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Objective: This paper aims at proposing a new machine-learning based model to improve the calculation of mealtime insulin boluses (MIB) in type 1 diabetes (T1D) therapy using continuous glucose monitoring (CGM) data. Indeed, MIB is still often computed through the standard formula (SF), which does not account for glucose rate-of-change ( $\Delta$ G), causing critical hypo/hyperglycemic episodes. Methods: Four candidate models for MIB calculation, based on multiple linear regression (MLR) and least absolute shrinkage and selection operator (LASSO) are developed. The proposed models are assessed in silico , using the UVa/Padova T1D simulator, in different mealtime scenarios and compared to the SF and three $\Delta$ G-accounting variants proposed in the literature. An assessment on real data, by retrospectively analyzing 218 glycemic traces, is also performed. Results: All four tested models performed better than the existing techniques. LASSO regression with extended feature-set including quadratic terms (LASSO $_Q$ ) produced the best results. In silico, LASSO $_Q$ reduced the error in estimating the optimal bolus to only 0.86 U (1.45 U of SF and 1.36–1.44 U of literature methods), as well as hypoglycemia incidence (from 44.41% of SF and 44.60–45.01% of literature methods, to 35.93%). Results are confirmed by the retrospective application to real data. Conclusion: New models to improve MIB calculation accounting for CGM- $\Delta$ G and easy-to-measure features can be developed within a machine learning framework. Particularly, in this paper, a new LASSO $_Q$ model was developed, which ensures better glycemic control than SF and other literature methods. Significance: MIB dosage with the proposed LASSO $_Q$ model can potentially reduce the risk of adverse events in T1D therapy.

We present a new mobile platform to be used in clinical trials aimed at both collecting data and assessing new technologies and treatments for diabetes care. The main components of the platform are a mobile app, that automatically collects data from continuous glucose monitoring sensors and activity trackers, and also allows users to manually log daily events; a cloud database for safe data storage; a web interface, which allows clinicians to monitor patients’ status in real-time. The platform is modular and highly customizable for a multitude of purposes in clinical research. Preliminary tests performed for daily-life data gathering by both clinicians and users are extremely encouraging.

Introduction: In persons with type 1 diabetes (T1D) insulin dosing can be adjusted based on trend arrows derived from continuous glucose monitoring (CGM). We propose a slide rule with narrower blood glucose intervals and more classes of insulin sensitivity than are available in current models. Methods: The slide rule was tested in silico, in which a meal was simulated in 100 virtual subjects and the insulin bolus was calculated either in the standard way based on the insulin-to-carbohydrate ratio and the correction factor or according to the slide rule, following which the percentage time spent in range (70–180 mg/dl; %TIR), hypoglycemia (< 70 mg/dl; %THYPO), and hyperglycemia (> 180 mg/dl; %THYPER) was compared between the methods during the 4 h after the meal. Slide rule performance was also tested in real life by analyzing the same variables at during the 4 h postprandial period in 27 individuals with T1D. Only meals starting while the rate of change was at least 1 mg/dl per minute (increasing or decreasing) were considered for analysis. Results: In silico, when the preprandial trend arrow was increasing, our slide rule reduced %THYPER and increased %TIR (p < 0.05), whereas when the preprandial trend arrow was decreasing, it reduced %THYPO and slightly increased %THYPER (p < 0.05). In real life, our slide rule kept subjects on target for 70.8 and 91.6% of postprandial time when preprandial trend arrows were increasing or decreasing, respectively. Conclusion: The proposed slide rule performed well both in silico and in real life, suggesting that it could be safely adopted by individuals with T1D to improve glucose control.

Background: Providing real-time magnitude and direction of glucose rate-of-change (ROC) via trend arrows represents one of the major strengths of continuous glucose monitoring (CGM) sensors in managing type 1 diabetes (T1D). Several literature methods were proposed to adjust the standard formula (SF) used for insulin bolus calculation by accounting for glucose ROC, but each of them provides different suggestions, making it difficult to understand which should be applied in practice. This work aims at performing an extensive in-silico assessment of their performance and safety. Methods: The methods of Buckingham (BU), Scheiner (SC), Pettus/Edelman (PE), Klonoff/Kerr (KL), Aleppo/Laffel (AL), Ziegler (ZI), and Bruttomesso (BR) were evaluated using the UVa/Padova T1D simulator, in single-meal scenarios, where ROC and glucose at mealtime varied between [-2,+2] mg/dL/min and [80,200] mg/dL, respectively. Efficacy of postprandial glucose control was quantitatively assessed by time in, above and below range (TIR, TAR, and TBR, respectively). Results: For negative ROCs, all methods proved to increase TIR and decrease TAR and TBR vs SF, with KL, PE, and BR being the most effective. For positive ROCs, a general worsening of the performances is present, only BR improved the glycemic control when mealtime glucose was close to hypoglycemia, while SC resulted the safest in the other conditions. Conclusions: Insulin bolus adjustment methods are effective for negative ROCs, but they generally appear to overdose for positive ROCs, calling for safer strategies in such a scenario. These results can be useful in outlining guidelines to identify which adjustment to apply based on the mealtime condition.

Type 1 diabetes (T1D) therapy requires multiple daily insulin injections to compensate the lack of endogenous insulin production due to β-cells destruction. An empirical standard formula (SF) is commonly used for such a task. Unfortunately, SF does not include information on glucose dynamics, e.g. the glucose rate-of-change (ROC) provided by continuous glucose monitoring (CGM) sensor. Hence, SF can sometimes lead to under/overestimations that can cause critical hypo/hyperglycemic episodes during/after the meal. Recently, to overcome this limitation, we proposed new linear regression models, integrating ROC information and personalized features. Despite the first encouraging results, the nonlinear nature of the problem calls for the application of nonlinear models. In this work, random forest (RF) and gradient boosting tree (GBT), nonlinear machine learning methodologies, were investigated. A dataset of 100 virtual subjects, opportunely divided into training and testing sets, was used. For each individual, a single-meal scenario with different meal conditions (preprandial ROC, BG and meal amounts) was simulated. The assessment was performed both in terms of accuracy in estimating the optimal bolus and glycemic control. Results were compared to the best performing linear model previously developed. The two tree-based models proposed lead to a statistically significant improvement of glycemic control compared to the linear approach, reducing the time spent in hypoglycemia (from 32.49% to 27.57-25.20% for RF and GBT, respectively). These results represent a preliminary step to prove that nonlinear machine learning techniques can improve the estimation of insulin bolus in T1D therapy. Particularly, RF and GBT were shown to outperform the previously linear models proposed.Clinical Relevance— Insulin bolus estimation with nonlinear machine learning techniques reduces the risk of adverse events in T1D therapy.

Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, every 1–5 min, the current blood glucose concentration and its rate-of-change, two key pieces of information for improving the determination of exogenous insulin administration and the prediction of forthcoming adverse events, such as hypo-/hyper-glycemia. The current research in diabetes technology is putting considerable effort into developing decision support systems for patient use, which automatically analyze the patient’s data collected by CGM sensors and other portable devices, as well as providing personalized recommendations about therapy adjustments to patients. Due to the large amount of data collected by patients with T1D and their variety, artificial intelligence (AI) techniques are increasingly being adopted in these decision support systems. In this paper, we review the state-of-the-art methodologies using AI and CGM sensors for decision support in advanced T1D management, including techniques for personalized insulin bolus calculation, adaptive tuning of bolus calculator parameters and glucose prediction.

Mathematical physiological models of type 1 diabetes (T1D) glucose-insulin dynamics have been of great help in designing and preliminary assessing new algorithm for glucose control. Derivation of models at the individual level is however difficult because of identifiability issues. Recently, fitting these models against data of real patients with T1D has been made possible by both the use of Bayesian estimation techniques and the availability of individual datasets including plasma glucose and insulin concentration samples gathered in clinical protocols. The aim of this work is to make a step further and develop a methodology able to estimate the parameters of T1D physiological models using easily accessible data only, i.e. continuous glucose monitoring (CGM) sensor, carbohydrate intakes (CHO), and exogenous insulin infusion (I) data. The methodology is tested on synthetic data of 100 patients generated by a composite model of glucose-insulin dynamics. To solve identifiability problems, a Bayesian approach numerically implemented by Markov Chain Monte Carlo (MCMC) has been used to obtain point estimates and confidence intervals of model unknown parameters exploiting a priori knowledge available from the literature. Results show goodness of model fit and acceptable precision of parameter estimates. The methodology is also successful in reconstructing of "non-accessible" glucose-insulin fluxes, i.e. glucose rate of appearance and plasma insulin. These preliminary results encourage further development of this framework and its assessment in more challenging setups.

The recent announcement of the production of new low-cost continuous glucose monitoring (CGM) sensors, the approval of marketed CGM sensors for making treatment decisions, and new reimbursement criteria have the potential to revolutionize CGM use. After briefly summarizing current CGM applications, we discuss how, in our opinion, these changes are expected to extend CGM utilization beyond diabetes patients, for example, to subjects with prediabetes or even healthy individuals. We also elaborate on how the integration of CGM data with other relevant information, for example, health records and other medical device/wearable sensor data, will contribute to creating a digital data ecosystem that will improve our understanding of the etiology and complications of diabetes and will facilitate the development of data analytics for personalized diabetes management and prevention.

Background: In type 1 diabetes (T1D) therapy, the calculation of the meal insulin bolus is performed according to a standard formula (SF) exploiting carbohydrate intake, carbohydrate-to-insulin ratio, correction factor, insulin on board, and target glucose. Recently, some approaches were proposed to account for preprandial glucose rate of change (ROC) in the SF, including those by Scheiner and by Pettus and Edelman. Here, the aim is to develop a new approach, based on neural networks (NN), to optimize and personalize the bolus calculation using continuous glucose monitoring information and some easily accessible patient parameters. Method: The UVa/Padova T1D Simulator was used to simulate data of 100 virtual adults in a single-meal noise-free scenario with different conditions in terms of meal amount and preprandial blood glucose and ROC values. An NN was trained to learn the optimal insulin dose using the SF parameters, ROC, body weight, insulin pump basal infusion rate and insulin sensitivity as features. The performance of the NN for meal bolus calculation was assessed by blood glucose risk index (BGRI) and compared to the methods by Scheiner and by Pettus and Edelman. Results: The NN approach brings to a small but statistically significant ( P < .001) reduction of BGRI value, equal to 0.37, 0.23, and 0.20 versus SF, Scheiner, and Pettus and Edelman, respectively. Conclusion: This preliminary study showed the potentiality of using NNs for the personalization and optimization of the meal insulin bolus calculation. Future work will deal with more realistic scenarios including technological and physiological/behavioral sources of variability.

In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate insulin pump delivery, such as low-glucose insulin suspension and the artificial pancreas, are starting to become a reality, their elevated cost and performance below user expectations is hindering their adoption. Hence, a decision support system that helps people with type 1 diabetes, on multiple daily injections or insulin pump therapy, to avoid undesirable overnight blood glucose fluctuations (hyper- or hypoglycaemic) is an attractive alternative. In this paper, we introduce a novel data-driven approach to predict the quality of overnight glycaemic control in people with type 1 diabetes by analyzing commonly gathered data during the day-time period (continuous glucose monitoring data, meal intake and insulin boluses). The proposed approach is able to predict whether overnight blood glucose concentrations are going to remain within or outside the target range, and therefore allows the user to take the appropriate preventive action (snack or change in basal insulin). For this purpose, a number of popular established machine learning algorithms for binary classification were evaluated and compared on a publicly available clinical dataset (i.e., OhioT1DM). Although there is no clearly superior classification algorithm, this study indicates that, by using commonly gathered data in type 1 diabetes management, it is possible to predict the quality of overnight glycaemic control with reasonable accuracy (AUC-ROC = 0.7).

In Type 1 diabetes (T1D) standard treatment, the mitigation of hypoglycemia is achieved by the assumption of small amounts of carbohydrates (CHO), called hypotreatments (HTs), as soon as hypoglycemia is revealed. However, since CHO takes time to reach the blood stream, hypoglycemia cannot be totally avoided. Our purpose is to evaluate in-silico the effectiveness of preventive HTs and to propose a new real-time algorithm for the mitigation/avoidance of hypoglycemia, based on continuous glucose monitoring (CGM) sensor data. To such a purpose, the algorithm exploits the "dynamic risk" non linear-function that, by combining CGM value and trend, allows predicting the forthcoming hypoglycemic event. The algorithm is tested in an ideal noise-free environment on 100 virtual subjects (VSs) generated by the UVA/Padova T1D simulator and undergoing a single-meal experiment, with induced post-meal hypoglycemia. Compared to a reference HT rule, which suggest to assume HTs when hypoglycemia is detected, the algorithm reduces, on median [25th – 75th percentiles], both the time spent in hypoglycemia (from 36 [29 – 43] min to 10 [0 – 20] min) and the post-treatment rebound (from 136 [121 – 148] mg/dl to 114 [98 – 130] mg/dl). In conclusion, the proposed real-time algorithm efficiently generates preventive HTs that allow to almost totally avoid hypoglycemia. Future work will concern to modify the algorithm for detecting in advance the severity of the hypoglycemic episode –since performance are influenced on the hypoglycemic episode aggressiveness level- and to assess algorithm in a more challenging environment, including CGM measurement error.

Type 1 diabetes (TID) therapy is based on multiple daily injections of exogenous insulin. The so-called insulin bolus calculators facilitate insulin dose calculation to the patients by implementing a standard formula SF which, besides some patient-related parameters, also considers the current value of blood glucose concentration (BG), normally measured by the patient through a fingerprick device. The recent approval by the U.S. Food and Drug Administration to use the measurements collected by wearable continuous glucose monitoring (CGM) sensors for insulin dosing of fers new perspectives. Indeed, CGM sensors provide real-time information on both glucose concentration and rate of change, currently not considered in the SF. The purpose of this work is to preliminary investigate the possibility of using neural networks (NN)s for the calculation of meal insulin bolus dose exploiting CGM-based information. Using the UVa/Padova TID Simulator, we generated data of 100 subjects in 9-h, single-meal, noise-free scenarios. In particular, for each subject we analyzed different meal conditions in terms of carbohydrate intakes, preprandial BG and glucose rate-of -change. Then, a fully-connected feedforward NN was trained, with the aim of estimating the insulin bolus needed to obtain the best glycemic outcomes according to the blood glucose risk index (BGRI). Preliminary results show that by using the NN to calculate insulin doses lower BGRI values are obtained, on average, compared to the SF. These results encourage further development of the approach and its assessment in more challenging scenarios.

Background: The standard formula (SF) used in bolus calculators (BCs) determines meal insulin bolus using “static” measurement of blood glucose concentration (BG) obtained by self-monitoring of blood glucose (SMBG) fingerprick device. Some methods have been proposed to improve efficacy of SF using “dynamic” information provided by continuous glucose monitoring (CGM), and, in particular, glucose rate of change (ROC). This article compares, in silico and in an ideal framework limiting the exposition to possibly confounding factors (such as CGM noise), the performance of three popular techniques devised for such a scope, that is, the methods of Buckingham et al (BU), Scheiner (SC), and Pettus and Edelman (PE). Method: Using the UVa/Padova Type 1 diabetes simulator we generated data of 100 virtual subjects in noise-free, single-meal scenarios having different preprandial BG and ROC values. Meal insulin bolus was computed using SF, BU, SC, and PE. Performance was assessed with the blood glucose risk index (BGRI) on the 9 hours after meal. Results: On average, BU, SC, and PE improve BGRI compared to SF. When BG is rapidly decreasing, PE obtains the best performance. In the other ROC scenarios, none of the considered methods prevails in all the preprandial BG conditions tested. Conclusion: Our study showed that, at least in the considered ideal framework, none of the methods to correct SF according to ROC is globally better than the others. Critical analysis of the results also suggests that further investigations are needed to develop more effective formulas to account for ROC information in BCs.

By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.

Many multicellular systems consist of coupled cells that work as a syncytium. The pancreatic islet of Langerhans is a well-studied example of such a microorgan. The islets are responsible for secretion of glucose-regulating hormones, mainly glucagon and insulin, which are released in distinct pulses. In order to observe pulsatile insulin secretion from the β-cells within the islets, the cellular responses must be synchronized. It is now well established that gap junctions provide the electrical nearest-neighbor coupling that allows excitation waves to spread across islets to synchronize the β-cell population. Surprisingly, functional coupling analysis of calcium responses in β-cells shows small-world properties, i.e., a high degree of local coupling with a few long-range "short-cut" connections that reduce the average path-length greatly. Here, we investigate how such long-range functional coupling can appear as a result of heterogeneity, nearest-neighbor coupling, and wave propagation. Heterogeneity is also able to explain a set of experimentally observed synchronization and wave properties without introducing all-or-none cell coupling and percolation theory. Our theoretical results highlight how local biological coupling can give rise to functional small-world properties via heterogeneity and wave propagation.

In the daily management of type 1 diabetes (T1D), determining the correct insulin dose to be injected at meal-time is fundamental to achieve optimal glycemic control. Wearable sensors, such as continuous glucose monitoring (CGM) devices, are instrumental to achieve this purpose. In this paper, we show how CGM data, together with commonly recorded inputs (carbohydrate intake and bolus insulin), can be used to develop an algorithm that allows classifying, at meal-time, the post-prandial glycemic status (i.e., blood glucose concentration being too low, too high, or within target range). Such an outcome can then be used to improve the efficacy of insulin therapy by reducing or increasing the corresponding meal bolus dose. A state-of-the-art T1D simulation environment, including intraday variability and a behavioral model, was used to generate a rich in silico dataset corresponding to 100 subjects over a two-month scenario. Then, an extreme gradient-boosted tree (XGB) algorithm was employed to classify the post-prandial glycemic status. Finally, we demonstrate how the XGB algorithm outcome can be exploited to improve glycemic control in T1D through real-time adjustment of the meal insulin bolus. The proposed XGB algorithm obtained good accuracy at classifying post-prandial glycemic status (AUROC = 0.84 [0.78, 0.87]). Consequently, when used to adjust, in real-time, meal insulin boluses obtained with a bolus calculator, the proposed approach improves glycemic control when compared to the baseline bolus calculator. In particular, percentage time in target [70, 180] mg/dL was improved from 61.98 (&plusmn, 13.89) to 67.00 (&plusmn, 11.54, p &lt, 0.01) without increasing hypoglycemia.