Updated Software for Automated Assessment of Glucose Variability and Quality of Glycemic Control in Diabetes

Glycemic response to meals with a high glycemic index differs between morning and evening: a randomized cross-over controlled trial among students with early or late chronotype

PURPOSE

Glycemic response to the same meal depends on daytime and alignment of consumption with the inner clock, which has not been examined by individual chronotype yet. This study examined whether the 2-h postprandial and 24-h glycemic response to a meal with high glycemic index (GI) differ when consumed early or late in the day among students with early or late chronotype.

METHODS

From a screening of 327 students aged 18-25 years, those with early (n = 22) or late (n = 23) chronotype participated in a 7-day randomized controlled cross-over intervention study. After a 3-day observational phase, standardized meals were provided on run-in/washout (days 4 and 6) and intervention (days 5 and 7), on which participants received a high GI meal (GI = 72) in the morning (7 a.m.) or in the evening (8 p.m.). All other meals had a medium GI. Continuous glucose monitoring was used to measure 2-h postprandial and 24-h glycemic responses and their variability.

RESULTS

Among students with early chronotype 2-h postprandial glucose responses to the high GI meal were higher in the evening than in the morning (iAUC: 234 (± 92) vs. 195 (± 91) (mmol/L) × min, p = 0.042). Likewise, mean and lowest 2-h postprandial glucose values were higher when the high GI meal was consumed in the evening (p < 0.001; p = 0.017). 24-h glycemic responses were similar irrespective of meal time. Participants with late chronotype consuming a high GI meal in the morning or evening showed similar 2-h postprandial (iAUC: 211 (± 110) vs. 207 (± 95) (mmol/L) × min, p = 0.9) and 24-h glycemic responses at both daytimes.

CONCLUSIONS

Diurnal differences in response to a high GI meal are confined to those young adults with early chronotype, whilst those with a late chronotype seem vulnerable to both very early and late high GI meals. Registered at clinicaltrials.gov (NCT04298645; 22/01/2020).

What Is the Influence of HIV Serostatus on HbA1c? A Prospective Analysis Using Continuous Glucose Monitoring

OBJECTIVE

Type 2 diabetes (T2D) is reported to be more common in people living with HIV (PLWH). Clinical guidelines recommend screening for diabetes in PLWH, but there is no agreed method due to studies reporting HbA1c is falsely low in PLWH. These studies were performed in the early HIV era when participants were taking older preparations of antiretroviral therapy that are rarely used today. We aimed to investigate whether HIV serostatus influences HbA1c.

RESEARCH DESIGNS AND METHODS

We conducted a prospective cohort study of PLWH and age- and sex-matched HIV-negative participants who were purposely recruited from clinics in Brighton, U.K. Each participant wore a Dexcom G6 continuous glucose monitor (CGM) for up to 10 days, had glucose measured during an oral glucose tolerance test, and fructosamine and paired HbA1c were measured. We performed regression analysis to assess the influence of HIV on HbA1c and used a separate model for CGM glucose, venous glucose, and fructosamine. In addition, we included predictor variables used in previous studies that explored HbA1c discrepancy.

RESULTS

We recruited 60 PLWH (90% men, 50% with T2D, mean ± SD age 57 ± 10.7 years, 100% undetectable viral load) and 48 people without HIV (92% men, 30% with T2D, mean age 57.7 ± 8.9 years). We found that HIV serostatus did not have a significant influence on HbA1c within the regression models.

CONCLUSIONS

We performed a comprehensive assessment of glycemia to assess whether HIV serostatus influences HbA1c. We did not find any strong evidence that HIV serostatus influenced HbA1c. The results of our study support incorporating HbA1c into routine clinical blood work in PLWH.

An Update on the iglu Software Package for Interpreting Continuous Glucose Monitoring Data

Background: Continuous glucose monitors (CGMs) are increasingly used to provide detailed quantification of glycemic control and glucose variability. An open-source R package iglu has been developed to assist with automatic CGM metrics computation and data visualization, providing a comprehensive list of implemented CGM metrics. Motivated by the recent international consensus statement on CGM metrics and recommendations from recent reviews of available CGM software, we present an updated version of iglu with improved accessibility and expanded functionality. Methods: The functionality was expanded to include automated computation of hypo- and hyperglycemia episodes with corresponding visualizations, composite metrics of glycemic control (glycemia risk index and personal glycemic state), and glycemic metrics associated with postprandial excursions. The algorithm for mean amplitude of glycemic excursions has been updated for improved accuracy, and the corresponding visualization has been added. Automated hierarchical clustering capabilities have been added to facilitate statistical analysis. Accessibility was improved by providing support for the automatic processing of common data formats, expanding the graphical user interface, and providing mirrored functionality in Python. Results: The updated version of iglu has been released to the Comprehensive R Archive Network (CRAN) as version 4. The corresponding Python wrapper has been released to the Python Package Index (PyPI) as version 1. The new functionality has been demonstrated using CGM data from 19 subjects with prediabetes and type 2 diabetes. Conclusions: An updated version of iglu provides comprehensive and accessible software for analyses of CGM data that meets the needs of researchers with varying levels of programming experience. It is freely available on CRAN and on GitHub at https://github.com/irinagain/iglu.

Increased glycemic variability in pregnant women with Roux-en-Y gastric bypass compared with sleeve gastrectomy

INTRODUCTION

Bariatric surgery is associated with adverse pregnancy outcomes such as reduced birth weight and premature birth. One possible mechanism for this is increased glycemic variability (GV) which occurs after bariatric surgery. The objective of this study was to compare the effect of Roux-en-Y gastric bypass (RYGB) versus vertical sleeve gastrectomy (SG) on GV during pregnancy and to investigate the relationships of GV, type of bariatric surgery and maternal and neonatal outcomes.

RESEARCH DESIGN AND METHODS

Fourteen pregnant women after RYGB and 14 after SG were investigated with continuous glucose monitoring in their second or third trimester in this observational study carried out as part of routine clinical care.

RESULTS

Pregnant women with RYGB had similar mean interstitial glucose values but significantly increased indices of GV and a lower %time in range 3.9-7.8 mmol/L (70-140 mg/dL), compared with SG.

CONCLUSIONS

Pregnant women who have undergone RYGB have greater GV during pregnancy compared with those who have undergone SG. Further research is needed to establish the relationship between GV and pregnancy outcomes to determine the preferred bariatric operation in women of reproductive age, and whether interventions to reduce GV might improve outcomes.

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

BACKGROUND

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

METHODS

A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

RESULTS

A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

CONCLUSION

This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

Associations of predominant night-eating with plasma glycemic status and continuous glucose monitoring measures among pregnant women

BACKGROUND & AIMS

To examine whether predominant night-eating, defined as more than 50% of total daily energy intake consumed between 1900 and 0659 h, is associated with glycemic outcomes in pregnancy.

METHODS

This was a prospective cohort study of 277 healthy pregnant women with complete 4-day dietary intake records at 18-24 weeks gestation, recruited from KK Women's and Children's Hospital, Singapore. Primary outcomes were fasting, 1-h, and 2-h plasma glucose after a 75-g oral glucose tolerance test at 24-28 weeks gestation. Secondary outcomes were gestational diabetes mellitus (GDM), fasting insulin, homeostasis model assessment of insulin resistance (HOMA2-IR), β-cell function (HOMA2-%B), and continuous glucose monitoring (CGM) measures. Glucose variables in continuous form were loge-transformed before analyses.

RESULTS

Predominant night-eating (11.6%) was associated with higher fasting glucose (geometric mean ratio (95% confidence interval) 1.05 (1.01, 1.08)) and 1-h glucose (1.11 (1.01, 1.21)), but not with 2-h glucose or GDM risk. Predominant night-eating women had lower fasting insulin (0.77 (0.63, 0.95)), lower HOMA2-IR (0.78 (0.64, 0.97)), and lower HOMA2-%B (0.77 (0.67, 0.89)) than their predominant day-eating counterparts. For CGM measures, predominant night-eating was associated with higher mean glucose (1.07 (1.00, 1.15)), higher glucose management indicator (1.05 (1.00, 1.10)), and higher overall glucose levels throughout 24 h (1.10 (1.02, 1.19)). All these associations were adjusted for socio-demographic, lifestyle factors, and diet composition.

CONCLUSION

Predominant night-eating was mainly associated with less desirable glycemic outcomes during pregnancy. Future studies should explore dietary interventions aimed at reducing consumption of relatively more calories at night than day during pregnancy.

Accuracy and impact on quality of life of real-time continuous glucose monitoring in children with hyperinsulinaemic hypoglycaemia

Objective

Continuous glucose monitoring (CGM) is the standard of care for glucose monitoring in children with diabetes, however there are limited data reporting their use in hyperinsulinaemic hypoglycaemia (HH). Here, we evaluate CGM accuracy and its impact on quality of life in children with HH.

Methods

Real-time CGM (Dexcom G5 and G6) was used in children with HH aged 0-16years. Data from self-monitoring capillary blood glucose (CBG) and CGM were collected over a period of up to 28days and analysed. Quality of life was assessed by the PedsQL4.0 general module and PedsQL2.0 family impact module, completed by children and their parents/carers before and after CGM insertion. Analysis of accuracy metrics included mean absolute relative difference (MARD) and proportion of CGM values within 15, 20, and 30% or 15, 20, and 30 mg/dL of reference glucose values >100 mg/dL or ≤100 mg/dL, respectively (% 15/15, % 20/20, % 30/30). Clinical reliability was assessed with Clarke error grid (CEG) analyses.

Results

Prospective longitudinal study with data analysed from 40 children. The overall MARD between reference glucose and paired CGM values (n=4,928) was 13.0% (Dexcom G5 12.8%, Dexcom G6 13.1%). The proportion of readings meeting %15/15 and %20/20 were 77.3% and 86.4%, respectively, with CEG analysis demonstrating 97.4% of all values in zones A and B. Within the hypoglycaemia range (<70 mg/dL), the median ARD was 11.4% with a sensitivity and specificity of 64.2% and 91.3%, respectively. Overall PedsQL child report at baseline and endpoint were 57.6 (50.5 - 75.8) and 87.0 (82.9 - 91.2), and for parents were 60.3 (44.8 - 66.0) and 85.3 (83.7 - 91.3), respectively (both p<0.001).

Conclusion

Use of CGM for children with HH is feasible, with clinically acceptable accuracy, particularly in the hypoglycaemic range. Quality of life measures demonstrate significant improvement after CGM use. These data are important to explore use of CGM in disease indications, including neonatal and paediatric diabetes, cystic fibrosis and glycogen storage disorders.

Continuous glucose monitoring in patients with post-bariatric hypoglycaemia reduces hypoglycaemia and glycaemic variability

AIM

To determine whether continuous glucose monitoring (CGM) can reduce hypoglycaemia in patients with post-bariatric hypoglycaemia (PBH).

MATERIALS AND METHODS

In an open-label, nonrandomized, pre-post design with sequential assignment, CGM data were collected in 22 individuals with PBH in two sequential phases: (i) masked (no access to sensor glucose or alarms); and (ii) unmasked (access to sensor glucose and alarms for low or rapidly declining sensor glucose). Twelve participants wore the Dexcom G4 device for a total of 28 days, while 10 wore the Dexcom G6 device for a total of 20 days.

RESULTS

Participants with PBH spent a lower percentage of time in hypoglycaemia over 24 hours with unmasked versus masked CGM (<3.3 mM/L, or <60 mg/dL: median [median absolute deviation {MAD}] 0.7 [0.8]% vs. 1.4 [1.7]%, P = 0.03; <3.9 mM/L, or <70 mg/dL: median [MAD] 2.9 [2.5]% vs. 4.7 [4.8]%; P = 0.04), with similar trends overnight. Sensor glucose data from the unmasked phase showed a greater percentage of time spent between 3.9 and 10 mM/L (70-180 mg/dL) (median [MAD] 94.8 [3.9]% vs. 90.8 [5.2]%; P = 0.004) and lower glycaemic variability over 24 hours (median [MAD] mean amplitude of glycaemic excursion 4.1 [0.98] vs. 4.4 [0.99] mM/L; P = 0.04). During the day, participants also spent a greater percentage of time in normoglycaemia with unmasked CGM (median [MAD] 94.2 [4.8]% vs. 90.9 [6.2]%; P = 0.005), largely due to a reduction in hyperglycaemia (>10 mM/L, or 180 mg/dL: median [MAD] 1.9 [2.2]% vs. 3.9 [3.6]%; P = 0.02).

CONCLUSIONS

Real-time CGM data and alarms are associated with reductions in low sensor glucose, elevated sensor glucose, and glycaemic variability. This suggests CGM allows patients to detect hyperglycaemic peaks and imminent hypoglycaemia, allowing dietary modification and self-treatment to reduce hypoglycaemia. The use of CGM devices may improve safety in PBH, particularly for patients with hypoglycaemia unawareness.

Long-Term Glucose Forecasting for Open-Source Automated Insulin Delivery Systems: A Machine Learning Study with Real-World Variability Analysis

Glucose forecasting serves as a backbone for several healthcare applications, including real-time insulin dosing in people with diabetes and physical activity optimization. This paper presents a study on the use of machine learning (ML) and deep learning (DL) methods for predicting glucose variability (GV) in individuals with open-source automated insulin delivery systems (AID). A three-stage experimental framework is employed in this work to systematically implement and evaluate ML/DL methods on a large-scale diabetes dataset collected from individuals with open-source AID. The first stage involves data collection, the second stage involves data preparation and exploratory analysis, and the third stage involves developing, fine-tuning, and evaluating ML/DL models. The performance and resource costs of the models are evaluated alongside relative and proportional errors for 17 GV metrics. Evaluation of fine-tuned ML/DL models shows considerable accuracy in glucose forecasting and variability analysis up to 48 h in advance. The average MAE ranges from 2.50 mg/dL for long short-term memory models (LSTM) to 4.94 mg/dL for autoregressive integrated moving average (ARIMA) models, and the RMSE ranges from 3.7 mg/dL for LSTM to 7.67 mg/dL for ARIMA. Model execution time is proportional to the amount of data used for training, with long short-term memory models having the lowest execution time but the highest memory consumption compared to other models. This work successfully incorporates the use of appropriate programming frameworks, concurrency-enhancing tools, and resource and storage cost estimators to encourage the sustainable use of ML/DL in real-world AID systems.

AGATA: A Toolbox for Automated Glucose Data Analysis

BACKGROUND

Analyzing continuous glucose monitoring (CGM) data is a mandatory step for multiple purposes spanning from reporting clinical trial outcomes to developing new algorithms for diabetes management. This task is repetitive, and scientists struggle in computing literature glucose control metrics and waste time in reproducing possibly complex plots and reports. For this reason, to provide the diabetes technology community a unified tool, here we present Automated Glucose dATa Analysis (AGATA), an automated glucose data analysis toolbox developed in MATLAB/Octave.

METHODS

Automated Glucose dATa Analysis is an open-source software program to visualize and preprocess CGM data, compute glucose control metrics, detect adverse events, evaluate the effectiveness of users' prediction algorithms, and compare study arms. Automated Glucose dATa Analysis can be used as a standalone computer application accessible through a dedicated graphical user interface, particularly suitable for clinicians, or by integrating its functionalities in user-defined MATLAB/Octave scripts, which fits the need of researchers and developers. To demonstrate its features, we used AGATA to analyze CGM data of two subjects extracted from a publicly available data set of individuals with type one diabetes. Finally, AGATA's features are compared against those of 12 noncommercial software programs for CGM data analysis.

RESULTS

Using AGATA, we easily preprocessed, analyzed, and visualized CGM data in a handy way, in compliance with the requirements and the standards defined in the literature. Compared to the other considered software programs, AGATA offers more functionalities and capabilities.

CONCLUSION

Automated Glucose dATa Analysis is easy to use and reduces the burden of CGM data analysis. It is freely available in GitHub at https://github.com/gcappon/agata.

Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data

The advancement of technology in the field of glycemic control has led to the widespread use of continuous glucose monitoring (CGM), which can be nowadays obtained from wearable devices equipped with a minimally invasive sensor, that is, transcutaneous needle type or implantable, and a transmitter that sends information to a receiver or smart device for data storage and display. This work aims to review the currently available software packages and tools for the analysis of CGM data. Based on the purposes of this work, 12 software packages have been identified from the literature, published until December 2021, namely: GlyCulator, EasyGV (Easy Glycemic Variability), CGM-GUIDE^© (Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation), GVAP (Glycemic Variability Analyzer Program), Tidepool, CGManalyzer, cgmanalysis, GLU, CGMStatsAnalyser, iglu, rGV, and cgmquantify. Comparison of available software packages and tools has been done in terms of main characteristics (i.e., publication year, presence of a graphical user interface, availability, open-source code, number of citations, programming language, supported devices, supported data format and organization of the data structure, documentation, presence of a toy example, video tutorial, data upload and download, measurement-units conversion), preprocessing procedures, data display options, and computed metrics; also, each of the computed metrics has been analyzed in terms of its adherence to the American Diabetes Association (ADA) 2017 international consensus on CGM data analysis and the ADA 2019 international consensus on time in range. Eventually, the agreement between metrics computed by different software and tools has been investigated. Based on such comparison, usability and complexity of data management, as well as the possibility to perform customized or patients-group analyses, have been discussed by highlighting limitations and strengths, also in relation to possible different user categories (i.e., patients, clinicians, researchers). The information provided could be useful to researchers interested in working in the diabetic research field as to clinicians and endocrinologists who need tools capable of handling CGM data effectively.

A New Analysis Tool for Continuous Glucose Monitor Data

BACKGROUND

With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools.

METHODS

In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual's CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies.

RESULTS

In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability.

CONCLUSIONS

We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

Large-Scale Data Analysis for Glucose Variability Outcomes with Open-Source Automated Insulin Delivery Systems

Open-source automated insulin delivery (AID) technologies use the latest continuous glucose monitors (CGM), insulin pumps, and algorithms to automate insulin delivery for effective diabetes management. Early community-wide adoption of open-source AID, such as OpenAPS, has motivated clinical and research communities to understand and evaluate glucose-related outcomes of such user-driven innovation. Initial OpenAPS studies include retrospective studies assessing high-level outcomes of average glucose levels and HbA1c, without in-depth analysis of glucose variability (GV). The OpenAPS Data Commons dataset, donated to by open-source AID users with insulin-requiring diabetes, is the largest freely available diabetes-related dataset with over 46,070 days’ worth of data and over 10 million CGM data points, alongside insulin dosing and algorithmic decision data. This paper first reviews the development toward the latest open-source AID and the performance of clinically approved GV metrics. We evaluate the GV outcomes using large-scale data analytics for the n = 122 version of the OpenAPS Data Commons. We describe the data cleaning processes, methods for measuring GV, and the results of data analysis based on individual self-reported demographics. Furthermore, we highlight the lessons learned from the GV outcomes and the analysis of a rich and complex diabetes dataset and additional research questions that emerged from this work to guide future research. This paper affirms previous studies’ findings of the efficacy of open-source AID.

Towards targeted dietary support for shift workers with type 2 diabetes (Shift-Diabetes study): A mixed-methods case study protocol

BACKGROUND

Blood glucose is higher in people working night shifts compared to day workers. Changes to eating behaviour, activity and sleep patterns in addition to circadian disruption are likely to impact glucose management in night-shift workers with type 2 diabetes.

AIM

To investigate current dietary intake and glucose variability during night work, including barriers and facilitators to dietary behaviour in this context.

METHODS

A mixed-methods case study will be conducted. Shift workers with type 2 diabetes working in a hospital setting will be recruited to this two-part study. Part 1: 70 participants will complete a 10-day observational study collecting data on continuous glucose, diet (self-report diary), sleep and physical activity during a period covering night work, rest days and non-night workdays. Mean glucose concentration and variability, and the mean healthy diet index score, will be compared between days of night work, non-night work and rest, after adjusting for other individual factors (sleep/physical activity/demographics). Part 2: A sample (n~13) will complete semi-structured interviews based on behavioural science frameworks to explore barriers/enablers to dietary behaviour when working night shifts. This will inform a quantitative survey to explore the generalisability of interview findings.

DISCUSSION

Findings from Part 1 and 2 will be triangulated to identify potential intervention strategies to address key barriers and enablers to healthier eating, and in turn improved glucose control, in shift workers with type 2 diabetes. This will be facilitated through stakeholder consultation and application of behavioural science frameworks. Shift-Diabetes study registration: ISRCTN11764942.

Comparison of Cgmanalysis, a Free Open-Source Continuous Glucose Monitoring Data Management and Analysis Software, with Commercially Available CGM Platforms: Data Standardization for Diabetes Technology Research

Background: Cgmanalysis is an open-source software based on the R programming language for data management and descriptive analysis of data from continuous glucose monitoring (CGM). We sought to validate the summary measures calculated by cgmanalysis against the results from proprietary software associated with four CGM commercially available models. Methods: Two weeks of data from 188 patients with type 1 diabetes using commercially available CGMs. Freestyle Libre Gen 1 (n = 53), Medtronic Guardian 3 (n = 52), Dexcom G6 reported by Dexcom Clarity (n = 48), and Dexcom G6 reported by Tandem (n = 35) were analyzed using proprietary software and cgmanalysis. Agreement was assessed using scatterplots, Bland-Altman plots, and equivalence tests. Results: Good agreement was obtained for all glycemic summary measures for all CGMs assessed. None of the differences between the cgmanalysis package and the manufacturers' software were outside the prespecified bounds of equivalence. Conclusions: Cgmanalysis is a validated open-source software to analyze commercially available CGM data and can be used to standardize diabetes technology research.

Switching from Insulin Degludec plus Dipeptidyl Peptidase-4 Inhibitor to Insulin Degludec/Liraglutide Improves Glycemic Variability in Patients with Type 2 Diabetes: A Preliminary Prospective Observation Study

Incretins reduce glycemic variability (GV) in patients with type 2 diabetes, but it is unknown whether switching from a combination of basal insulin and a DPP-4 inhibitor to insulin degludec/liraglutide (IDegLira) improves GV. We performed an exploratory prospective observational study to compare the effect of IDegLira and the combination on GV. We recruited hospitalized patients with type 2 diabetes who had stable glycemic control with insulin degludec (≤16 units/day) and taking a DPP-4 inhibitor. GV was analyzed using continuous glucose monitoring (CGM) before and after switching the medication to IDegLira. The principal endpoint was the change in mean amplitude of glycemic excursions (MAGE). Other indices of GV and CGM parameters were analyzed as the secondary endpoints. Fifteen participants were enrolled and 12 completed the study. In these participants, the DPP-4 inhibitor and insulin degludec were discontinued, and the equivalent dose of IDegLira was commenced. Switching to IDegLira significantly improved MAGE from 74.9 (60.3, 97.7) mg/dL to 64.8 (52.0, 78.2) mg/dL (P < 0.05), as well as other indices of GV and 24-hour mean blood glucose concentration. Analysis of the ambulatory glucose profile showed marked reductions in postprandial glucose concentration. Nocturnal glucose concentration was similar under the two treatment regimens. IDegLira improved GV as well as the mean and the postprandial glucose concentration by switching from insulin degludec plus DPP-4 inhibitor combination. IDegLira might be beneficial for patients being treated with low-dose basal insulin.

Improved glycaemia during the Covid-19 pandemic lockdown is sustained post-lockdown and during the "Eat Out to Help Out" Government Scheme, in adults with Type 1 diabetes in the United Kingdom

AIMS

The majority of studies report that the Covid-19 pandemic lockdown did not have a detrimental effect on glycaemia. We sought to explore the impact of lockdown on glycaemia and whether this is sustained following easing of restrictions.

METHODS

Retrospective, observational analysis in adults and children with type 1 diabetes attending a UK specialist centre, using real-time or intermittently scanned continuous glucose monitoring. Data from the following 28-day time periods were collected: (i) pre-lockdown; (ii) during lockdown; (iii) immediately after lockdown; and (iv) a month following relaxation of restrictions (coinciding with Government-subsidised restaurant food). Data were analysed for times in glycaemic ranges and are expressed as median (IQR).

RESULTS

145 adults aged 35.5 (25.8-51.3) years with diabetes duration of 19.0 (7.0-29.0) years on multiple daily injections of insulin (60%) and continuous insulin infusion (40%) were included. In adults, % time in range (70-180mg/dL) increased during lockdown (60.2 (45.2-69.3)%) compared to pre-lockdown (56.7 (43.5-65.3)%; p<0.001). This was maintained in the post-lockdown time periods. Similarly, % time above range (>180mg/dL) reduced in lockdown compared to pre-lockdown (p = 0.01), which was sustained thereafter. In children, no significant changes to glycaemia were observed during lockdown. In multivariable analysis, a greater increase in %TIR 3.9-10mmol/L (70-180mg/dL) during lockdown was associated with higher levels of deprivation (coefficient: 4.208, 95% CI 0.588 to 7.828; p = 0.02).

CONCLUSIONS

Glycaemia in adults improved during lockdown, with people from more deprived areas most likely to benefit. This effect was sustained after easing of restrictions, with government-subsidised restaurant eating having no adverse impact on glycaemia.

Safety and Feasibility of the PEPPER Adaptive Bolus Advisor and Safety System: A Randomized Control Study

Background: The Patient Empowerment through Predictive Personalized Decision Support (PEPPER) system provides personalized bolus advice for people with type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system, which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalized carbohydrate recommendations, and dynamic bolus insulin constraint. We evaluated the safety and efficacy of the PEPPER system compared to a standard bolus calculator. Methods: This was an open-labeled multicenter randomized controlled crossover study. Following 4-week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12 weeks. Participants then crossed over after a washout period. The primary end-point was percentage time in range (TIR, 3.9-10.0 mmol/L [70-180 mg/dL]). Secondary outcomes included glycemic variability, quality of life, and outcomes on the safety system and insulin recommender. Results: Fifty-four participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3-49.8) years, diabetes duration 21.0 (11.5-26.0) years, and HbA1c 61.0 (58.0-66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 [52.1-67.8] % vs. 58.4 [49.6-64.3] %, respectively, P = 0.27). For quality of life, participants reported higher perceived hypoglycemia with the PEPPER system despite no objective difference in time spent in hypoglycemia. Conclusions: The PEPPER system was safe, but did not change glycemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confirm overall effectiveness. Clinical trial registration: ClinicalTrials.gov NCT03849755.

Roux-en-Y Gastric Bypass Increases Glycemic Variability and Time in Hypoglycemia in Patients With Obesity and Prediabetes or Type 2 Diabetes: A Prospective Cohort Study

OBJECTIVE

Roux-en-Y gastric bypass (RYGB) is an established treatment for type 2 diabetes and obesity. The study objective was to establish RYGB's effects on glycemic variability (GV) and hypoglycemia.

RESEARCH DESIGN AND METHODS

This was a prospective observational study of 10 participants with obesity and prediabetes or type 2 diabetes who underwent RYGB. Patients were studied before RYGB (Pre) and 1 month, 1 year, and 2 years postsurgery with continuous glucose measurement (CGM). A mixed-meal test (MMT) was conducted at Pre, 1 month, and 1 year.

RESULTS

After RYGB, mean CGM decreased (at 1 month, 1 year, and 2 years), and GV increased (at 1 year and 2 years). Five of the 10 participants had a percent time in range (%TIR) <3.0 mmol/L (54 mg/dL) greater than the international consensus target of 1% at 1 or 2 years. Peak glucagon-like peptide-1 (GLP-1) and glucagon area under the curve during MMT were positively and negatively associated, respectively, with contemporaneous %TIR <3.0 mmol/L.

CONCLUSIONS

Patients undergoing RYGB are at risk for development of postbariatric hypoglycemia due to a combination of reduced mean glucose, increased GV, and increased GLP-1 response.