GlyCulator 3.0: A Fast, Easy-to-Use Analytical Tool for CGM Data Analysis, Aggregation, Center Benchmarking, and Data Sharing

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.

Comparison of Computational Statistical Packages for the Analysis of Continuous Glucose Monitoring Data with a Reference Software, "Ambulatory Glucose Profile," in Type 1 Diabetes

Objective: To compare the accuracy of commonly used continuous glucose monitoring (CGM) analysis programs with ambulatory glucose profile (AGP) and Dexcom Clarity (DC) in analyzing CGM metrics in patients with type 1 diabetes (T1D). Research Methods: CGM data up to 90 days from 152 adults using the same CGM and automated insulin delivery system with T1D were collected. Six of the 19 CGM analysis programs (CDGA, cgmanalysis, Glyculator, iglu, EasyGV, and GLU) were selected to compare with AGP and DC. Metrics were compared etween all tools with two one-sided t-tests equivalence testing. For the equivalence test, the acceptable range of deviation was set as ±2 mg/dL for mean glucose, ±2% for time in range (TIR), ±1% for time above range (TAR), time above range level 1 (TAR1), time above range level 2 (TAR2), and coefficient of variation (CV). Results: All packages were compared with each other for all CGM metrics, and most of them had statistically significant differences for at least some metrics. All tools were equivalent to AGP for mean glucose, TIR, TAR, TAR1, and TAR2 within ±2 mg/dL, ±2%, ±1%, ±1% and 1%, respectively. CDGA, Glyculator, cgmanalysis, and iglu were not equivalent to AGP for CV within ±1%. All tools were equivalent to DC for mean glucose, TIR, and TAR2 within ±2 mg/dL, ±2%, and ±1%, respectively. Glyculator was not equivalent for TAR1, TAR, and CV. CGDA, cgmanalysis, and iglu were not equivalent to DC for TAR1 and TAR. EasyGV and GLU were not equivalent for TAR within ±1%. Conclusions: CGM analysis programs reported CGM metrics statistically differently, but these differences may not be applicable in clinical practice. The equivalence test also confirmed that the differences are negligible for TIR and mean glucose, while they can be important for hyperglycemic ranges and CV. A standardization for CGM data handling and analysis is necessary for clinical studies reporting CGM-generated outcomes.

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.

Novel Protocol for the Use of Advanced Hybrid Closed-Loop System in Adolescents Engaged in Contact Sports

INTRODUCTION

Managing exercise remains challenging for adolescent athletes with type 1 diabetes (T1D), especially in contact sports. Even the use of hybrid closed loops can cause problems due to the need to disconnect the pump during some training or competitions. This study evaluated the efficacy of a novel protocol for the use of an advanced hybrid closed-loop system in adolescent football players with T1D during a sports camp.

METHODS

Eleven boys aged 14.9 years (25-75th percentile: 14-15.5), with a diabetes duration of 5.7 years (5.2-7) and regular training schedules in junior football leagues, participated in the study. They started AHCL (MiniMed780G, Medtronic) therapy a month before a week-long sports camp and were observed during the sports camp and the preceding week. Daily camp activities included two 1.5-h training sessions. Protocol included a 90-min temporary target of 150 mg/dL before and insulin pump disconnection during training. Physical activity was tracked using wGT3X-BT Actigraph monitors.

RESULTS

The camp provided conditions of demanding physical activity (6.6 [6-6.9] h/day of moderate-to-vigorous intensity). After starting AHCL, the average participant time spent in the target glucose range (70-180 mg/dL) was 79.34 ± 8.46%, and no significant change was observed during the camp (mean difference +0.79 ± 8.24%, p = 0.7581). Median glucose levels dropped by 10.91 ± 12.08 mg/dL (p = 0.0134), and time in the tight target range increased by 11.41 ± 11.60% (p = 0.0008) without increasing the time below range (<70 mg/dL) or glycemic variability. During the camp, daily insulin dose and basal/bolus ratio remained comparable with baseline, but the relative amount of automated bolus insulin decreased by 14.24 ± 4.65% (p < 0.0001).

CONCLUSION

The predefined regimen, including a temporary target before and disconnection of AHCL during football training, was safe and may provide satisfactory glucose control in active adolescents with T1D. This protocol could be adapted for use in other intensive contact sports.

Continuous glycemic monitoring in managing diabetes in adult patients with wolfram syndrome

AIMS

In this study we evaluated the use of Continuous Glucose Monitoring system in adults with insulin-dependent diabetes in the course of Wolfram syndrome (WFS) in comparison to patients with type 1 diabetes (T1D).

METHODS

Individuals with WFS (N = 10) used continuous glucose monitoring for 14 days and were compared with 30 patients with T1D matched using propensity score for age and diabetes duration. Glycemic variability was calculated with Glyculator 3.0.

RESULTS

We revealed significant differences in glycemic indices between adults with Wolfram syndrome-related diabetes and matched comparison group. Patients with Wolfram syndrome presented lower mean glucose in 24-h and nighttime records [24h: 141.1 ± 30.4mg/dl (N = 10) vs 164.9 ± 31.3mg/dl (N = 30), p = 0.0427; nighttime: 136.7 ± 39.6mg/dl vs 166.2 ± 32.1mg/dl (N = 30), p = 0.0442]. Moreover, they showed lower standard deviation of sensor glucose over all periods [24h: 50.3 ± 9.2mg/dl (N = 10) vs 67.7 ± 18.7 mg/dl (N = 30), p = 0.0075; daytime: 50.8 ± 8.7mg/dl (N = 10) vs 67.4 ± 18.0mg/dl (N = 30), p = 0.0082; nighttime: 45.1 ± 14.9mg/dl (N = 10) vs 65.8 ± 23.2mg/dl (n = 30), p = 0.0119] and coefficient of variation at night [33.3 ± 5.8% (N = 10) vs 40.5 ± 8.8% (N = 30), p = 0.0210]. Additionally, WFS patients displayed lower time in high-range hyperglycemia (> 250mg/dl) across all parts of day [24h: 4.6 ± 3.8% (N = 10) vs 13.4 ± 10.5% (N = 30), p = 0.0004; daytime: 4.7 ± 3.9% (N = 10) vs 13.8 ± 11.2% (N = 30), p = 0.0005; nighttime: 4.2 ± 5.5% (N = 10) vs 12.1 ± 10.3% (N = 30), p = 0.0272].

CONCLUSIONS

Adult patients with Wolfram syndrome show lower mean blood glucose, less extreme hyperglycemia, and lower glycemic variability in comparison to patients with type 1 diabetes.

Glycemic variability is associated with sural nerve conduction velocity in outpatients with type 2 diabetes: Usefulness of a new point-of-care device for nerve conduction studies

AIMS/INTRODUCTION

Although several studies have shown the association between continuous glucose monitoring (CGM)-derived glycemic variability (GV) and diabetic peripheral neuropathy, no studies have focused on outpatients or used NC-stat®/DPNCheck™, a new point-of-care device for nerve conduction study (NCS). We investigated the association between CGM-derived GV and NCS using DPNCheck™ in outpatients with type 2 diabetes, and further analyzed the difference in results between patients with and without well-controlled HbA1c levels.

MATERIALS AND METHODS

All outpatients with type 2 diabetes using the CGM device (FreeStyle Libre Pro®) between 2017 and 2022 were investigated. Sural nerve conduction was evaluated by sensory nerve action potential (SNAP) amplitude and sensory conduction velocity (SCV) using DPNCheck™. Associations of CGM-derived GV metrics with SNAP amplitude and SCV were investigated.

RESULTS

In total, 304 outpatients with type 2 diabetes were included. In a linear regression model, most CGM-derived GV metrics except for the mean amplitude of glucose excursion and low blood glucose index were significantly associated with SCV, but not with SNAP amplitude. The significant associations of most CGM-derived GV metrics with SCV remained after adjustment for possible confounding factors, but not after adjustment for glycated hemoglobin (HbA1c). Most CGM-derived GV metrics were significantly associated with SCV after adjustment for HbA1c in patients with a HbA1c ≤ 6.9%, but not in those with a HbA1c ≥ 7.0%.

CONCLUSIONS

In outpatients with type 2 diabetes, multiple CGM-derived GV metrics were significantly associated with SCV obtained by DPNCheck™. GV may have independent impacts on peripheral nerve function, particularly in patients with well-controlled HbA1c levels.

Short term e-bicycle riding results in favorable cardiometabolic shifts in moderately active adults

PURPOSE

Electric bikes (EB) are a form of active transportation with demonstrated health benefits. The purpose of this study was to determine the influence of riding an EB for one week on indices of cardiometabolic health in middle-aged adults.

METHODS

Adults (n = 22; age = 57.1 ± 11.3 year; BMI = 27.7 ± 4.9) participated in a 2 week study. During Week 1, participants were instructed to continue regular activities. Starting Week 2 participants were provided an EB to ride at least 3 days for a minimum of 30 min·day-1. Physical activity (PA) and glucose were measured continuously. Body composition, blood lipids, glucose, insulin, hemoglobin A1c (HbA1c), plasma endothelin-1 (ET-1), and carotid-femoral pulse wave velocity (cf-PWV) were measured on days 1 and 14.Data and Statistical analyses or Statistics. Each participant served as their own control. Paired t-tests compared dependent variables between week 1 (without EB) and week 2 (with EB).

RESULTS

When provided an EB for one week, moderate to vigorous PA increased by 6-9 min·day-1 (P < 0.05) and sedentary time decreased by ~ 77 min·day-1 (P < 0.05). Data from 24 h continuous glucose monitoring showed the percentage of time in healthy range (70-120 mg·dl-1 glucose) increased (P < 0.05) from week 1 to week 2. Compared to day 1, cf-PWV was lower at day 14 (P < 0.05) following one week of riding an EB.

CONCLUSION

Moderately-active, middleaged adults showed improved continuous glucose regulation and lower central arterial stiffness following one week of riding an EB.

Lisdexamphetamine versus methylphenidate for paediatric patients with attention-deficit hyperactivity disorder and type 1 diabetes (LAMAinDiab): protocol for a multicentre, randomised cross-over clinical trial in an outpatient telemedicine-supported setting

INTRODUCTION

Attention deficit hyperactivity disorder (ADHD) affects 5%-10% of paediatric population and is reportedly more common in children with type 1 diabetes (T1D), exacerbating its clinical course. Proper treatment of ADHD in such patients may thus provide neurological and metabolic benefits. To test this, we designed a non-commercial second phase clinical trial comparing the impact of different pharmacological interventions for ADHD in children with T1D.

METHODS AND ANALYSIS

This is a multicentre, randomised, open-label, cross-over clinical trial in children and adolescents with ADHD and T1D. The trial will be conducted in four reference paediatric diabetes centres in Poland. Over 36 months, eligible patients with both T1D and ADHD (aged 8-16.5 years, T1D duration >1 year) will be offered participation. Patients' guardians will undergo online once-weekly training sessions behaviour management for 10 weeks. Afterward, children will be randomised to methylphenidate (long-release capsule, doses 18-36-54 mg) versus lisdexamphetamine (LDX, 30-50-70 mg). Pharmacotherapy will continue for 6 months before switching to alternative medication. Throughout the trial, the participants will be evaluated every 3 months by their diabetologist and online psychological assessments. The primary endpoint (ADHD symptom severity, Conners 3.0 questionnaire) will be assessed by a blinded investigator. Secondary endpoints will include HbA1c, continuous glucose monitoring indices and quality-of-life (PedsQL).

ETHICS AND DISSEMINATION

The trial is approved by Bioethical Committee at Medical University of Lodz and Polish regulatory agency (RNN/142/22/KE, UR/DBL/D/263/2022). The results will be communicated to the research and clinical community, and Polish agencies responsible for healthcare policy. Patient organisations focused on paediatric T1D will be notified by a consortium member. We hope to use the trial's results to promote collaboration between mental health professionals and diabetes teams, evaluate the economic feasibility of using LDX in patients with both diseases and the long run improve ADHD treatment in children with T1D.

TRIAL REGISTRATION NUMBERS

EU Clinical Trials Register (EU-CTR, 2022-001906-24) and NCT05957055.