Nonadjunctive Use of Continuous Glucose Monitoring for Diabetes Treatment Decisions

Digital twins and artificial intelligence in metabolic disease research

Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.

Accuracy of a Continuous Glucose Monitor During Pediatric Type 1 Diabetes Inpatient Admissions

Objective: Continuous glucose monitors (CGMs) used for type 1 diabetes management are associated with lower hemoglobin A1c. CGMs are not approved for inpatient use, when close glucose monitoring and intensive insulin management are essential for optimal health. Accuracy data from adult hospitalizations have been published, but pediatric data are limited. Design and Methods: This retrospective review of Dexcom G6 data from youth with type 1 diabetes during hospitalization assessed CGMs and matched (within 5 min) point-of-care (POC) and laboratory glucose values. Glucose values >400 and <40 mg/dL were excluded due to sensor reporting capabilities. Standard methods for CGM accuracy were used including mean absolute relative difference (MARD), Clarke Error Grids, and percentage of CGM values within 15%/20%/30% if glucose value is >100 mg/dL and 15/20/30 mg/dL if value is ≤100 mg/dL. Results: A total of 1120 POC and 288 laboratory-matched pairs were collected from 83 unique patients (median age 12.0 years, 68.7% non-Hispanic white, 54.2% male) during 100 admissions. For POC values, overall, MARD was 11.8%, that on the medical floor was 13.5%, and that in the intensive care unit was 7.9%. The MARD for all laboratory values was 6.5%. In total, 98% of matched pairs were within Clarke Error Grid A and B zones. Conclusions: Findings from our pediatric population were similar to accuracy reported in hospitalized adults, indicating the potential role for CGM use during pediatric hospitalizations. Additional research is needed to assess accuracy under various conditions, including medication use, as well as development of safe hospital protocols for successful CGM implementation for routine inpatient care.

Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error Due to Changes in Sensor Location

BACKGROUND

Continuous glucose monitors (CGMs) have become important tools for providing estimates of glucose to patients with diabetes. Recently, neural networks (NNs) have become a common method for forecasting glucose values using data from CGMs. One method of forecasting glucose values is a time-delay feedforward (FF) NN, but a change in the CGM location on a participant can increase forecast error in a FF NN.

METHODS

In response, we examined a NN with gated recurrent units (GRUs) as a method of reducing forecast error due to changes in sensor location.

RESULTS

We observed that for 13 participants with type 2 diabetes wearing blinded CGMs on both arms for 12 weeks (FreeStyle Libre Pro-Abbott), GRU NNs did not produce significantly different errors in glucose prediction due to sensor location changes (P < .05).

CONCLUSION

We observe that GRU NNs can mitigate error in glucose prediction due to differences in CGM location.

Feasibility and acceptability of the use of flash glucose monitoring encountered by Indigenous Australians with type 2 diabetes mellitus: initial experiences from a pilot study

BACKGROUND

Type 2 diabetes mellitus (T2DM) is highly prevalent within the Indigenous Australian community. Novel glucose monitoring technology offers an accurate approach to glycaemic management, providing real-time information on glucose levels and trends. The acceptability and feasibilility of this technology in Indigenous Australians with T2DM has not been investigated.

OBJECTIVE

This feasibility phenomenological study aims to understand the experiences of Indigenous Australians with T2DM using flash glucose monitoring (FGM).

METHODS

Indigenous Australians with T2DM receiving injectable therapy (n = 8) who used FGM (Abbott Freestyle Libre) for 6-months, as part of a clinical trial, participated in semi-structured interviews. Thematic analysis of the interviews was performed using NVivo12 Plus qualitative data analysis software (QSR International).

RESULTS

Six major themes emerged: 1) FGM was highly acceptable to the individual; 2) FGM's convenience was its biggest benefit; 3) data from FGM was a tool to modify lifestyle choices; 4) FGM needed to be complemented with health professional support; 5) FGM can be a tool to engage communities in diabetes management; and 6) cost of the device is a barrier to future use.

CONCLUSIONS

Indigenous Australians with T2DM had positive experiences with FGM. This study highlights future steps to ensure likelihood of FGM is acceptable and effective within the wider Indigenous Australian community.

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

Methods for Insulin Bolus Adjustment Based on the Continuous Glucose Monitoring Trend Arrows in Type 1 Diabetes: Performance and Safety Assessment in an In Silico Clinical Trial

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.

Examining Sensor Agreement in Neural Network Blood Glucose Prediction

Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting (N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant (P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.

3D CoMoO4 nanoflake arrays decorated disposable pencil graphite electrode for selective and sensitive enzyme-less electrochemical glucose sensors

Three-dimensional (3D) cobalt molybdate (CoMoO₄) hierarchical nanoflake arrays on pencil graphite electrode (PGE) (CoMoO₄/PGE) are actualized via one-pot hydrothermal technique. The morphological features comprehend that the CoMoO₄ nanoflake arrays expose the 3D, open, porous, and interconnected network architectures on PGE. The formation and growth mechanisms of CoMoO₄ nanostructures on PGE are supported with different structural and morphological characterizations. The constructed CoMoO₄/PGE is operated as an electrocatalytic probe in enzyme-less electrochemical glucose sensor (ELEGS), confronting the impairments of cost- and time-obsessed conventional electrode polishing and catalyst amendment progressions and obliged the employment of a non-conducting binder. The wide-opened interior and exterior architectures of CoMoO₄ nanoflake arrays escalate the glucose utilization efficacy, whilst the intertwined nanoflakes and graphitic carbon layers, respectively, of CoMoO₄ and PGE articulate the continual electron mobility and catalytically active channels of CoMoO₄/PGE. It jointly escalates the ELEGS concerts of CoMoO₄/PGE including high sensitivity (1613 μA mM^-1 cm^-2), wide linear glucose range (0.0003-10 mM), and low detection limit (0.12 µM) at a working potential of 0.65 V (vs. Ag/AgCl) together with the good recovery in human serum. Thus, the fabricated CoMoO₄/PGE extends exclusive virtues of modest electrode production, virtuous affinity, swift response, and excellent sensitivity and selectivity, exposing innovative prospects to reconnoitring the economically viable ELEGSs with binder-free, affordable cost, and expansible 3D electrocatalytic probes.

Incorporating Glucose Variability into Glucose Forecasting Accuracy Assessment Using the New Glucose Variability Impact Index and the Prediction Consistency Index: An LSTM Case Example

BACKGROUND

In this work, we developed glucose forecasting algorithms trained and evaluated on a large dataset of free-living people with type 1 diabetes (T1D) using closed-loop (CL) and sensor-augmented pump (SAP) therapies; and we demonstrate how glucose variability impacts accuracy. We introduce the glucose variability impact index (GVII) and the glucose prediction consistency index (GPCI) to assess the accuracy of prediction algorithms.

METHODS

A long-short-term-memory (LSTM) neural network was designed to predict glucose up to 60 minutes in the future using continuous glucose measurements and insulin data collected from 175 people with T1D (41,318 days) and evaluated on 75 people (11,333 days) from the Tidepool Big Data Donation Dataset. LSTM was compared with two naïve forecasting algorithms as well as Ridge linear regression and a random forest using root-mean-square error (RMSE). Parkes error grid quantified clinical accuracy. Regression analysis was used to derive the GVII and GPCI.

RESULTS

The LSTM had highest accuracy and best GVII and GPCI. RMSE for CL was 19.8 ± 3.2 and 33.2 ± 5.4 mg/dL for 30- and 60-minute prediction horizons, respectively. RMSE for SAP was 19.6 ± 3.8 and 33.1 ± 7.3 mg/dL for 30- and 60-minute prediction horizons, respectively; 99.6% and 97.6% of predictions were within zones A+B of the Parkes error grid at 30- and 60-minute prediction horizons, respectively. Glucose variability was strongly correlated with RMSE (R≥0.64, P < 0.001); GVII and GPCI demonstrated a means to compare algorithms across datasets with different glucose variability.

CONCLUSIONS

The LSTM model was accurate on a large real-world free-living dataset. Glucose variability should be considered when assessing prediction accuracy using indices such as GVII and GPCI.

Phenomenological-based model of glucose transport from liver to abdominal subcutaneous adipose tissue

BACKGROUND

A good treatment for type 1 diabetes mellitus (T1DM) requires accurate measurements of blood glucose levels. Continuous glucose monitors (CGM) measure the glucose concentration in the interstitial fluid of the abdominal subcutaneous adipose tissue. However, glucose measured in the abdominal interstitial fluid does not represent blood glucose concentrations accurately due to the complex blood transport through the body and glucose diffusion in interstitial fluid.

METHODS

To gain insight into this problem, a phenomenological-based semiphysical model (PBSM) of glucose transport by volumetric flow and diffusion from the bloodstream to interstitial fluid was constructed. A published 10-step modeling procedure was used to obtain a model for glucose transport time through the blood vessels and from the blood capillaries to the interstitial fluid, glucose diffusion within the interstitial fluid, and glucose diffusion through the semipermeable coating of the sensor needle. For this model, a healthy person is considered at rest with average parameters.

RESULTS

The simulations performed using the PBSM allow obtaining the glucose transport time from the liver to the sensor needle. In this way, it is possible to reconstruct an accurate dynamic measurement of blood glucose from the measurements in the interstitial fluid of the abdominal subcutaneous adipose tissue.

CONCLUSIONS

PBSMs with parameters interpretability illustrate the connection of glucose concentrations in the interstitial fluid with that currently in the blood. Implementing this model in a CGM will result in more reliable measurements of blood glucose levels for T1DM treatment.

Physical Activity and Psychological Stress Detection and Assessment of Their Effects on Glucose Concentration Predictions in Diabetes Management

OBJECTIVE

Continuous glucose monitoring (CGM) enables prediction of the future glucose concentration (GC) trajectory for making informed diabetes management decisions. The glucose concentration values are affected by various physiological and metabolic variations, such as physical activity (PA) and acute psychological stress (APS), in addition to meals and insulin. In this work, we extend our adaptive glucose modeling framework to incorporate the effects of PA and APS on the GC predictions.

METHODS

A wristband conducive of use by free-living ambulatory people is used. The measured physiological variables are analyzed to generate new quantifiable input features for PA and APS. Machine learning techniques estimate the type and intensity of the PA and APS when they occur individually and concurrently. Variables quantifying the characteristics of both PA and APS are integrated as exogenous inputs in an adaptive system identification technique for enhancing the accuracy of GC predictions. Data from clinical experiments illustrate the improvement in GC prediction accuracy.

RESULTS

The average mean absolute error (MAE) of one-hour-ahead GC predictions with testing data decreases from 35.1 to 31.9 mg/dL (p-value = 0.01) with the inclusion of PA information, and it decreases from 16.9 to 14.2 mg/dL (p-value = 0.006) with the inclusion of PA and APS information.

CONCLUSION

The first-ever glucose prediction model is developed that incorporates measures of physical activity and acute psychological stress to improve GC prediction accuracy.

SIGNIFICANCE

Modeling the effects of physical activity and acute psychological stress on glucose concentration values will improve diabetes management and enable informed meal, activity and insulin dosing decisions.

Suicide and Self-inflicted Injury in Diabetes: A Balancing Act

Glycemic control in type 1 diabetes mellitus (T1DM) remains a challenge for many, despite the availability of modern diabetes technology. While technologies have proven glycemic benefits and may reduce excess mortality in some populations, both mortality and complication rates remain significantly higher in T1DM than the general population. Diabetes technology can reduce some burdens of diabetes self-management, however, it may also increase anxiety, stress, and diabetes-related distress. Additional workload associated with diabetes technologies and the dominant focus on metabolic control may be at the expense of quality-of-life. Diabetes is associated with significantly increased risk of suicidal ideation, self-harm, and suicide. The risk increases for those with diabetes and comorbid mood disorder. For example, the prevalence of depression is significantly higher in people with diabetes than the general population, and thus, people with diabetes are at even higher risk of suicide. The Center for Disease Control and Prevention reported a 24% rise in US national suicide rates between 1999 and 2014, the highest in 30 years. In the United Kingdom, 6000 suicides occur annually. Rates of preventable self-injury mortality stand at 29.1 per 100 000 population. Individuals with diabetes have an increased risk of suicide, being three to four times more likely to attempt suicide than the general population. Furthermore, adolescents aged 15 to 19 are most likely to present at emergency departments for self-inflicted injuries (9.6 per 1000 visits), with accidents, alcohol-related injuries, and self-harm being the strongest risk factors for suicide, the second leading cause of death among 10 to 24 year olds. While we have developed tools to improve glycemic control, we must be cognizant that the psychological burden of chronic disease is a significant problem for this vulnerable population. It is crucial to determine the psychosocial and behavioral predictors to uptake and continued use of technology in order to aid the identification of those individuals most likely to realize benefits of any intervention as well as those individuals who may require more support to succeed with technology.

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

BACKGROUND

Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia.

METHODS

A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes.

RESULTS

In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68).

CONCLUSION

An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.

Closed-Loop Control with Unannounced Exercise for Adults with Type 1 Diabetes using the Ensemble Model Predictive Control

This paper presents an individualized Ensemble Model Predictive Control (EnMPC) algorithm for blood glucose (BG) stabilization and hypoglycemia prevention in people with type 1 diabetes (T1D) who exercise regularly. The EnMPC formulation can be regarded as a simplified multi-stage MPC allowing for the consideration of N _en scenarios gathered from the patient's recent behavior. The patient's physical activity behavior is characterized by an exercise-specific input signal derived from the deconvolution of the patient's continuous glucose monitor (CGM), accounting for known inputs such as meal, and insulin pump records. The EnMPC controller was tested in a cohort of in silico patients with representative inter-subject and intra-subject variability from the FDA-accepted UVA/Padova simulation platform. Results show a significant improvement on hypoglycemia prevention after 30 min of mild to moderate exercise in comparison to a similarly tuned baseline controller (rMPC); with a reduction in hypoglycemia occurrences (< 70 mg/dL), from 3.08% ± 3.55 with rMPC to 0.78% ± 2.04 with EnMPC (P < 0.05).

Lag Time Remains with Newer Real-Time Continuous Glucose Monitoring Technology During Aerobic Exercise in Adults Living with Type 1 Diabetes

Background: Real-time continuous glucose monitoring (CGM) devices help detect glycemic excursions associated with exercise, meals, and insulin dosing in patients with type 1 diabetes (T1D). However, the delay between interstitial and blood glucose may result in CGM underestimating the true change in glycemia during activity. The purpose of this study was to examine CGM discrepancies during exercise and the meal postexercise versus self-monitoring of blood glucose (SMBG). Methods: Seventeen adults with T1D using insulin pump therapy and CGM completed 60 min of aerobic exercise on three occasions. A standardized meal was given 30 min postexercise. SMBG was measured during exercise and in recovery using OmniPod® Personal Diabetes Manager (PDM; Insulet, Billerica, MA) with built-in glucose meter (FreeStyle; Abbott Laboratories, Abbott Park, IL), while CGM was measured with Dexcom G4® with 505 algorithm (n = 4) or G5® (n = 13), which were calibrated with subjects' own PDM. Results: SMBG showed a large drop in glycemia during exercise, while CGM showed a lag of 12 ± 11 (mean ± standard deviation) minutes and bias of -7 ± 19 mg/dL/min during activity. Mean absolute relative difference (MARD) for CGM versus SMBG was 13 (6-22)% [median (interquartile range)] during exercise and 8 (5-14)% during mealtime. Clarke error grids showed CGM values were in zones A and B 94%-99% of the time for SMBG. Conclusion: In summary, the drop in CGM lags behind the drop in blood glucose during prolonged aerobic exercise by 12 ± 11 min, and MARD increases to 13 (6-22)% during exercise as well. Therefore, if hypoglycemia is suspected during exercise, individuals should confirm glucose levels with a capillary glucose measurement.

Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review

For individuals affected by Type 1 diabetes (T1D), a chronic disease in which the pancreas does not produce any insulin, maintaining the blood glucose (BG) concentration as much as possible within the safety range (70–180 mg/dl) allows avoiding short- and long-term complications. The tuning of exogenous insulin infusion can be difficult, especially because of the inter- and intra-day variability of physiological and behavioral factors. Continuous glucose monitoring (CGM) sensors, which monitor glucose concentration in the subcutaneous tissue almost continuously, allowed improving the detection of critical hypo- and hyper-glycemic episodes. Moreover, their integration with insulin pumps for continuous subcutaneous insulin infusion allowed developing algorithms that automatically tune insulin dosing based on CGM measurements in order to mitigate the incidence of critical episodes. In this work, we aim at reviewing the literature on methods for CGM-based automatic attenuation or suspension of basal insulin with a focus on algorithms, their implementation in commercial devices and clinical evidence of their effectiveness and safety.

Non-adjunctive flash glucose monitoring system use during summer-camp in children with type 1 diabetes: The free-summer study

BACKGROUND

A factory-calibrated sensor for intermittently scanned continuous glucose monitoring (isCGM) is accurate and safe in children with type 1 diabetes (T1D). Data on isCGM effectiveness as a replacement for self-monitoring of blood glucose (SMBG) in this population is scarce.

OBJECTIVE

The aim of this study was to evaluate the non-adjunctive use of isCGM in children with T1D during 2 weeks in a challenging summer-camp setting.

METHODS

In this two-arm, parallel, randomized, outpatient clinical trial we enrolled 46 children (25 females, mean ± SD: age 11.1 ± 2.6 years, glycated hemoglobin (HbA1c) 7.4% ± 0.7%): 26 in the isCGM group were blinded for the SMBG and insulin dosing was isCGM-based, whereas 20 in the control group were blinded for isCGM and performed SMBG-based insulin dosing. The primary outcome of intention-to-treat analysis was between-group difference in the proportion of time within range 3.9 to 10 mmol/L (TIR).

RESULTS

There was no significant difference in TIR (3.9-10 mmol/L) between the two groups. In participants with suboptimal metabolic control (HbA1c > 7%) we observed a significant reduction in time spent above 10 mmol/L (P < 0.05) and an improvement in TIR (P = 0.05) in the isCGM group. No severe hypoglycemic events or serious adverse events occurred. Overall mean absolute relative difference (MARD) between isCGM and SMBG was 18.3%, with median absolute relative difference (ARD) of 8%. Consensus error grid analysis demonstrated 82.2% and 95.2% of results in zone A, and zone A + B, respectively.

CONCLUSION

The non-adjunctive use of isCGM was as safe and effective as SMBG, and reduced time spent in hyperglycemia in a sub-population of children with T1D with suboptimal glycemic control.

TRIAL REGISTRATION

NCT03182842.

Continuous glucose monitoring: a review of the evidence, opportunities for future use and ongoing challenges

The advent of devices that can track interstitial glucose levels, which are closely related to blood glucose levels, on a near continuous basis, has facilitated better insights into patterns of glycaemia. Continuous glucose monitoring (CGM) therefore allows for more intensive monitoring of blood glucose levels and potentially improved glycaemic control. In the context of the announcement on 1 April 2017 that the Australian Government will fund CGM monitoring for people with type 1 diabetes under the age of 21 years, this paper provides a review of the evidence for CGM and some of the ongoing challenges. There is evidence that real-time CGM in type 1 diabetes improves HbA1c and hypoglycaemia, while in type 2 diabetes, the evidence is less robust. Initial barriers to widespread implementation of CGM included issues with accuracy and user friendliness; however, as the technology has evolved, these issues have largely improved. Ongoing barriers include cost, and weaker evidence for their benefit in certain populations such as those with type 2 diabetes and less glycaemic variability. CGM has the potential to reduce healthcare costs, although real-world studies, including cost-effectiveness analyses, are needed in this area.

Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications

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.

Identifiability Analysis of Three Control-Oriented Models for Use in Artificial Pancreas Systems

OBJECTIVE

Our aim is to analyze the identifiability of three commonly used control-oriented models for glucose control in patients with type 1 diabetes (T1D).

METHODS

Structural and practical identifiability analysis were performed on three published control-oriented models for glucose control in patients with type 1 diabetes (T1D): the subcutaneous oral glucose minimal model (SOGMM), the intensive control insulin-nutrition-glucose (ICING) model, and the minimal model control-oriented (MMC). Structural identifiability was addressed with a combination of the generating series (GS) approach and identifiability tableaus whereas practical identifiability was studied by means of (1) global ranking of parameters via sensitivity analysis together with the Latin hypercube sampling method (LHS) and (2) collinearity analysis among parameters. For practical identifiability and model identification, continuous glucose monitor (CGM), insulin pump, and meal records were selected from a set of patients (n = 5) on continuous subcutaneous insulin infusion (CSII) that underwent a clinical trial in an outpatient setting. The performance of the identified models was analyzed by means of the root mean square (RMS) criterion.

RESULTS

A reliable set of identifiable parameters was found for every studied model after analyzing the possible identifiability issues of the original parameter sets. According to an importance factor ([Formula: see text]), it was shown that insulin sensitivity is not the most influential parameter from the dynamical point of view, that is, is not the parameter impacting the outputs the most of the three models, contrary to what is assumed in the literature. For the test data, the models demonstrated similar performance with most RMS values around 20 mg/dl (min: 15.64 mg/dl, max: 51.32 mg/dl). However, MMC failed to identify the model for patient 4. Also, considering the three models, the MMC model showed the higher parameter variability when reidentified every 6 hours.

CONCLUSION

This study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.

A deep learning approach to adherence detection for type 2 diabetics

Diabetes has become one of the biggest health problems in the world. In this context, adherence to insulin treatment is essential in order to avoid life-threatening complications. In this pilot study, a novel adherence detection algorithm using Deep Learning (DL) approaches was developed for type 2 diabetes (T2D) patients, based on simulated Continuous Glucose Monitoring (CGM) signals. A large and diverse amount of CGM signals were simulated for T2D patients using a T2D adapted version of the Medtronic Virtual Patient (MVP) model for T1D. By using these signals, different classification algorithms were compared using a comprehensive grid search. We contrast a standard logistic regression baseline to Multi- Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The best classification performance with an average accuracy of 77:5% was achieved with CNN. Hence, this indicates the potential of DL, when considering adherence detection systems for T2D patients.

Continuous Glucose Monitoring and Insulin Informed Advisory System with Automated Titration and Dosing of Insulin Reduces Glucose Variability in Type 1 Diabetes Mellitus

BACKGROUND

Glucose variability (GV) remains a key limiting factor in the success of diabetes management. While new technologies, for example, accurate continuous glucose monitoring (CGM) and connected insulin delivery devices, are now available, current treatment standards fail to leverage the wealth of information generated. Expert systems, from automated insulin delivery to advisory systems, are a key missing element to richer, more personalized, glucose management in diabetes.

METHODS

Twenty four subjects with type 1 diabetes mellitus (T1DM), 15 women, 37 ± 11 years of age, hemoglobin A1c 7.2% ± 1%, total daily insulin (TDI) 46.7 ± 22.3 U, using either an insulin pump or multiple daily injections with carbohydrate counting, completed two randomized crossover 48-h visits at the University of Virginia, wearing Dexcom G4 CGM, and using either usual care or the UVA decision support system (DSS). DSS consisted of a combination of automated insulin titration, bolus calculation, and CHO treatment advice. During each admission, participants were exposed to a variety of meal sizes and contents and two 45-min bouts of exercise. GV and glucose control were assessed using CGM.

RESULTS

The use of DSS significantly reduced GV (coefficient of variation: 0.36 ± 08. vs. 0.33 ± 0.06, P = 0.045) while maintaining glycemic control (average CGM: 155.2 ± 27.1 mg/dL vs. 155.2 ± 23.2 mg/dL), by reducing hypoglycemia exposure (%<70 mg/dL: 3.8% ± 4.6% vs. 1.8% ± 2%, P = 0.018), with nonsignificant trends toward reduction of significant hyperglycemia overnight (%>250 mg/dL: 5.3% ± 9.5% vs. 1.9% ± 4.6%) and at mealtime (11.3% ± 14.8% vs. 5.8% ± 9.1%).

CONCLUSIONS

A CGM/insulin informed advisory system proved to be safe and feasible in a cohort of 24 T1DM subjects. Use of the system may result in reduced GV and improved protection against hypoglycemia.

Technology in the management of type 1 diabetes mellitus - current status and future prospects

Type 1 diabetes mellitus (T1DM) represents 5-10% of diabetes cases worldwide. The incidence of T1DM is increasing, and there is no immediate prospect of a cure. As such, lifelong management is required, the burden of which is being eased by novel treatment modalities, particularly from the field of diabetes technologies. Continuous glucose monitoring has become the standard of care and includes factory-calibrated subcutaneous glucose monitoring and long-term implantable glucose sensing. In addition, considerable progress has been made in technology-enabled glucose-responsive insulin delivery. The first hybrid insulin-only closed-loop system has been commercialized, and other closed-loop systems are under development, including dual-hormone glucose control systems. This Review focuses on well-established diabetes technologies, including glucose sensing, pen-based insulin delivery, data management and data analytics. We also cover insulin pump therapy, threshold-based suspend, predictive low-glucose suspend and single-hormone and dual-hormone closed-loop systems. Clinical practice recommendations for insulin pump therapy and continuous glucose monitoring are presented, and ongoing research and future prospects are highlighted. We conclude that the management of T1DM is improved by diabetes technology for the benefit of the majority of people with T1DM, their caregivers and guardians and health-care professionals treating patients with T1DM.

Bridging technology and clinical practice: innovating inpatient hyperglycaemia management in non-critical care settings

Emerging evidence shows that suboptimal glycaemic control is associated with increased morbidity and length of stay in hospital. Various guidelines for safe and effective inpatient glycaemic control in the non-critical care setting have been published. In spite of this, implementation in practice remains limited because of the increasing number of people with diabetes admitted to hospital and staff work burden. The use of technology in the outpatient setting has led to improved glycaemic outcomes and quality of life for people with diabetes. There remains an unmet need for technology utilisation in inpatient hyperglycaemia management in the non-critical care setting. Novel technologies have the potential to provide benefits in diabetes care in hospital by improving efficacy, safety and efficiency. Rapid analysis of glucose measurements by point-of-care devices help facilitate clinical decision-making and therapy adjustment in the hospital setting. Glucose treatment data integration with computerized glucose management systems underpins the effective use of decision support systems and may streamline clinical staff workflow. Continuous glucose monitoring and automation of insulin delivery through closed-loop systems may provide a safe and efficacious tool for hospital staff to manage inpatient hyperglycaemia whilst reducing staff workload. This review summarizes the evidence with regard to technological methods to manage inpatient glycaemic control, their limitations and the future outlook, as well as potential strategies by healthcare organizations such as the National Health Service to mediate the adoption, procurement and use of diabetes technologies in the hospital setting.

Human Factors and Data Logging Processes With the Use of Advanced Technology for Adults With Type 1 Diabetes: Systematic Integrative Review

Background

People with type 1 diabetes (T1D) undertake self-management to prevent short and long-term complications. Advanced technology potentially supports such activities but requires consideration of psychological and behavioral constructs and usability issues. Economic factors and health care provider capacity influence access and uptake of advanced technology. Previous reviews have focused upon clinical outcomes or were descriptive or have synthesized studies on adults with those on children and young people where human factors are different.

Objective

This review described and examined the relationship between human factors and adherence with technology for data logging processes in adults with T1D.

Methods

A systematic literature search was undertaken by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Quality appraisal was undertaken and data were abstracted and categorized into the themes that underpinned the human factor constructs that were examined.

Results

A total of 18 studies were included. A total of 6 constructs emerged from the data analysis: the relationship between adherence to data logging and measurable outcomes; satisfaction with the transition to advanced technology for self-management; use of advanced technology and time spent on diabetes-related activities; strategies to mediate the complexities of diabetes and the use of advanced technology; cognition in the wild; and meanings, views, and perspectives from the users of technology.

Conclusions

Increased treatment satisfaction was found on transition from traditional to advanced technology use—insulin pump and continuous glucose monitoring (CGM); the most significant factor was when blood glucose levels were consistently <7.00 mmol/L (P ≤.01). Participants spent considerable time on their diabetes self-care. Logging of data was positively correlated with increasing age when using an app that provided meaningful feedback (regression coefficient=55.8 recordings/year; P ≤.01). There were benefits of CGM for older people in mediating complexities and fears of hypoglycemia with significant differences in well-being (P ≤.001).

Qualitative studies explored the contextual use and uptake of technology. The results suggested frustrations with CGM, continuous subcutaneous insulin infusion, calibration of devices, and alarms. Furthermore implications for “body image” and the way in which “significant others” impacted on the behavior and attitude of the individual toward technology use. There were wide variations in the normal use of and interaction with technology across a continuum of sociocultural contexts, which has implications for the way in which future technologies should be designed.

Quantitative studies were limited by small sample sizes, making it difficult to generalize findings to other contexts. This was further limited by a sample that was predominantly white, well-controlled, and engaged with self-care. The use of critical appraisal frameworks demonstrated where research into human factors and data logging processes of individuals could be improved. This included engaging people in the design of the technology, especially hard-to-reach or marginalized groups.

Review of a commercially available hybrid closed-loop insulin-delivery system in the treatment of Type 1 diabetes

Type 1 diabetes is an important medical condition causing significant burden and morbidity to those persons affected by it. Improvements in insulin products, insulin delivery and glucose monitoring technology have all contributed to reductions in long-term complications and hypoglycemia. This article reviews the Medtronic 670G device and summarizes the data supporting how this product reduces the burden and increases the safety of insulin dosing in Type 1 diabetes.

Wearable physiological systems and technologies for metabolic monitoring

Wearable sensors allow continuous monitoring of metabolites for diabetes, sports medicine, exercise science, and physiology research. These sensors can continuously detect target analytes in skin interstitial fluid (ISF), tears, saliva, and sweat. In this review, we will summarize developments on wearable devices and their potential applications in research, clinical practice, and recreational and sporting activities. Sampling skin ISF can require insertion of a needle into the skin, whereas sweat, tears, and saliva can be sampled by devices worn outside the body. The most widely sampled metabolite from a wearable device is glucose in skin ISF for monitoring diabetes patients. Continuous ISF glucose monitoring allows estimation of the glucose concentration in blood without the pain, inconvenience, and blood waste of fingerstick capillary blood glucose testing. This tool is currently used by diabetes patients to provide information for dosing insulin and determining a diet and exercise plan. Similar technologies for measuring concentrations of other analytes in skin ISF could be used to monitor athletes, emergency responders, warfighters, and others in states of extreme physiological stress. Sweat is a potentially useful substrate for sampling analytes for metabolic monitoring during exercise. Lactate, sodium, potassium, and hydrogen ions can be measured in sweat. Tools for converting the concentrations of these analytes sampled from sweat, tears, and saliva into blood concentrations are being developed. As an understanding of the relationships between the concentrations of analytes in blood and easily sampled body fluid increases, then the benefits of new wearable devices for metabolic monitoring will also increase.

Generation of an immortalized mesenchymal stem cell line producing a secreted biosensor protein for glucose monitoring

Diabetes is a chronic disease characterized by high levels of blood glucose. Diabetic patients should normalize these levels in order to avoid short and long term clinical complications. Presently, blood glucose monitoring is dependent on frequent finger pricking and enzyme based systems that analyze the drawn blood. Continuous blood glucose monitors are already on market but suffer from technical problems, inaccuracy and short operation time. A novel approach for continuous glucose monitoring is the development of implantable cell-based biosensors that emit light signals corresponding to glucose concentrations. Such devices use genetically modified cells expressing chimeric genes with glucose binding properties. MSCs are good candidates as carrier cells, as they can be genetically engineered and expanded into large numbers. They also possess immunomodulatory properties that, by reducing local inflammation, may assist long operation time.

Here, we generated a novel immortalized human MSC line co-expressing hTERT and a secreted glucose biosensor transgene using the Sleeping Beauty transposon technology. Genetically modified hMSCs retained their mesenchymal characteristics. Stable transgene expression was validated biochemically. Increased activity of hTERT was accompanied by elevated and constant level of stem cell pluripotency markers and subsequently, by MSC immortalization. Furthermore, these cells efficiently suppressed PBMC proliferation in MLR transwell assays, indicating that they possess immunomodulatory properties. Finally, biosensor protein produced by MSCs was used to quantify glucose in cell-free assays. Our results indicate that our immortalized MSCs are suitable for measuring glucose concentrations in a physiological range. Thus, they are appropriate for incorporation into a cell-based, immune-privileged, glucose-monitoring medical device.

Practical Considerations on the Use of Continuous Glucose Monitoring in Pediatrics and Older Adults and Nonadjunctive Use

Clinical use of continuous glucose monitoring (CGM) devices has grown over the past 15 years from a niche concept to becoming standard of care for patients with type 1 diabetes (T1D). With the December 2016 Food and Drug Administration approval for diabetes treatment decisions directly from CGM values (nonadjunctive use) without finger-stick confirmation, the uptake and scope of CGM use will likely further expand. With this expansion, it is important to consider the role and impact of CGM technology in specific settings and high-risk populations, such as the young and the elderly. In pediatric patients, CGM concerns include limited body surface area, difficulty keeping sensors adhered, and the role of nonadjunctive use in the school setting. In older adults, Medicare did not, until very recently, cover CGM devices and as such, their use had been limited by lack of reimbursement. As CGM use will likely expand in clinical practice given the nonadjunctive indication, guidelines and recommendations for clinical practice are warranted. In this article, we discuss recent research on CGM use in the special populations of children and older adults and provide initial guidelines for nonadjunctive use in clinical practice.

Exercise management in type 1 diabetes: a consensus statement

Type 1 diabetes is a challenging condition to manage for various physiological and behavioural reasons. Regular exercise is important, but management of different forms of physical activity is particularly difficult for both the individual with type 1 diabetes and the health-care provider. People with type 1 diabetes tend to be at least as inactive as the general population, with a large percentage of individuals not maintaining a healthy body mass nor achieving the minimum amount of moderate to vigorous aerobic activity per week. Regular exercise can improve health and wellbeing, and can help individuals to achieve their target lipid profile, body composition, and fitness and glycaemic goals. However, several additional barriers to exercise can exist for a person with diabetes, including fear of hypoglycaemia, loss of glycaemic control, and inadequate knowledge around exercise management. This Review provides an up-to-date consensus on exercise management for individuals with type 1 diabetes who exercise regularly, including glucose targets for safe and effective exercise, and nutritional and insulin dose adjustments to protect against exercise-related glucose excursions.

How Can We Realize the Clinical Benefits of Continuous Glucose Monitoring?

Controlling glycemia in diabetes remains key to prevent complications in this condition. However, glucose levels can undergo large fluctuations secondary to daily activities, consequently creating management difficulties. The current review summarizes the basics of glucose management in diabetes by addressing the main glycemic parameters. The advantages and limitation of HbA1c, the gold standard measure of glucose control, are discussed together with the clinical importance of hypoglycemia and glycemic variability. The review subsequently moves focus to glucose monitoring techniques in diabetes, assessing advantages and limitations. Monitoring glucose levels is crucial for effective and safe adjustment of hypoglycemic therapy, particularly in insulin users. Self-monitoring of blood glucose (SMBG), based on capillary glucose testing, remains one of the most widely used methods to monitor glucose levels, given the relative accuracy, familiarity, and manageable costs. However, patient inconvenience and the sporadic nature of SMBG limit clinical effectiveness of this approach. In contrast, continuous glucose monitoring (CGM) provides a more comprehensive picture of glucose levels, but these systems are expensive and require constant calibration which, together with concerns over accuracy of earlier devices, restrict CGM use to special groups of patients. The newer flash continuous glucose monitoring (FCGM) system, which is more affordable than conventional CGM devices and does not require calibration, offers an alternative glucose monitoring strategy that comprehensively analyzes glucose profile while sparing patients the inconvenience of capillary glucose testing for therapy adjustment or CGM calibration. The fast development of new CGM devices will gradually displace SMBG as the main glucose testing method. Avoiding the inconvenience of SMBG and optimizing glycemia through alternative glucose testing strategies will help to reduce the risk of complications and improve quality of life in patients with diabetes.

Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges

Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones.

Predicting Insulin Treatment Scenarios with the Net Effect Method: Domain of Validity

BACKGROUND

A simulation methodology based on the net effect, a signal estimated from continuous glucose monitoring (CGM) and insulin data accounting for sources of glucose variability, for example, meals and exercise, has been proposed. This method has been recently used to "replay" real-life treatment scenarios and determine the minimal level of CGM sensor accuracy required for nonadjunctive use. Given the potential of the net effect method, it is important to assess its domain of validity.

METHODS

The UVA/Padova type 1 diabetes simulator is used to generate glucose and insulin data. The net effect signal is estimated and used to predict the glucose profiles resulting from the following therapy modifications: (1) basal insulin increase/decrease, (2) bolus reduction to prevent hypoglycemia, (3) bolus addition after CGM hyperalarms, (4) hypotreatment addition after CGM hypoalarms. Results of the net effect method are compared with the reference provided by the UVA/Padova simulator.

RESULTS

The net effect method (1) well predicts the effect of small basal insulin adjustments (±10%), but overestimates time in hypo/hyperglycemia for larger adjustments (±50%); (2) underestimates the bolus reduction required to prevent hypoglycemia; (3) underestimates time in hyperglycemia when introducing correction boluses; and (4) overestimates time in hypoglycemia when introducing hypotreatments.

CONCLUSIONS

The net effect method is reliable for small adjustments of basal insulin, while outside this domain of validity it can provide inaccurate results.

Glucose: archetypal biomarker in diabetes diagnosis, clinical management and research

The clinical utility of diabetes biomarkers can be considered in terms of diagnosis, management and prediction of long-term vascular complications. Glucose satisfies all of these requirements. Thresholds of hyperglycemia diagnostic of diabetes reflect inflections that confer a risk of developing long-term microvascular complications. Degrees of hyperglycemia (impaired fasting glucose, impaired glucose tolerance) that lie below the diagnostic threshold for diabetes identify individuals at risk of progression to diabetes and/or development of atherothrombotic cardiovascular disease. Self-measured glucose levels usefully complement hemoglobin A1c levels to guide daily management decisions. Continuous glucose monitoring provides detailed real-time data that is of value in clinical decision making, assessing response to new diabetes drugs and the development of closed-loop artificial pancreas technology.