Glucose Control System Research Articles

Mixted Optimization With Multi-Peaks Diet for Controlling the Glycemic Profil of the Diabetic Patients

Objective: The objective of this study is to develop an efficient glucose management system based on an original control model with mixed constraints (dynamic and static constraints) to manage diets with several thresholds, using Bergman’s minimal model and Fischer’s meal models. Theoretical Framework: Personalized and healthy diets play a significant role in controlling the progression of diseases such as type 2 diabetes. Existing glucose management methods are predominantly based on dynamic mathemati- cal models and focus on diets with a single peak. However, diets with multiple thresholds better reflect realistic dietary patterns and enable more comprehen- sive glucose control. Method: The proposed model combines two key components: an objective function that minimizes the total error between the basic and generated glucose and the quantity of diet, and constraints that incorporate dynamic differential equations (Bergman’s minimal model) and static constraints representing the interval between two sensitive meals. To solve this model, the integral in the objective function is discretized using the trapezoidal rule, and dynamic con- straints are approximated using the Euler-Cauchy method. A genetic algorithm is employed to optimize the resulting static model. Several study cases, including global, partial, and one-off scenarios, are considered. Results and Discussion: Numerical simulations demonstrate that the pro- posed system effectively estimates sufficient personalized diets, grading the glycemic profile within a defined confidence zone. This approach accounts for three critical dimensions: the quantity of food, its distribution, and the intervals between successive meals. Optimal management of these parameters results in maximum dietary intake, low glucose generation, and minimal insulin secretion, enabling diabetics to maintain a near-normal lifestyle. Research Implications: This study provides a realistic and innovative frame- work for glucose management, considering the complexities of real-life dietary patterns. It highlights the significance of integrating dynamic and static con- straints in glucose control systems and contributes to the design of personalized dietary strategies for individuals with diabetes. Originality/Value: This work contributes to the literature by introducing a novel glucose management system that considers diets with multiple thresholds, enabling better control of glycemic profiles and improving the quality of life for individuals with diabetes.

A physiologically-based quantitative systems pharmacology model for mechanistic understanding of the response to alogliptin and its application in patients with renal impairment.

Alogliptin is a highly selective inhibitor of dipeptidyl peptidase-4 and primarily excreted as unchanged drug in the urine, and differences in clinical outcomes in renal impairment patients increase the risk of serious adverse reactions. In this study, we developed a comprehensive physiologically-based quantitative systematic pharmacology model of the alogliptin-glucose control system to predict plasma exposure and use glucose as a clinical endpoint to prospectively understand its therapeutic outcomes with varying renal function. Our model incorporates a PBPK model for alogliptin, DPP-4 activity described by receptor occupancy theory, and the crosstalk and feedback loops for GLP-1-GIP-glucagon, insulin, and glucose. Based on the optimization of renal function-dependent parameters, the model was extrapolated to different stages renal impairment patients. Ultimately our model adequately describes the pharmacokinetics of alogliptin, the progression of DPP-4 inhibition over time and the dynamics of the glucose control system components. The extrapolation results endorse the dose adjustment regimen of 12.5mg once daily for moderate patients and 6.25mg once daily for severe and ESRD patients, while providing additional reflections and insights. In clinical practice, our model could provide additional information on the in vivo fate of DPP4 inhibitors and key regulators of the glucose control system.

Periodic insulin stimulation of Akt: Dynamic steady states and robustness

The periodic secretion of insulin is a salient feature of the blood glucose control system in vivo. Insulin levels in the blood exhibit oscillations on multiple time scales - rapid, ultradian, and circadian - and the improved metabolic regulation resulting from pulsatile insulin release has been well established. Although numerous mathematical models investigating the causal mechanisms of insulin oscillations have appeared in the literature, to date there has been comparatively little attention given to the influence of periodic insulin stimulation on downstream components of the insulin signalling pathway. In this paper, we explore the effect of high frequency periodic insulin stimulation on Akt (also known as PKB), a crucial crosstalk node in the insulin signalling pathway that coordinates metabolic and mitogenic processes in the cell. We analyse a mathematical model of Akt translocation to the plasma membrane under both single step insulin perturbations and periodic insulin stimulation with an emphasis on - but not limited to - the physiological range of parameter values. It was shown that the system rapidly attains a robust dynamic steady state entrained to the periodic insulin stimulation. Moreover, the translocation of Akt to the plasma membrane in the model permits a sufficient level of phosphorylation to trigger downstream metabolic regulators. However, the modelling also indicated that further investigation of this activation process is required to determine whether the response of Akt is a key determinant of the enhanced metabolic control observed under periodic insulin stimulation.

A Quantitative Systems Pharmacology Model of the Incretin Hormones GIP and GLP1, Glucagon, Glucose, Insulin, and the Small Molecule DPP-4 Inhibitor, Linagliptin

In the current study, we established a comprehensive quantitative systems pharmacology (QSP) model using linagliptin as the model drug, where drug disposition, drug intervention on dipeptidyl peptidase-4 (DPP-4), glucose-dependent insulinotropic peptide (GIP), Glucagon-like peptide-1 (GLP-1), glucagon, glucose, and insulin are integrated together with the cross talk and feedback loops incorporated among the whole glycemic control system. In the final linagliptin QSP model, the complicated disposition of linagliptin was characterized by a 2-compartment pharmacokinetic (PK) model with an enterohepatic cycling (EHC) component as well as target-mediated drug disposition (TMDD) processes occurring in both tissues and plasma, and the inhibitory effect of linagliptin on DPP-4 was determined by the linagliptin-DPP-4 complex in the central compartment based on target occupancy principle. The integrated GIP-GLP1-glucagon-glucose-insulin system contains five indirect response models as the "skeleton" structure with 12 feedback loops incorporated within the glucose control system. Our model adequately characterized the substantial nonlinear PK of linagliptin, time course of DPP-4 inhibition, as well as the kinetics of GIP, GLP-1, glucagon, and glucose simultaneously in humans. Our model provided valuable insights on linagliptin pharmacokinetics/pharmacodynamics and complicated glucose homeostasis. Since the glucose regulation modeling framework within the QSP model is "drug-independent", our model can be easily adopted by others to evaluate the effect of other DPP-4 inhibitors on the glucose control system. In addition, our QSP model, which contains more components than other reported glucose regulation models, can potentially be used to evaluate the effect of combination antidiabetic therapy targeting different components of glucose control system.

Robustness of closed-loop glucose control systems

Robustness of closed-loop glucose control systems

112-LB: Proof-of-Concept Testing of an Artificial Intelligence–Based Fully Closed-Loop System in Hospitalized Patients with Diabetes

Objective: Achieving tight glucose control safely and effectively is challenging in the Intensive Care Unit (ICU). We conducted the first-in-human test of an artificial intelligence (AI)-based fully autonomous glucose control system in a simulated ICU setting. Methods: We admitted seven participants with type1 and type 2 (n=6) diabetes mellitus for 24-hour closed-loop glucose control sessions that included three unannounced meals. The AI-based system uses glucose control software that controls intravenous insulin and glucose infusions based on real-time continuous glucose monitoring derived data from two real-time CGM devices. The primary efficacy endpoint was the percentage of glucose values within the range of 70-180 mg/dL. The primary safety endpoint was the percentage of glucose values < 70 mg/dL. Results: Participants were on average 48±9.7 years-old, 71% female, and BMI 36±7.8 m/kg2. Mean time in the target range (70-180 mg/dl) was 87.8%. Mean time in the hypoglycemic range (< 70 mg/dL) was 0.2%, Figure. No adverse events occurred. Conclusion: This first in-human results show this novel fully autonomous closed-loop glucose control system designed for the ICU setting has the potential to achieve unparalleled glycemic control with minimal risk of hypoglycemia and paves the way for further research in the actual ICU setting. Disclosure F. Zahedi tajrishi: None. F. J. Pasquel: Consultant; Dexcom, Inc., Medscape, Research Support; Dexcom, Inc., Ideal Medical Technologies. G. Davis: Consultant; Medscape, Research Support; Insulet Corporation. M. Perez-guzman: None. L. I. Guerrero arroyo: None. G. Blanco: None. J. Varghese: None. S. Zahid: None. J. Dejournett: Stock/Shareholder; Ideal Medical Technologies. L. Dejournett: Employee; Ideal Medical Technologies. Funding Ideal Medical Technologies; Dexcom, Inc.

The influence of mare's lactational status during early pregnancy on metabolism of foals up to 28 days of age

The influence of a mare's nutrition on the fetus plays a significant role in future health, growth and performance, affecting insulin and glucose control systems. When maternal nutrition is altered through concentrate supplementation, higher insulin concentrations are required to maintain euglycemia in the mare, which can lead to metabolic reprogramming in the foal. The "easy keeper" condition (obese animals and/or difficult to lose weight), as well as susceptibility to endocrinopathic laminitis can result from this intrauterine reprogramming. It is common to use lactating mares as embryo recipients, however, little is known about the effect on the mare's metabolism and influence on the foal's development. Our study aimed to compare the insulin, glucose and others metabolism markers of foals born from lactating (L) and non-lactating (NL) recipient mares, from day 1-28 of life. We used 12 normal foals, offspring of Mangalarga Paulista mares aged 5 to 15 years. Mares from the lactating group continued lactation from ovulation until a maximum of 170 days of pregnancy. Foal evaluations were performed the day after birth (D1) and at 7, 14, 21 and 28 days of age. Glucose (mg/dL), insulin (µU/mL), triglycerides (mg/dL), non-esterified fatty acids (NEFA; mmol/dL) and cortisol (µg/dL) were analyzed. We used factorial arrangements between groups (L, NL) and time (D1, D7,D14, D21 and D28). The results presented are the mean and standard error of the mean. There was no statistically significant interaction in any of the variables. There was no difference between groups, but insulin, glucose, triglycerides and cortisol changed over time. Insulin concentrations were lower on D1 and equal at other times (D1:3.82±0.27; D7:6.28±0.59; D14:7.89±0.98; D21:6.97±0.69; D28:5.87±0.88; P=0.002). Glucose was lower on D28, which was equal to D21 (D1:144.83±5.27; D7:148.33±3.96; D14: 147.25±4.27; D21: 140.25±3.04; D28: 133.33±3.75; P=0.02). Triglycerides were higher on D1 and D7, on D14 were equal to these times and to D21 and on D28 were lower than other times, being equal to D21 (D1: 72.64±21.95; D7: 65.59±8.38; D14: 51.92±7.73; D21: 41±3.47; D28: 34.05±2.84; P<0.01). Cortisol was higher on D1 and lower on all other times (D1: 0.83±0.09; D7: 0.42±0.06; D14: 0.43±0.04; D21: 0.57±0.06; D28: 0.6±0.07; P=0.003). In conclusion, some characteristics change with foal age such as insulin, glucose, triglycerides and cortisol. The lactating recipient mare status during the initial third of pregnancy does not influence the postnatal characteristics of the foal up to 28 days of life. Acknowledgements: FAPESP (2017/05425-0 and 2020/10260-3) and CAPES for financial support.

Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records.

Diabetes is an increasingly common disease that poses an immense challenge to public health. Hyperglycemia is also a common complication in clinical patients in the intensive care unit, increasing the rate of infection and mortality. The accurate and real-time prediction of blood glucose concentrations after each short-acting insulin injection has great clinical significance and is the basis of all intelligent blood glucose control systems. Most previous prediction methods require long-term continuous blood glucose records from specific patients to train the prediction models, resulting in these methods not being used in clinical practice. In this study, we construct 13 deep neural networks with different architectures to atomically predict blood glucose concentrations after arbitrary independent insulin injections without requiring continuous historical records of any patient. Using our proposed models, the best root mean square error of the prediction results reaches 15.82 mg/dL, and 99.5% of the predictions are clinically acceptable, which is more accurate than previously proposed blood glucose prediction methods. Through the re-validation of the models, we demonstrate the clinical practicability and universal accuracy of our proposed prediction method.

Time With Glucose Level in Target Range Among Children and Adolescents With Type 1 Diabetes After a Software Update to a Closed-Loop Glucose Control System

Time With Glucose Level in Target Range Among Children and Adolescents With Type 1 Diabetes After a Software Update to a Closed-Loop Glucose Control System

Automated versus conventional perioperative glycemic control in adult diabetic patients undergoing open heart surgery

BackgroundIntraoperative glycemic variability is associated with increased risks of mortality and morbidity and an increased incidence of hyperglycemia after cardiac surgery. Accordingly, clinicians tend to use a tight glucose control to maintain perioperative blood glucose levels and therefore the need to develop a less laborious automated glucose control system is important especially in diabetic patients at a higher risk of developing complications.MethodsPatients, aged between 40 and 75 years old, undergoing open heart surgery were randomized to either an automated protocol (experimental) or to the conventional technique at our institution (control).ResultsWe showed that the percentage of patients maintained between 7.8–10 mmol.l−1 was not statistically different between the two groups, however, through an additional analysis, we showed that the proportion of patients whose glucose levels maintained between a safety level of 6.7–10 mmol.l−1 was significantly higher in the experimental group compared to control group, 14 (26.7%) vs 5 (17.2%) P = 0.025. In addition, the percentage of patients who had at least one intraoperative hyperglycemic event was significantly higher in the control group compared to the experimental group, 17 (58.6%) vs 5 (16.7%), P < 0.001 with no hypoglycemic events in the experimental group compared to two events in the control group. We also showed that longer surgeries can benefit more from using the automated glucose control system, particularly surgeries lasting more than 210 min.ConclusionWe concluded that the automated glucose control pump in diabetic patients undergoing open heart surgeries maintained most of the patients within a predefined glucose range with a very low incidence of hyperglycemic events and no incidence of hypoglycemic events.Trial registrationRegistered with clinicaltrials.gov (NCT #NCT03314272, Principal investigator Roland Kaddoum, date of registration: 19/10/2017).

Zone-MPC Automated Insulin Delivery Algorithm Tuned for Pregnancy Complicated by Type 1 Diabetes.

Type 1 diabetes (T1D) increases the risk for pregnancy complications. Increased time in the pregnancy glucose target range (63-140 mg/dL as suggested by clinical guidelines) is associated with improved pregnancy outcomes that underscores the need for tight glycemic control. While closed-loop control is highly effective in regulating blood glucose levels in individuals with T1D, its use during pregnancy requires adjustments to meet the tight glycemic control and changing insulin requirements with advancing gestation. In this paper, we tailor a zone model predictive controller (zone-MPC), an optimization-based control strategy that uses model predictions, for use during pregnancy and verify its robustness in-silico through a broad range of scenarios. We customize the existing zone-MPC to satisfy pregnancy-specific glucose control objectives by having (i) lower target glycemic zones (i.e., 80-110 mg/dL daytime and 80-100 mg/dL overnight), (ii) more assertive correction bolus for hyperglycemia, and (iii) a control strategy that results in more aggressive postprandial insulin delivery to keep glucose within the target zone. The emphasis is on leveraging the flexible design of zone-MPC to obtain a controller that satisfies glycemic outcomes recommended for pregnancy based on clinical insight. To verify this pregnancy-specific zone-MPC design, we use the UVA/Padova simulator and conduct in-silico experiments on 10 subjects over 13 scenarios ranging from scenarios with ideal metabolic and treatment parameters for pregnancy to extreme scenarios with such parameters that are highly deviant from the ideal. All scenarios had three meals per day and each meal had 40 grams of carbohydrates. Across 13 scenarios, pregnancy-specific zone-MPC led to a 10.3 ± 5.3% increase in the time in pregnancy target range (baseline zone-MPC: 70.6 ± 15.0%, pregnancy-specific zone-MPC: 80.8 ± 11.3%, p < 0.001) and a 10.7 ± 4.8% reduction in the time above the target range (baseline zone-MPC: 29.0 ± 15.4%, pregnancy-specific zone-MPC: 18.3 ± 12.0, p < 0.001). There was no significant difference in the time below range between the controllers (baseline zone-MPC: 0.5 ± 1.2%, pregnancy-specific zone-MPC: 3.5 ± 1.9%, p = 0.1). The extensive simulation results show improved performance in the pregnancy target range with pregnancy-specific zone MPC, suggest robustness of the zone-MPC in tight glucose control scenarios, and emphasize the need for customized glucose control systems for pregnancy.

A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems.

Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. In order to move towards fully automated closed-loop glucose control, we propose an algorithm based on a deep learning framework that performs multitask quantile regression, for both meal detection and carbohydrate estimation. Our proposed method is evaluated in silico on 10 adult subjects from the UVa/Padova simulator with a Bio-inspired Artificial Pancreas (BiAP) control algorithm over a 2 month period. Three different configurations of the AP are evaluated -BiAP without meal announcement (BiAP-NMA), BiAP with meal announcement (BiAP-MA), and BiAP with meal detection (BiAP-MD). We present results showing an improvement of BiAP-MD over BiAP-NMA, demonstrating 144.5 ± 6.8 mg/dL mean blood glucose level (−4.4 mg/dL, 0.01) and 77.8 ± 6.3% mean time between 70 and 180 mg/dL (+3.9%, 0.001). This improvement in control is realised without a significant increase in mean in hypoglycaemia (+0.1%, 0.4). In terms of detection of meals and snacks, the proposed method on average achieves 93% precision and 76% recall with a detection delay time of 38 ± 15 min (92% precision, 92% recall, and 37 min detection time for meals only). Furthermore, BiAP-MD handles hypoglycaemia better than BiAP-MA based on CVGA assessment with fewer control errors (10% vs. 20%). This study suggests that multitask quantile regression can improve the capability of AP systems for postprandial glucose control without increasing hypoglycaemia.

A Multitask Learning Approach to Personalized Blood Glucose Prediction.

Blood glucose prediction algorithms are key tools in the development of decision support systems and closed-loop insulin delivery systems for blood glucose control in diabetes. Deep learning models have provided leading results among machine learning algorithms to date in glucose prediction. However these models typically require large amounts of data to obtain best personalised glucose prediction results. Multitask learning facilitates an approach for leveraging data from multiple subjects while still learning accurate personalised models. In this work we present results comparing the effectiveness of multitask learning over sequential transfer learning, and learning only on subject-specific data with neural network and support vector regression. The multitask learning approach shows consistent leading performance in predictive metrics at both short-term and long-term prediction horizons. We obtain a predictive accuracy (RMSE) of 18.8 ±2.3, 25.3 ±2.9, 31.8 ±3.9, 41.2 ±4.5, 47.2 ±4.6mg/dL at 30, 45, 60, 90, and 120min prediction horizons respectively, with at least 93% clinically acceptable predictions using the Clarke Error Grid (EGA) at each prediction horizon. We also identify relevant prior information such as glycaemic variability that can be incorporated to improve predictive performance at long-term prediction horizons. Furthermore, we show consistent performance - ≤ 5% change in both RMSE and EGA (Zone A) - in rare cases of adverse glycaemic events with 1-6 weeks of training data. In conclusion, a multitask approach can allow for deploying personalised models even with significantly less subject-specific data without compromising performance.

Problems and methods of a closed-loop blood glucose control system construction

The automation of the insulin therapy in the closed-loop blood glucose control system is accompanied by such problems as delays of the feedback and the controlling action effect on blood glucose dynamics, difficulties in low and high glucose levels advance detection and timely compensation, a human factor, complexity of a mathematical model of control object building, evaluation of blood glucose control efficiency. In the paper, these automation problems and methods for their compensation are considered. The closed-loop system based on the model predictive control, adaptive adjustment of the controlling action and decision-making support was developed. Experimental testing with UVA/Padova Type 1 Diabetes Mellitus Simulator showed that the application of suggested methods in the closed-loop allows increasing the efficiency of the blood glucose maintaining in the target range.

An independent central point OPTICS clustering algorithm for semi-supervised outlier detection of continuous glucose measurements

• A novel semi-supervised outlier detection method is designed for outlier detection of CGM measurements. • The ICP-OPTICS algorithm is proposed for outlier detection and an optimization function is developed to improve the clustering performance. • The proposed method achieves good accuracy and reveals the superiority applied to blood glucose datasets. Continuous glucose monitoring (CGM) collects a host of time-series sensor data and is committed to fully automated systems for glucose control as an essential part. Outlier data in CGM measurements caused by faults may seriously affect the computation of insulin infusion rates and endanger the safety of patients. In this paper, a semi-supervised outlier detection method is proposed for anomaly detection of glucose concentration measurements based on a density-based clustering algorithm, named independent central point OPTICS (ICP-OPTICS). An optimization function is designed to improve the clustering performance and solve the problem of outlier detection with the distance measurement and information entropy of weighted time series. The proposed method in the application can be configured automatically with no prior knowledge except only some clean samples. The UVa/Padova simulator and real dataset are used to evaluate the performance of the method. Compared with the present work, the statistical results show that the method is much better in effectiveness and superiority. Furthermore, the proposed method also provides a possible reference significance for outlier detection in other practice applications.

Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions

This paper introduces methods to estimate aspects of physical activity and sedentary behavior from three-axis accelerometer data collected with a wrist-worn device at a sampling rate of 32 [Hz] on adults with type 1 diabetes (T1D) in free-living conditions. In particular, we present two methods able to detect and grade activity based on its intensity and individual fitness as sedentary, mild, moderate or vigorous, and a method that performs activity classification in a supervised learning framework to predict specific user behaviors. Population results for activity level grading show multi-class average accuracy of 99.99%, precision of 98.0±2.2%, recall of 97.9±3.5% and F1 score of 0.9±0.0. As for the specific behavior prediction, our best performing classifier, gave population multi-class average accuracy of 92.43±10.32%, precision of 92.94±9.80%, recall of 92.20±10.16% and F1 score of 92.56±9.94%. Our investigation showed that physical activity and sedentary behavior can be detected, graded and classified with good accuracy and precision from three-axial accelerometer data collected in free-living conditions on people with T1D. This is particularly significant in the context of automated glucose control systems for diabetes, in that the methods we propose have the potential to inform changes in treatment parameters in response to the intensity of physical activity, allowing patients to meet their glycemic targets.

Application of model reference adaptive control and modified Smith predictor to control blood glucose in type 1 diabetic patients

Abstract In this paper, a new adaptive control structure for time-delayed systems has been proposed, combining the model reference adaptive control with the modified Smith predictor (SP). due to the extensive variability of the patients’ metabolism, an in silico clinical trial consisting of 30 patients is utilized to simulate the personal variability in the glucose control system. all the patients have random changes and sinusoidal oscillations in their parameters of the Dalla Man glucose-insulin model. based on the quantitative and qualitative indicators, the performance of the proposed algorithm is compared to a PID controller of the Smith predictor structure. the results of the simulation show that the proposed control scheme is successful against the interpatient variation in fasting conditions, meal disruption rejection, and in terms of robustness. Insulin therapy often leads to high fluctuations in blood glucose and hypoglycemic events in patients with type 1 diabetes. On the other hand, a closed blood glucose loop control with an artificial pancreas can improve the patients’ quality of life. In this paper, the physiological behaviour of the system is modeled inversely using the daily patients’ data acquired by the GIM simulator.

FDA approval of dapagliflozin for chronic kidney disease: a remarkable achievement?

Chronic kidney disease (CKD) affects 12% of the population worldwide,1 is a major cause of morbidity and mortality, and consumes health-care resources.2 A primary therapeutic goal for the treatment of CKD is prevention of kidney failure and cardiovascular disease (CVD).3,4 Healthy lifestyle behaviours, blood pressure (BP) lowering, maintaining glucose control (in people with diabetes), and renin and angiotensin aldosterone system (RAAS) blockers have been the cornerstone of therapy for patients with CKD since the late 1990s.

Central nervous system regulation of organismal energy and glucose homeostasis.

Growing evidence implicates the brain in the regulation of both immediate fuel availability (for example, circulating glucose) and long-term energy stores (that is, adipose tissue mass). Rather than viewing the adipose tissue and glucose control systems separately, we suggest that the brain systems that control them are components of a larger, highly integrated, 'fuel homeostasis' control system. This conceptual framework, along with new insights into the organization and function of distinct neuronal systems, provides a context within which to understand how metabolic homeostasis is achieved in both basal and postprandial states. We also review evidence that dysfunction of the central fuel homeostasis system contributes to the close association between obesity and type 2 diabetes, with the goal of identifying more effective treatment options for these common metabolic disorders.

Meal detection and carbohydrate estimation based on a feedback scheme with application to the artificial pancreas

• A simple automatic meal detection and carbohydrate amount estimation from readings of blood glucose (BG) and insulin infusion was developed. • It can improve the artificial pancreas control system coupling it with the main regulator. • The methodology can help the off-line reconstruction of the carbohydrate intake signal which allows a reliable identification of a control-relevant model. • The developed algorithm can produce on-line prediction of meal onset and amount of carbohydrates ingested. • The performance of the algorithm was assessed with both virtual data and clinical data obtaining 97% rate of detection and estimation. Current glucose control systems automatically regulate basal insulin infusion, but users still need to manually announce meals (major disturbances) to dose prandial insulin boluses. This issue needs to be solved to reach a fully automated artificial pancreas. Automatic meal detection and carbohydrate amount estimation from readings of blood glucose (BG) and insulin infusion can improve the artificial pancreas control system from two possible paths: (i) the off-line reconstruction of the carbohydrate intake signal which allows a reliable identification of a control-relevant model, and (ii) the on-line prediction of meal onset and amount of carbohydrates ingested, which allows safety supervision of manually entered meal announcements. The aim of this work is the item (i), for which an automatic algorithm is developed to detect the consumption of a meal and estimate its carbohydrate amount in people with type 1 diabetes. The unknown input estimation is based on a feedback scheme where the measured BG is compared with a BG prediction. Glycemic behavior is predicted using a personalized model by means of the patient's functional insulin therapy parameters defined by the treating physician. The proposed algorithm is evaluated with data extracted from the 30-patient cohort of the UVA/Padova simulator approved by the FDA and with retrospective data from 11 real patients of a diabetes center. Diabetes care data from free-living adult patients were collected during regular screening and the meals were identified by experts. For the in silico dataset, the detection accuracy is near 100%, the absolute error of the estimation of ingested carbohydrates is 10% on average, and the average bias of meal onset estimation is 5 min. For the clinical dataset, the meal detection performance is 98% and the estimation accuracy measures are 13% and 2 min, respectively. In this work, the impact of reconstructing the carbohydrate intake signal on the identification proved to be beneficial. In addition, the feedback scheme and the easily personalized prediction model make the strategy efficient.