Individuals living with type 1 diabetes (T1D) face important challenges when engaging in physical activity (PA), as it necessitates careful management of blood glucose levels often through insulin adjustments and carbohydrate intake. Integrating PA detection into sensor augmented insulin pumps (SAP) is a promising strategy to enhance glycemic control by suggesting basal insulin reduction or carbohydrate ingestion in the critical 24 h after the PA detection.We have developed a real-time, model-based module for PA detection based solely on measured glucose levels, insulin infusion rates, and carbohydrate intake. The approach is based on the monitoring of the magnitude as well as various statistical properties of the prediction residuals, i.e., the discrepancies between actual sensor-measured glucose levels and model-predicted levels.We tested our algorithm on the Type 1 Diabetes and Exercise Initiative (T1DEXI) dataset, which includes structured sessions of aerobic, resistance, and interval exercises. In a dataset containing all three activity types, the detection approach based on the median of the prediction residuals successfully detected an average of 59% of PA instances, while keeping the false alarms to 3.5 per considered timeframe, when considering models tailored to each participant. When using a population model identified on in-silico data from the UVa/Padova T1D simulator, the approach successfully detected 62% of PAs, while keeping the false alarms to 3.6 per considered timeframe.These encouraging findings open the possibility of integrating PA detection into SAP systems without the need for additional physiological signals, thus enabling improved glucose management.

A panel of experts in the use of continuous glucose monitoring (CGM) data in the treatment of diabetes met in Burlingame, California on October 27, 2025 to discuss the utility of the glycemia risk index (GRI) for clinical care research and population health management. The GRI composite metric is a single number (on a 0-100 percentile scale-lower is better) based on an expert-determined weighting of the seven individual components in the existing ambulatory glucose profile (AGP). The GRI describes the quality of glycemia based on glucose values collected in a 14-day CGM tracing, thus providing additional insights into CGM profiles beyond the AGP. During the meeting, the mathematical derivation of the GRI metric was presented along with its use for adult and pediatric individuals with diabetes and cancer who require medications that can adversely affect the glucose concentration. Examples where the GRI provided useful insights into the quality of CGM tracings were also discussed by the expert panel. In addition, a new smartphone application, the GRI Calculator, was presented. This app calculates the GRI of a CGM tracing and provides visualization of sequential CGM tracings for a specific individual. The GRI provides a reference measurement for the accuracy of artificial intelligence (AI) models assigning levels of glycemic quality to CGM tracings intended to match the assessments of clinicians. The GRI is now part of the data visualization panel for the Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE-2) project, which standardizes both CGM and insulin dosing data. Further exploration of the potential value of the GRI for non-insulin users needs to be undertaken. The panel unanimously recommended that CGM manufacturers and developers of data visualization software for CGMs add the GRI to their data platforms for insulin users.

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Total daily energy expenditure (TDEE) is impacted by many medical conditions, such as diabetes. In the case of type 1 diabetes (T1D), individuals need to have an accurate assessment of the energy expenditure in real-time to avoid dietary imbalance, and improve glycemic control. This work proposes a hierarchical Long Short-Term Memory (LSTM)-based modeling approach to predict real-time continuous energy expenditure, expressed as metabolic equivalents (METs), for individuals with T1D on a 24-hour basis by leveraging the step count and heart rate data from a wrist-band smartwatch. To deal with the inter-and intra-individual variability, the proposed model uses three different LSTMs to capture population, activity-type and subject scale information. To evaluate the impact of the components of the hierarchy, the performance of the proposed hierarchical model was assessed at each level. The results show that the combination of population data, such as heart rate and step counts, with individual data in a hierarchical architecture helps to achieve superior prediction performance, than using only individual heart rate and step counts data.Additionally, compared to non-hierarchical modeling, the hierarchical modeling can provide precise and individualized prediction of the METs categories, as it allows the integration of the variation at different levels of the hierarchy. This model can be used to augment current automated insulin delivery (AID) systems to adapt insulin infusion according to the predicted activity intensity and compensate for glycemic perturbations due to exercise.

The development of automated insulin delivery systems has seen tremendous improvements from individual components to interoperable system combinations of devices and new drugs besides insulin. The components have become progressively smaller, more accurate, and more user friendly. This article summarizes the history of the artificial pancreas from the earliest concepts to fully functional systems to research into further improvements in the future. The authors include many of the developers of this technology who received research support from the National Institute of Diabetes and Digestive and Kidney Diseases at various stages to develop these systems.

Objective: A common challenge in model identification with clinical data is incomplete and sometimes imprecise information. In this work, we provide a method to reconstruct the corrupted input data in a clinical dataset and, jointly identify the person-specific parameters of a metabolic model describing meal-insulin-glucose-dynamics for people with type 1 diabetes (T1D). Method: The proposed method is an algorithm that iterates between nonlinear least-squares and mixed-integer quadratic programming to optimize model parameters in conjunction with sparse corrections to the input data. In order to handle long stretches of data, the optimization problem is designed to be i) computationally tractable, and ii) robust against the potential presence of significant inaccuracies corrupting a data portion. Moreover, since the pattern of the inaccuracies is specific to each person, we propose a personalized hyperparameter tuning approach. The method is applied on clinical data from 13 people with T1D. Identified model performance is compared to the performance of model identified with standard least squares (LS) method. Results: Compared to LS, identifying corrections in conjunction with model parameters on training data lead to an improvement in the model prediction capabilities on unseen data with an average 2.2% improvement in MARD for two-hour prediction horizon (p-value = 0.0006). Conclusions: The proposed method is effective in model identification for clinical data with unknown inaccuracies in the inputs. Significance: Personalized models with high accuracy can inform treatment decisions and lead to better glucose control outcomes in people with T1D.

Artificial pancreas (AP) systems, also called automated insulin delivery systems, have improved the time in range of glucose levels, reduced the daily burden of the user for glucose regulation, and improved their quality of life. Several commercially available AP systems operate in hybrid closed-loop mode that requires manual information from the user for meals and exercise. This article summarizes the progress on mathematical models of glucose-insulin dynamics, continuous glucose monitoring systems, and insulin pumps that form the building blocks of AP systems, the shift from animal studies to in silico clinical trials that accelerated the rate of progress in AP technologies and the efforts for developing the next-generation AP systems, and the fully automated AP that eliminates manual inputs and mitigates the effects of disturbances to glucose homeostasis-meals, physical activities, acute stress, and variations in sleep characteristics. A section is devoted to discuss the unique glycemic management challenges faced by women with diabetes across the lifespan (menstrual cycle, menopause, pregnancy) and summarize progress made to reduce their impact on glycemic management.

Background: Managing exercise in type 1 diabetes is challenging, in part, because different types of exercises can have diverging effects on glycemia. The aim of this work was to develop a classification model that can classify an exercise event (structured or unstructured) as aerobic, interval, or resistance for the purpose of incorporation into an automated insulin delivery (AID) system.Methods: A long short-term memory network model was developed with real-world data from 30-min structured sessions of at-home exercise (aerobic, resistance, or mixed) using triaxial accelerometer, heart rate, and activity duration information. The detection algorithm was used to classify 15 common free-living and unstructured activities and relate each to exercise-associated change in glucose.Results: A total of 1610 structured exercise sessions were used to train, validate, and test the model. The accuracy for the structured exercise sessions in the testing set was 72% for aerobic, 65% for interval, and 77% for resistance. In addition, we tested the classifier on 3328 unstructured sessions. We validated the session-associated change in glucose against the expected change during exercise for each type. Mean and standard deviation of the change in glucose of -20.8 (40.3) mg/dL were achieved for sessions classified as aerobic, -16.2 (39.0) mg/dL for sessions classified as interval, and -11.6 (38.8) mg/dL for sessions classified as resistance.Conclusions: The proposed algorithm reliably identified physical activity associated with expected change in glucose, which could be integrated into an AID system to manage the exercise disturbance in glycemia according to the predicted class.

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.

Background: The amount and type of food consumed impacts the glycemic response and insulin needs of people with type 1 diabetes mellitus (T1DM). Daily variability in consumption, re fl ected in diet quality, may acutely impact glycemic levels and insulin needs. Objective: Type 1 Diabetes Exercise Initiative (T1DEXI) data were examined to evaluate the impact of daily diet quality on near-term glycemic control and interaction with exercise. Methods: Using the Remote Food Photography Method, < 8 d of dietary intake data were analyzed per participant. Diet quality was quantified with the Healthy Eating Index-2015 (HEI), where a score of 100 indicates the highest-quality diet. Each participant day was classified as low HEI ( < 57) or high HEI ( > 57) based on the mean of nationally reported HEI data. Within participants, the relationship between diet quality and subsequent glycemia measured by continuous glucose monitoring (CGM) and total insulin dose usage was evaluated using a paired t -test and robust regression models. Results: Two hundred twenty-three adults (76% female) with mean f SD age, HbA1c, and body mass index (BMI) of 37 f 14 y, 6.6% f 0.7%, and 25.1 f 3.6 kg/m(2) , respectively, were included in these analyses. The mean HEI score was 56 across all participant days. On high HEI days (mean, 66 f 4) compared with low HEI days (mean, 47 f 5), total time in range (70 - 180 mg/dL) was greater (77.2% f 14% compared with 75.7% f 14%, respectively, P = 0.01), whereas time above 180 mg/dL (19% f 14% compared with 21% f 15%, respectively, P = 0.004), mean glucose (143 f 22 compared with 145 f 22 mg/dL, respectively, P = 0.02), and total daily insulin dose (0.52 f 0.18 compared with 0.54 f 0.18 U/kg/d, respectively, P = 0.009) were lower. The interaction between diet quality and exercise on glycemia was not signi fi cant. Conclusions: Higher HEI scores correlated with improved glycemia and lower insulin needs, although the impact of diet quality was modest and smaller than the previously reported impact of exercise.

Aims: To evaluate factors affecting within-participant reproducibility in glycemic response to different forms of exercise. Methods: Structured exercise sessions ~30 minutes in length from the Type 1 Diabetes Exercise Initiative (T1DEXI) study were used to assess within-participant glycemic variability during and after exercise. The effect of several pre-exercise factors on the within-participant glycemic variability was evaluated. Results: Data from 476 adults with type 1 diabetes were analyzed. A participant’s change in glucose during exercise was reproducible within 15 mg/dL of the participant’s other exercise sessions only 32% of the time. Participants who exercised with lower and more consistent glucose level, insulin on board (IOB), and carbohydrate intake at exercise start had less variability in glycemic change during exercise. Participants with lower mean glucose ( P < .001), lower glucose coefficient of variation (CV) ( P < .001), and lower % time <70 mg/dL ( P = .005) on sedentary days had less variable 24-hour post-exercise mean glucose. Conclusions: Reproducibility of change in glucose during exercise was low in this cohort of adults with T1D, but more consistency in pre-exercise glucose levels, IOB, and carbohydrates may increase this reproducibility. Mean glucose variability in the 24 hours after exercise is influenced more by the participant’s overall glycemic control than other modifiable factors.

Adults with type 1 diabetes should perform daily physical activity to help maintain health and fitness, but the influence of daily step counts on continuous glucose monitoring (CGM) metrics are unclear. This analysis used the Type 1 Diabetes Exercise Initiative (T1DEXI) dataset to investigate the effect of daily step count on CGM-based metrics. In a 4 week free-living observational study of adults with type 1 diabetes, with available CGM and step count data, we categorised participants into three groups—below (<7000), meeting (7000–10,000) or exceeding (>10,000) the daily step count goal—to determine if step count category influenced CGM metrics, including per cent time in range (TIR: 3.9–10.0 mmol/l), time below range (TBR: <3.9 mmol/l) and time above range (TAR: >10.0 mmol/l). A total of 464 adults with type 1 diabetes (mean±SD age 37±14 years; HbA1c 48.8±8.1 mmol/mol [6.6±0.7 https://doi.org/10.25934/PR00008428 ).

CONTEXT:Adults with type 1 diabetes (T1D) face the necessity of balancing the benefits of exercise with the potential hazards of hypoglycemia. OBJECTIVE:This work aimed to assess whether impaired awareness of hypoglycemia (IAH) affects exercise-associated hypoglycemia in adults with T1D. METHODS:We compared continuous glucose monitoring (CGM)-measured glucose during exercise and for 24 hours following exercise from 95 adults with T1D and IAH (Clarke score ≥4 or ≥1 severe hypoglycemic event within the past year) to 95 "aware" adults (Clarke score ≤2 and no severe hypoglycemic event within the past year) matched on sex, age, insulin delivery modality, and glycated hemoglobin A1c. A total of 4236 exercise sessions, and 1794 exercise days and 839 sedentary days, defined as 24 hours following exercise or a day without exercise, respectively, were available for analysis. RESULTS:Participants with IAH exhibited a nonsignificant trend toward greater decline in glucose during exercise compared to "aware" (-21 ± 44 vs -19 ± 43 mg/dL [-1.17 ± 2.44 vs -1.05 ± 2.39 mmol/L], adjusted group difference of -4.2 [95% CI, -8.4 to 0.05] mg/dL [-0.23 95% CI, -.47 to 0.003 mmol/L]; P = .051). Individuals with IAH had a higher proportion of days with hypoglycemic events below 70 mg/dL [3.89 mmol/L] (≥15 minutes <70 mg/dL [<3.89 mmol/L]) both on exercise days (51% vs 43%; P = .006) and sedentary days (48% vs 30%; P = .001). The increased odds of experiencing a hypoglycemic event below 70 mg/dL (<3.89 mmol/L) for individuals with IAH compared to "aware" did not differ significantly between exercise and sedentary days (interaction P = .36). CONCLUSION:Individuals with IAH have a higher underlying risk of hypoglycemia than "aware" individuals. Exercise does not appear to differentially increase risk for hypoglycemia during the activity, or in the subsequent 24 hours for IAH compared to aware individuals with T1D.

Background Customized and standard automated insulin delivery (AID) systems for use in pregnancies of women with preexisting type 1 diabetes (T1D) are being developed and tested to achieve pregnancy appropriate continuous glucose monitoring (CGM) targets. Guidance on the use of CGM for treatment decisions during pregnancy in the United States is limited. Methods Ten pregnant women with preexisting T1D participated in a trial evaluating at-home use of a pregnancy-specific AID system. Seven-point self-monitoring of blood glucose (SMBG) was compared to the closest sensor glucose (Dexcom G6 CGM) value biweekly to assess safety and reliability based on the 20%/20 mg/dL criteria. Results All participants completed the study with 7 participants satisfying the safety and reliability criteria with a mean absolute relative difference of 10.3%. Three participants did not fulfill the criteria, mainly because the frequency of SMBG did not meet the requirements. Conclusion Dexcom G6 CGM is safe and accurate in the real-world setting for use in pregnant women with preexisting T1D with reduced SMBG testing as part of a pregnancy-specific AID system.

The ability to shift circadian phase in vivo has the potential to offer substantial health benefits. However, the blood–brain barrier prevents the absorption of the majority of large and many small molecules, posing a challenge to neurological pharmaceutical development. Motivated by the presence of the circadian molecule KL001, which is capable of causing phase shifts in a circadian oscillator, we investigated the pharmacokinetics of different neurological pharmaceuticals on the dynamics of circadian phase. Specifically, we developed and validated five different transport models that describe drug concentration profiles of a circadian pharmaceutical at the brain level under oral administration and designed a nonlinear model predictive control (MPC)-based framework for phase resetting. Performance of the novel control algorithm based on the identified pharmacokinetic models was demonstrated through simulations of real-world misalignment scenarios due to jet lag. The time to achieve a complete phase reset for 11-h phase delay ranged between 48 and 72 h, while a 5-h phase advance was compensated in 30 to 60 h. This approach provides mechanistic insight into the underlying structure of the circadian oscillatory system and thus leads to a better understanding of the feasibility of therapeutic manipulations of the system.

Background: Managing glycemia during and after exercise events in type 1 diabetes (T1D) is challenging since these events can have wide-ranging effects on glycemia depending on the event timing, type, intensity. To this end, advanced physical activity-informed technologies can be beneficial for improving glucose control. Methods: We propose a real-time physical activity detection and classification framework, which builds upon random forest models. This module automatically detects exercise sessions and predicts the activity type and intensity from tri-axial accelerometer, heart rate, and continuous glucose monitoring records. Results: Data from 19 adults with T1D who performed structured sessions of either aerobic, resistance, or high-intensity interval exercise at varying times of day were used to train and test this framework. The exercise onset and completion were both predicted within 1 minute with an average accuracy of 81% and 78%, respectively. Activity type and intensity were identified within 2.38 minutes and from the exercise onset. On participants assigned to the test set, the average accuracy for activity type and intensity classification was 74% and 73%, respectively, if exercise was announced. For unannounced exercise events, the classification accuracy was 65% for the activity type and 70% for its intensity. Conclusions: The proposed module showed high performance in detection and classification of exercise in real-time within a minute of exercise onset. Integration of this module into insulin therapy decisions can help facilitate glucose management around physical activity.

Despite several developments in artificial pancreas technology, postprandial glycemic regulation remains to be a major challenge for type 1 diabetes management. Typically, the large spike in blood glucose concentration induced by meals require an appropriate dose of bolus insulin. Although matching bolus insulin to carbohydrate intake has been shown to improve glycemic regulation, current state-of-the-art meal bolus calculators depend on patient-specific parameters and/or historical clinical data, which may not be easily available. In this paper, we propose a model-free safe and personalized bolus calculator algorithm that is based on safe contextual Bayesian optimization. The proposed algorithm neither requires any patient-specific parameters, nor historical clinical data. Furthermore, the proposed algorithm focuses on patient safety, and ensures satisfaction of the safety-critical hypoglycemia constraint with high probability. In silico experiments conducted on the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator, as well as on a cohort of 50 virtual patients based on the Hovorka T1D model in open-loop mode, demonstrate that our algorithm is able to quickly learn the optimum bolus insulin dose for the announced meals using only the patient's CGM data while ensuring patient safety.

Heterocyclic aromatic amines (HAAs) are potent carcinogenic agents found in charred meats and cigarette smoke. However, few eukaryotic resistance genes have been identified. We used Saccharomyces cerevisiae (budding yeast) to identify genes that confer resistance to 2-amino-3-methylimidazo[4,5-f]quinoline (IQ). CYP1A2 and NAT2 activate IQ to become a mutagenic nitrenium compound. We introduced an expression vector that contains human CYP1A2 and NAT2 genes into selected mutant strains and the diploid yeast deletion collection. The deletion libraries expressing CYP1A2 and NAT2 or no human genes were exposed to either 400 or 800 μM IQ for five or ten generations. DNA barcodes were sequenced using the Illumina HiSeq 2500 platform and statistical significance was determined for exactly matched barcodes. Four screens for IQ resistance in the “humanized” collection identified 1160 unique ORFs, of which 337 were validated or duplicated in at least two screens. Two screens of the original yeast library identified 101 genes that overlap with the 337 previously identified. Selected genes were validated by growth curves, competitive growth assays, or trypan blue assays. Prominent among both sets are ribosomal protein genes, while nitrogen metabolism, cell wall synthesis, and phosphatase genes were identified among the “humanized” library. Protein complexes identified included the casein kinase 2 (CK2) and histone chaperone (HIR) complex. DNA repair genes included NTG1, RAD18, RAD9, PSY2 and UBC13 . Polymorphisms in human NTHL1 , the NTG1 ortholog, and RAD18 are risk factors for colon cancer. These studies thus provoke questions of whether genetic risk factors for colon cancer confer more HAA-associated toxicity.