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

Development of a three-dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetes

INTRODUCTION

Despite the improvements in diabetes management by continuous glucose monitoring (CGM) it is difficult to capture the complexity of CGM data in one metric. We aimed to develop a clinically relevant multidimensional scoring model with the capacity to identify the most alarming CGM episodes and/or patients from a large cohort.

RESEARCH DESIGN AND METHODS

Retrospective CGM data from 2017 to 2020 available in electronic medical records were collected from n=613 individuals with type 1 diabetes (total 82 114 days). A scoring model was developed based on three metrics; glycemic variability percentage, low blood glucose index and high blood glucose index. Values for each dimension were normalized to a numeric score between 0-100. To identify the most representative score for an extended time period, multiple ways to combine the mean score of each dimension were evaluated. Correlations of the scoring model with CGM metrics were computed. The scoring model was compared with interpretations of a clinical expert board (CEB).

RESULTS

The dimension of hypoglycemia must be weighted to be representative, whereas the other two can be represented by their overall mean. The scoring model correlated well with established CGM metrics. Applying a score of ≥80 as the cut-off for identifying time periods with a 'true' target fulfillment (ie, reaching all targets for CGM metrics) resulted in an accuracy of 93.4% and a specificity of 97.1%. The accuracy of the scoring model when compared with the CEB was high for identifying the most alarming CGM curves within each dimension of glucose control (overall 86.5%).

CONCLUSIONS

Our scoring model captures the complexity of CGM data and can identify both the most alarming dimension of glycemia and the individuals in most urgent need of assistance. This could become a valuable tool for population management at diabetes clinics to enable healthcare providers to stratify care to the patients in greatest need of clinical attention.

Short-term glycemic variability in non-diabetic, non-obese dogs assessed by common glycemic variability indices

Glycemic variability (GV) refers to swings in blood glucose levels and is an emerging measure of glycemic control in clinical practice. It is associated with micro- and macrovascular complications and poor clinical outcomes in diabetic humans. Although an integral part of patient assessment in human patients, it is to a large extent neglected in insulin-treated diabetic dogs. This prospective pilot study was performed to describe canine within-day GV in non-diabetic dogs with the aim to provide a basis for the interpretation of daily glucose profiles, and to promote GV as an accessible tool for future studies in veterinary medicine. Interstitial glucose concentrations of ten non-diabetic, non-obese beagles were continuously measured over a 48-h period using a flash glucose monitoring system. GV was assessed using the common indices MAGE (mean amplitude of glycemic excursion), GVP (Glycemic variability percentage) and CV (coefficient of variation). A total of 2260 sensor measurements were obtained, ranging from 3.7 mmol/L (67 mg/dL) to 8.5 mmol/L (153 mg/dL). Glucose profiles suggested a meal-dependent circadian rhythmicity with small but significant surges during the feeding periods. No differences in GV indices were observed between day and night periods (p > 0.05). The MAGE (mmol/L), GVP (%) and CV (%) were 0.86 (± 0.19), 7.37 (± 1.65), 6.72 (± 0.89) on day one, and 0.83 (± 0.18), 6.95 (± 1.52), 6.72 (± 1.53) on day two, respectively. The results of this study suggest that GV is low in non-diabetic dogs and that glucose concentrations are kept within narrow ranges.

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.

Key indices of glycaemic variability for application in diabetes clinical practice

Near normal glycaemic control in diabetes consists to target daily glucose fluctuations and quarterly HbA1c oscillations in addition to overall glucose exposure. Consequently, the prerequisite is to define simple, and mathematically undisputable key metrics for the short- and long-term variability in glucose homeostasis. As the standard deviations (SD) of either glucose or HbA1c are dependent on their means, the coefficient of variation (CV = SD/mean) should be applied instead as it that avoids the correlation between the SD and mean values. A CV glucose of 36% is the most appropriate threshold between those with stable versus labile glucose homeostasis. However, when near normal mean glucose concentrations are achieved a lower CV threshold of <27 % is necessary for reducing the risk for hypoglycaemia to a minimal rate. For the long-term variability in glucose homeostasis, a CVHbA1c < 5 % seems to be a relevant recommendation for preventing adverse clinical outcomes.

Continuous glucose monitoring-based composite metrics: a review and assessment of performance in recent-onset and long-duration type 1 diabetes

This study examined correlations between continuous glucose monitoring (CGM)-based composite metrics and standard glucose metrics within CGM data sets from individuals with recent-onset and long-duration type 1 diabetes. First, a literature review and critique of published CGM-based composite metrics was undertaken. Second, composite metric results were calculated for the two CGM data sets and correlations with six standard glucose metrics were examined. Fourteen composite metrics met selection criteria; these metrics focused on overall glycemia (n = 8), glycemic variability (n = 4), and hypoglycemia (n = 2), respectively. Results for the two diabetes cohorts were similar. All eight metrics focusing on overall glycemia strongly correlated with glucose time in range; none strongly correlated with time below range. The eight overall glycemia-focused and two hypoglycemia-focused composite metrics were all sensitive to automated insulin delivery therapeutic intervention. Until a composite metric can adequately capture both achieved target glycemia and hypoglycemia burden, the current two-dimensional CGM assessment approach may offer greatest clinical utility.

The Use of Extreme Value Statistics to Characterize Blood Glucose Curves and Patient Level Risk Assessment of Patients With Type I Diabetes

OBJECTIVE

Characterizing blood glucose curves and providing precise patient level risk assessment of hyperglycemia using extreme value statistics and comparing these assessments with traditional indicators of glycemic variability which are not designed to specifically capture the risk of hyperglycemia.

RESEARCH DESIGN AND METHODS

One year return level (blood glucose level exceeded exactly once every year on average) and probability of exceeding and expected time spent above certain thresholds (600 and 400 mg/dL) per year were calculated. As a comparison, traditional metrics for glycemic variability were determined too. The effect of body mass index on extremes was also investigated using non-stationary models. Metrics were calculated on a dataset containing 170.8 patient-years of measurements of 226 patients.

RESULTS

Nine high-risk patients were identified with the novel metrics: their estimated time spent above 600 mg/dL per year were above 2 hours. These patients were at moderate risk according to the traditional metrics. Higher body mass index was associated with more extreme glucose levels.

CONCLUSIONS

Through these estimates it is possible to assess each patient's individual clinical risk of hyperglycemia even beyond the observed blood glucose levels and detection limits. Additionally, it allows the assessment of the impact of clinical characteristics and treatments on blood glucose control in a novel, mathematically well-founded and potentially clinically more useful way than the already existing indicators.

Analysis of "Multicenter Evaluation Study Comparing a New Factory-Calibrated Real-Time Continuous Glucose Monitoring System to Existing Flash Glucose Monitoring System"

In an article in the Journal of Diabetes Science and Technology, Ji et al. report on the accuracy of a new factory calibrated continuous glucose monitoring system, the AiDEX CGM (Microtech Medical Company, Ltd., Hangzhou, China). This is the first report from a new manufacturer of a highly accurate factory calibrated CGM. The authors report that the accuracy of the AiDEX CGM is comparable to previous results from Abbott Diabetes Care and Dexcom. However, the study protocol was significantly different from previous studies. This study is the first of numerous studies likely from other manufacturers of CGM technology. It raises the question of how to evaluate sensor performance in the coming era of mass adoption of CGM and increased use of automated insulin delivery systems.

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.

Feasibility of Using a Factory-Calibrated Continuous Glucose Monitoring System to Diagnose Type 2 Diabetes

Context: Plasma glucose or A1C criteria can be used to establish the diagnosis of type 2 diabetes (T2D). Objective: We examined whether continuous glucose monitoring (CGM) data from a single 10-day wear period could form the basis of an alternative diagnostic test for T2D. Design: We developed a binary classification diagnostic CGM (dCGM) algorithm using a dataset of 716 individual CGM sensor sessions from 563 participants with associated A1C measurements from seven clinical trials. Data from 470 participants were used for training and 93 participants for testing (49 normoglycemic [A1C <5.7%], 27 prediabetes, and 17 T2D [A1C ≥6.5%] not using pharmacotherapy). dCGM performance was evaluated against the accompanying A1C measurement, which was assumed to provide the correct diagnosis. Results: The dCGM algorithm's overall sensitivity, specificity, positive predictive value, and negative predictive value were 71%, 93%, 71%, and 93%, respectively. At other clinically relevant A1C thresholds, dCGM specificity among normoglycemic participants was 98% (48/49 correctly classified), and for participants with suboptimally controlled diabetes (A1C ≥7%, above the American Diabetes Association recommended A1C goal) the sensitivity was 100% (8/8 participants correctly diagnosed with T2D). Conclusions: Classifications based on the dCGM algorithm were in good agreement with traditional methods based on A1C. The dCGM algorithm may provide an alternative method for screening and diagnosing T2D, and warrants further investigation.

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.

Effects of structured Ramadan Nutrition Plan on glycemic control and variability using continuous glucose monitoring in individuals with type 2 diabetes: A pilot study

BACKGROUND AND AIMS

Continuous glucose monitoring (CGM) has been increasingly used in recent years to evaluate glycemic control and variability in individuals with diabetes observing Ramadan fasting. However, the effectiveness of the Ramadan Nutrition Plan (RNP) in individuals with type 2 diabetes (T2D) using CGM-derived measures has not been investigated. The study aimed to evaluate the effects of structured RNP versus standard care using CGM in individuals with T2D.

METHODS

This parallel non-randomized interventional study with patients' preference design involved 21 individuals with T2D (mean age: 49 ± 10 years, BMI: 30.0 ± 6.2 kg/m²). Participants chose to receive either structured RNP (sRNT; structured Ramadan Nutrition Therapy group; n = 14) or standard care (SC; n = 7). Participants wore CGM 5 days before Ramadan and during Ramadan. CGM-derived measures of glycemic variability were calculated using Glyculator version 2.0.

RESULTS

Compared to the SC group, the sRNT group significantly reduced their fasting blood glucose levels, HbA1c, total cholesterol, diastolic blood pressure, and increased dietary fiber intake. CGM data showed the sRNT group had significantly lower average sensor glucose, peak sensor value, estimated A1c, percentage and duration of time-above-range, J-index, mean amplitude of glycemic excursion (MAGE), and continuous overall net glycemic action (CONGA); and a significantly higher percentage of time-in-range (TIR).

CONCLUSIONS

The structured RNP significantly improved clinical outcomes, glycemic control and variability in individuals with T2D. The study highlights the importance of utilizing CGM sensor data to monitor glycemic excursions during Ramadan fasting. Adequately powered randomized controlled trials are needed to confirm the findings.

A Framework for Integrating Continuous Glucose Monitor-Derived Metrics into Economic Evaluations in Type 1 Diabetes

Economic models in type 1 diabetes have relied on a change in haemoglobin A1c as the link between the blood glucose trajectory and long-term clinical outcomes, including microvascular and macrovascular disease. The landscape has changed in the past decade with the availability of regulatory approved, accurate and convenient continuous glucose monitoring devices and their ability to track patients' glucose levels over time. The data emerging from continuous glucose monitoring have enriched the clinical understanding of the disease and indirectly of patients' behaviour. This has triggered the development of new measures proposed to better define the quality of glycaemic control, beyond haemoglobin A1c. The objective of this paper is to review recent developments in clinical knowledge brought into focus with the application of continuous glucose monitoring devices, and to discuss potential approaches to incorporate the concepts into economic models in type 1 diabetes. Based on a targeted review and a series of multidisciplinary workshops, an influence diagram was developed that captures newer concepts (e.g. continuous glucose monitoring metrics) that can be integrated into economic models and illustrates their association with more established concepts. How the additional continuous glucose monitoring-based indicators of glycaemic control may contribute to economic modelling beyond haemoglobin A1c, and more accurately reflect the economic value of novel type 1 diabetes treatments, is discussed.

Ultra-long-acting recombinant insulin for the treatment of diabetes mellitus in dogs

BACKGROUND

For the treatment of diabetes mellitus (DM) in dogs, novel insulins with decreased injection frequency while maintaining safety and efficacy are desirable. Insulin fused with immunoglobulin-fragment-crystallizable (Fc) has an ultra-long plasma half-life because it recycles through cells, protected from proteolysis.

HYPOTHESIS

Glycemic control can be achieved in diabetic dogs with a recombinant fusion protein of a synthetic insulin and canine Fc (AKS-218d) administered subcutaneously once-weekly.

ANIMALS

Five client-owned dogs with naturally occurring DM.

METHODS

Prospective clinical trial in dogs with DM that were recruited from the UC Davis Veterinary Teaching Hospital and local veterinary clinics. Dogs previously controlled using intermediate-acting insulin q12h were transitioned to once-weekly injections of a preliminary construct identified as AKS-218d. The dose of AKS-218d was titrated weekly for 8 weeks based on clinical response and continuous interstitial glucose monitoring. Clinical signs, body weight, serum fructosamine concentrations, and mean interstitial glucose concentrations (IG) over the preceding week were compared between baseline (before AKS-218d) and during the last week of treatment. Data were compared using nonparametric paired tests.

RESULTS

Once-weekly AKS-218d, compared to baseline twice-daily insulin therapy, resulted in no significant changes in clinical signs, median (range) body weight (+0.4 kg [-0.5-1.1]; P = .6), fructosamine concentration (-75 mmol/L [-215 to +126]; P = .4), or mean IG (+81 mg/dL [-282 to +144]; P = .8). No adverse reactions were reported.

CONCLUSION

Control of clinical signs, body weight, and maintenance of glycemia was achieved with this once-weekly novel insulin construct in 4 of 5 dogs.

Outpatient Randomized Crossover Automated Insulin Delivery Versus Conventional Therapy with Induced Stress Challenges

Background: Automated insulin delivery (AID) systems have not been evaluated in the context of psychological and pharmacological stress in type 1 diabetes. Our objective was to determine glycemic control and insulin use with Zone Model Predictive Control (zone-MPC) AID system enhanced for states of persistent hyperglycemia versus sensor-augmented pump (SAP) during outpatient use, including in-clinic induced stress. Materials and Methods: Randomized, crossover, 2-week trial of zone-MPC AID versus SAP in 14 adults with type 1 diabetes. In each arm, each participant was studied in-clinic with psychological stress induction (Trier Social Stress Test [TSST] and Socially Evaluated Cold Pressor Test [SECPT]), followed by pharmacological stress induction with oral hydrocortisone (total four sessions per participant). The main outcomes were 2-week continuous glucose monitor percent time in range (TIR) 70-180 mg/dL, and glucose and insulin outcomes during and overnight following stress induction. Results: During psychological stress, AID decreased glycemic variability percentage by 13.4% (P = 0.009). During pharmacological stress, including the following overnight, there were no differences in glucose outcomes and total insulin between AID and physician-assisted SAP. However, with AID total user-requested insulin was lower by 6.9 U (P = 0.01) for pharmacological stress. Stress induction was validated by changes in heart rate and salivary cortisol levels. During the 2-week AID use, TIR was 74.4% (vs. SAP 63.1%, P = 0.001) and overnight TIR was 78.3% (vs. SAP 63.1%, P = 0.004). There were no adverse events. Conclusions: Zone-MPC AID can reduce glycemic variability and the need for user-requested insulin during pharmacological stress and can improve overall glycemic outcomes. Clinical Trial Identifier NCT04142229.

Dried chicory root improves bowel function, benefits intestinal microbial trophic chains and increases faecal and circulating short chain fatty acids in subjects at risk for type 2 diabetes

We investigated the impact of dried chicory root in a randomised, placebo-controlled trial with 55 subjects at risk for type 2 diabetes on bowel function, gut microbiota and its products, and glucose homeostasis. The treatment increased stool softness (+1.1 ± 0.3 units; p = 0.034) and frequency (+0.6 ± 0.2 defecations/day; p < 0.001), strongly modulated gut microbiota composition (7 % variation; p = 0.001), and dramatically increased relative levels (3-4-fold) of Anaerostipes and Bifidobacterium spp., in a dose-dependent, reversible manner. A synthetic community, including selected members of these genera and a Bacteroides strain, generated a butyrogenic trophic chain from the product. Faecal acetate, propionate and butyrate increased by 25.8 % (+13.0 ± 6.3 mmol/kg; p = 0.023) as did their fasting circulating levels by 15.7 % (+7.7 ± 3.9 μM; p = 0.057). In the treatment group the glycaemic coefficient of variation decreased from 21.3 ± 0.94 to 18.3 ± 0.84 % (p = 0.004), whereas fasting glucose and HOMA-ir decreased in subjects with low baseline Blautia levels (-0.3 ± 0.1 mmol/L fasting glucose; p = 0.0187; -0.14 ± 0.1 HOMA-ir; p = 0.045). Dried chicory root intake rapidly and reversibly affects bowel function, benefits butyrogenic trophic chains, and promotes glycaemic control.

Utility of Continuous Glucose Monitoring vs Meal Study in Detecting Hypoglycemia After Gastric Bypass

CONTEXT

Gastric bypass (GB) increases postprandial glucose excursion, which in turn can predispose to the late complication of hypoglycemia. Diagnosis remains challenging and requires documentation of symptoms associated with low glucose and relief of symptom when glucose is normalized (Whipple triad).

OBJECTIVE

To compare the yield of mixed meal test (MMT) and continuous glucose monitoring system (CGMS) in detecting hypoglycemia after GB.

SETTING

The study was conducted at General Clinical Research Unit, Cincinnati Children's Hospital (Cincinnati, OH, USA).

METHODS

Glucose profiles were evaluated in 15 patients with documented recurrent clinical hypoglycemia after GB, 8 matched asymptomatic GB subjects, and 9 healthy weight-matched nonoperated controls using MMT in a control setting and CGMS under free-living conditions.

RESULTS

Patients with prior GB had larger glucose variability during both MMT and CGMS when compared with nonsurgical controls regardless of their hypoglycemic status. Sensitivity (71 vs 47%) and specificity (100 vs 88%) of MMT in detecting hypoglycemia was superior to CGMS.

CONCLUSIONS

Our findings indicate that a fixed carbohydrate ingestion during MMT is a more reliable test to diagnose GB-related hypoglycemia compared with CGMS during free-living state.

Gradient variability coefficient: a novel method for assessing glycemic variability and risk of hypoglycemia

OBJECTIVE

Despite the clinical importance of glycemic variability and hypoglycemia, thus far, there is no consensus on the optimum method for assessing glycemic variability and risk of hypoglycemia simultaneously.

RESEARCH DESIGN AND METHODS

A novel metric, the gradient variability coefficient (GVC), was proposed for characterizing glycemic variability and risk of hypoglycemia. A total of 208 daily records of CGM encompassing 104 patients with T1DM and 2380 daily records from 1190 patients with T2DM were obtained in our study. Simulated CGM waveforms were used to assess the ability of GVC and other metrics to capture the amplitude and frequency of glucose fluctuations. In addition, the association between GVC and the risk of hypoglycemia was evaluated by receiver operating characteristic (ROC) curve.

RESULTS

The results of simulated CGM waveforms indicated that, compared with the widely used metrics of glycemic variability including standard deviation of sensor glucose (SD), coefficient of variation (CV), and mean amplitude of glycemic excursion (MAGE), GVC could reflect both the amplitude and frequency of glucose oscillations. In addition, the area under the curve (AUC) of ROC was 0.827 in T1DM and 0.873 in T2DM, indicating good performance in predicting hypoglycemia.

CONCLUSIONS

The proposed GVC might be a clinically useful tool in characterizing glycemic variability and the assessment of hypoglycemia risk in patients with diabetes.

Glycaemia dynamics in gestational diabetes mellitus

Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed.

Carbohydrate-Energy Replacement Following High-Intensity Interval Exercise Blunts Next-Day Glycemic Control in Untrained Women

Background

Improved glycemic control has been reported for ∼24 h following low-volume high-intensity interval exercise (HIIE), but it is unclear if this is a direct effect of exercise or an indirect effect of the exercise-induced energy deficit. The purpose of this study was to investigate the effect of carbohydrate-energy replacement after low-volume HIIE on 24 h glycemic control in women.

Methods

Seven untrained women (age: 22 ± 2 yr; BMI: 22 ± 3 kg/m2; VO2peak: 33 ± 7 ml/kg/min) completed three 2-day trials in the mid-follicular phase of the menstrual cycle. Continuous glucose monitoring was used to measure blood glucose concentrations during, and for 24 h following three conditions: (1) HIIE followed by a high-carbohydrate energy replacement drink (EX-HC); (2) HIIE followed by a non-caloric taste-matched placebo drink (EX-NC); and (3) seated control with no drink (CTL). HIIE involved an evening session (1,700 h) of 10 × 1-min cycling efforts at ∼90% maximal heart rate with 1 min recovery. Diet was standardized and identical across all three 2-day trials, apart from the post-exercise carbohydrate drink in EX-HC, which was designed to replenish the exercise-induced energy expenditure. Postprandial glycemic responses to the following days breakfast, snack, lunch, and dinner, as well as 24 h indices of glycemic control, were analyzed.

Results

The day after HIIE, postprandial glycemia following breakfast and snack were reduced in EX-NC compared to EX-HC, as reflected by lower 3 h glucose mean (breakfast: 5.5 ± 0.5 vs. 6.7 ± 1, p = 0.01, Cohen’s d = 1.4; snack: 4.9 ± 0.3 vs. 5.7 ± 0.8 mmol/L, p = 0.02, d = 1.4) and/or area under the curve (AUC) (breakfast: 994 ± 86 vs. 1,208 ± 190 mmol/L x 3 h, p = 0.01, d = 1.5). Postprandial glycemic responses following lunch and dinner were not different across conditions (p &gt; 0.05). The 24 h glucose mean (EX-NC: 5.2 ± 0.3 vs. EX-HC: 5.7 ± 0.7 mmol/L; p = 0.02, d = 1.1) and AUC (EX-NC: 7,448 ± 425 vs. EX-HC: 8,246 ± 957 mmol/L × 24 h; p = 0.02, d = 1.1) were reduced in EX-NC compared to EX-HC.

Conclusion

Post-exercise carbohydrate-energy replacement attenuates glycemic control the day following a single session of low-volume HIIE in women.

Glycemic Variability Within 1 Year Following Surgery for Stage II-III Colon Cancer

OBJECTIVE

To examine glycemic variability within 1 month and 1 year following surgery among adult patients, with and without Type 2 Diabetes (T2D), treated for stage II-III colon cancer.

METHOD

A retrospective analysis of electronic health record data was conducted. Glycemic variability (i.e., standard deviation [SD] and coefficient of variation [CV] of > 2 blood glucose measures) was assessed within 1 month and within 1 year following colon surgery. Chi-square (χ²), Fisher's exact, and Mann-Whitney U tests were used for the analyses.

RESULTS

Among the sample of 165 patients with stage II-III colon cancer, those with T2D had higher glycemic variability compared to patients without T2D (p < .001), with values within 1 month following surgery (SD = 44.69 mg/dL, CV = 27.4%) vs (SD = 20.55 mg/dL, CV = 17.53%); and within 1 year following surgery (SD = 45.04 mg/dL, CV = 29.04%) vs (SD = 21.36 mg/dL, CV = 18.6%). Associations were found between lower body mass index and higher glycemic variability (i.e., SD [r = -.413, p < .05] and CV [r = -.481, p < .01]) within 1 month following surgery in patients with T2D. Higher preoperative glucose was associated with higher glycemic variability (i.e., SD r = .448, p < .01) within 1 year in patients with T2D. Demographic and clinical characteristics were weakly associated with glycemic variability in patients without T2D.

CONCLUSIONS

Patients with stage II-III colon cancer with T2D experienced higher glycemic variability within 1 month and within 1 year following surgery compared to those without T2D. Associations between glycemic variability and demographic and clinical characteristics differed by T2D status. Further research in prospective studies is warranted.

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.

Towards a Rational Balanced Pancreatic and Islet Allocation Schema

Allocation of donated organs for transplantation is a complex process that considers numerous factors such as donor, organ and candidate characteristics and practical issues such as geography. Whole pancreas and isolated islet transplantation are lifesaving for certain individuals with diabetes. Herein, we suggest a revised allocation schema that matches donor characteristics with candidate medical condition while allowing for geographic considerations. It is hoped that adoption of this schema will shorten allocation time, decrease organ waste and optimize the parity between organ donor characteristics and candidate state of health.

Quality of Glycemic Control: Assessment Using Relationships Between Metrics for Safety and Efficacy

Numerous methods have been proposed as measures of quality of glycemic control resulting in confusion regarding the best choice of metric to use by clinicians and researchers. Some methods use a single metric such as HbA1c, Mean Glucose, %Time In Range (%TIR), or Coefficient of Variation (%CV). Others use a combination of up to seven metrics, for example, Q-Score, Comprehensive Glucose Pentagon (CGP), and Personal Glycemic State (PGS). A recently proposed Composite continuous Glucose monitoring index utilizes three metrics: %TIR, Time Below Range (%TBR), and standard deviation (SD) of glucose. This review proposes that only two metrics can be sufficient when monitoring an individual patient or when comparing two or more forms of management interventions. These two metrics comprise (1) a measure of efficacy such as Mean Glucose, HbA1c, %TIR, or %Time Above Range (%TAR) and (2) a measure of safety based on risk of hypoglycemia such as %TBR, Low Blood Glucose Index (LBGI), or frequency of specified types of hypoglycemic events per patient year. By analysis of the two-dimensional graphical and statistical relationships between metrics for safety and efficacy and by testing identity versus nonidentity of these relationships, one can improve sensitivity for detection of the effects of medications and of other therapeutic interventions, avoid the need for arbitrary scoring systems for glucose values falling within versus outside the target range, and offer the advantage of conceptual and practical simplicity.

Glycaemic variabilities: Key questions in pursuit of clarity

After years of intensive investigation, the definition of glycaemic variability remains unclear and the term variability in glucose homoeostasis might be more appropriate covering both short and long-term glycaemic variability. For the latter, we remain in the search of an accurate definition and related targets. Recent work leads us to consider that the within-subject variability of HbA1c calculated from consecutive determinations of HbA1c at regular time-intervals could be the most relevant index for assessing the long-term variability with a threshold value of 5% (%CV = SD of HbA1c/mean HbA1c) to separate stability from lability of HbA1c. Presently, no one can deny that short- and long-term glucose variability should be maintained within their lower ranges to limit the incidence of hypoglycaemia. Usually, therapeutic strategies aimed at reducing post-meal glucose excursions, i.e. the major contributor to daily glucose fluctuations, exert a beneficial effect on the short-term glucose variability. This explains the effectiveness of adjunct therapies with either GLP- receptor agonists or SGLT inhibitors in type 2 diabetes. In type 1 diabetes, the application of a CGM device alone reduces the short-term glycaemic variability. In contrast, sophisticated insulin delivery does not necessarily lead to such reductions despite marked downward shifts of 24-hour glycaemic profiles. Such contrasting observations raise the question as to whether the prolonged wear of CGM devices is or not the major causative factor for improvement in glucose variability among intensively insulin-treated persons with type 1 diabetes.

Analysis on frosting of heat exchanger and numerical simulation of heat transfer characteristics using BP neural network learning algorithm

The study is aimed at the frosting problem of the air source heat pump in the low temperature and high humidity environment, which reduces the service life of the system. First, the frosting characteristics at the evaporator side of the air source heat pump system are analyzed. Then, a new defrost technology is proposed, and dimensional theory and neural network are combined to predict the transfer performance of the new system. Finally, an adaptive network control algorithm is proposed to predict the frosting amount. This algorithm optimizes the traditional neural network algorithm control process, and it is more flexible, objective, and reliable in the selection of the hidden layer, the acquisition of the optimal function, and the selection of the corresponding learning rate. Through model performance, regression analysis, and heat transfer characteristics simulation, the effectiveness of this method is further confirmed. It is found that, the new air source heat pump defrost system can provide auxiliary heat, effectively regulating the temperature and humidity. The mean square error is 0.019827, and the heat pump can operate efficiently under frosting conditions. The defrost system is easy to operate, and facilitates manufactures designing for different regions under different conditions. This research provides reference for energy conservation, emission reduction, and sustainable economic development.

An ultra-long-acting recombinant insulin for the treatment of diabetes mellitus in cats

BACKGROUND

Treatment of diabetes mellitus (DM) in cats typically requires insulin injections q12h-q24h, posing a major compliance barrier for caregivers. Novel treatments enabling decreased injection frequency while maintaining safety are highly desirable. Insulin fused with feline immunoglobulin fragment crystallizable (Fc) has an ultra-long plasma half-life because it recycles through cells where it is protected from proteolysis.

HYPOTHESIS

Glycemic control can be achieved in diabetic cats with a recombinant fusion protein of a synthetic insulin and feline Fc (AKS-267c) administered SC weekly.

ANIMALS

Five cats with spontaneous DM.

METHODS

Cats previously controlled using insulin glargine q12h were transitioned to once-weekly injection of AKS-267c. The dose of AKS-267c was titrated weekly for 7 weeks based on continuous glucose monitoring. Clinical signs, body weight, fructosamine concentrations, and mean interstitial glucose concentrations (IG) were compared between baseline (week 0, on insulin glargine) and the last week of treatment. Data were assessed for normality and compared using parametric or nonparametric paired tests (as appropriate).

RESULTS

After 7 weeks of once-weekly injections, compared to baseline, there were no significant changes in clinical signs, body weight (median [range] gain, 0.1 kg [-0.1 to +0.7]; P = .5), fructosamine (-60 mmol/L [-338 to +206]; P = .6), and mean IG concentrations (change = -153 mmol/L [-179 to +29]; P = .3), and no adverse reactions were reported.

CONCLUSION

Successful control of clinical signs and maintenance of glycemia was achieved with this once-weekly novel insulin treatment. The efficacy and safety of this novel formulation should be further assessed in a large clinical trial.

Day-to-day variability of porcine lente, insulin glargine 300 U/mL and insulin degludec in diabetic dogs

Background

Day‐to‐day variability impacts safety of insulin therapy and the choice of monitoring strategies. Side‐by‐side comparisons of insulin formulations in diabetic dogs are scarce.

Hypothesis/Objectives

Insulin glargine 300 U/mL (IGla300) and insulin degludec (IDeg) are associated with less day‐to‐day glucose variability compared to porcine lente (PL) in diabetic dogs.

Animals

Seven intact male purpose‐bred beagles with toxin‐induced diabetes.

Methods

In this repeated measured study, PL, IGla300 and IDeg were compared in 2 phases: once‐daily (q24h) and twice‐daily (q12h) administration. Interstitial glucose concentrations (IG) were measured continuously throughout the study. For each formulation, maximal q24h dose was determined using the same algorithm (while avoiding hypoglycemia) and then maintained for 72 hours. In phase 2, 70% of the maximal q24h dose was administered q12h and maintained for 5 days regardless of hypoglycemia. Coefficient of variation (CV) and glycemic variability percentage (GVP) were calculated to determine day‐to‐day and intraday variability, respectively.

Results

There was no difference in day‐to‐day variability between PL, IGla300, and IDeg in the q24h phase. In the q12h phase, day‐to‐day variability was higher (P = .01) for PL (CV = 42.6 ± 6.8%) compared to IGla300 and IDeg (CV = 30.1 ± 7.7%, 25.2 ± 7.0%, respectively). The GVP of PL was lower (P = .02) compared to IGla300. There was no difference between PL, IGla300 and IDeg in %time IG < 70 mg/dL.

Conclusions and Clinical Importance

Insulin degludec and IGla300 administered q12h were associated with lower day‐to‐day variability, which might be advantageous in minimizing monitoring requirements without increasing the risk of hypoglycemia.

Comparison of pharmacodynamics between insulin glargine 100 U/mL and insulin glargine 300 U/mL in healthy cats

Insulin glargine (IGla) is a synthetic human-recombinant insulin analog that is used routinely in people as a q24h basal insulin. The 300 U/mL (U300) formulation of IGla is associated with longer duration of action and less within-day variability, making it a better basal insulin compared with the 100 U/mL (U100) formulation. We hypothesized that in healthy cats, IGlaU300 has a flatter time-action profile and longer duration of action compared with IGlaU100. Seven healthy neutered male, purpose-bred cats were studied in a randomized, crossover design. Pharmacodynamics of IGlaU100 and IGlaU300 (0.8 U/kg, subcutaneous) were determined by the isoglycemic clamp method. The time-action profile of IGlaU300 was flatter compared with IGlaU100 as demonstrated by lower peak (5.6 ± 1.1 mg/kg/min vs 8.3 ± 1.9 mg/kg/min, respectively; P = 0.04) with no difference in total metabolic effect (ME; P = 0.7) or duration of action (16.8 h ± 4.7 h vs 13.4 h ± 2.6 h; P = 0.2). The greater fraction of ME in the 12- to 24-h period postinjection (35 ± 23% vs 7 ± 8% respectively; P = 0.048) and lower intraday GIR% variability (7.8 ± 3.7% vs 17.4 ± 8.2% respectively; P = 0.03) supports a flatter time-action profile of IGlaU300. There were no differences in onset and end of the action. In summary, although both formulations have a similar duration of action that is well below 24 h, the ME of IGlaU300 is more evenly distributed over a 24 h period in healthy cats, making it a better candidate for once-daily injection in diabetics compared with IGlaU100.

Interpreting blood GLUcose data with R package iglu

Continuous Glucose Monitoring (CGM) data play an increasing role in clinical practice as they provide detailed quantification of blood glucose levels during the entire 24-hour period. The R package iglu implements a wide range of CGM-derived metrics for measuring glucose control and glucose variability. The package also allows one to visualize CGM data using time-series and lasagna plots. A distinct advantage of iglu is that it comes with a point-and-click graphical user interface (GUI) which makes the package widely accessible to users regardless of their programming experience. Thus, the open-source and easy to use iglu package will help advance CGM research and CGM data analyses. R package iglu is publicly available on CRAN and at https://github.com/irinagain/iglu.

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.

Determining the glycaemic responses of foods: conventional and emerging approaches

A low-glycaemic diet is crucial for those with diabetes and cardiovascular diseases. Information on the glycaemic index (GI) of different ingredients can help in designing novel food products for such target groups. This is because of the intricate dependency of material source, composition, food structure and processing conditions, among other factors, on the glycaemic responses. Different approaches have been used to predict the GI of foods, and certain discrepancies exist because of factors such as inter-individual variation among human subjects. Besides other aspects, it is important to understand the mechanism of food digestion because an approach to predict GI must essentially mimic the complex processes in the human gastrointestinal tract. The focus of this work is to review the advances in various approaches for predicting the glycaemic responses to foods. This has been carried out by detailing conventional approaches, their merits and limitations, and the need to focus on emerging approaches. Given that no single approach can be generalised to all applications, the review emphasises the scope of deriving insights for improvements in methodologies. Reviewing the conventional and emerging approaches for the determination of GI in foods, this detailed work is intended to serve as a state-of-the-art resource for nutritionists who work on developing low-GI foods.

The effect of the ghrelin-receptor agonist capromorelin on glucose metabolism in healthy cats

Somatostatin secretion from islet delta cells is important in maintaining low glycemic variability (GV) by providing negative feedback to beta cells and inhibiting insulin secretion. Capromorelin is a ghrelin-receptor agonist that activates the growth hormone secretagogue receptor on delta cells. We hypothesized that in cats, capromorelin administration will result in decreased GV at the expense of reduced insulin secretion and glucose tolerance. Seven healthy cats were treated with capromorelin from days 1-30. After the first day, fasting blood glucose increased (+13 ± 3 mg/dL, P < 0.0001), insulin decreased (+128 ± 122 ng/dL, P = 0.03), and glucagon was unchanged. Blood glucose was increased throughout an intravenous glucose tolerance test on day 1 with blunting of first-phase insulin response ([FPIR] 4,931 ± 2,597 ng/L/15 min) compared with day -3 (17,437 ± 8,302 ng/L/15 min, P = 0.004). On day 30, FPIR was still blunted (9,993 ± 4,285 ng/L/15 min, P = 0.045), but glucose tolerance returned to baseline. Mean interstitial glucose was increased (+19 ± 6 mg/dL, P = 0.03) on days 2-4 but returned to baseline by days 27-29 (P = 0.3). On days 2-4, GV was increased (SD = 9.7 ± 3.2) compared with baseline (SD = 5.0 ± 1.1, P = 0.02) and returned to baseline on days 27-29 (SD = 6.1 ± 1.1, P = 0.16). In summary, capromorelin caused a decline in insulin secretion and glycemic control and an increase in glucose variability early in the course of treatment, but these effects diminished toward the end of 30 d of treatment.

Randomized Crossover Comparison of Automated Insulin Delivery Versus Conventional Therapy Using an Unlocked Smartphone with Scheduled Pasta and Rice Meal Challenges in the Outpatient Setting

Background: Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of prescribed meals. We evaluated performance of our interoperable artificial pancreas system (iAPS) in the at-home setting, running on an unlocked smartphone, with scheduled meal challenges in a randomized crossover trial. Methods: Ten adults with type 1 diabetes completed 2 weeks of AID-based control and 2 weeks of conventional therapy in random order where they consumed regular pasta or extra-long grain white rice as part of a complete dinner meal on six different occasions in both arms (each meal thrice in random order). Surveys assessed satisfaction with AID use. Results: Postprandial differences in conventional therapy were 10,919.0 mg/dL × min (95% confidence interval [CI] 3190.5-18,648.0, P = 0.009) for glucose area under the curve (AUC) and 40.9 mg/dL (95% CI 4.6-77.3, P = 0.03) for peak continuous glucose monitor glucose, with rice showing greater increases than pasta. White rice resulted in a lower estimate over pasta by a factor of 0.22 (95% CI 0.08-0.63, P = 0.004) for AUC under 70 mg/dL. These glycemic differences in both meal types were reduced under AID-based control and were not statistically significant, where 0-2 h insulin delivery decreased by 0.45 U for pasta (P = 0.001) and by 0.27 U for white rice (P = 0.01). Subjects reported high overall satisfaction with the iAPS. Conclusions: The AID system running on an unlocked smartphone improved postprandial glucose control over conventional therapy in the setting of challenging meals in the outpatient setting. Clinical Trial Registry: clinicaltrials.gov NCT03767790.

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis

Like many wearables, flash glucose monitoring relies on user compliance and is subject to missing data. As recent research is beginning to utilise glucose technologies as behaviour change tools, it is important to understand whether missing data are tolerable. Complete Freestyle Libre data files were amputed to remove 1-6 h of data both at random and over mealtimes (breakfast, lunch, and dinner). Absolute percent errors (MAPE) and intraclass correlation coefficients (ICC) were calculated to evaluate agreement and reliability. Thirty-two (91%) participants provided at least 1 complete day (24 h) of data (age: 44.8 ± 8.6 years, female: 18 (56%); mean fasting glucose: 5.0 ± 0.6 mmol/L). Mean and continuous overall net glycaemic action (CONGA) (60 min) were robust to data loss (MAPE ≤3%). Larger errors were calculated for standard deviation, coefficient of variation (CV) and mean amplitude of glycaemic excursions (MAGE) at increasing missingness (MAPE: 2%-10%, 2%-9%, and 4%-18%, respectively). ICC decreased as missing data increased, with most indicating excellent reliability (>0.9) apart from certain MAGE ICCs, which indicated good reliability (0.84-0.9). Researchers and clinicians should be aware of the potential for larger errors when reporting standard deviation, CV, and MAGE at higher rates of data loss in nondiabetic populations. But where mean and CONGA are of interest, data loss is less of a concern. Novelty: As research now utilises flash glucose monitoring as behavioural change tools in nondiabetic populations, it is important to consider the influence of missing data. Glycaemic variability indices of mean and CONGA are robust to data loss, but standard deviation, CV, and MAGE are influenced at higher rates of missingness.

Low Blood Glucose Index and Hypoglycaemia Risk: Insulin Glargine 300 U/mL Versus Insulin Glargine 100 U/mL in Type 2 Diabetes

INTRODUCTION

We examined differences in hypoglycaemia risk between insulin glargine 300 U/mL (Gla-300) and insulin glargine 100 U/mL (Gla-100) in individuals with type 2 diabetes (T2DM) using the low blood glucose index (LBGI).

METHODS

Daily profiles of self-monitored plasma glucose (SMPG) from the EDITION 2, EDITION 3 and SENIOR treat-to-target trials of Gla-300 versus Gla-100 were used to compute the LBGI, which is an established metric of hypoglycaemia risk. The analysis also examined documented (blood glucose readings < 3.0 mmol/L [54 mg/dL]) symptomatic hypoglycaemia (DSH).

RESULTS

Overall LBGI in EDITION 2 and SENIOR and night-time LBGI in all three trials were significantly (p < 0.05) lower with Gla-300 versus Gla-100. The largest differences between Gla-300 and Gla-100 were observed during the night. In all three trials, individual LBGI results correlated with the observed number of DSH episodes per participant (EDITION 2 [r = 0.35, p < 0.001]; EDITION 3 [r = 0.26, p < 0.001]; SENIOR [r = 0.30, p < 0.001]). Participants at moderate risk of experiencing hypoglycaemia (defined as LBGI > 1.1) reported 4- to 8-fold more frequent DSH events than those at minimal risk (LBGI ≤ 1.1) (p ≤  0.009).

CONCLUSIONS

The LBGI identified individuals with T2DM at risk for hypoglycaemia using SMPG data and correlated with the number of DSH events. Using the LBGI metric, a lower risk of hypoglycaemia with Gla-300 than Gla-100 was observed in all three trials. The finding that differences in LBGI are greater at night is consistent with previously published differences in the pharmacokinetic profiles of Gla-300 and Gla-100, which provides the physiological foundation for the presented results.

Characterization of Daily Glycemic Variability in Subjects with Type 1 Diabetes Using a Mixture of Metrics

Background: Glycemic variability (GV) is an important component of glycemic control for patients with type 1 diabetes (T1D). The inadequacy of existing measurements lies in the fact that they view the variability from different aspects, so that no consensus has been reached among physicians as to which metrics to use in practice. Moreover, although GV, from 1 day to another, can show very different patterns, few metrics have been dedicated to daily evaluations. Materials and Methods: A reference (stable glycemia) statistical model is built based on a combination of daily computed canonical glycemic control metrics including variability. The metrics are computed for subjects from the TRIMECO islet transplantation trial, selected when their β-score (composite score for grading success) is ≥6 after a transplantation. Then, for any new daily glycemia recording, its likelihood with respect to this reference model provides a multimetric score of daily GV severity. In addition, determining the likelihood value that best separates the daily glycemia with β-score = 0 from that with β-score ≥6, we propose an objective decision rule to classify daily glycemia into "stable" or "unstable." Results: The proposed characterization framework integrates multiple standard metrics and provides a comprehensive daily GV index, based on which, long-term variability evaluations and investigations on the implicit link between variability and β-score can be carried out. Evaluation, in a daily GV classification task, shows that the proposed method is highly concordant to the experience of diabetologists. Conclusion: A multivariate statistical model is proposed to characterize the daily GV of subjects with T1D. The model has the advantage to provide a single variability score that gathers the information power of a number of canonical scores, too partial to be used individually. A reliable decision rule to classify daily variability measurements into stable or unstable is also provided.

Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices

BACKGROUND

Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted from continuous glucose monitoring (CGM) data, can distinguish between stages of diabetes progression. We showed that 25 indices driving a logistic regression classifier can differentiate between healthy and nonhealthy individuals; whereas 37 GV indices and four individual parameters, feeding a polynomial-kernel support vector machine (SVM), can further distinguish between impaired glucose tolerance (IGT) and type 2 diabetes (T2D). The latter approach has some limitations to interpretability (complex model, extensive index pool). In this article, we try to obtain the same performance with a simpler classifier and a parsimonious subset of indices.

METHODS

We analyzed the data of 62 subjects with IGT or T2D. We selected 17 interpretable GV indices and four parameters (age, sex, BMI, waist circumference). We trained a SVM on the data of a baseline visit and tested it on the follow-up visit, comparing the results with the state-of-art methods.

RESULTS

The linear SVM fed by a reduced subset of 17 GV indices and four basic parameters achieved 82.3% accuracy, only marginally worse than the reference 87.1% (41-features polynomial-kernel SVM). Cross-validation accuracies were comparable (69.6% vs 72.5%).

CONCLUSION

The proposed SVM fed by 17 GV indices and four parameters can differentiate between IGT and T2D. Using a simpler model and a parsimonious set of indices caused only a slight accuracy deterioration, with significant advantages in terms of interpretability.

Prediction of Nocturnal Hypoglycemia From Continuous Glucose Monitoring Data in People With Type 1 Diabetes: A Proof-of-Concept Study

BACKGROUND

Intensive insulin therapy has documented benefits but may also come at the expense of a higher risk of hypoglycemia. Hypoglycemia is associated with higher all-cause mortality and nocturnal hypoglycemia has been associated with the sudden dead-in-bed syndrome. This proof-of-concept study sought to investigate if nocturnal hypoglycemia can be predicted.

METHOD

Continuous glucose monitoring, meal, insulin, and demographics data from 463 people with type 1 diabetes were obtained from a clinical trial. A total of 4721 nights without or with hypoglycemia (429) were available including data from three consecutive days before the night. Thirty-two features were calculated based on these data. Data were split into 20% participants for evaluation and 80% for training. The optimal feature subset was found from forward selection of the 80% participants with linear discriminant analysis as basis for the classifier.

RESULTS

The forward selection resulted in a feature subset of four features. The evaluation resulted in an area under the receiver operating characteristics curve (ROC-AUC) of 0.79 leading to a sensitivity and a specificity of, e.g., 75% and 70%.

CONCLUSIONS

It was possible to predict nocturnal hypoglycemic episodes with a ROC-AUC of 0.79. A warning at bedtime about nocturnal hypoglycemia could be of great help for people with diabetes to enable preventive actions. Further development of the proposed algorithm is needed for implementation in everyday practice.

Glycemic variability indices can be used to diagnose islet transplantation success in type 1 diabetic patients

AIMS

High glycemic variability (GV) is the major indication for islet transplantation (IT) in patients with type 1 diabetes (T1D). The actual criteria used to assess graft function do not consider GV improvement. Our study aimed to describe GV indices' evolution in T1D patients who benefited from IT during the TRIMECO trial and to evaluate if thresholds might be defined to diagnose IT success.

METHODS

We collected data from 29 patients of the TRIMECO trial, a clinical trial (NCT01148680) comparing the metabolic efficacy of IT with intensive insulin therapy. Based on CGM data, we analyzed mean glucose level and four GV indices (standard deviation, coefficient of variation, MAGE and GVP) before (M0) and 6 months (M6) after IT.

RESULTS

Each GV index decreased significantly between M0 and M6: SD 53.9 mg/dL [44.6-61.5] versus 20.1 mg/dL [13.5-24.3]; CV 35.2% [30.6-37.7] versus 17.3% [12.0-20.5]; MAGE 134.9 mg/dl [111.2-155.8] versus 51.9 mg/dL [32.4-62.4]; GVP 35.3% [24.9-47.2] versus 12.2% [6.2-18.8] (p ≤ 0.0001). Thresholds diagnosing IT success at 6 months post-transplant were an SD at 22.76 mg/dL (sensibility 88.89%, specificity 80.00%), a CV at 17.47% (sensibility 88.89%, specificity 70.00%), a MAGE at 54.81 mg/dL (sensibility 88.89%, specificity 80.00%) and a GVP at 12.27% (sensibility 88.89%, specificity 70.00%).

CONCLUSIONS

This study confirms a positive impact of IT on GV. The proposed thresholds allow an easy evaluation of IT success using only CGM data and may be a clinical tool for the follow-up of transplanted patients.

Positioning time in range in diabetes management

Recent upswings in the use of continuous glucose monitoring (CGM) technologies have given people with diabetes and healthcare professionals unprecedented access to a range of new indicators of glucose control. Some of these metrics are useful research tools and others have been welcomed by patient groups for providing insights into the quality of glucose control not captured by conventional laboratory testing. Among the latter, time in range (TIR) is an intuitive metric that denotes the proportion of time that a person's glucose level is within a desired target range (usually 3.9-10.0 mmol/l [3.5-7.8 mmol/l in pregnancy]). For individuals choosing to use CGM technology, TIR is now often part of the expected conversation between patient and healthcare professional, and consensus recommendations have recently been produced to facilitate the adoption of standardised TIR targets. At a regulatory level, emerging evidence linking TIR to risk of complications may see TIR being more widely accepted as a valid endpoint in future clinical trials. However, given the skewed distribution of possible glucose values outside of the target range, TIR (on its own) is a poor indicator of the frequency or severity of hypoglycaemia. Here, the state-of-the-art linking TIR with complications risk in diabetes and the inverse association between TIR and HbA_1c are reviewed. Moreover, the importance of including the amount and severity of time below range (TBR) in any discussions around TIR and, by inference, time above range (TAR) is discussed. This review also summarises recent guidance in setting 'time in ranges' goals for individuals with diabetes who wish to make use of these metrics. For most people with type 1 or type 2 diabetes, a TIR >70%, a TBR <3.9 mmol/l of <4%, and a TBR <3.0 mmol/l of <1% are recommended targets, with less stringent targets for older or high-risk individuals and for those under 25 years of age. As always though, glycaemic targets should be individualised and rarely is that more applicable than in the personal use of CGM and the data it provides.

Comparison of Glycemic Variability Indices: Blood Glucose, Risk Index, and Coefficient of Variation in Predicting Adverse Outcomes for Patients Undergoing Cardiac Surgery

OBJECTIVES

Fluctuations in blood glucose (glycemic variability) increase the risk of adverse outcomes. No universally accepted tool for glycemic variability exists during the perioperative period. The authors compared 2 measures of glycemic variability-(1) coefficient of variation (CV) and (2) the Blood Glucose Risk Index (BGRI)-in predicting adverse outcomes after cardiac surgery.

DESIGN

Prospective, observational study.

SETTING

Single-center, teaching hospital.

PARTICIPANTS

A total of 1,963 adult patients undergoing cardiac surgery.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Postoperative blood glucose levels were measured hourly for the first 24 hours and averaged every 4 hours (4, 8, 12, 16, 20, and 24 hours). Glycemic variability was measured by CV and the BGRI. The primary outcome, major adverse events (MAEs), was a predefined composite of postoperative complications (death, reoperation, deep sternal infection, stroke, pneumonia, renal failure, tamponade, and myocardial infarction). Logistic regression models were constructed to evaluate the association. Predictive ability was measured using C-statistics. Major adverse events were seen in 170 (8.7%) patients. Only the fourth quartile of CV showed association (odds ratio [OR] 1.91; 95% confidence interval [CI] [1.19-3.14]; p = 0.01), whereas BGRI was related significantly to MAE (OR 1.20; 95% CI [1.10-1.32]; p < 0.0001). The predictive ability of CV and BGRI increased on adding the standard Society of Thoracic Surgeons (STS) risk index. The C-statistic for STS was 0.68, whereas STS + CV was 0.70 (p = 0.012) and STS + BGRI was 0.70 (p = 0.012).

CONCLUSION

Both CV and the BGRI had good predictive ability. The BGRI being a continuous variable could be a preferred measure of glycemic variability in predicting adverse outcomes (cutoff value 2.24) after cardiac surgery.

A Novel CGM Metric-Gradient and Combining Mean Sensor Glucose Enable to Improve the Prediction of Nocturnal Hypoglycemic Events in Patients with Diabetes

Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, and it is often asymptomatic. A novel CGM metric-gradient was proposed in this paper, and a method of combining mean sensor glucose (MSG) and gradient was presented for the prediction of nocturnal hypoglycemia. For this purpose, the data from continuous glucose monitoring (CGM) encompassing 1,921 patients with diabetes were analyzed, and a total of 302 nocturnal hypoglycemic events were recorded. The MSG and gradient values were calculated, respectively, and then combined as a new metric (i.e., MSG+gradient). In addition, the prediction was conducted by four algorithms, namely, logistic regression, support vector machine, random forest, and long short-term memory. The results revealed that the gradient of CGM showed a downward trend before hypoglycemic events happened. Additionally, the results indicated that the specificity and sensitivity based on the proposed method were better than the conventional metrics of low blood glucose index (LBGI), coefficient of variation (CV), mean absolute glucose (MAG), lability index (LI), etc., and the complex metrics of MSG+LBGI, MSG+CV, MSG+MAG, and MSG+LI, etc. Specifically, the specificity and sensitivity were greater than 96.07% and 96.03% at the prediction horizon of 15 minutes and greater than 87.79% and 90.07% at the prediction horizon of 30 minutes when the proposed method was adopted to predict nocturnal hypoglycemic events in the aforementioned four algorithms. Therefore, the proposed method of combining MSG and gradient may enable to improve the prediction of nocturnal hypoglycemic events. Future studies are warranted to confirm the validity of this metric.

Monitoring of Pediatric Type 1 Diabetes

Regular self-monitoring of blood glucose levels, and ketones when indicated, is an essential component of type 1 diabetes (T1D) management. Although fingerstick blood glucose monitoring has been the standard of care for decades, ongoing rapid technological developments have resulted in increasingly widespread use of continuous glucose monitoring (CGM). This article reviews recommendations for self-monitoring of glucose and ketones in pediatric T1D with particular emphasis on CGM and factors that impact the accuracy and real-world use of this technology.

Arterial stiffness in children and adolescents with and without continuous insulin infusion

Background Arterial stiffness is a consequence of aging, but there are several diseases that contribute to this process. The evaluation of pulse wave velocity (PWV) allows a dynamic evaluation of vascular distensibility and the detection of atherosclerosis at an early stage. It was intended to evaluate the PWV in children and adolescents with type 1 diabetes mellitus (T1DM) and to compare their outcome according to the type of treatment used. Methods Forty-eight patients were randomly selected. Inclusion criteria: T1DM, under intensive insulin therapy (multiple daily insulin administrations [MDI] or continuous insulin infusion system [CIIS]). Exclusion criteria: existence of another chronic pathology or microvascular complications. Echocardiography was performed and three measurements of PWV were done, with their mean calculated. Results Most of the children and adolescents presented a PWV ≥ the 75th centile. There was a statistically significant difference for hemoglobin A1c (HbA1c) (7.8 in CIIS vs. 9 in MDI, p < 0.05). There were not statistically significant differences in the PWV between the two groups. This can be attributed to the fact that children with CIIS are those who previously presented greater glycemic instability. There was a significant correlation between PWV and disease duration (Pearson's correlation coefficient [r] = 0.314, p = 0.036). Conclusions This study showed that in children and adolescents with T1DM, there is an important prevalence of arterial stiffness, translated by an increase in PWV. This increase in PWV appears to exist even in very young children with little disease evolution time.

Patterns of Glycemic Variability During a Diabetes Self-Management Educational Program

Background: Variations in blood glucose levels over a given time interval is termed as glycemic variability (GV). Higher GV is associated with higher diabetes-related complications. The current study was done with the aim of detecting the sensitivity of various GV indices among individuals with type 2 diabetes mellitus of different glycemic control status. Methods: We performed a longitudinal study among individuals with type 2 diabetes mellitus (T2DM) who were participating in a two-week diabetes self-management education (DSME) program. Participants were categorized by their HbA1c as poor (≥8%), acceptable (7%–8%), and optimal control (<7%). Continuous glucose monitoring (CGM) sensors recorded interstitial glucose every 15 min from day 1. The evaluated GV measures include standard deviation (SD), coefficient of variation (CV), mean amplitude of glycemic excursion (MAGE), continuous overlapping net glycemic action (CONGA), mean of daily difference for inter-day variation (MODD), high blood glucose index (HBGI), and low blood glucose index (LBGI). Results: A total of 41 study participants with 46347 CGM values were available for analysis. Of 41 participants, 20 (48.7%) were in the poor, 10 (24.3%) in the acceptable, and 11 (26.8%) in the optimal control group. The GV indices (SD; CV; MODD; MAGE; CONGA; HBGI) of poorly controlled (77.43; 38.02; 45.82; 216.63; 14.10; 16.62) were higher than acceptable (50.02; 39.32; 30.79; 138.01; 8.87; 5.56) and optimal (34.15; 29.46; 24.56; 126.15; 8.67; 3.13) control group. Glycemic variability was reduced in the poorly and acceptably controlled groups by the end of the 2-week period. There was a rise in LBGI in the optimally controlled group, indicating pitfalls of tight glycemic control. Conclusion: Indices of glycemic variability are useful complements, and changes in it can be demonstrated within short periods.

Applications of Variability Analysis Techniques for Continuous Glucose Monitoring Derived Time Series in Diabetic Patients

Methods from non-linear dynamics have enhanced understanding of functional dysregulation in various diseases but received less attention in diabetes. This retrospective cross-sectional study evaluates and compares relationships between indices of non-linear dynamics and traditional glycemic variability, and their potential application in diabetes control. Continuous glucose monitoring provided data for 177 subjects with type 1 (n = 22), type 2 diabetes (n = 143), and 12 non-diabetic subjects. Each time series comprised 576 glucose values. We calculated Poincaré plot measures (SD1, SD2), shape (SFE) and area of the fitting ellipse (AFE), multiscale entropy (MSE) index, and detrended fluctuation exponents (α1, α2). The glycemic variability metrics were the coefficient of variation (%CV) and standard deviation. Time of glucose readings in the target range (TIR) defined the quality of glycemic control. The Poincaré plot indices and α exponents were higher (p < 0.05) in type 1 than in the type 2 diabetes; SD1 (mmol/l): 1.64 ± 0.39 vs. 0.94 ± 0.35, SD2 (mmol/l): 4.06 ± 0.99 vs. 2.12 ± 1.04, AFE (mmol²/l²): 21.71 ± 9.82 vs. 7.25 ± 5.92, and α1: 1.94 ± 0.12 vs. 1.75 ± 0.12, α2: 1.38 ± 0.11 vs. 1.30 ± 0.15. The MSE index decreased consistently from the non-diabetic to the type 1 diabetic group (5.31 ± 1.10 vs. 3.29 ± 0.83, p < 0.001); higher indices correlated with lower %CV values (r = -0.313, p < 0.001). In a subgroup of type 1 diabetes patients, insulin pump therapy significantly decreased SD1 (-0.85 mmol/l), SD2 (-1.90 mmol/l), and AFE (-16.59 mmol²/l²), concomitantly with %CV (-15.60). The MSE index declined from 3.09 ± 0.94 to 1.93 ± 0.40 (p = 0.001), whereas the exponents α1 and α2 did not. On multivariate regression analyses, SD1, SD2, SFE, and AFE emerged as dominant predictors of TIR (β = -0.78, -1.00, -0.29, and -0.58) but %CV as a minor one, though α1 and MSE failed. In the regression models, including SFE, AFE, and α2 (β = -0.32), %CV was not a significant predictor. Poincaré plot descriptors provide additional information to conventional variability metrics and may complement assessment of glycemia, but complexity measures produce mixed results.

Glucose Variability: A Review of Clinical Applications and Research Developments

Glycemic variability (GV) is a major consideration when evaluating quality of glycemic control. GV increases progressively from prediabetes through advanced T2D and is still higher in T1D. GV is correlated with risk of hypoglycemia. The most popular metrics for GV are the %Coefficient of Variation (%CV) and standard deviation (SD). The %CV is correlated with risk of hypoglycemia. Graphical display of glucose by date, time of day, and day of the week, and display of simplified glucose distributions showing % of time in several ranges, provide clinically useful indicators of GV. SD is highly correlated with most other measures of GV, including interquartile range, mean amplitude of glycemic excursion, mean of daily differences, and average daily risk range. Some metrics are sensitive to the frequency, periodicity, and complexity of glycemic fluctuations, including Fourier analysis, periodograms, frequency spectrum, multiscale entropy (MSE), and Glucose Variability Percentage (GVP). Fourier analysis indicates progressive changes from normal subjects to children and adults with T1D, and from prediabetes to T2D. The GVP identifies novel characteristics for children, adolescents, and adults with type 1 diabetes and for adults with type 2. GVP also demonstrated small rapid glycemic fluctuations in people with T1D when using a dual-hormone closed-loop control. MSE demonstrated systematic changes from normal subjects to people with T2D at various stages of duration, intensity of therapy, and quality of glycemic control. We describe new metrics to characterize postprandial excursions, day-to-day stability of glucose patterns, and systematic changes of patterns by day of the week. Metrics for GV should be interpreted in terms of percentiles and z-scores relative to identified reference populations. There is a need for large accessible databases for reference populations to provide a basis for automated interpretation of GV and other features of continuous glucose monitoring records.

Metrics to Evaluate Quality of Glycemic Control: Comparison of Time in Target, Hypoglycemic, and Hyperglycemic Ranges with "Risk Indices"

OBJECTIVE

We sought to cross validate several metrics for quality of glycemic control, hypoglycemia, and hyperglycemia.

RESEARCH DESIGN AND METHODS

We analyzed the mathematical properties of several metrics for overall glycemic control, and for hypo- and hyperglycemia, to evaluate their similarities, differences, and interrelationships. We used linear regression to describe interrelationships and examined correlations between metrics within three conceptual groups.

RESULTS

There were consistently high correlations between %Time in range (%TIR) and previously described risk indices (M₁₀₀, Blood Glucose Risk Index [BGRI], Glycemic Risk Assessment Diabetes Equation [GRADE], Index of Glycemic Control [IGC]), and with J-Index (J). There were also high correlations among %Hypoglycemia, Low Blood Glucose Index (LBGI), percentage of GRADE attributable to hypoglycemia (GRADE_{%Hypoglycemia}), and Hypoglycemia Index, but negligible correlation with J. There were high correlations of percentage of time in hyperglycemic range (%Hyperglycemia) with High Blood Glucose Index (HBGI), percentage of GRADE attributable to hyperglycemia (GRADE_{%Hyperglycemia}), Hyperglycemia Index, and J. %TIR is highly negatively correlated with %Hyperglycemia but very weakly correlated with %Hypoglycemia. By adjusting the parameters used in IGC, Hypoglycemia Index, Hyperglycemia Index, or in M_R, one can more closely approximate the properties of BGRI, LBGI, or HBGI, and of GRADE, GRADE_{%Hypoglycemia}, or GRADE_{%Hyperglycemia}.

CONCLUSIONS

Simple readily understandable criteria such as %TIR, %Hypoglycemia, and %Hyperglycemia are highly correlated with and appear to be as informative as "risk indices." The J-Index is sensitive to hyperglycemia but insensitive to hypoglycemia.