Nocturnal Glucose Metabolism in Type 1 Diabetes: A Study Comparing Single Versus Dual Tracer Approaches

Adaptive Biobehavioral Control: A Pilot Analysis of Human-Machine Coadaptation in Type 1 Diabetes

Background: While it is well recognized that an automated insulin delivery (AID) algorithm should adapt to changes in physiology, it is less understood that the individual would also have to adapt to the AID system. The adaptive biobehavioral control (ABC) method presented here attempts to compensate for this deficiency by including AID into an information cloud-based ecosystem. Methods: The Web Information Tool (WIT) implements the ABC concept via the following: (1) a Physiological Adaptation Module (PAM) that tracks metabolic changes and adapts AID parameters accordingly and (2) a Behavioral Adaptation Module (BAM) that provides information feedback. The safety of WIT (primary outcome) was assessed in an 8-week randomized, two-arm parallel pilot study. All participants used the Control-IQ® AID system enhanced with PAM, but only those in the Experimental group had access to BAM. Secondary glycemic outcomes were computed using the 2-week baseline period and the last 2 weeks of treatment. Results: Thirty participants with type 1 diabetes (T1D) completed all study procedures (17 female/13 male; age: 40 ± 14 years; HbA1c: 6.6% ± 0.5%). No severe hypoglycemia, DKA, or other serious adverse events were reported. Comparing the Experimental and Control groups, no significant difference was observed in time in range (70-180 mg/dL): 74.6% vs 73.8%, adjusted mean difference: 2.65%, 95% CI (-1.12%,6.41%), P = 0.161. Time in 70-140 mg/dL was significantly higher in the Experimental group: 50.7% vs 49.2%, 5.71% (0.44%,10.97%), P = 0.035, without increased time below range: 0.54% (-0.09%,1.17%), P = 0.089. Conclusion: The results demonstrate that it is safe to integrate an AID system into the WIT ecosystem. Validation in a full-scale study is ongoing.

Supervised and Unsupervised Approaches for the Real-Time Detection of Undesired Insulin Suspension Caused by Malfunctions

BACKGROUND

Automated insulin delivery (AID) systems, permit improved treatment of type 1 diabetes (T1D). Unfortunately, malfunctioning in the insulin pump or in the infusion set can prevent insulin from being administered, reducing the AID efficacy and posing the patient at risk. Different data-driven methods available in the literature can be used to deal with the problem of automatically detecting complete insulin suspension in real-time. This article investigates both supervised and unsupervised strategies and proposes a fair comparison under either population or personalized settings.

METHODS

Several algorithms are compared using data generated through the UVA/Padova T1D simulator, a computer simulator widely used to test control strategies in silico and accepted by the Food and Drugs Administration (FDA) as a substitute to animal pre-clinical trials. Two synthetic data sets, each consisting of 100 virtual subjects monitored for 1 month, were generated. Occasional faults of the insulin pump are simulated as complete occlusions by suspending the therapy administration. Personalized algorithms are investigated with unsupervised approaches only, since personalized labels are hardly available.

RESULTS

In the population scenario, the supervised approach outperforms the unsupervised strategy. In particular, logistic regression and random forest achieves a recall of 72% and 82%, with 0.12 and 0.21 false positives (FP) per day, respectively. In the personalized setting scenario, the unsupervised algorithms are tailored on each patient and outperform the population ones, in particular isolation forest achieves a recall 80% and 0.06 FPs per day.

CONCLUSIONS

This article suggests that unsupervised personalized approach, by addressing the large variability in glucose response among individuals with T1D, is superior to other one-fits-all approaches in detecting insulin suspensions caused by malfunctioning. Population methodologies can be effectively used while waiting to collect sufficient patient data, when the system is installed on a new patient.

The Minimally-Invasive Oral Glucose Minimal Model: Estimation of Gastric Retention, Glucose Rate of Appearance, and Insulin Sensitivity From Type 1 Diabetes Data Collected in Real-Life Conditions

OBJECTIVE

Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (R[Formula: see text]), and insulin sensitivity (S[Formula: see text]). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM.

METHODS

Forty-seven individuals with T1D (weight =78±13 kg, age =42±10 yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data.

RESULTS

The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R[Formula: see text] model parameters were not significantly different using the MI-OMM and R-OMM (p 0.05) and the correlation between the two S[Formula: see text] was satisfactory ( ρ =0.77).

CONCLUSION

The MI-OMM is usable to estimate GR, R[Formula: see text], and S[Formula: see text] from data collected in real-life conditions with minimally-invasive technologies.

SIGNIFICANCE

Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R[Formula: see text], and S[Formula: see text]. DSS could finally exploit this information to improve diabetes management.

Toward a Fully Automated Artificial Pancreas System Using a Bioinspired Reinforcement Learning Design: In Silico Validation

OBJECTIVE

The automation of insulin treatment is the most challenge aspect of glucose management for type 1 diabetes owing to unexpected exogenous events (e.g., meal intake). In this article, we propose a novel reinforcement learning (RL) based artificial intelligence (AI) algorithm for a fully automated artificial pancreas (AP) system.

METHODS

A bioinspired RL designing method was developed for automated insulin infusion. This method has reward functions that imply the temporal homeostatic objective and discount factors that reflect an individual specific pharmacological characteristic. The proposed method was applied to a training method using an RL algorithm and was evaluated in virtual patients from the FDA approved UVA/Padova simulator with unannounced meal intakes.

RESULTS

For a single-meal experiment with preprandial fasting, the trained policy demonstrated fully automated regulation in both the basal and postprandial phases. In the in silico trial with a variation of insulin sensitivity and dawn phenomenon, the policy achieved a mean glucose of 124.72 mg/dL and percentage time in the normal range of 89.56%. The layer-wise relevance propagation provides interpretable information on AI-driven decision for robustness to sensor noise, automated postprandial regulation, and insulin stacking avoidance.

CONCLUSION

The AP algorithm based on the bioinspired RL approach enables fully automated blood glucose control with unannounced meal intake.

SIGNIFICANCE

The proposed framework can be extended to other drug-based treatments for systems with significant uncertainties.

Young-onset diabetes, nutritional therapy and novel insulin delivery systems: a report from the 21st Hong Kong Diabetes and Cardiovascular Risk Factors - East Meets West Symposium

The prevalence and incidence of young-onset diabetes are increasing in many parts of the world, with the most rapid increase occurring in Asia, where one in five people with diabetes are diagnosed below the age of 40 years. Accumulation of glycaemic burden from an early age significantly increases the lifetime risks of developing complications from diabetes. Despite impending health threats, young people fare worse in the control of blood glucose and other metabolic risk factors. Challenges in the management of young-onset diabetes are compounded by heterogeneity of the underlying causes, pathophysiology and clinical phenotypes in this group. Effective characterization of people with diabetes has implications in steering the choice of glucose-lowering drugs, which, in turn, determines the clinical outcome. Medical nutritional therapy is key to effective management of people with diabetes but dietary adherence is often suboptimal among younger individuals. A recently published consensus report on nutritional therapy addresses dietary management in people with prediabetes as well as diabetes, and summarizes clinical evidence regarding macronutrient and micronutrient composition as well as eating patterns in people with diabetes. For people with type 1 diabetes, automated insulin delivery systems have rapidly evolved since the concept was first introduced at the National Institute of Health and the Juvenile Diabetes Research Foundation in 2005. The subsequent development of a type 1 diabetes simulator, developed using detailed human physiology data on carbohydrate metabolism replaced the need for pre-clinical animal studies and facilitated the seamless progression to artificial pancreas human clinical trials.

Insulin Therapy in Adults with Type 1 Diabetes Mellitus: a Narrative Review

Here, we review insulin management options and strategies in nonpregnant adult patients with type 1 diabetes mellitus (T1DM). Most patients with T1DM should follow a regimen of multiple daily injections of basal/bolus insulin, but those not meeting individual glycemic targets or those with frequent or severe hypoglycemia or pronounced dawn phenomenon should consider continuous subcutaneous insulin infusion. The latter treatment modality could also be an alternative based on patient preferences and availability of reimbursement. Continuous glucose monitoring may improve glycemic control irrespective of treatment regimen. A glycemic target of glycated hemoglobin < 7% (53 mmol/mol) is appropriate for most nonpregnant adults. Basal insulin analogues with a reduced peak profile and an extended duration of action with lower intraindividual variability relative to neutral protamine Hagedorn insulin are preferred. The clinical advantages of basal analogues compared with older basal insulins include reduced injection burden, better efficacy, lower risk of hypoglycemic episodes (especially nocturnal), and reduced weight gain. For prandial glycemic control, any rapid-acting prandial analogue (aspart, glulisine, lispro) is preferred over regular human insulin. Faster-acting insulin aspart is a relatively new option with the advantage of better postprandial glucose coverage. Frequent blood glucose measurements along with patient education on insulin dosing based on carbohydrate counting, premeal blood glucose, and anticipated physical activity is paramount, as is education on the management of blood glucose under different circumstances.Plain Language Summary: Plain language summary is available for this article.

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.

Carbohydrate Restriction in Type 1 Diabetes: A Realistic Therapy for Improved Glycaemic Control and Athletic Performance?

Around 80% of individuals with Type 1 diabetes (T1D) in the United States do not achieve glycaemic targets and the prevalence of comorbidities suggests that novel therapeutic strategies, including lifestyle modification, are needed. Current nutrition guidelines suggest a flexible approach to carbohydrate intake matched with intensive insulin therapy. These guidelines are designed to facilitate greater freedom around nutritional choices but they may lead to higher caloric intakes and potentially unhealthy eating patterns that are contributing to the high prevalence of obesity and metabolic syndrome in people with T1D. Low carbohydrate diets (LCD; <130 g/day) may represent a means to improve glycaemic control and metabolic health in people with T1D. Regular recreational exercise or achieving a high level of athletic performance is important for many living with T1D. Research conducted on people without T1D suggests that training with reduced carbohydrate availability (often termed "train low") enhances metabolic adaptation compared to training with normal or high carbohydrate availability. However, these "train low" practices have not been tested in athletes with T1D. This review aims to investigate the known pros and cons of LCDs as a potentially effective, achievable, and safe therapy to improve glycaemic control and metabolic health in people with T1D. Secondly, we discuss the potential for low, restricted, or periodised carbohydrate diets in athletes with T1D.

Contribution of pancreatic α-cell function to insulin sensitivity and glycemic variability in patients with type 1 diabetes

AIMS/INTRODUCTION

To evaluate the contribution of pancreatic α-cell function to the dawn phenomenon, insulin sensitivity, hepatic glucose uptake and glycemic variability in patients with type 1 diabetes.

MATERIALS AND METHODS

In 40 patients with type 1 diabetes, arginine stimulation tests were carried out, and the area under the curve (AUC) of glucagon was measured using radioimmunoassays (AUCglc RIA ) and enzyme-linked immunosorbent assays (AUCglc ELISA ). The ratio of the insulin dose delivered by an artificial pancreas to maintain euglycemia between 04.00 and 08.00 hours or between 00.00 and 04.00 hours was measured as the dawn index. The glucose infusion rate and hepatic glucose uptake were measured using hyperinsulinemic euglycemic clamp and clamp oral glucose loading tests. Glycemic variability in 96 h was measured by continuous glucose monitoring.

RESULTS

The median dawn index (1.7, interquartile range 1.0-2.8) was not correlated with AUCglc RIA (R2  = 0.03, P = 0.39) or AUCglc ELISA (R2  = 0.04, P = 0.32). The median glucose infusion rate (7.3 mg/kg/min, interquartile range 6.4-9.2 mg/kg/min) was significantly correlated with AUCglc RIA (R2  = 0.20, P = 0.02) and AUCglc ELISA (R2  = 0.21, P = 0.02). The median hepatic glucose uptake (65.3%, interquartile range 40.0-87.3%) was not correlated with AUCglc RIA (R2  = 0.07, P = 0.26) or AUCglc ELISA (R2  = 0.26, P = 0.79). The standard deviation of glucose levels measured by continuous glucose monitoring was significantly correlated with AUCglc RIA (R2  = 0.11, P = 0.049), but not with AUCglc ELISA (R2  = 0.01, P = 0.75).

CONCLUSIONS

Pancreatic α-cell function contributed to insulin sensitivity in patients with type 1 diabetes.

Pharmacokinetics, Pharmacodynamics, and Modulation of Hepatic Glucose Production With Insulin Glargine U300 and Glargine U100 at Steady State With Individualized Clinical Doses in Type 1 Diabetes

OBJECTIVE

This study characterized the pharmacokinetics (PK), pharmacodynamics (PD), and endogenous (hepatic) glucose production (EGP) of clinical doses of glargine U300 (Gla-300) and glargine U100 (Gla-100) under steady-state (SS) conditions in type 1 diabetes mellitus (T1DM).

RESEARCH DESIGN AND METHODS

T1DM subjects (N = 18, age 40 ± 12 years, T1DM duration 26 ± 12 years, BMI 23.4 ± 2 kg/m², A1C 7.19 ± 0.52% [55 ± 5.7 mmol · mol-1^-1]) were studied after 3 months of Gla-300 or Gla-100 (evening dosing) titrated to fasting euglycemia (random, crossover) with the euglycemic clamp using individualized doses (Gla-300 0.35 ± 0.08, Gla-100 0.28 ± 0.07 units · kg^-1).

RESULTS

Plasma free insulin concentrations (free immunoreactive insulin area under the curve) were equivalent over 24 h with Gla-300 versus Gla-100 (point estimate 1.11 [90% CI 1.03; 1.20]) but were reduced in the first 6 h (0.91 [90% CI 0.86; 0.97]) and higher in the last 12 h postdosing (1.38 [90% CI 1.21; 1.56]). Gla-300 and Gla-100 both maintained 24 h euglycemia (0.99 [90% CI 0.98; 1.0]). The glucose infusion rate was equivalent over 24 h (1.03 [90% CI 0.88; 1.21]) but was lower in first (0.77 [90% CI 0.62; 0.95]) and higher (1.53 [90% CI 1.23; 1.92]) in the second 12 h with Gla-300 versus Gla-100. EGP was less suppressed during 0-6 h but more during 18-24 h with Gla-300. PK and PD within-day variability (fluctuation) was 50% and 17% lower with Gla-300.

CONCLUSIONS

Individualized, clinical doses of Gla-300 and Gla-100 resulted in a similar euglycemic potential under SS conditions. However, Gla-300 exhibited a more stable profile, with lower variability and more physiological modulation of EGP compared with Gla-100.

Paradigm Shifts in Nocturnal Glucose Control in Type 2 Diabetes

CONTEXT

A better understanding of nocturnal regulation of glucose homeostasis will provide the framework for designing rational therapeutic strategies to improve the management of overnight glucose in patients with type 2 diabetes (T2D).

OBJECTIVE

To establish the nocturnal pattern and regulation of glucose production (EGP) in humans and to determine whether the pattern is dysregulated in people with T2D.

DESIGN

Subjects were infused with [3-3H] glucose overnight. Arterial blood samples were drawn for hormones and analytes to estimate EGP throughout the night. Deuterium-labeled water was provided to measure gluconeogenesis (GNG) using the hexamethylenetetramine method of Landau.

SETTING

Mayo Clinic Clinical Research Trials Unit, Rochester, MN, USA.

PARTICIPANTS AND INTERVENTIONS

A total of 43 subjects [23 subjects with T2D and 20 nondiabetic (ND) subjects comparable for age and body mass index] were included in this study.

MAIN OUTCOME(S) MEASURE(S)

Glucose and EGP.

RESULTS

Plasma glucose, C-peptide, and glucagon concentrations were higher throughout the night, whereas insulin concentrations were higher in subjects with T2D vs ND subjects at 1:00 and 4:00 am but similar at 7:00 am. EGP was higher in the subjects with T2D than in the ND subjects throughout the night (P < 0.001). Glycogenolysis (GGL) fell and GNG rose, resulting in significantly higher (P < 0.001) rates of GNG at 4:00 and 7:00 am and significantly (P < 0.001) higher rates of GGL at 1:00, 4:00, and 7:00 am in T2D as compared with ND.

CONCLUSIONS

These data imply that optimal therapies for T2D for nocturnal/fasting glucose control should target not only the absolute rates of EGP but also the contributing pathways of GGL and GNG sequentially.

Greater early postprandial suppression of endogenous glucose production and higher initial glucose disappearance is achieved with fast-acting insulin aspart compared with insulin aspart

AIM

To investigate the mechanisms behind the lower postprandial glucose (PPG) concentrations achieved with fast-acting insulin aspart (faster aspart) than with insulin aspart (IAsp).

MATERIALS AND METHODS

In a randomized, double-blind, crossover trial, 41 people with type 1 diabetes received identical subcutaneous single faster aspart and IAsp doses (individualized for each participant), together with a standardized mixed meal (including 75 g carbohydrate labelled with [1-¹³ C] glucose). PPG turnover was determined by the triple-tracer meal method using continuous, variable [6-³ H] glucose and [6,6-² H₂ ] glucose infusion.

RESULTS

Insulin exposure within the first hour was 32% greater with faster aspart than with IAsp (treatment ratio faster aspart/IAsp 1.32 [95% confidence interval {CI} 1.18;1.48]; P < .001), leading to a 0.59-mmol/L non-significantly smaller PPG increment at 1 hour (ΔPG_1h ; treatment difference faster aspart-IAsp -0.59 mmol/L [95% CI -1.19; 0.01]; P = .055). The trend towards reduced ΔPG_1h with faster aspart was attributable to 12% greater suppression of endogenous glucose production (EGP; treatment ratio 1.12 [95% CI 1.01; 1.25]; P = .040) and 23% higher glucose disappearance (1.23 [95% CI 1.05; 1.45]; P = .012) with faster aspart than with IAsp during the first hour. Suppression of free fatty acid levels during the first hour was 36% greater for faster aspart than for IAsp (1.36 [95% CI 1.01;1.88]; P = .042).

CONCLUSIONS

The trend towards improved PPG control with faster aspart vs IAsp in this study was attributable to both greater early suppression of EGP and stimulation of glucose disappearance.

The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day

BACKGROUND

A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described.

METHOD

Intraday variability of insulin sensitivity (S_I) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily S_I. Thus, time-varying distributions of both subject's basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of "dawn" phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added.

RESULTS

One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data.

CONCLUSIONS

The new modifications introduced in the T1D simulator allow to extend its domain of validity from "single-meal" to "single-day" scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

A Novel Insulin/Glucose Model after a Mixed-Meal Test in Patients with Type 1 Diabetes on Insulin Pump Therapy

Current closed-loop insulin delivery methods stem from sophisticated models of the glucose-insulin (G/I) system, mostly based on complex studies employing glucose tracer technology. We tested the performance of a new minimal model (GLUKINSLOOP 2.0) of the G/I system to characterize the glucose and insulin dynamics during multiple mixed meal tests (MMT) of different sizes in patients with type 1 diabetes (T1D) on insulin pump therapy (continuous subcutaneous insulin infusion, CSII). The GLUKINSLOOP 2.0 identified the G/I system, provided a close fit of the G/I time-courses and showed acceptable reproducibility of the G/I system parameters in repeated studies of identical and double-sized MMTs. This model can provide a fairly good and reproducible description of the G/I system in T1D patients on CSII, and it may be applied to create a bank of “virtual” patients. Our results might be relevant at improving the architecture of upcoming closed-loop CSII systems.