose control depends on each subject's varying needs. In this context, physical activity represents one of the main factors altering insulin requirements and complicating treatment decisions. This work aims to develop and test in simulation a data-driven method to automatically incorporate physical activity into daily treatment decisions to optimize mealtime glycemic control in individuals with type 1 diabetes. METHODS: We leveraged glucose, insulin, meal and physical activity data collected from twenty-three individuals to develop a method that (i) tracks and quantifies the accumulated glycemic impact from daily physical activity in real-time, (ii) extracts an individualized routine physical activity profile, and (iii) adjusts insulin doses according to the prolonged changes in insulin needs due to deviations in daily physical activity in a personalized manner. We used the data replay simulation framework developed at the University of Virginia to "re-simulate" the clinical data and estimate the performances of the new decision support system for physical activity informed insulin dosing against standard insulin dosing. The paired t-test is used to compare the performances of dosing methods with p < 0.05 as the significance threshold. RESULTS: Simulation results show that, compared with standard dosing, the proposed physical-activity informed insulin dosing could result in significantly less time spent in hypoglycemia (15.3± 8% vs. 11.1± 4%, p = 0.007) and higher time spent in the target glycemic range (66.1± 11.7% vs. 69.6± 12.2%, p < 0.01) and no significant difference in the time spent above the target range(26.6± 1.4 vs. 27.4± 0.1, p = 0.5). CONCLUSIONS: Integrating daily physical activity, as measured by the step count, into insulin dose calculations has the potential to improve blood glucose control in daily life with type 1 diabetes.

Welfare of pigs at slaughter.

The killing of pigs for human consumption (slaughtering) can take place in a slaughterhouse or on farm. The processes of slaughtering that were assessed for welfare, from the arrival of pigs until their death, were grouped into three main phases: pre-stunning (including arrival, unloading from the truck, lairage, handling and moving of pigs); stunning (including restraint); and bleeding. Stunning methods were grouped into three categories: electrical, controlled atmosphere and mechanical. Twelve welfare consequences the pigs can be exposed to during slaughter were identified: heat stress, cold stress, fatigue, prolonged thirst, prolonged hunger, impeded movement, restriction of movements, resting problem, negative social behaviour, pain, fear and respiratory distress. Welfare consequences and relevant animal-based measures were described. In total, 30 welfare hazards that could occur during slaughter were identified and characterised, most of them related to stunning and bleeding. Staff were identified as the origin of 29 hazards, which were attributed to the lack of appropriate skill sets needed to perform tasks or to fatigue. Corrective and preventive measures for these hazards were assessed: measures to correct hazards were identified, and management was shown to have a crucial role in prevention. Outcome tables linking hazards, welfare consequences, animal-based measures, origins and preventive and corrective measures were developed for each process. Mitigation measures to minimise welfare consequences are proposed.

Stunning methods and slaughter of rabbits for human consumption.

This opinion on the killing of rabbits for human consumption ('slaughtering') responds to two mandates: one from the European Parliament (EP) and the other from the European Commission. The opinion describes stunning methods for rabbits known to the experts in the EFSA working group, which can be used in commercial practice, and which are sufficiently described in scientific and technical literature for the development of an opinion. These are electrical stunning, mechanical stunning with a penetrative and non-penetrative captive bolt and gas stunning. The latter method is not allowed in the EU anymore following Council Regulation (EC) No 1099/2009, but may still be practiced elsewhere in the world. Related hazards and welfare consequences are also evaluated. To monitor stunning effectiveness as requested by the EP mandate, the opinion suggests the use of indicators for the state of consciousness, selected on the basis of their sensitivity, specificity and ease of use. Similarly, it suggests indicators to confirm animals are dead before dressing. For the European Commission mandate, slaughter processes were assessed from the arrival of rabbits in containers until their death, and grouped in three main phases: pre-stunning (including arrival, unloading of containers from the truck, lairage, handling/removing of rabbits from containers), stunning (including restraint) and bleeding (including bleeding following stunning and bleeding during slaughter without stunning). Ten welfare consequences resulting from the hazards that rabbits can be exposed to during slaughter are identified: consciousness, animal not dead, thermal stress (heat or cold stress), prolonged thirst, prolonged hunger, restriction of movements, pain, fear, distress and respiratory distress. Welfare consequences and relevant animal-based measures (indicators) are described. Outcome tables linking hazards, welfare consequences, indicators, origins, preventive and corrective measures are developed for each process. Mitigation measures to minimise welfare consequences are also proposed.

Scientific opinion concerning the killing of rabbits for purposes other than slaughter.

Rabbits of different ages may have to be killed on-farm for purposes other than slaughter (where slaughter is defined as killing for human consumption) either individually or on a large scale (e.g. for production reasons or for disease control). The purpose of this opinion was to assess the risks associated to the on-farm killing of rabbits. The processes during on-farm killing that were assessed included handling, stunning and/or killing methods (including restraint). The latter were grouped into four categories: electrical methods, mechanical methods, controlled atmosphere method and lethal injection. In total, 14 hazards were identified and characterised, most of these related to stunning and/or killing. The staff was identified as the origin for all hazards, either due to lack of the appropriate skill sets needed to perform tasks or due to fatigue. Possible corrective and preventive measures were assessed: measures to correct hazards were identified for five hazards and the staff was shown to have a crucial role in prevention. Five welfare consequences of the welfare hazards to which rabbits can be exposed to during on-farm killing were identified: not being dead, consciousness, pain, fear and distress. Welfare consequences and relevant animal-based measures were described. Outcome tables linking hazards, welfare consequences, animal-based measures, origins, preventive and corrective measures were developed for each process. Mitigation measures to minimise welfare consequences are proposed.

Welfare of pigs during killing for purposes other than slaughter.

Pigs at different stages of the production cycle may have to be killed on-farm for purposes other than slaughter (where slaughter is defined as killing for human consumption) either individually (e.g. severely injured pigs) or on a large scale (e.g. unproductive animals or for disease control reasons). This opinion assessed the risks associated with the on-farm killing of pigs and included two phases: 1) handling and moving of pigs and 2) killing methods (including restraint). The killing methods were subdivided into four categories: electrical methods, mechanical methods, gas mixture methods and lethal injection. Four welfare consequences to which pigs can be exposed to during on-farm killing were identified: pain, fear, impeded movement and respiratory distress. Welfare consequences and relevant animal-based measures were described. In total, 28 hazards were associated with the welfare consequences; majority of the hazards (24) are related to Phase 2 (killing). The main hazards are associated with lack of staff skills and training, and poor-designed and constructed facilities. Staff was identified as an origin of all hazards, either due to lack of skills needed to perform appropriate killing or due to fatigue. Corrective measures were identified for 25 hazards. Outcome tables linking hazards, welfare consequences, animal-based measures, hazard origins, preventive and corrective measures were developed and mitigation measures proposed.

Estimation of Hemoglobin A1c from Continuous Glucose Monitoring Data in Individuals with Type 1 Diabetes: Is Time In Range All We Need?

Objective: To bridge the gap between laboratory-measured hemoglobin A1c (HbA1c) and continuous glucose monitoring (CGM)-derived time in target range (TIR), introducing TIR-driven estimated A1c (eA1c). Methods: Data from Protocol 1 (training data set) and Protocol 3 (testing data set) of the International Diabetes Closed-Loop Trial were used. Training data included 3 months of CGM recordings from 125 individuals with type 1 diabetes, and HbA1c at 3 months; testing data included 9 months of CGM recordings from 168 individuals, and HbA1c at 3, 6, and 9 months. Hemoglobin glycation was modeled by a first-order differential equation driven by TIR. Three model parameters were estimated in the training data set and fixed thereafter. A fourth parameter was estimated in the testing data set, to individualize the model by calibration with month 3 HbA1c. The accuracy of eA1c was assessed on months 6 and 9 HbA1c. Results: eA1c was tracked for each individual in the testing data set for 6 months after calibration. Mean absolute differences between HbA1c and eA1c 3- and 6-month postcalibration were 0.25% and 0.24%; Pearson's correlation coefficients were 0.93 and 0.93; percentages of eA1c within 10% from reference HbA1c were 97.6% and 96.3%, respectively. Conclusions: HbA1c and TIR are reflections of the same underlying process of glycemic fluctuation. Using a model individualized with one HbA1c measurement, TIR provides an accurate approximation of HbA1c for at least 6 months, reflecting blood glucose fluctuations and nonglycemic biological factors. Thus, eA1c is an intermediate metric that mathematically adjusts a CGM-based assessment of glycemic control to individual glycation rates.

Safety and Efficacy of Initializing the Control-IQ Artificial Pancreas System Based on Total Daily Insulin in Adolescents with Type 1 Diabetes.

Objective: To assess the safety and efficacy of a simplified initialization for the Tandem t:slim X2 Control-IQ hybrid closed-loop system, using parameters based on total daily insulin ("MyTDI") in adolescents with type 1 diabetes under usual activity and during periods of increased exercise. Research Design and Methods: Adolescents with type 1 diabetes 12-18 years of age used Control-IQ for 5 days at home using their usual parameters. Upon arrival at a 60-h ski camp, participants were randomized to either continue Control-IQ using their home settings or to reinitialize Control-IQ with MyTDI parameters. Control-IQ use continued for 5 days following camp. The effect of MyTDI on continuous glucose monitoring outcomes were analyzed using repeated measures analysis of variance (ANOVA): baseline, camp, and at home. Results: Twenty participants were enrolled and completed the study; two participants were excluded from the analysis due to absence from ski camp (1) and illness (1). Time in range was similar between both groups at home and camp. A tendency to higher time <70 mg/dL in the MyTDI group was present but only during camp (median 3.8% vs. 1.4%, P = 0.057). MyTDI users with bolus/TDI ratios >40% tended to show greater time in the euglycemic range improvements between baseline and home than users with ratios <40% (+16.3% vs. -9.0%, P = 0.012). All participants maintained an average of 95% time in closed loop (84.1%-100%). Conclusions: MyTDI is a safe, effective, and easy way to determine insulin parameters for use in the Control-IQ artificial pancreas. Future modifications to account for the influence of carbohydrate intake on MyTDI calculations might further improve time in range.