Background: The use of continuous glucose monitoring (CGM) devices in managing type 1 diabetes (T1D) has been associated with improved glycemic control in individuals with T1D. A key challenge for CGMs, however, is achieving accuracy, particularly under conditions where glucose levels may fluctuate rapidly, such as during exercise. Another factor contributing to blood glucose variability is the menstrual cycle, during which hormonal fluctuations affect insulin sensitivity, leading to variable glucose levels. This study aimed to assess the accuracy of FreeStyle Libre-3 (FSL3) during continuous moderate-intensity aerobic exercise (CONT) performed in the follicular and luteal phases of the menstrual cycle in females with T1D. Methods: Participants underwent CONT sessions on a cycle ergometer, one in the follicular phase and one in the luteal phase of the menstrual cycle, at the Research Laboratory of the Faculty of Physiotherapy. Glucose levels were measured every 10 min using FSL3 and the YSI 2500 as a gold standard. Measurements began 20 min before CONT and continued for 20 min after exercise. Results: A total of 26 females (mean age 32.2 ± 6.1 years and mean duration of diabetes 16.4 ± 8.4 years) participated in this study. FSL3 showed significant differences compared with YSI glucose data for both phases of the menstrual cycle (about 16 mg/dL higher in FSL3). There were no differences in mean absolute relative differences (MARDs) between the follicular (16.06%) and luteal (16.43) phases. Moreover, exercise did not affect MARDs, which were 14.21% pre-exercise and 17.63% postexercise for the follicular phase and 14.95% pre-exercise and 17.71% postexercise for the luteal phase. Conclusions: The findings suggest that the accuracy of FSL3 is not affected by CONT, showing good accuracy levels in both phases of the menstrual cycle. Thus, this study is the first to examine the influence of the menstrual cycle and exercise on the accuracy of a CGM device. The study was also prospectively registered at clinicaltrials.gov (NCT06086067).

AIMS:This study aimed to evaluate how pre-exercise glucose levels and menstrual cycle phase influence glucose responses to aerobic exercise in adults with type 1 diabetes (T1D) treated with multiple daily insulin injections, with the goal of improving personalized exercise management. MATERIALS AND METHODS:We analyzed 51 moderate-intensity, 30-min aerobic sessions (17 male, 34 female) from the TAILOR/1a study. Glucose (plasma and interstitial), heart rate, and anthropometric data were collected. Female participants performed sessions in both the follicular and luteal phases. Glucose trends during exercise were clustered using k-medoids and Dynamic Time Warping. Features from clinical, glucose, and heart rate data were extracted and correlated with cluster assignment. RESULTS:Two distinct glucose response patterns emerged: descending and stable. Higher pre-exercise glucose levels were associated with greater glucose decline and increased hypoglycemia risk, particularly in men. Female participants more frequently exhibited stable glucose profiles, particularly during the luteal phase. Fitness and body composition influenced cluster assignment: fitter individuals-particularly women-were more likely to exhibit stable glucose trends. Pre-exercise glucose was the strongest predictor of response. The menstrual cycle phase had a modest but noticeable effect on glucose dynamics. CONCLUSIONS:Glucose response to exercise in T1D is highly variable and influenced by pre-exercise glycemia, sex, fitness level, and menstrual cycle phase. Women, particularly during the luteal phase, demonstrated more stable glycemic responses. These findings support the need for individualized exercise recommendations and for integrating physiological and behavioral factors into predictive models for automated insulin dosing and exercise guidance in T1D.

Pramlintide's capability to delay gastric emptying has motivated its use in artificial pancreas systems, accompanying insulin as a control action. Due to the scarcity of pramlintide simulation models in the literature, in silico testing of insulin-plus-pramlintide strategies is not widely used. This work incorporates a recent pramlintide pharmacokinetics/pharmacodynamics model into the T1DM UVA/Padova simulator to adjust and validate four insulin-plus-pramlintide control algorithms. The proposals are based on an existing insulin controller and administer pramlintide either as independent boluses or as a ratio of the insulin infusion. The results of the insulin-pramlintide algorithms are compared against their insulin-alone counterparts, showing an improvement in the time in range between 3.00% and 10.53%, consistent with results reported in clinical trials in the literature. Future work will focus on individualizing the pramlintide model to the patients' characteristics and evaluating the implemented strategies under more challenging scenarios.

Artificial Pancreas systems facilitate glucose management for people with Type 1 Diabetes (T1D). However, insulin action is sometimes not enough to counteract every disturbance. One approach devoted to improving these systems consists of introducing additional control actions to the system that either act in the opposite direction to insulin or modify the system's dynamics to ease the control task, for instance, by attenuating disturbances, such as meals. The main representative of these systems would be insulin-plus-glucagon strategies. However, recent advances have introduced other hormones, such as pramlintide (an amylin analog), in closed-loop systems. Also, drugs developed for the treatment of Type 2 Diabetes have drawn attention to be used concomitantly with insulin delivery in T1D, such as GLP-1 receptor agonists, SGLT2 inhibitors, Metformin, etc. This work presents a summary of the most representative drugs being researched as adjunctive therapies in T1D and a qualitative review of their presence alongside AP systems. These therapies have diverse effects, and they provide some improvement to the patients' glucose metrics, but they still require further pharmaceutical developments since most present several adverse effects. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Background: Glucagon-like peptide 1 (GLP-1) is a hormone that promotes insulin secretion, delays gastric emptying, and inhibits glucagon secretion. The GLP-1 receptor agonists have been developed as adjunctive therapies for type 2 diabetes to improve glucose control. Recently, there has been an interest in introducing GLP-1 receptor agonists as adjunctive therapies in type 1 diabetes alongside automatic insulin delivery systems. The preclinical validation of these systems often relies on mathematical simulators that replicate the glucose dynamics of a person with diabetes. This review aims to explore mathematical models available in the literature to describe GLP-1 effects to be used in a type 1 diabetes simulator. Methods: Three databases were examined in the search for GLP-1 mathematical models. More than 1500 works were found after searching for specific keywords that were narrowed down to 39 works for full-text assessment. Results: A total of 23 works were selected describing GLP-1 pharmacokinetics and pharmacodynamics. However, none of the found models was designed for type 1 diabetes. An analysis is included of the available models’ features that could be translated into a GLP-1 receptor agonist model for type 1 diabetes. Conclusion: There is a gap in research in GLP-1 receptor agonists mathematical models for type 1 diabetes, which could be incorporated into type 1 diabetes simulators, providing a safe and inexpensive tool to carry out preclinical validations using these therapies.

An artificial pancreas system regulates blood glucose in people with type 1 diabetes by automating the appropriate insulin infusion rate calculation. Insulin cannot be removed once injected, and thus, the control input is constrained to be positive. Controllers designed without taking this constraint into consideration often deliver an excessive insulin dose after a meal intake (postprandial period), which may cause hypoglycemia, a condition related to harmful complications. The non-negativeness is usually handled indirectly through additional control structures that compensate for the saturation by cutting off the insulin flow in advance. The few approaches that consider the non-negativeness of insulin in the controller design process end up with high-order controllers, which are difficult to analyze. In this work, the design of a input-constrained PD controller for regulating postprandial glucose is explored. The set of feasible controller parameters is computed and related to the meal and insulin dynamics.

Pramlintide, an amylin analog, has been coming up as an agent in type 1 diabetes dual-hormone therapies (insulin/pramlintide). Since pramlintide slows down gastric emptying, it allows for easing glucose control and reducing the burden of meal announcements. Pre-clinical in silico evaluations are a key step in the development of any closed-loop strategy. However, mathematical models are needed, and pramlintide models in the literature are scarce. This work proposes a proof-of-concept pramlintide model, describing its subcutaneous pharmacokinetics (PK) and its effect on gastric emptying (PD). The model is validated with published populational (clinical) data. The model development is divided into three stages: intravenous PK, subcutaneous PK, and PD modeling. In each stage, a set of model structures are proposed, and their performance is assessed using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). In order to evaluate the modulation of the rate of gastric emptying, a literature meal model was used. The final pramlintide model comprises four compartments and a function that modulates gastric emptying depending on plasma pramlintide. Results show an appropriate fit for the data. Some aspects are left as open questions due to the lack of specific data (e.g., the influence of meal composition on the pramlintide effect). Moreover, further validation with individual data is necessary to propose a virtual cohort of patients.

The glucagon effect is understudied in type 1 diabetes (T1D) simulators, without a clear consensus on the pharmacodynamics of glucagon over glucose. Glucagon receptors dynamics could present a significant contribution to T1D simulators, making them more physiologically accurate without an excessive increase in complexity. This work analyzes the receptors model contributions to glucose dynamics using a model proposed in previous work. Then, the model is assessed from two different perspectives: (1) A clinical dataset of the influence of diet (high or low carbohydrate content) on two consecutive glucagon doses (100 and 500 μg) is used to identify the model parameters and (2) three other glucagon action models from the literature are also identified to serve as comparators. Different identification methods are used to adapt to the distinctive features of the dataset. The root mean square error (RMSE) and the Akaike Information Criterion (AIC) were the discerning metrics used to compare the models fittings. Results show that the receptors model offers the lowest RMSE and AIC in contrast to the comparators. This model will hence be helpful in the development of accurate T1D simulators.

Artificial pancreas systems should be designed considering different patient profiles, which is challenging from a control theory perspective. In this paper, a flexible-hybrid dual-hormone control algorithm for an artificial pancreas is proposed. The algorithm handles announced/unannounced meals by means of a non-interacting feedforward scheme that safely incorporates prandial boluses. Also, a coordination strategy is employed to distribute the counter-regulatory actions, which can be delivered as a continuous glucagon infusion via an automated pump, as an oral rescue carbohydrate recommendation, or as a rescue glucagon dose recommendation to be administrated through a glucagon pen. The different configurations of the proposed controller were evaluated in silico using a 14-day virtual scenario with random meal intakes and exercise sessions, achieving above 80% time-in-range and low time spent in hypoglycemia.

Background: This study aimed to evaluate the accuracy of Dexcom G6 (DG6) and FreeStyle Libre-2 (FSL2) during aerobic training and high-intensity interval training (HIIT) in individuals with type 1 diabetes. Methods: Twenty-six males (mean age 29.3 ± 6.3 years and mean duration of diabetes 14.9 ± 6.1 years) participated in this study. Interstitial glucose levels were measured using DG6 and FSL2, while plasma glucose levels were measured every 10 min using YSI 2500 as the reference for glucose measurements in this study. The measurements began 20 min before the start of exercise and continued for 20 min after exercise. Seven measurements were taken for each subject and exercise. Results: Both DG6 and FSL2 devices showed significant differences compared to YSI glucose data for both aerobic and HIIT exercises. Continuous glucose monitoring (CGM) devices exhibited superior performance during HIIT than aerobic training, with DG6 showing a mean absolute relative difference of 14.03% versus 31.98%, respectively. In the comparison between the two devices, FSL2 demonstrated significantly higher effectiveness in aerobic training, yet its performance was inferior to DG6 during HIIT. According to the 40/40 criteria, both sensors performed similarly, with marks over 93% for all ranges and both exercises, and above 99% for HIIT and in the >180 mg/dL range, which is in accordance with FDA guidelines. Conclusions: The findings suggest that the accuracy of DG6 and FSL2 deteriorates during and immediately after exercise but remains acceptable for both devices during HIIT. However, accuracy is compromised with DG6 during aerobic exercise. This study is the first to compare the accuracy of two CGMs, DG6, and FSL2, during two exercise modalities, using plasma glucose YSI measurements as the gold standard for comparisons. It was registered at clinicaltrials.gov (NCT06080542).

A crucial aspect of diabetes management is the comprehensive education of clinical staff, patients, and caretakers. The introduction of advanced technologies in artificial pancreas devices heightens this need, as patients must learn to manage their condition and navigate the evolving technological components of their treatment. A series of self-guided virtual laboratories has been developed to aid this process. These laboratories are designed to introduce diabetes management agents to the control engineering concepts used in artificial pancreas systems, as well as to the fundamental clinical and physiological aspects of daily diabetes management.

This paper validates a glucoregulatory model including glucagon receptors dynamics in the description of endogenous glucose production (EGP). A set of models from literature are selected for a head-to-head comparison in order to evaluate the role of glucagon receptors. Each EGP model is incorporated into an existing glucoregulatory model and validated using a set of clinical data, where both insulin and glucagon are administered. The parameters of each EGP model are identified in the same optimization problem, minimizing the root mean square error (RMSE) between the simulation and the clinical data. The results show that the RMSE for the proposed receptors-based EGP model was lower when compared to each of the considered models (Receptors approach: 7.13±1.71 mg/dl vs. 7.76±1.45 mg/dl (p=0.066), 8.45±1.38 mg/dl (p=0.011) and 8.99±1.62 mg/dl (p=0.007)). This raises the possibility of considering glucagon receptors dynamics in type 1 diabetes simulators.

Most advanced technologies for the treatment of type 1 diabetes, such as sensor-pump integrated systems or the artificial pancreas, require accurate glucose predictions on a given future time-horizon as a basis for decision-making support systems. Seasonal stochastic models are data-driven algebraic models that use recent history data and periodic trends to accurately estimate time series data, such as glucose concentration in diabetes. These models have been proven to be a good option to provide accurate blood glucose predictions under free-living conditions. These models can cope with patient variability under variable-length time-stamped daily events in supervision and control applications. However, the seasonal-models-based framework usually needs of several months of data per patient to be fed into the system to adequately train a personalized glucose predictor for each patient. In this work, an in silico analysis of the accuracy of prediction is presented, considering the effect of training a glucose predictor with data from a cohort of patients (population) instead of data from a single patient (individual). Feasibility of population data as an input to the model is asserted, and the effect of the dataset size in the determination of the minimum amount of data for a valid training of the models is studied. Results show that glucose predictors trained with population data can provide predictions of similar magnitude as those trained with individualized data. Overall median root mean squared error (RMSE) (including 25% and 75% percentiles) for the predictor trained with population data are {6.96[4.87,8.67], 12.49[7.96,14.23], 19.52[10.62,23.37], 28.79[12.96,34.57], 32.3[16.20,41.59], 28.8[15.13,37.18]} mg/dL, for prediction horizons (PH) of {15,30,60,120,180,240} min, respectively, while the baseline of the individually trained RMSE results are {6.37[5.07,6.70], 11.27[8.35,12.65], 17.44[11.08,20.93], 22.72[14.29,28.19], 28.45[14.79,34.38], 25.58[13.10,36.60]} mg/dL, both training with 16 weeks of data. Results also show that the use of the population approach reduces the required training data by half, without losing any prediction capability.

Accurate mathematical models are needed to simulate and validate dual-hormone control algorithms for Artificial pancreas systems. Glucagon receptors are a key component for glucagon to have effect on glucose. However, they are not usually taken into consideration in the development of models for type 1 diabetes (T1D). In previous work, a model proposal integrating glucagon receptor dynamics into endogenous glucose production (EGP) was successfully validated under single glucagon boluses. In this work, this model is further validated using a dataset of clinical data where two glucagon doses (100 and 500 µg) are consecutively administered to 10 patients with T1D in two different settings: low and high-carb diets (total of 20 datasets). The model performance is compared to three other EGP models from literature under different identification criteria. The receptors model achieved the lowest root mean squared error regardless of the diet and the individualization method.

The histopathological features in the central nervous system (CNS) developing during the active phase of tetanus antibody formation in the cerebrospinal fluid (CSF) as induced in 15 rabbits were studied. The measurement of antibody titres in serum and CSF by electroimmunodiffusion and histological examination were done sequentially at the 1st, 3rd, 5th, 7th and 9th days after cisternal secondary inoculation with fluid tetanus toxoid. Tetanus antibodies appeared in serum after the 1st and in CSF after the 5th day. Decreasing values of CSF total protein were found. The meaning of an elevated Q ratio as observed in this situation of strong antibody formation in the CSF was enhanced. The histopathological features in the central nervous system consisted of perivascular inflammatory infiltration, at first polymorphic and then composed almost exclusively of mononuclear cells with a predominantly leptomeningeal and subpial localization, which might represent the origin of CSF tetanus antibodies. The localization was related to the contact zone between the antigen- and antibody-containing compartments, respectively the subarachnoid space and vascularized structures of the brain and spinal cord. Four control rabbits presented neither tetanus antibodies in the CSF nor perivascular inflammatory infiltration in the CNS. Similarity between the present experimental results and the immunopathological features of the primary demyelinating diseases provides some useful information about the immunological inflammatory events in these diseases.