Evening and overnight closed-loop control versus 24/7 continuous closed-loop control for type 1 diabetes: a randomised crossover trial

Consensus Report on Glucagon-Like Peptide-1 Receptor Agonists as Adjunctive Treatment for Individuals With Type 1 Diabetes Using an Automated Insulin Delivery System

With increasing prevalence of obesity and cardiovascular diseases, there is a growing interest in the use of glucagon-like peptide-1 receptor agonists (GLP-1RAs) as an adjunct therapy in type 1 diabetes (T1D). The GLP-1RAs are currently not approved by the US Food and Drug Administration for the treatment of T1D in the absence of randomized controlled trials documenting efficacy and safety of these agents in this population. The Diabetes Technology Society convened a series of three consensus meetings of clinicians and researchers with expertise in diabetes technology, GLP-1RA therapy, and T1D management. The project was aimed at synthesizing current literature and providing conclusions on the use of GLP-1RA therapy as an adjunct to automated insulin delivery (AID) systems in adults with T1D. The expert panel members met virtually three times on January 17, 2024, and April 24, 2024, and August 14, 2024, to discuss topics ranging from physiology and outcomes of GLP-1RAs in T1D to limitations of current sensors, algorithms, and insulin for AID systems. The panelists also identified research gaps and future directions for research. The panelists voted to in favor of 31 recommendations. This report presents the consensus opinions of the participants that, in adults with T1D using AID systems, GLP-1RAs have the potential to (1) provide effective adjunct therapy and (2) improve glycemic and metabolic outcomes without increasing the risk of severe hypoglycemia or diabetic ketoacidosis.

An evaluation of how exercise position statement guidelines are being used in the real world in type 1 diabetes: Findings from the type 1 diabetes exercise initiative (T1DEXI)

AIMS

Position statement guidelines should help people with type 1 diabetes (T1D) improve glucose outcomes during exercise.

METHODS

In a 4-week observational study, continuous glucose, insulin, and nutrient data were collected from 561 adults with T1D. Glucose outcomes were calculated during exercise, post-exercise, and overnight, and were compared for sessions when participants used versus did not use exercise guidelines for open-loop (OL) and automated insulin delivery (AID) therapy.

RESULTS

Guidelines requiring behaviour modification were rarely used while guidelines not requiring modification were often used. The guideline recommending reduced meal insulin before exercise was associated with lower time <3.9 mmol/L during exercise (-2.2 %, P=0.02) for OL but not significant for AID (-1.4 %, P=0.27). Compared to exercise with low glucose (<3.9 mmol/L) in prior 24-hours, sessions without recent low glucose had lower time <3.9 mmol/L during exercise (-1.2 %, P<0.001). The AID guideline for no carbohydrates before exercise when CGM is flat, or increasing, was not associated with improved glycaemia.

CONCLUSIONS

Free-living datasets may be used to evaluate usage and benefit of position statement guidelines. Evidence suggests OL participants who reduced meal insulin prior to exercise and did not have low glucose in the prior 24 h had less time below range.

Evidence from clinical trials on high-risk medical devices in children: a scoping review

BACKGROUND

Meeting increased regulatory requirements for clinical evaluation of medical devices marketed in Europe in accordance with the Medical Device Regulation (EU 2017/745) is challenging, particularly for high-risk devices used in children.

METHODS

Within the CORE-MD project, we performed a scoping review on evidence from clinical trials investigating high-risk paediatric medical devices used in paediatric cardiology, diabetology, orthopaedics and surgery, in patients aged 0-21 years. We searched Medline and Embase from 1st January 2017 to 9th November 2022.

RESULTS

From 1692 records screened, 99 trials were included. Most were multicentre studies performed in North America and Europe that mainly had evaluated medical devices from the specialty of diabetology. Most had enrolled adolescents and 39% of trials included both children and adults. Randomized controlled trials accounted for 38% of the sample. Other frequently used designs were before-after studies (21%) and crossover trials (20%). Included trials were mainly small, with a sample size <100 participants in 64% of the studies. Most frequently assessed outcomes were efficacy and effectiveness as well as safety.

CONCLUSION

Within the assessed sample, clinical trials on high-risk medical devices in children were of various designs, often lacked a concurrent control group, and recruited few infants and young children.

IMPACT

In the assessed sample, clinical trials on high-risk medical devices in children were mainly small, with variable study designs (often without concurrent control), and they mostly enrolled adolescents. We provide a systematic summary of methodologies applied in clinical trials of medical devices in the paediatric population, reflecting obstacles in this research area that make it challenging to conduct adequately powered randomized controlled trials. In view of changing European regulations and related concerns about shortages of high-risk medical devices for children, our findings may assist competent authorities in setting realistic requirements for the evidence level to support device conformity certification.

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

OBJECTIVE

Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid.

METHODS

Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources.

SIGNIFICANCE

These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

Utility and precision evidence of technology in the treatment of type 1 diabetes: a systematic review

BACKGROUND

The greatest change in the treatment of people living with type 1 diabetes in the last decade has been the explosion of technology assisting in all aspects of diabetes therapy, from glucose monitoring to insulin delivery and decision making. As such, the aim of our systematic review was to assess the utility of these technologies as well as identify any precision medicine-directed findings to personalize care.

METHODS

Screening of 835 peer-reviewed articles was followed by systematic review of 70 of them (focusing on randomized trials and extension studies with ≥50 participants from the past 10 years).

RESULTS

We find that novel technologies, ranging from continuous glucose monitoring systems, insulin pumps and decision support tools to the most advanced hybrid closed loop systems, improve important measures like HbA1c, time in range, and glycemic variability, while reducing hypoglycemia risk. Several studies included person-reported outcomes, allowing assessment of the burden or benefit of the technology in the lives of those with type 1 diabetes, demonstrating positive results or, at a minimum, no increase in self-care burden compared with standard care. Important limitations of the trials to date are their small size, the scarcity of pre-planned or powered analyses in sub-populations such as children, racial/ethnic minorities, people with advanced complications, and variations in baseline glycemic levels. In addition, confounders including education with device initiation, concomitant behavioral modifications, and frequent contact with the healthcare team are rarely described in enough detail to assess their impact.

CONCLUSIONS

Our review highlights the potential of technology in the treatment of people living with type 1 diabetes and provides suggestions for optimization of outcomes and areas of further study for precision medicine-directed technology use in type 1 diabetes.

Patient-reported outcomes for older adults on CamAPS FX closed loop system

AIMS

Use of the CamAPS FX hybrid closed loop (CL) system is associated with improved time in range and glycated haemoglobin A1c across the age span, but little is known about its effects on patient-reported outcomes (PROs).

METHODS

This open-label, randomized, multi-site study compared CamAPS FX to sensor-augmented pump (SAP) in a sample of older adults (≥60 years) with type 1 diabetes (T1D). Thirty-five older adults completed PROs surveys at the start of the study and after each period of 16 weeks using either CL or SAP. At the end of the study, 19 participated in interviews about their experiences with CL.

RESULTS

Results examining the 16 weeks of CL use showed that the overall Diabetes Distress Scale score and two subscales (powerlessness and physician distress) improved significantly along with trust on the Glucose Monitoring Satisfaction Survey. User experience interview responses were consistent in noting benefits of 'improved glycaemic control' and 'worrying less about diabetes'.

CONCLUSION

In this sample of older adults with T1D who have previously shown glycaemic benefit, there are indicators of improved PROs and subjective user experience benefits.

Clinically-Similar Clusters of Daily CGM Profiles: Tracking the Progression of Glycemic Control Over Time

BACKGROUND

The adoption of CGM results in vast amounts of data, but their interpretation is still more art than exact science. The International Consensus on Time in Range (TIR) proposed the widely accepted TIR system of metrics, which we now take forward by introducing a finite and fixed set of clinically-similar clusters (CSCs), such that the TIR metrics of the daily CGM profiles within a cluster are homogeneous.

METHODS

CSC definition and validation used 204,710 daily CGM profiles in health, type 1 and type 2 diabetes (T1D, T2D), on different treatments. The CSCs were defined using 23,916 daily CGM profiles (Training set), and the final fixed set of CSCs was obtained using another 37,758 profiles (Validation set). The Testing set (143,036 profiles) was used to establish the robustness and generalizability of the CSCs.

RESULTS

The final set of CSCs contains 32 clusters. Any daily CGM profile was classifiable to a single CSC which approximated common glycemic metrics of the daily CGM profile, as evidenced by regression analyses with 0 intercept (R-squares≥0.83, e.g., correlation≥0.91), for all TIR and several other metrics. The CSCs distinguished CGM profiles in health, T2D, and T1D on different treatments, and allowed tracking of the daily changes in a person's glycemic control over time.

CONCLUSION

Daily CGM profiles can be classified into one of 32 prefixed CSCs, which enables a host of applications, e.g. tabulated data interpretation and algorithmic approaches to treatment, database indexing, pattern recognition, and tracking disease progression.

Switching to an advanced hybrid closed-loop system in real-world practice improves hypoglycemia awareness and metabolic control in adults with type 1 diabetes, particularly in those with impaired perception of hypoglycemia symptoms

AIMS

To evaluate the efficacy of an advance closed-loop (AHCL) system in restoring awareness of hypoglycemia in patients with type 1 diabetes (T1D).

METHODS

We conducted a prospective study including 46 subjects with T1D flash glucose monitoring (FGM) or continuous glucose monitoring (CGM) switching to a Minimed 780G® system. Patients were classified in three groups according to the therapy used before switching to Minimed® 780G: multiple dose insulin (MDI) therapy + FGM (n = 6), continuous subcutaneous insulin infusion + FGM (n = 21), and sensor-augmented pump with predictive low-glucose suspend (n = 19). FGM/CGM data were analyzed at baseline, after 2 and 6 months on AHCL. Clarke's score of hypoglycemia awareness was compared at baseline and 6 months recordings. We also compared the efficacy of the AHCL system in improving A_1c among patients with appropriate perception of symptoms of hypoglycemia compared to those presenting with impaired awareness of hypoglycemia (IAH).

RESULTS

Participants had a mean age of 37 ± 15 and a diabetes duration of 20 ± 10 years. At baseline, 12 patients (27%) showed IAH as defined by a Clarke's score ≥ 3. Patients with IAH were older and had lower estimated glomerular filtration rate (eGFR) compared with those who did not have IAH; with no differences in baseline CGM metrics or A_1c. An overall decrease in A_1c was observed after 6 months on AHCL system (from 6.9 ± 0.5% to 6.7 ± 0.6%, P < 0.001), regardless of prior insulin therapy. The improvement in metabolic control was greater in patients with IAH, showing a reduction in A_1c from 6.9 ± 0.5 to 6.4 ± 0.4% vs 6.9 ± 0.5 to 6.8 ± 0.6% (P = 0.003), showing a parallel increase in total daily boluses of insulin and automatic bolus correction administered by the AHCL system. In patients with IAH Clarke's score decreased from 3.6 ± 0.8 at baseline to 1.9 ± 1.6 after 6 months (P < 0.001). After 6 months on AHCL system, only 3 patients (7%) presented with a Clarke's score ≥ 3, resulting in an absolute risk reduction of 20% (95% confidence interval: 7-32) of having IAH.

CONCLUSIONS

Switching from any type of insulin administration to AHCL system improves restoration of hypoglycemia awareness and metabolic control in patients with T1D, particularly in adults with impaired perception of hypoglycemia symptoms.

TRIAL REGISTRATION

ClinicalTrial.gov ID NCT04900636.

Consensus Recommendations for the Use of Automated Insulin Delivery Technologies in Clinical Practice

The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

Effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes (T1D): systematic review and meta-analysis

BACKGROUND

The purpose of this study was to assess the effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes.

METHODS

We searched PubMed, EMBASE, Cochrane Central, and the Web of Science to December 2021. The eligibility criteria for study selection were randomized controlled trials comparing artificial pancreas systems (MPC, PID, and fuzzy algorithms) with conventional insulin therapy in type 1 diabetes patients. The heterogeneity of the overall results was identified by subgroup analysis of two factors including the intervention duration (overnight and 24 h) and the follow-up periods (< 1 week, 1 week to 1 month, and > 1 month).

RESULTS

The meta-analysis included a total of 41 studies. Considering the effect on the percentage of time maintained in the target range between the MPC-based artificial pancreas and conventional insulin therapy, the results showed a statistically significantly higher percentage of time maintained in the target range in overnight use (10.03%, 95% CI [7.50, 12.56] p < 0.00001). When the follow-up period was considered, in overnight use, the MPC-based algorithm showed a statistically significantly lower percentage of time maintained in the hypoglycemic range (-1.34%, 95% CI [-1.87, -0.81] p < 0.00001) over a long period of use (> 1 month).

CONCLUSIONS

Overnight use of the MPC-based artificial pancreas system statistically significantly improved glucose control while increasing time maintained in the target range for outpatients with type 1 diabetes. Results of subgroup analysis revealed that MPC algorithm-based artificial pancreas system was safe while reducing the time maintained in the hypoglycemic range after an overnight intervention with a long follow-up period (more than 1 month).

Automated Insulin Delivery with SGLT2i Combination Therapy in Type 1 Diabetes

Background: Use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) as adjunct therapy to insulin in type 1 diabetes (T1D) has been previously studied. In this study, we present data from the first free-living trial combining low-dose SGLT2i with commercial automated insulin delivery (AID) or predictive low glucose suspend (PLGS) systems. Methods: In an 8-week, randomized, controlled crossover trial, adults with T1D received 5 mg/day empagliflozin (EMPA) or no drug (NOEMPA) as adjunct to insulin therapy. Participants were also randomized to sequential orders of AID (Control-IQ) and PLGS (Basal-IQ) systems for 4 and 2 weeks, respectively. The primary endpoint was percent time-in-range (TIR) 70-180 mg/dL during daytime (7:00-23:00 h) while on AID (NCT04201496). Findings: A total of 39 subjects were enrolled, 35 were randomized, 34 (EMPA; n = 18 and NOEMPA n = 16) were analyzed according to the intention-to-treat principle, and 32 (EMPA; n = 16 and NOEMPA n = 16) completed the trial. On AID, EMPA versus NOEMPA had higher daytime TIR 81% versus 71% with a mean estimated difference of +9.9% (confidence interval [95% CI] 0.6-19.1); p = 0.04. On PLGS, the EMPA versus NOEMPA daytime TIR was 80% versus 63%, mean estimated difference of +16.5% (95% CI 7.3-25.7); p < 0.001. One subject on SGLT2i and AID had one episode of diabetic ketoacidosis with nonfunctioning insulin pump infusion site occlusion contributory. Interpretation: In an 8-week outpatient study, addition of 5 mg daily empagliflozin to commercially available AID or PLGS systems significantly improved daytime glucose control in individuals with T1D, without increased hypoglycemia risk. However, the risk of ketosis and ketoacidosis remains. Therefore, future studies with SGLT2i will need modifications to closed-loop control algorithms to enhance safety.

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.

Better TIR, HbA1c, and less hypoglycemia in closed-loop insulin system in patients with type 1 diabetes: a meta-analysis

The study aimed to evaluate the effectiveness and safety of long-term use of closed-loop insulin system (CLS) in non-pregnant patients with type 1 diabetes mellitus (T1DM) using systematic review and meta-analysis. A literature search was performed using MEDLINE, EMBASE, and the Cochrane Library. Randomized controlled trials (RCTs) on long-term use (not less than 8 weeks) of CLS in patients with T1DM were selected. Meta-analysis was performed with RevMan V.5.3.5 to compare CLS with controls (continuous subcutaneous insulin infusion with blinded continuous glucose monitoring or unblinded sensor-augmented pump therapy or multiple daily injections or predictive low-glucose suspend system) in adults and children with type 1 diabetes. Research quality evaluation was conducted using the Cochrane risk of bias tool. Eleven RCTs (817 patients) that satisfied the eligibility criteria were included in the meta-analysis. Compared with controls, the CLS group had a favorable effect on the proportion of time with sensor glucose level in 3.9-10 mmol/L (10.32%, 8.70% to 11.95%), above 10 mmol/L (-8.89%, -10.57% to -7.22%), or below 3.9 mmol/L (-1.09%, -1.54% to -0.64%) over 24 hours. The CLS group also had lower glycated hemoglobin levels (-0.30%, -0.41% to -0.19%), and glucose variability, coefficient of variation of glucose, and SD were lower by 1.41 (-2.38 to -0.44, p=0.004) and 6.37 mg/dL (-9.19 mg/dL to -3.55 mg/dL, p<0.00001). There were no significant differences between the CLS and the control group in terms of daily insulin dose, quality of life assessment, and satisfaction with diabetes treatment. CLS is a better solution than control treatment in optimizing blood glucose management in patients with T1DM. CLS could become a common means of treating T1DM in clinical practice.

Efficacy and safety of closed-loop insulin delivery versus sensor-augmented pump in the treatment of adults with type 1 diabetes: a systematic review and meta-analysis of randomized-controlled trials

BACKGROUND

Controversy remains regarding whether closed-loop (CL) insulin delivery or insulin sensor-augmented pump (SAP) delivery is more efficient for clinical treatment. Therefore, we aimed to compare the efficacy and safety of CL insulin delivery systems versus insulin SAP delivery for adults with type 1 diabetes (T1D).

METHODS

Embase, Ovid MEDLINE, PubMed, ScienceDirect, Scopus, the Cochrane Library, and other databases were searched for related articles, and we analyzed the average blood glucose (BG), time in range (TIR), and adverse effects (AEs) as primary endpoints to evaluate efficacy and safety.

RESULTS

Of 1616 articles, 12 randomized-controlled trials (RCTs) were included in the final analysis. Regarding BG control efficacy, CL insulin delivery resulted better outcomes than SAP therapy with regard to the average BG value, which was detected and recorded by continuous glucose monitoring (mean difference [MD][mmol/L]:  - 0.25 95% confidence interval [CI]  - 0.42 to - 0.08, p = 0.003); TIR 3.9-10 mmol/L (MD [%]: 7.91 95% CI 4.45-11.37, p < 0.00001). Similar results were observed for the secondary outcomes including low blood glucose index (LBGI) (MD:  - 0.41 95% CI - 0.55 to - 0.26, p < 0.00001), high blood glucose index (HBGI) (MD:  - 2.56 95% CI - 3.38 to - 1.74, p < 0.00001), and standard deviation (SD) of glucose variability (MD [mmol/L]: -0.25 95% CI - 0.44 to - 0.06, p = 0.01). Furthermore, SAP therapy was associated with more adverse effects (risk ratio: 0.20 95% CI 0.07-0.52, p = 0.001) than CL insulin delivery, and one of the most common adverse effects was hypoglycemia.

CONCLUSIONS

CL insulin delivery appears to be a better treatment method than SAP therapy for adults with T1D because of its increased BG control efficacy and decreased number of hypoglycemic events.

Lived Experience of Advanced Hybrid Closed-Loop Versus Hybrid Closed-Loop: Patient-Reported Outcomes and Perspectives

This article reports on the lived experience of Medtronic advanced hybrid closed-loop (AHCL) in comparison to first generation hybrid closed-loop (HCL) in a randomized, open-label, two-period crossover trial. Patient-reported outcome (PROs) measures were administered before randomization and at the end of each study period in 113 adolescents and young adults with type 1 diabetes. Glucose monitoring satisfaction subscales for emotional burden and behavioral burden improved significantly (P < 0.01) over time with use of AHCL versus HCL and co-occurred with glycemic improvements (reduced percent time above 180 mg/dL during the day and no change in % time less than 54 mg/dL across 24 h) and greater time in Auto Mode. PROs, including distress, technology attitudes, and hypoglycemia confidence, were not different. AHCL use was associated with improved glucose monitoring satisfaction. Satisfaction was greater in those participants who had more appreciable glycemic benefit and stayed in Auto Mode more often. Clinical Trial Registration number: NCT03040414.

One Year Real-World Use of the Control-IQ Advanced Hybrid Closed-Loop Technology

Background: The t:slim X2™ insulin pump with Control-IQ^® technology from Tandem Diabetes Care is an advanced hybrid closed-loop system that was first commercialized in the United States in January 2020. Longitudinal glycemic outcomes associated with real-world use of this system have yet to be reported. Methods: A retrospective analysis of Control-IQ technology users who uploaded data to Tandem's t:connect^® web application as of February 11, 2021 was performed. Users age ≥6 years, with >2 weeks of continuous glucose monitoring (CGM) data pre- and >12 months post-Control-IQ technology initiation were included in the analysis. Results: In total 9451 users met the inclusion criteria, 83% had type 1 diabetes, and the rest had type 2 or other forms of diabetes. The mean age was 42.6 ± 20.8 years, and 52% were female. Median percent time in automation was 94.2% [interquartile range, IQR: 90.1%-96.4%] for the entire 12-month duration of observation, with no significant changes over time. Of these users, 9010 (96.8%) had ≥75% of their CGM data available, that is, sufficient data for reliable computation of CGM-based glycemic outcomes. At baseline, median percent time in range (70-180 mg/dL) was 63.6 (IQR: 49.9%-75.6%) and increased to 73.6% (IQR: 64.4%-81.8%) for the 12 months of Control-IQ technology use with no significant changes over time. Median percent time <70 mg/dL remained consistent at ∼1% (IQR: 0.5%-1.9%). Conclusion: In this real-world use analysis, Control-IQ technology retained, and to some extent exceeded, the results obtained in randomized controlled trials, showing glycemic improvements in a broad age range of people with different types of diabetes.

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).

[New Developments in Type 1 Diabetes]

MONITORING

With the increasing prevalence of continuous glucose monitoring (CGM) systems, time in range (TIR) is gaining importance as a parameter for optimization of glycemic control in patients with type 1 diabetes mellitus. Recent studies showed improved prevention of cardiovascular events and pregnancy complications in patients with optimized TIR. In addition to TIR, it is recommended to consider Time below Range (TBR) as well to include an assessment of hypoglycemia.

HYPOGLYCEMIA

Hypoglycemia remains a clinically relevant complication of therapy for type 1 diabetes mellitus. With the approval of nasal glucagon, there is now an alternative to traditional injections as an easy-to-use emergency therapy. With the development of the glucagon analogue Dasiglucagon, which is stable in the liquid state, a classic emergency pen with a ready-to-use solution will also potentially be available in the near future.

INSULIN THERAPY

The new fast-acting insulin aspart (FIASP) offers new opportunities for blood glucose optimization in type 1 diabetes patients. Furthermore, the first hybrid closed-loop system for the treatment of type 1 diabetes mellitus was approved in Germany in 2019. This system automatically adjusts the basal rate based to blood glucose levels measured by CGM. When used adequately, a hybrid closed-loop system allows for improved glycemic control, particularly of nocturnal blood glucose.

COMPLEMENTARY THERAPIES

Since 2019, the SGLT-2 inhibitor dapagliflozin and the combined SGLT-1/2 inhibitor sotagliflozin have been approved for the therapy of inadequately controlled type 1 diabetics with a BMI above 27 kg/m² and no elevated risk of diabetic ketoacidosis. The most relevant side effect is atypical normoglycemic ketoacidosis, which is why initial risk assessment and adequate training of the patient to perform and interpret ketone body and pH measurements during therapy are of central importance.

Sleep and diabetes-specific psycho-behavioral outcomes of a new automated insulin delivery system in young children with type 1 diabetes and their parents

BACKGROUND

Data on the use of Control-IQ, the latest FDA-approved automated insulin delivery (AID) system for people with T1D 6 years of age or older is still scarce, particularly regarding nonglycemic outcomes. Children with T1D and their parents are at higher risk for sleep disturbances. This study assesses sleep, psycho-behavioral and glycemic outcomes of AID compared to sensor-augmented pump therapy (SAP) therapy in young children with T1D and their parents.

METHODS

Thirteen parents and their young children (ages 7-10) on insulin pump therapy were enrolled. Children completed an initial 4-week study with SAP using their own pump and a study CGM followed by a 4-week phase of AID. Sleep outcomes for parents and children were evaluated through actigraphy watches. Several questionnaires were administered at baseline and at the end of each study phase. CGM data were used to assess glycemic outcomes.

RESULTS

Actigraphy data did not show any significant change from SAP to AID, except a reduction of number of parental awakenings during the night (p = 0.036). Parents reported statistically significant improvements in Pittsburgh Sleep Quality Index total score (p = 0.009), Hypoglycemia Fear Survey total score (p = 0.011), diabetes-related distress (p = 0.032), and depression (p = 0.023). While on AID, time in range (70-180 mg/dL) significantly increased compared to SAP (p < 0.001), accompanied by a reduction in hyperglycemia (p = 0.001).

CONCLUSIONS

These results suggest that use of AID has a positive impact on glycemic outcomes in young children as well as sleep and diabetes-specific quality of life outcomes in their parents.

The physiological basis of insulin therapy in people with diabetes mellitus

Insulin therapy has been in use now for 100 years, but only recently insulin replacement has been based on physiology. The pancreas secretes insulin at continuously variable rates, finely regulated by sensitive arterial glucose sensing. Pancreatic insulin is delivered directlyin the portal blood to insulinize preferentially the liver. In the fasting state, insulin is secreted at a low rate to modulate hepatic glucose output. After liver extraction (50%), insulin concentrations in peripheral plasma are 2.4-4 times lower than in portal, but still efficacious to restrain lipolysis. In the prandial condition, insulin is secreted rapidly in large amounts to increase portal and peripheral concentrations to peaks 10-20 times greater vs the values of fasting within 30-40 min from meal ingestion. The prandial portal hyperinsulinemia fully suppresses hepatic glucose production while peripheral hyperinsulinemia increases glucose utilization, thus limitating the post-prandial plasma glucose elevation. Physiology of insulin indicates that insulin should be replaced in people with diabetes mimicking the pancreas, i.e. in a basal-bolus mode, for fasting and prandial state, respectively. Despite the presently ongoing limitations (subcutaneous and peripheral rather than portal and intravenous insulin delivery), basal-bolus insulin allows people with diabetes to achieve A1c in the range with minimal risk of hypoglycaemia, to prevent vascular complications and to ensure good quality of life.

Comparison Between Closed-Loop Insulin Delivery System (the Artificial Pancreas) and Sensor-Augmented Pump Therapy: A Randomized-Controlled Crossover Trial

Objective: Several studies have shown that closed-loop automated insulin delivery (the artificial pancreas) improves glucose control compared with sensor-augmented pump therapy. We aimed to confirm these findings using our automated insulin delivery system based on the iPancreas platform. Research Design and Methods: We conducted a two-center, randomized crossover trial comparing automated insulin delivery with sensor-augmented pump therapy in 36 adults with type 1 diabetes. Each intervention lasted 12 days in outpatient free-living conditions with no remote monitoring. The automated insulin delivery system used a model predictive control algorithm that was a less aggressive version of our earlier dosing algorithm to emphasize safety. The primary outcome was time in the range 3.9-10.0 mmol/L. Results: The automated insulin delivery system was operational 90.2% of the time. Compared with the sensor-augmented pump therapy, automated insulin delivery increased time in range (3.9-10.0 mmol/L) from 61% (interquartile range 53-74) to 69% (60-73; P = 0.006) and increased time in tight target range (3.9-7.8 mmol/L) from 37% (30-49) to 45% (35-51; P = 0.011). Automated insulin delivery also reduced time spent below 3.9 and 3.3 mmol/L from 3.5% (0.8-5.4) to 1.6% (1.1-2.7; P = 0.0021) and from 0.9% (0.2-2.1) to 0.5% (0.2-1.1; P = 0.0122), respectively. Time spent below 2.8 mmol/L was 0.2% (0.0-0.6) with sensor-augmented pump therapy and 0.1% (0.0-0.4; P = 0.155) with automated insulin delivery. Conclusions: Our study confirms findings that automated insulin delivery improves glucose control compared with sensor-augmented pump therapy. ClinicalTrials.gov no. NCT02846831.

Assessing the efficacy, safety and utility of closed-loop insulin delivery compared with sensor-augmented pump therapy in very young children with type 1 diabetes (KidsAP02 study): an open-label, multicentre, multinational, randomised cross-over study protocol

INTRODUCTION

Diabetes management in very young children remains challenging. Glycaemic targets are achieved at the expense of high parental diabetes management burden and frequent hypoglycaemia, impacting quality of life for the whole family. Our objective is to assess whether automated insulin delivery can improve glycaemic control and alleviate the burden of diabetes management in this particular age group.

METHODS AND ANALYSIS

The study adopts an open-label, multinational, multicentre, randomised, crossover design and aims to randomise 72 children aged 1-7 years with type 1 diabetes on insulin pump therapy. Following screening, participants will receive training on study insulin pump and study continuous glucose monitoring devices. Participants will be randomised to 16-week use of the hybrid closed-loop system (intervention period) or to 16-week use of sensor-augmented pump therapy (control period) with 1-4 weeks washout period before crossing over to the other arm. The order of the two study periods will be random. The primary endpoint is the between-group difference in time spent in the target glucose range from 3.9 to 10.0 mmol/L based on sensor glucose readings during the 16-week study periods. Analyses will be conducted on an intention-to-treat basis. Key secondary endpoints are between group differences in time spent above and below target glucose range, glycated haemoglobin and average sensor glucose. Participants' and caregivers' experiences will be evaluated using questionnaires and qualitative interviews, and sleep quality will be assessed. A health economic analysis will be performed.

ETHICS AND DISSEMINATION

Ethics approval has been obtained from Cambridge East Research Ethics Committee (UK), Ethics Committees of the University of Innsbruck, the University of Vienna and the University of Graz (Austria), Ethics Committee of the Medical Faculty of the University of Leipzig (Germany) and Comité National d'Ethique de Recherche (Luxembourg). The results will be disseminated by peer-reviewed publications and conference presentations.

TRIAL REGISTRATION NUMBER

NCT03784027.

Glycemic Outcomes of Use of CLC Versus PLGS in Type 1 Diabetes: A Randomized Controlled Trial

OBJECTIVE

Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system.

RESEARCH DESIGN AND METHODS

After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14-72 years; mean HbA_1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70-180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study.

RESULTS

All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P < 0.001). Time >180 mg/dL was lower in the CLC group than PLGS group (difference = -6.0%; 95% CI -8.4%, -3.7%; P < 0.001) while time <54 mg/dL was similar (0.04%; 95% CI -0.05%, 0.13%; P = 0.41). HbA_1c after 13 weeks was lower on CLC than PLGS (7.2% [55 mmol/mol] vs. 7.5% [56 mmol/mol], difference -0.34% [-3.7 mmol/mol]; 95% CI -0.57% [-6.2 mmol/mol], -0.11% [1.2 mmol/mol]; P = 0.0035).

CONCLUSIONS

Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA_1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS.

Closed-loop control in insulin pumps for type-1 diabetes mellitus: safety and efficacy

INTRODUCTION

Type 1 diabetes is a lifelong disease with high management burden. The majority of people with type 1 diabetes fail to achieve glycemic targets. Algorithm-driven automated insulin delivery (closed-loop) systems aim to address these challenges. This review provides an overview of commercial and emerging closed-loop systems.

AREAS COVERED

We review safety and efficacy of commercial and emerging hybrid closed-loop systems. A literature search was conducted and clinical trials using day-and-night closed-loop systems during free-living conditions were used to report on safety data. We comment on efficacy where robust randomized controlled trial data for a particular system are available. We highlight similarities and differences between commercial systems.

EXPERT OPINION

Study data shows that hybrid closed-loop systems are safe and effective, consistently improving glycemic control when compared to standard therapy. While a fully closed-loop system with minimal burden remains the end-goal, these hybrid closed-loop systems have transformative potential in diabetes care.

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes

Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.