Evaluation of an open access software for calculating glucose variability parameters of a continuous glucose monitoring system applied at pediatric intensive care unit

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.

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.

Glucose Fluctuation and Severe Internal Carotid Artery Siphon Stenosis in Type 2 Diabetes Patients

The impact of glucose fluctuation on intracranial artery stenosis remains to be elucidated. This study aimed to investigate the association between glucose fluctuation and intracranial artery stenosis. This was a cross-sectional study of type 2 diabetes mellitus (T2DM) patients equipped with the FreeStyle Libre Pro continuous glucose monitoring system (Abbott Laboratories) between February 2019 and June 2020. Glucose fluctuation was evaluated according to the standard deviation (SD) of blood glucose, coefficient of variation (%CV), and mean amplitude of glycemic excursions (MAGE). Magnetic resonance angiography was used to evaluate the degree of intracranial artery stenosis. Of the 103 patients, 8 patients developed severe internal carotid artery (ICA) siphon stenosis (≥70%). SD, %CV, and MAGE were significantly higher in the severe stenosis group than in the non-severe stenosis group (<70%), whereas there was no significant intergroup difference in the mean blood glucose and HbA1c. Multivariable logistic regression analysis adjusted for sex showed that SD, %CV, and MAGE were independent factors associated with severe ICA siphon stenosis. In conclusion, glucose fluctuation is significantly associated with severe ICA siphon stenosis in T2DM patients. Thus, glucose fluctuation can be a target of preventive therapies for intracranial artery stenosis and ischemic stroke.

GLU: a software package for analysing continuously measured glucose levels in epidemiology

Continuous glucose monitors (CGM) record interstitial glucose levels 'continuously', producing a sequence of measurements for each participant (e.g. the average glucose level every 5 min over several days, both day and night). To analyse these data, researchers tend to derive summary variables such as the area under the curve (AUC), to then use in subsequent analyses. To date, a lack of consistency and transparency of precise definitions used for these summary variables has hindered interpretation, replication and comparison of results across studies. We present GLU, an open-source software package for deriving a consistent set of summary variables from CGM data. GLU performs quality control of each CGM sample (e.g. addressing missing data), derives a diverse set of summary variables (e.g. AUC and proportion of time spent in hypo-, normo- and hyper- glycaemic levels) covering six broad domains, and outputs these (with quality control information) to the user. GLU is implemented in R and is available on GitHub at https://github.com/MRCIEU/GLU. Git tag v0.2 corresponds to the version presented here.

Correlations Between Glycemic Parameters Obtained from Continuous Glucose Monitoring and Hemoglobin A1c and Glycoalbumin Levels in Type 2 Diabetes Mellitus

It is difficult to detect glycemic excursions using CGM in daily clinical practice. We retrospectively analyzed CGM data in type T2DM to define the correlations between HbA1c and GA levels at admission and the parameters representing glycemic excursions measured by CGM, including the mean amplitude of glycemic excursions (MAGE) and standard deviation (SD). The MAGE correlated significantly with GA and HbA1c, but not with the GA/HbA1c ratio. The SD correlated significantly with GA, HbA1c, and GA/HbA1c. Multivariate analysis identified the GA value to be the most reflective of MAGE. Patients were divided into 2 groups using a MAGE cutoff value of 75 mg/dl, which reflects stable diabetes. There was a significant difference in GA, but not HbA1c, between the groups with low and high mean amplitudes of glycemic excursions. Receiver operating characteristic curve analysis indicated that the cutoff for GA for identifying patients with MAGE of ≤75 mg/dl was 18.1%. Our study identified GA to be the most reflective of glycemic excursions in patients with T2DM. GA can be a useful index of glycemic excursions and treatment optimization to prevent arteriosclerosis.

A Prospective Longitudinal Study on the Relationship Between Glucose Fluctuation and Cognitive Function in Type 2 Diabetes: PROPOSAL Study Protocol

INTRODUCTION

Although the risk of dementia among patients with type 2 diabetes mellitus (T2DM) is double that of those without T2DM, the mechanism remains to be elucidated and the glycemic goal to prevent progression of cognitive impairment is unclear. Results from cross-sectional studies suggest that glucose fluctuations are associated with impairment of cognitive function among T2DM patients. Therefore, the aim of the longitudinal study described here is to evaluate the relationships between glucose fluctuation indexes assessed by continuous glucose monitoring (CGM) and cognitive function among elderly patients with T2DM.

METHODS

This will be a prospective, single-center, 2-year longitudinal study in which a total of 100 elderly patients with T2DM showing mild cognitive impairment (MCI) will be enrolled. Glucose fluctuations, assessed using the FreeStyle Libre Pro continuous glucose monitoring system (Abbott Laboratories), and results of cognitive tests, namely the Montreal Cognitive Assessment (MoCA) and Alzheimer's Disease Assessment Scale (ADAS), will be evaluated at baseline, 1-year visit and 2-year visit. The primary endpoint is the relationships between indexes of glucose fluctuation and change in MoCA and ADAS scores. Secondary endpoints are the relationships between the indexes of glucose fluctuation or cognitive scores and the following: indexes representing intracranial lesions obtained by magnetic resonance imaging and angiography of the head; Geriatric Depression Scale score; Apathy Scale score; carotid intima-media thickness assessed by echography; inflammatory markers; fasting glucose; glycated hemoglobin; blood pressure; and the development of cardiovascular and renal events.

PLANNED OUTCOMES

The current study is scheduled for completion in June 2022. The results could lead to the elucidation of novel glycemic goals to prevent the progression of cognitive impairment and/or of relationships between glucose fluctuations and cognitive function among T2DM patients. The findings of the study will be reported in publications and conference presentations.

TRIAL REGISTRATION

University Hospital Medical Information Network Clinical Trial Registry (UMIN000038546).

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

Background: Glycemic variability is an important factor to consider in diabetes management. It can be assessed with multiple glycemic variability metrics and quality of control indices based on continuous glucose monitoring (CGM) recordings. For this, a robust repeatable calculation is important. A widely used tool for automated assessment is the EasyGV software. The aim of this work is to implement new methods of glycemic variability assessment in EasyGV and to validate implementation of each glucose metric in EasyGV against a reference implementation of the calculations. Methods: Validation data used came from the JDRF CGM study. Validation of the implementation of metrics that are available in EasyGV software v9 was carried out and the following new methods were added and validated: personal glycemic state, index of glycemic control, times in ranges, and glycemic variability percentage. Reference values considered gold standard calculations were derived from MATLAB implementation of each metric. Results: The Pearson correlation coefficient was above 0.98 for all metrics, except for mean amplitude of glycemic excursion (r = 0.87) as EasyGV implements a fuzzy logic approach to assessment of variability. Bland-Altman plots demonstrated validation of the new software. Conclusions: The new freely available EasyGV software v10 (www.phc.ox.ac.uk/research/technology-outputs/easygv) is a validated robust tool for analyzing different glycemic variabilities and control metrics.

Associations between acute glucose control and peripheral nerve structure and function in type 1 diabetes

AIM

To examine the associations between continuous overlapping net glycaemic action (CONGA), percentage time in hyperglycaemia (%HG) or normoglycaemia (%NG) and peripheral nerve structure and function in type 1 diabetes.

METHODS

Twenty-seven participants with type 1 diabetes underwent continuous glucose monitoring followed by corneal confocal microscopy and nerve excitability assessments. CONGA, %HG (> 10.0 mmol/l) and %NG (3.9-10.0 mmol/l) were correlated against corneal nerve fibre length and density in the central cornea and inferior whorl region, corneal microneuromas, and a nerve excitability score while controlling for age, sex, diabetes duration and HbA_1c .

RESULTS

An increase in CONGA [median 2.5 (2.0-3.1) mmol/l] or %HG (mean 46 ± 18%) was associated with a worse nerve excitability score (r = -0.433, P = 0.036 and r = -0.670, P = 0.0012, respectively). By contrast, greater %NG (51 ± 17%) correlated with better nerve excitability scores (r = 0.672, P = 0.0011). Logistic regression revealed that increasing %HG increased the likelihood of abnormal nerve function [odds ratio (OR) 1.11, 95% confidence interval (CI) 1.01-1.23; P = 0.037). An increase in CONGA and %HG were associated with worsening nerve conduction measures, whereas longer %NG correlated with improved nerve conduction variables. CONGA and %HG were associated with inferior whorl corneal nerve fibre length (r = 0.483, P = 0.034 and r = 0.591, P = 0.021, respectively) and number of microneuromas (r = 0.433, P = 0.047 and r = 0.516, P = 0.020, respectively).

CONCLUSIONS

Short-term measures of glucose control are associated with impaired nerve function and alterations in corneal nerve morphology.

Hypoglycemia in blood glucose level in type 2 diabetic Japanese patients by continuous glucose monitoring

BACKGROUND

Hypoglycemia is associated with cardiovascular diseases, increased risk of death. Therefore, it is important to avoid hypoglycemia. The aim of this study was to characterize hypoglycemia according to glycated hemoglobin (HbA1c) level and determine the contributing factors in type 2 diabetes mellitus (T2DM), using continuous glucose monitoring (CGM).

METHODS

T2DM patients (n = 293) receiving inpatient care were divided into five groups according to HbA1c level on admission (Group 1: ≥ 6 to < 7%, Group 2: ≥ 7 to < 8%, Group 3: ≥ 8 to < 9%, Group 4: ≥ 9 to < 10%, and Group 5: ≥ 10%). The frequency of hypoglycemia and factors associated with hypoglycemia were analyzed.

RESULTS

Hypoglycemia occurred in 15 patients (5.1%), including 4 (8%), 4 (6%), and 7 (10%) patients of Groups 1, 2, and 3, respectively, but in none of groups 4 and 5. Patients with hypoglycemia of Groups 1 had low insulin secretion and were high among insulin users, those of Groups 2 had low homeostasis model assessment of insulin resistance (HOMA-IR). Those of Group 2 and 3 had significantly lower mean blood glucose levels, those of Group 3 only had significantly lower maximum blood glucose level and percentage of AUC > 180 mg/dL. In any of the HbA1c groups, variations in blood glucose level were significantly larger in patients with hypoglycemia than without.

CONCLUSIONS

Hypoglycemia occurred in patients with a wide range of HbA1c on admission (range 6-9%), suggesting that prediction of hypoglycemia based on HbA1c alone is inappropriate. Among patients with low HbA1c, strict control sometimes induce hypoglycemia. Among patients with high HbA1c, the possibility of hypoglycemia should be considered if there is a marked discrepancy between HbA1c and randomly measured blood glucose level. Larger variations in blood glucose level induce hypoglycemia in any of the HbA1c groups. The treatment to reduce variations in blood glucose level is important to prevent hypoglycemia.

Effect of short-term treatment with sitagliptin or glibenclamide on daily glucose fluctuation in drug-naïve Japanese patients with type 2 diabetes mellitus

AIMS

To compare the effect of a dipeptidyl peptidase-4 inhibitor (DPP4-i) and a sulfonylurea (SU) on daily glucose fluctuation in drug-naïve Japanese patients with type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

A total of 53 drug-naïve Japanese patients with T2DM (HbA1c, 7.0%-9.0%; fasting plasma glucose, 6.1 mmol/L or higher) were randomly assigned to either sitagliptin 50 mg qd or glibenclamide 2.5 mg per day (given in divided doses) in a 1:1 ratio. A continuous glucose monitoring (CGM) device was used to obtain 24-hour glucose profiles for each patient at baseline and at Week 2. The primary study endpoint was change from baseline in mean amplitude of glucose excursion (MAGE) during a 24-hour period. A key secondary endpoint was change from baseline in the standard deviation (SD) of 24-hour glucose levels.

RESULTS

After 2 weeks of treatment, a numerically greater reduction in MAGE from baseline was observed in the sitagliptin group compared with the glibenclamide group, but the between-treatment difference was not statistically significant (LS mean difference [95% CI]: -0.48 mmol/L [-1.31, 0.34]; P = .245). However, a significantly greater reduction in the change from baseline in SD was observed in the sitagliptin group compared with the glibenclamide group (LS mean difference [95% CI]: -0.33 mmol/L [-0.62, -0.03]; P = .029).

CONCLUSIONS

This study suggests that the DPP4 inhibitor sitagliptin has a greater ability to reduce daily glucose fluctuation than the SU glibenclamide in drug-naïve Japanese patients with T2DM. ClinicalTrials.gov: NCT02318693.

A randomized controlled trial to compare the effects of sulphonylurea gliclazide MR (modified release) and the DPP-4 inhibitor vildagliptin on glycemic variability and control measured by continuous glucose monitoring (CGM) in Brazilian women with type 2 diabetes

AIMS

This study aims to evaluate whether there is a difference between the effects of vildagliptin and gliclazide MR (modified release) on glycemic variability (GV) in women with type 2 diabetes (T2DM) as evaluated by continuous glucose monitoring (CGM).

METHODS

An open-label, randomized study was conducted in T2DM women on steady-dose metformin monotherapy which were treated with 50 mg vildagliptin twice daily or 60-120 mg of gliclazide MR once daily. CGM and GV indices calculation were performed at baseline and after 24 weeks.

RESULTS

In total, 42 patients (age: 61.9 ± 5.9 years, baseline glycated hemoglobin (HbA1c): 7.3 ± 0.56) were selected and 37 completed the 24-week protocol. Vildagliptin and gliclazide MR reduced GV, as measured by the mean amplitude of glycemic excursions (MAGE, p = 0.007 and 0.034, respectively). The difference between the groups did not reach statistical significance. Vildagliptin also significantly decreased the standard deviation of the mean glucose (SD) and the mean of the daily differences (MODD) (p = 0.007 and 0.030).

CONCLUSIONS

Vildagliptin and gliclazide MR similarly reduced the MAGE in women with T2DM after 24 weeks of treatment. Further studies are required to attest differences between vildagliptin and gliclazide MR regarding glycemic variability.

Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data

BACKGROUND

High levels of glycemic variability are still observed in most patients with diabetes with severe insulin deficiency. Glycemic variability may be an important risk factor for acute and chronic complications. Despite its clinical importance, there is no consensus on the optimum method for characterizing glycemic variability.

METHOD

We developed a simple new metric, the glycemic variability percentage (GVP), to assess glycemic variability by analyzing the length of the continuous glucose monitoring (CGM) temporal trace normalized to the duration under evaluation. The GVP is similar to other recently proposed glycemic variability metrics, the distance traveled, and the mean absolute glucose (MAG) change. We compared results from distance traveled, MAG, GVP, standard deviation (SD), and coefficient of variation (CV) applied to simulated CGM traces accentuating the difference between amplitude and frequency of oscillations. The GVP metric was also applied to data from clinical studies for the Dexcom G4 Platinum CGM in subjects without diabetes, with type 2 diabetes, and with type 1 diabetes (adults, adolescents, and children).

RESULTS

In contrast to other metrics, such as CV and SD, the distance traveled, MAG, and GVP all captured both the amplitude and frequency of glucose oscillations. The GVP metric was also able to differentiate between diabetic and nondiabetic subjects and between subjects with diabetes with low, moderate, and high glycemic variability based on interquartile analysis.

CONCLUSION

A new metric for the assessment of glycemic variability has been shown to capture glycemic variability due to fluctuations in both the amplitude and frequency of glucose given by CGM data.

Intensive insulin therapy combined with metformin is associated with reduction in both glucose variability and nocturnal hypoglycaemia in patients with type 2 diabetes

BACKGROUND

The effect on glucose variability in patients with intensive insulin therapy has not been fully understood. This observational study investigated the different glucose variability and hypoglycaemia patterns in type 2 diabetes patients treated with continuous subcutaneous insulin infusion (CSII) or multiple daily injections (MDI) with or without metformin administration.

METHODS

During hospitalization, a total of 501 patients with poor glycaemic control and in initial treatment with either CSII alone (n = 187), CSII + Metformin (n = 81), MDI alone (n = 146), or MDI + Metformin (n = 87) were involved in the final analysis. Data obtained from continuous glucose monitoring were used to assess blood glucose fluctuation and nocturnal hypoglycaemia.

RESULTS

Among the 4 groups, no difference was found in mean blood glucose levels. Results in parameters reflecting glucose fluctuation: continuous overlapping net glycaemic action in CSII + Metformin and mean amplitude of glycaemic excursions in MDI + Metformin were significantly lower than those in either CSII alone or MDI alone, respectively, even after adjustment (P = .031 and .006). Frequency of nocturnal hypoglycaemia was significantly decreased in CSII + Metformin as compared with CSII alone (0.6% vs 1.8%) and in MDI + Metformin as compared with MDI alone (1.6% vs 2.3%), with the highest frequency observed in MDI alone and the lowest in CSII + Metformin (all between group P < .001). Consistent results were obtained in between-group comparisons for hypoglycaemia duration. Subgroup analysis matched with baseline body mass index, and glycated haemoglobin and fasting blood glucose further confirmed these findings.

CONCLUSION

Metformin added to initial CSII or MDI therapy is associated with a reduction in both glucose fluctuation and nocturnal hypoglycaemic risk in patients with type 2 diabetes.

Twenty-four-hour variations in blood glucose level in Japanese type 2 diabetes patients based on continuous glucose monitoring

Aims/Introduction

High fluctuations in blood glucose are associated with various complications. The correlation between glycated hemoglobin (HbA1c) level and fluctuations in blood glucose level has not been studied in Japanese patients with type 2 diabetes. In the present study, blood glucose profile stratified by HbA1c level was evaluated by continuous glucose monitoring (CGM) in Japanese type 2 diabetes patients.

Materials and Methods

Our retrospective study included 294 patients with type 2 diabetes who were divided by HbA1c level into five groups (≥6.0 to <7.0%, ≥7.0 to <8.0%, ≥8.0 to <9.0%, ≥9.0 to <10.0% and ≥10%). The correlation between HbA1c level and CGM data was analyzed. The primary end‐point was the difference in blood glucose fluctuations among the HbA1c groups.

Results

The mean blood glucose level increased significantly with increasing HbA1c (P_trend < 0.01). The standard deviation increased with increases in HbA1c (P_trend < 0.01). The mean amplitude of glycemic excursions did not vary significantly with HbA1c. The levels of maximum blood glucose, minimum blood glucose, each preprandial blood glucose, each postprandial maximum blood glucose, range of increase in postprandial glucose from pre‐meal to after breakfast, the area under the blood concentration–time curve >180 mg/dL and percentage of the area under the blood concentration–time curve >180 mg/dL were higher with higher HbA1c. Mean glucose level and pre‐breakfast blood glucose level were significant and independent determinants of HbA1c.

Conclusions

In Japanese patients treated for type 2 diabetes, the mean amplitude of glycemic excursions did not correlate with HbA1c, making it difficult to assess blood glucose fluctuations using HbA1c. Parameters other than HbA1c are required to evaluate fluctuations in blood glucose level in patients receiving treatment for type 2 diabetes.