Diabetes Technology: Monitoring, Analytics, and Optimal Control

Bioelectronic medicine: Preclinical insights and clinical advances

The nervous system maintains homeostasis and health. Homeostatic disruptions underlying the pathobiology of many diseases can be controlled by bioelectronic devices targeting CNS and peripheral neural circuits. New insights into the regulatory functions of the nervous system and technological developments in bioelectronics drive progress in the emerging field of bioelectronic medicine. Here, we provide an overview of key aspects of preclinical research, translation, and clinical advances in bioelectronic medicine.

External validation of a classifier of daily continuous glucose monitoring (CGM) profiles

As continuous glucose monitoring (CGM) sensors generate ever increasing amounts of CGM data, the need for methods to simplify the storage and analysis of this data becomes increasingly important. Lobo et al. developed a classifier of daily CGM profiles as an initial step in addressing this need. The classifier has several important applications including, but not limited to, data compression, data encryption, and indexing of databases. While the classifier has already successfully classified 99.0% of the 42,595 daily CGM profiles in a Test Set, this work presents an external validation using an external validation set (EVal Set) derived from 8 publicly available data sets. The Test Set and the EVal Set differ in terms of (but not limited to) demographics, data collection time periods, and data collection geographies. The classifier successfully classified 98.2% of the 137,030 daily CGM profiles in the EVal Set. Furthermore, each of the 483 distinct groups of classified daily CGM profiles from the EVal Set retains the same clinical characteristics as the corresponding group from the Test Set, as desired. Finally, the set of unclassified daily CGM profiles from the EVal Set retains the same statistical characteristics as the set of unclassified daily CGM profiles from the Test Set, as desired. These results establish the robustness and generalizability of the classifier: the performance of the classifier is unchanged despite the marked differences between the Test Set and the EVal Set.

A data-driven approach to classifying daily continuous glucose monitoring (CGM) time series

According to the World Health Organization, about 422 million people worldwide have type 1 or type 2 diabetes (T1D, T2D), with the latter accounting for 90-95% of cases. Safe and effective treatment of patients with diabetes requires accurate and frequent monitoring of their blood sugar levels. Continuous glucose monitoring (CGM) is a monitoring technology developed to address this need, and its use among U.S. T1D patients has increased from 6% in 2011 to 38% in 2018 and continues to increase worldwide in both T1D and T2D. This paper presents a data-driven approach to determine Ω, a finite set of representative daily profiles (motifs) such that almost any daily CGM profile generated by a patient can be matched to one of the motifs in Ω. The training data set (9,741 profiles) was used to identify 8 candidate sets of motifs, while the validation data set (14,175 profiles) was used to select the final set Ω. The robustness of Ω was established by using it to successfully classify (match against a representative daily profile in Ω) 99.0% of 42,595 daily CGM profiles in the testing data set. All data sets contained daily CGM profiles from six studies involving T1D and T2D patients using a variety of treatment modes, including daily insulin injections, insulin pumps, or artificial pancreas (AP). The classified profiles can be used in predictive modeling, decision support, and automated control systems (e.g., AP).

The Differential and Combined Action of Insulin Glargine and Lixisenatide on the Fasting and Postprandial Components of Glucose Control

BACKGROUND

iGlarLixi is an injectable combination of long acting insulin glargine (iGlar) and glucagon-like peptide 1 receptor agonist lixisenatide in a fixed ratio, which was proven safe and effective for the treatment of type 2 diabetes. Lixisenatide and iGlar act differently on fasting and postprandial plasma glucose (fasting plasma glucose [FPG] and postprandial glucose [PPG]). Here, we deconstruct quantitatively their respective FPG and PPG effects.

METHOD

This post hoc study analyzes data from the Lixilan-O trial, where 1170 subjects with type 2 diabetes were randomly assigned to 30 weeks of once daily injections of lixisenatide, iGlar, and iGlarLixi (1:2:2). The FPG and PPG components of glucose control were assessed in terms of mean glucose (fasting mean plasma glucose [FMPG] and prandial mean plasma glucose [PMPG], respectively). The MPGP was computed across all meals as a delta between post- and premeal glucose; glucose variability was measured by the high blood glucose index (HBGI) (fasting HBGI and prandial HBGI [PHBGI], respectively), and glycemic exposure measured by area under the curve (AUC) computed overall. All metrics were derived from seven-point self-monitoring glucose profiles.

RESULTS

Insulin glargine lowered significantly FMPG by 15.3 mg/dL (P < .01) without any significant change in PMPG. Lixisenatide, when added to iGlar, reduced PMPG by 9.7 mg/dL (P < .01), AUC by 96.3 mg∙h/dL (P < .01), and PHBGI by 2.4 (P < .01), primarily due to attenuation of PPG and without significant change in mean FPG.

CONCLUSION

Insulin glargine and lixisenatide act selectively on FPG and PPG. Their combination iGlarLixi offers more effective glucose control than its components due to the cumulative effect on FPG and PPG, which is evidenced by reduced average glycemia, glycemic exposure, and glucose variability.

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.

Decreased complexity of glucose dynamics in diabetes in rhesus monkeys

Until recently, preclinical and clinical work on diabetes has focused on the understanding of blood glucose elevation and its detrimental metabolic sequelae. The advent of continuous glucose monitoring (CGM) technology now allows real time monitoring of blood glucose levels as a time series, and thus the exploration of glucose dynamics at short time scales. Previous work has shown decreases in the complexity of glucose dynamics, as measured by multiscale entropy (MSE) analysis, in diabetes in humans, mice, and rats. Analyses for non-human primates (NHP) have not been reported, nor is it known if anti-diabetes compounds affect complexity of glucose dynamics. We instrumented four healthy and six diabetic rhesus monkeys with CGM probes in the carotid artery and collected glucose values at a frequency of one data point per second for the duration of the sensors' life span. Sensors lasted between 45 and 78 days. Five of the diabetic rhesus monkeys were also administered the anti-diabetic drug liraglutide daily beginning at day 39 of the CGM monitoring period. Glucose levels fluctuated during the day in both healthy and diabetic rhesus monkeys, peaking between 12 noon - 6 pm. MSE analysis showed reduced complexity of glucose dynamics in diabetic monkeys compared to healthy animals. Although liraglutide decreased glucose levels, it did not restore complexity in diabetic monkeys consistently. Complexity varied by time of day, more strongly for healthy animals than for diabetic animals. And by dividing the monitoring period into 3-day or 1-week subperiods, we were able to estimate within-animal variability of MSE curves. Our data reveal that decreased complexity of glucose dynamics is a conserved feature of diabetes from rodents to NHPs to man.

Automated closed-loop control of diabetes: the artificial pancreas

The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.