Improved quality of glycemic control and reduced glycemic variability with use of continuous glucose monitoring. Diabetes Technol Ther. 2009;11:717-723. [PMID: 19905888 DOI: 10.1089/dia.2009.0077]

Time With Rapid Change of Glucose

BACKGROUND

A variety of metrics are used to describe glycemic variation, some of which may be difficult to comprehend or require complex strategies for smoothing of the glucose curve. We aimed to describe a new metric named time with rapid change of glucose (TRC), which is presented as percentage of time, similar to time above range (TAR), time in range (TIR), and time below range (TBR).

METHOD

We downloaded glucose data for 90 days from 159 persons with type 1 diabetes using the Abbott Freestyle Libre version 1. We defined TRC as the proportion of time (%) with an absolute rate of change of glucose > 1.5 mmol/L/15 minutes (1.8mg/dL/min) corresponding to a minimum rate of change for glucose in the 3.9-10.0 mmol/L (70-180 mg/dL) range within 1 hour. TRC is related to the other glucose variability metrics: CV within day (CVw) and mean amplitude of glycemic excursion (MAGE).

RESULTS

The more than 1.27 million glucose rates were t-location scale distributed with SD 0.91 mmol/L/15 min (1.1 mg/dL/15 min). The median TRC was 6.9% (IQR 4.5%-9.5%). The proportion of TRC with positive slope was 3.9% (2.6%-5.3%) and significantly higher than the proportion with negative slope 2.8% (1.5%-4.4%) P < .001. TRC correlated with CVw and MAGE (Spearman's correlation coefficient .56 and .65, respectively, P < .001).

CONCLUSION

TRC is proposed as an easily perceived metric to compare the performance of hybrid or fully automated closed-loop insulin delivery systems to obtain glucose homeostasis.

Glucose sensor with predictive alarm for hypoglycaemia: Improved glycaemic control in adolescents with type 1 diabetes

AIM

Hypoglycaemic events are linked to microvascular and macrovascular complications in people with type 1 diabetes. We aimed to evaluate the efficacy of glucose sensor [real-time continuous glucose monitoring (RT-CGM)] with predictive alarm (PA) in reducing the time spent below the range (%TBR <70 mg/dl) in a group of adolescents with type 1 diabetes (AwD).

MATERIALS AND METHODS

This was a crossover, monocentric and randomized study. RT-CGM was set with Alarm on Threshold (AoT) at 70 mg/dl) or PA for hypoglycaemia (20 m before threshold). Twenty AwD were enrolled and randomized to either a PA/AoT or AoT/PA treatment sequence, in a 1:1 ratio. The two groups (PA vs. AoT) were compared using two-way repeated measures ANOVA taking account of the carryover effect.

RESULTS

AwD using PA for hypoglycaemia spent less time in severe hypoglycaemia (%TBR2 <54 mg/dl; 0.32 ± 0.31 vs. 0.91 ± 0.90; p < .02) and hypoglycaemia (%TBR <70 mg/dl; 1.68 ± 1.06 vs. 2.90 ± 2.05; p < .02), with better glycaemia risk index (51.3 ± 11.0 vs. 61.5 ± 12.6; p ≤ .01).

CONCLUSION

The use of RT-CGM with PA for hypoglycaemia technology in AwD using multiple daily insulin injection treatment could significantly reduce the risk of having hypoglycaemic events resulting in an improved quality of glucose control.

CLINICAL TRIAL REGISTRATION NUMBER

NCT05574023.

Association between perioperative glucose profiles assessed by the continuous glucose monitoring (CGM) system and prognosis in patients with ST-segment elevation myocardial infarction (STEMI): protocol for a cohort study

INTRODUCTION

ST-segment elevation myocardial infarction (STEMI) presents a serious cardiovascular condition requiring prompt intervention. Dysglycaemia has been identified as a significant risk factor impacting STEMI prognosis. However, limited research has focused on comprehensively examining the association between glucose dynamics during the perioperative period and patient outcomes. This study aims to address this gap by leveraging continuous glucose monitoring (CGM) technology to gain real-time insights into glucose fluctuations and their potential impact on STEMI prognosis.

METHODS AND ANALYSIS

This is a multicentre, prospective, 3-year follow-up cohort study. Between May 2023 and May 2024, 550 eligible STEM patients who underwent percutaneous coronary intervention are expected to be recruited. Using the CGM system, continuous glucose levels will be collected throughout the perioperative phase. Key clinical parameters, including cardiac biomarkers, angiographic findings and major adverse cardiovascular events, will be assessed in relation to glucose profile.

ETHICS AND DISSEMINATION

The study was approved by the Medical Research Ethics Committee of The First Affiliated Hospital of University of Science and Technology of China and will be conducted in accordance with the moral, ethical and scientific principles of the Declaration of Helsinki. Written informed consent will be obtained from all participants before any study-related procedures are implemented. Study results will be disseminated through conferences and peer-reviewed scientific journals.

TRIAL REGISTRATION NUMBER

ChiCTR2300069662.

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.

Emerging Diabetes Technologies: Continuous Glucose Monitors/Artificial Pancreases

Over the past decade there have been many advances in diabetes technologies, such as continuous glucose monitors (CGM s), insulin-delivery devices, and hybrid closed loop systems . Now most CGMs (Medtronic-Guardian, Dexcom-G6, and Abbott-Libre-2) have MARD values of < 10%, in contrast to two decades ago when the MARD used to be > 20%. In addition, the majority of the new CGMs do not require calibrations, and the latest CGMs last for 10-14 days. An implantable 6-months CGM by Eversense-3 is now approved in the USA and Europe. Recently, the FDA approved Libre 3 which provides real-time glucose values every minute. Even though it is approved as an iCGM it is not interoperable with automatic-insulin-delivery (AID) systems. The newer CGMs that are likely to be launched in the next few months in the USA include the 10-11 days Dexcom G7 (60% smaller than the existing G6), and the 7-days Medtronic Guardian 4. Most of the newer CGM have several features like automatic initialization, easy insertion, predictive alarms, and alerts. It has also been noticed that an arm insertion site might have better accuracy than abdomen or other sites, like the buttock for kids. Lag time between YSI and different sensors have been reported differently, sometimes it is down to 2-3 min; however, in many instances, it is still 15-20 min, especially when the rate of change of glucose is > 2 mg/min. We believe that in the next decade there will be a significant increase in the number of people who use CGM for their day-to-day diabetes care.

The use of long-acting insulin degludec in adult patients with type 2 diabetes mellitus in real clinical practice in Russia

BACKGROUND: Effective glycaemic control remains the most important task in managing the risks of Diabetes type 2 complications development. In this regard, the choice of insulin preparations with minimal variability of action is of utmost importance since this approach allows achieving the maximum treatment effectiveness and adequate safety level.

AIM: The aim of this study was to investigate insulin degludec treatment effect on glycemic control in adult patients with Diabetes Mellitus (DM) type 2 in a real-world clinical setting in the Russian Federation.

MATERIALS AND METHODS: The open prospective study was conducted in 2020–2021 in 35 clinical centers in 31 cities of the Russian Federation. The study included adult patients with type 2 DM treated according to Russian routine clinical practice. The prospective follow-up period was 26 weeks. The main study endpoints were changes in HbA1c level, fasting plasma glucose, insulin daily doses, number, and characteristics of different types of hypoglycaemia episodes and adverse events (AEs), and patient preferences compared to previous treatment.

RESULTS: The study enrolled 494 patients. By the end of follow-up period:

CONCLUSION: The study demonstrated a statistically significant improvement in glycemic control, accompanied by basal insulin dose decrease combined with the absence of severe episodes of hypoglycemia, and significant decrease of nonsevere episodes (total and nocturnal). These results led to a large proportion of patients wanting to continue insulin degludec treatment preferring the medicine over previous treatment.

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.

Impact of anxiety, depression and disease-related distress on long-term glycaemic variability among subjects with Type 1 diabetes mellitus

BACKGROUND

Anxiety, depression, and disease-related distress are linked to worse overall glycaemic control, in terms of HbA1c. This study was aimed to evaluate whether traits of these emotional disorders are associated with long-term glycaemic variability in subjects with Type 1 diabetes.

METHODS

Longitudinal retrospective study. Six-year HbA1c data (2014-2019) from 411 subjects with Type 1 diabetes who had participated in a previous study to design a diabetes-specific quality of life questionnaire in the year 2014 were included. Scores for Spanish versions of the Hospital Anxiety and Depression Scale (HADS) and Problem Areas in Diabetes (PAID) scale were obtained at baseline, along with sociodemographic and clinical data. Long-term glycaemic variability was measured as the coefficient of variation of HbA1c (HbA1c-CV). The association between HADS and PAID scores and HbA1c-CV was analysed with Spearman correlations and multiple regression models, both linear and additive, including other covariates (age, sex, diabetes duration time, type of treatment, baseline HbA1c, use of anxiolytic or antidepressant drugs, education level and employment status).

RESULTS

Scores of depression, anxiety and distress were positively and significantly correlated to HbA1c-CV in univariate analyses. Multiple regression study demonstrated an independent association only for diabetes distress score (p < 0.001). Age, diabetes duration time, baseline HbA1c, education level and employment status were also significantly associated with HbA1c-CV. However, when subjects were analyzed separately in two age groups, distress scores were associated with HbA1c-CV only among those aged 25 years or older, while anxiety scores, but not distress, were associated with HbA1c-CV among those younger than 25 years.

CONCLUSIONS

Psychological factors, particularly disease-related distress and anxiety, are associated with long-term glycaemic variability in subjects with Type 1 diabetes.

Alterations in Glycemic Variability, Vascular Health, and Oxidative Stress following a 12-Week Aerobic Exercise Intervention-A Pilot Study

The state of being overweight or obese leads to an increased risk of development of cardiometabolic disease. Increases in glycemic variability have been associated with greater induction of oxidative stress and declined vascular health, which may be exacerbated by higher weight status and improved through exercise. The purpose of this study was to examine the impact of a twelve-week aerobic exercise intervention on continuous glucose monitor (CGM) assessed glucose concentrations and glycemic variability, and biomarkers of vascular health and oxidative stress in overweight or obese adults. Eight adults (Age = 48.9 ± 5.2 years; BMI = 29.4 ± 8.3 kg/m2) completed a twelve-week aerobic exercise intervention. Participants walked three times per week at moderate intensity for ~150 minutes each week. All participants wore a CGM for seven consecutive days at baseline and post-intervention. On the final day of monitoring, a fasting blood sample was collected, and an oral glucose tolerance test (OGTT) was performed. Intra- and inter-day glycemic variability was assessed as the mean amplitude of glycemic excursions, continuous overlapping net glycemic action of one-, two-, and four-hour, and the mean observation of daily differences. Plasma concentrations of nitric oxide (NO) and myeloperoxidase (MPO) were measured, and their ratio was calculated (NO:MPO). No CGM-assessed glucose concentrations or measures of glycemic variability changed from baseline to post-intervention. MPO concentration decreased (24.8 ± 8.2 ng/mL to 16.4 ± 4.6 ng/mL, p < 0.01), the NO:MPO ratio improved (3.5:1 to 6.4:1, p < 0.01) following the twelve-week intervention. Individual level changes in body weight and V̇O₂peak were found. In conclusion, twelve weeks of aerobic exercise reduced oxidative stress and improved the propensity to vasodilate but did not alter CGM-assessed glucose concentrations or glycemic variability in this group of overweight or obese non-diabetic adults. These findings may be due to individual changes in body weight or V̇O2peak, which necessitates further research to explore their influence on these outcomes of interest.

Improved Glycemic Control and Variability: Application of Healthy Ingredients in Asian Staples

A reduction in carbohydrate intake and low-carbohydrate diets are often advocated to prevent and manage diabetes. However, limiting or eliminating carbohydrates may not be a long-term sustainable and maintainable approach for everyone. Alternatively, diet strategies to modulate glycemia can focus on the glycemic index (GI) of foods and glycemic load (GL) of meals. To assess the effect of a reduction in glycemic load of a 24 h diet by incorporating innovative functional ingredients (β-glucan, isomaltulose) and alternative low GI Asian staples (noodles, rice)on glycemic control and variability, twelve Chinese men (Age: 27.0 ± 5.1 years; BMI:21.6 ± 1.8kg/m²) followed two isocaloric, typically Asian, 24h diets with either a reduced glycemic load (LGL) or high glycemic load (HGL) in a randomized, single-blind, controlled, cross-over design. Test meals included breakfast, lunch, snack and dinner and the daily GL was reduced by 37% in the LGL diet. Continuous glucose monitoring provided 24 h glycemic excursion and variability parameters: incremental area under the curve (iAUC), max glucose concentration (Max), max glucose range, glucose standard deviation (SD), and mean amplitude of glycemic excursion (MAGE), time in range (TIR). Over 24h, the LGL diet resulted in a decrease in glucose Max (8.12 vs. 6.90 mmol/L; p = 0.0024), glucose range (3.78 vs. 2.21 mmol/L; p = 0.0005), glucose SD (0.78 vs. 0.43 mmol/L; p = 0.0002), mean amplitude of glycemic excursion (2.109 vs. 1.008; p < 0.0001), and increase in 4.5-6.5mmol/L TIR (82.2 vs. 94.6%; p = 0.009), compared to the HGL diet. The glucose iAUC, MAX, range and SD improved during the 2 h post-prandial window of each LGL meal, and this effect was more pronounced later in the day. The current results validate the dietary strategy of incorporating innovative functional ingredients (β-glucan, isomaltulose) and replacing Asian staples with alternative low GI carbohydrate sources to reduce daily glycemic load to improve glycemic control and variability as a viable alternative to the reduction in carbohydrate intake alone. These observations provide substantial public health support to encourage the consumption of staples of low GI/GL to reduce glucose levels and glycemic variability. Furthermore, there is growing evidence that the role of chrononutrition, as reported in this paper, requires further examination and should be considered as an important addition to the understanding of glucose homeostasis variation throughout the day.

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.

Flash Continuous Glucose Monitoring: A Practical Guide and Call to Action for Pharmacists

Despite advances in diabetes technology, the proportion of patients with type 2 diabetes achieving recommended glycemic goals remains suboptimal. There is a growing interest in flash continuous glucose monitoring (CGM) among patients, pharmacists and providers. Pharmacists are well positioned to collaborate with patients and providers in ambulatory care or community-based settings to allow a greater number of patients with diabetes to harness the benefits of flash CGM. The purpose of this narrative review is to provide pharmacists with a background on flash CGM technology, review the data supporting pharmacist-driven flash CGM services, and address common questions that arise in pharmacy practice surrounding flash CGM.

Improved Real-World Glycemic Control With Continuous Glucose Monitoring System Predictive Alerts

BACKGROUND

Most standalone real-time continuous glucose monitoring (RT-CGM) systems provide predictive low and high sensor glucose (SG) threshold alerts. The durations and risk of low and high SG excursions following Guardian™ Connect CGM system predictive threshold alerts were evaluated.

METHODS

Continuous glucose monitoring system data uploaded between January 2, 2017 and May 22, 2018 by 3133 individuals using multiple daily injections (MDIs) or continuous subcutaneous insulin infusion (CSII) therapy were deidentified and retrospectively analyzed. Glucose excursions were defined as SG values that went beyond a preset low or high SG threshold for ≥15 minutes. For a control group, thresholds were based on the median of the low SG threshold limit (70 mg/dL) and the high SG threshold limit (210 mg/dL) preset by all system users. During periods when alerts were not enabled, timestamps were identified when a predictive alert would have been triggered. The time before low horizon was 17.5 minutes and the time before high horizon was 15 minutes, of all users who enabled alerts. Excursions occurring after a low SG or high SG predictive alert were segmented into prevented, ≤20, 20-60, and >60 minutes.

RESULTS

Excursions were prevented after 59% and 39% of low and high SG predictive alerts, respectively. The risk of a low or high excursion occurring was 1.9 (P < 0.001, 95% CI, 1.88-1.93) and 3.3 (P < 0.001, 95% CI, 3.20-3.30) times greater, respectively, when alerts were not enabled.

CONCLUSIONS

The predictive alerts of the RT-CGM system under study can help individuals living with diabetes prevent some real-world low and high SG excursions. This can be especially important for those unable to reach or maintain glycemic control with basic RT-CGM or CSII therapy.

User-centered design of an air quality feedback technology to promote adoption of clean cookstoves

BACKGROUND

Recent work has examined behavioral reactivity associated with personal awareness of electronic sensors monitoring the use of environmental health products, including cookstoves. These studies suggest that sensors could be used as behavior change tools.

OBJECTIVE

We present a human-centered design approach toward the development of a household air quality feedback technology intended to improve consistent and exclusive use of liquid petroleum gas (LPG) stoves provided as part of a health efficacy study.

METHODS

We found through a consultation process that households may be behaviorally triggered by reminders of the health and environmental impacts of cooking practices and may respond to both auditory and visual feedback. Based on these insights, we designed and validated a system linking air particulate monitoring with persistent visual feedback and a dynamic audio alarm.

RESULTS

Data collected over 14 days in participants households show that the system is able to detect sudden rises in household indoor air pollution and to communicate that information to household members.

SIGNIFICANCE

This device could be used as a tool to raise awareness of air pollution associated in order to stimulate adoption of cleaner cooking technologies.

Glycemic variability in newly diagnosed diabetic cats treated with the glucagon-like peptide-1 analogue exenatide extended release

Background

Glycemic variability (GV) is an indicator of glycemic control and can be evaluated by calculating the SD of blood glucose measurements. In humans with diabetes mellitus (DM), adding a glucagon‐like peptide‐1 (GLP‐1) analogue to conventional therapy reduces GV. In diabetic cats, the influence of GLP‐1 analogues on GV is unknown.

Objective

To evaluate GV in diabetic cats receiving the GLP‐1 analogue exenatide extended release (EER) and insulin.

Animals

Thirty client‐owned cats with newly diagnosed spontaneous DM.

Methods

Retrospective study. Blood glucose curves from a recent prospective placebo‐controlled clinical trial generated 1, 3, 6, 10, and 16 weeks after starting therapy were retrospectively evaluated for GV. Cats received either EER (200 μg/kg) or 0.9% saline SC once weekly, insulin glargine and a low‐carbohydrate diet. Mean blood glucose concentrations were calculated and GV was assessed by SD. Data were analyzed using nonparametric tests.

Results

In the EER group, GV (mean SD [95% confidence interval]) was lower at weeks 6 (1.69 mmol/L [0.9‐2.48]; P = .02), 10 (1.14 mmol/L [0.66‐1.62]; P = .002) and 16 (1.66 mmol/L [1.09‐2.23]; P = .02) compared to week 1 (4.21 mmol/L [2.48‐5.93]) and lower compared to placebo at week 6 (3.29 mmol/L [1.95‐4.63]; P = .04) and week 10 (4.34 mmol/L [2.43‐6.24]; P < .000). Cats achieving remission (1.21 mmol/L [0.23‐2.19]) had lower GV compared to those without remission (2.96 mmol/L [1.97‐3.96]; P = .01) at week 6.

Conclusions and Clinical Importance

The combination of EER, insulin, and a low‐carbohydrate diet might be advantageous in the treatment of newly diagnosed diabetic cats.

Applications and pitfalls of hemoglobin A1C and alternative methods of glycemic monitoring

INTRODUCTION

Intensive glycemic control minimizes the risks of microvascular complications in diabetes. A1C is a convenient estimate of mean blood glucose, but is not the only marker available. The practical use and limitations of alternative markers and continuous glucose monitors are the focus of this review.

METHODS

PubMed and the Cochrane Library were searched for studies concerning applications or limitations of A1C, fructosamine, glycated albumin, 1,5-anhydroglucitol, skin autofluorescence, and continuous glucose monitoring. Papers reporting on strengths, limitations, or comparisons of these methods were reviewed for inclusion.

RESULTS

A1C reflects three months of glycemic control and is not an ideal marker in all patient populations. Fructosamine and glycated albumin reflect mean blood glucose over three weeks. 1,5-Anhydroglucitol can measure hyperglycemic excursions in days to weeks. Continuous glucose monitors provide immediate feedback for timely intervention to reduce glycemic excursions and can assess glycemic variability. Current barriers to continuous glucose monitor use include inexperience, cost, discomfort, and medication interference.

CONCLUSIONS

Many promising alternative glycemic markers exist. The main limitations for all alternative methods of glycemic monitoring are a lack of standardization for clinically useful cut-offs or guidelines, and a lack of long-term data on their association with complications, particularly in varied patient populations.

A Review of Continuous Glucose Monitoring-Based Composite Metrics for Glycemic Control

We performed a literature review of composite metrics for describing the quality of glycemic control, as measured by continuous glucose monitors (CGMs). Nine composite metrics that describe CGM data were identified. They are described in detail along with their advantages and disadvantages. The primary benefit to using composite metrics in clinical practice is to be able to quickly evaluate a patient's glycemic control in the form of a single number that accounts for multiple dimensions of glycemic control. Very little data exist about (1) how to select the optimal components of composite metrics for CGM; (2) how to best score individual components of composite metrics; and (3) how to correlate composite metric scores with empiric outcomes. Nevertheless, composite metrics are an attractive type of scoring system to present clinicians with a single number that accounts for many dimensions of their patients' glycemia. If a busy health care professional is looking for a single-number summary statistic to describe glucose levels monitored by a CGM, then a composite metric has many attractive features.

Increased Preoperative Glucose Variability Is Associated with Adverse Perioperative Outcomes Following Orthopedic Surgery in Patients with Type 2 Diabetes Mellitus

The association between glucose variability (GV) and adverse perioperative outcomes in type 2 diabetes mellitus (T2DM) patients undergoing orthopedic surgery was investigated. A retrospective cohort study was performed by analyzing data on T2DM patients receiving continuous blood glucose (BG) monitoring and continuous subcutaneous insulin infusion treatment due to poorly controlled preoperative BG prior to orthopedic surgery. GV was assessed with coefficient of variation (CV). Postoperative and perioperative CV, hypoglycemia cases, and other perioperative outcomes (diabetes preparation time [DPT], length of stay [LOS], and perioperative and infective complication cases) were analyzed. Results showed that a total of 168 patients were grouped into preoperative CV tertiles: 1st (n=56): 0-0.2921, 2nd (n=58): 0.2922-0.3779, and 3rd (n=54): 0.3780-0.5750. Fasting blood glucose (FBG), perioperative CV, rate of hypoglycemia cases (OR: 5.53, 95%CI: 2.43-12.59) (all P<0.001) and DPT (P=0.024) were higher in the 3rd than in the 1st tertile. After adjustments of covariates, regression analysis indicated that the 3rd tertile was associated with increased perioperative CV (adjusted coefficient=0.515, P<0.001), DPT (adjusted coefficient =0.169, P=0.073), rate of hypoglycemia cases (OR: 6.72, 95%CI: 2.69-16.82, P<0.001) and perioperative complication cases (OR: 2.50, 95%CI: 0.90-7.01, P=0.080). In conclusion, preoperative GV is associated with increased perioperative GV and adverse perioperative outcomes including longer DPT and higher rates of hypoglycemia and perioperative complications.

Glycemic Variability and Hypoglycemic Excursions With Continuous Glucose Monitoring Compared to Intermittently Scanned Continuous Glucose Monitoring in Adults With Highest Risk Type 1 Diabetes

BACKGROUND

The I-HART CGM study has shown that real-time continuous glucose monitoring (rtCGM) has greater beneficial impact on hypoglycemia than intermittently scanned continuous glucose monitoring (iscCGM) in adults with type 1 diabetes at high risk (Gold score ≥4 or recent severe hypoglycemia using insulin injections). In this subanalysis, we present the impact of rtCGM and iscCGM on glycemic variability (GV).

METHODS

Forty participants were recruited to this parallel group study. Following two weeks of blinded rtCGM (DexcomG4), participants were randomized to rtCGM (Dexcom G5; n = 20) or iscCGM (Freestyle Libre; n = 20) for eight weeks. An open-extension phase enabled participants on rtCGM to continue for a further eight weeks and those on iscCGM to switch to rtCGM over this period. Glycemic variability measures at baseline, 8- and 16-week endpoints were compared between groups.

RESULTS

At the eight-week endpoint, between-group differences demonstrated significant reduction in several GV measures with rtCGM compared to iscCGM (GRADE%hypoglycemia, index of glycemic control [IGC], and average daily risk range [ADRR]; P < .05). Intermittently scanned continuous glucose monitoring reduced mean average glucose and glycemic variability percentage and GRADE%hyperglycemia compared with rtCGM (P < .05). At 16 weeks, the iscCGM group switching to rtCGM showed significant improvement in GRADE%hypoglycemia, personal glycemic status, IGC, and ADRR.

CONCLUSION

Our data suggest most, but not all, GV measures improve with rtCGM compared with iscCGM, particularly those measures associated with the risk of hypoglycemia. Selecting appropriate glucose monitoring technology to address GV in this high-risk cohort is important to minimize the risk of glucose extremes and severe hypoglycemia.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov NCT03028220.

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.

Time in range and HbA1C after 6 months with a multidisciplinary program for children and adolescents with diabetes mellitus, real world data from Mexico City

BACKGROUND

Multidisciplinary interventions may be useful for children and adolescents with diabetes mellitus (DM), especially in areas where new blood glucose monitoring and control technologies are difficult to access.

METHODS

PAANDA, a care program for adolescents and children with diabetes, was implemented in patients aged 0 to 18 years and 11 months. The effect of the intervention was determined by self-blood glucose monitoring (SBGM) and glycosylated hemoglobin (HbA_1C ) levels at start and after 6 months.

RESULTS

A total of 121 patients with DM were evaluated, mean age of 14.27 years (SD: 4.60 years). Blood glucose measurements in range (70-120 mg/dL pre-prandial or 70-180 mg/dL post-prandial) increased by 20.67% before breakfast, 8.14% after breakfast (both P-value <.001), 5.02% before lunch (P-value = .02), 8.66% after lunch (P-value <.001), 11.50% before dinner (P-value <.001), 11.87% after dinner (P-value <.001), and 8.00% at dawn (P-value = .001). This change was accompanied by fewer values in the hyperglycemic category (-19.49% before breakfast, -7.73% after breakfast, both P-value <.001) and hypoglycemia (-1.18%). HbA_1C levels decreased significantly 1.8% (P-value = .018). Multivariate logistic regression analysis showed an increase in glycemic control associated with each month after the intervention time in the PAANDA program (P-value <.001 for all the time points evaluated) and a significant decrease in glycemic variability.

CONCLUSIONS

The multidisciplinary PAANDA intervention had a beneficial effect on glycemic control, with an improved time in range in a population of children and adolescents with DM.

Non-invasive platform to estimate fasting blood glucose levels from salivary electrochemical parameters

Diabetes is a metabolic disorder that affects more than 400 million people worldwide. Most existing approaches for measuring fasting blood glucose levels (FBGLs) are invasive. This work presents a proof-of-concept study in which saliva is used as a proxy biofluid to estimate FBGL. Saliva collected from 175 volunteers was analysed using portable, handheld sensors to measure its electrochemical properties such as conductivity, redox potential, pH and K+, Na+ and Ca2+ ionic concentrations. These data, along with the person's gender and age, were trained and tested after casewise annotation with their true FBGL values using a set of mathematical algorithms. An accuracy of 87.4 ± 1.7% and a mean relative deviation of 14.1% (R 2 = 0.76) was achieved using a mathematical algorithm. All parameters except the gender were found to play a key role in the FBGL determination process. Finally, the individual electrochemical sensors were integrated into a single platform and interfaced with the authors' algorithm through a simple graphical user interface. The system was revalidated on 60 new saliva samples and gave an accuracy of 81.67 ± 2.53% (R 2 = 0.71). This study paves the way for rapid, efficient and painless FBGL estimation from saliva.

Continuous Glucose Monitoring: A Brief Review for Primary Care Practitioners

Glycated hemoglobin A1c (HbA1c) is routinely used as a marker of average glycemic control, but it fails to provide data on hypoglycemia and glycemic variability, both of which are associated with adverse clinical outcomes. Self-monitoring of blood glucose (SMBG), particularly in insulin-treated patients, is a cornerstone in the management of patients with diabetes. SMBG helps with treatment decisions that aim to reduce high glucose levels while avoiding hypoglycemia and limiting glucose variability. However, repeated SMBG can be inconvenient to patients and difficult to maintain in the long term. By contrast, continuous glucose monitoring (CGM) provides a convenient, comprehensive assessment of blood glucose levels, allowing the identification of high and low glucose levels, in addition to evaluating glycemic variability. CGM using newer detection and visualization systems can overcome many of the limitations of an HbA1c-based approach while addressing the inconvenience and fragmented glucose data associated with SMBG. When used together with HbA1c monitoring, CGM provides complementary information on glucose levels, thus facilitating the optimization of diabetes therapy while reducing the fear and risk of hypoglycemia. Here we review the capabilities and benefits of CGM, including cost-effectiveness data, and discuss the potential limitations of this glucose-monitoring strategy for the management of patients with diabetes. FUNDING: Sanofi US, Inc.

Clinical Implications of Lipohypertrophy Among People with Type 1 Diabetes in India

BACKGROUND

Lipohypertrophy (LH) at insulin injection sites is a common but preventable complication in type 1 diabetes mellitus (T1DM). We evaluated the prevalence, contributing risk factors, and consequences of LH, specifically the glycemic variability (GV) among T1DM patients.

METHODS

This is a cross-sectional study conducted at a tertiary care center in India, wherein 139 subjects with T1DM were randomly selected and evaluated for the presence of LH through visual and palpation examinations. Demography, anthropometry, and injecting practices were evaluated using a validated questionnaire and their effect on LH was determined. Subsequently, the effect of LH on GV and unexplained hypoglycemia (UH) was studied. Mean glucose, mean amplitude of glycemic excursions (MAGEs), and continuous overlapping net glycemic action (CONGA) were assessed in a subset of patients who injected insulin alternately in LH and non-LH sites.

RESULTS

The overall prevalence of LH was 69.8%, and was significantly higher in adults than in children (P = 0.038). Improper rotation of sites (P < 0.0001) and insulin syringe reusage for more than five times (P = 0.009) significantly increase the risk of LH. The presence of LH has a significant effect on GV and UH with adjusted odds ratios of 17.65 (P < 0.0001) and 28.02 (P < 0.0001), respectively. Ambulatory glucose monitoring on a subset of patients confirmed that the mean glucose, MAGE, and CONGA were higher when subjects injected insulin at LH sites than at non-LH sites.

CONCLUSIONS

Improper rotation of sites and reuse of needles are the leading causes of LH in Indian T1DM patients, which, in turn, significantly increases the risk of GV and UH.

Metformin add-on continuous subcutaneous insulin infusion on precise insulin doses in patients with type 2 diabetes

To investigate whether metformin add-on to the continuous subcutaneous insulin infusion (Met + CSII) therapy leads to a significant reduction in insulin doses required by type 2 diabetes (T2D) patients to maintain glycemic control, and an improvement in glycemic variation (GV) compared to CSII only therapy. We analyzed data from our two randomized, controlled open-label trials. Newly diagnoses T2D patients were randomized assigned to receive either CSII therapy or Met + CSII therapy for 4 weeks. Subjects were subjected to a 4-day continuous glucose monitoring (CGM) at the endpoint. Insulin doses and GV profiles were analyzed. The primary endpoint was differences in insulin doses and GV between the two groups. A total of 188 subjects were admitted as inpatients. Subjects in metformin add-on therapy required significantly lower total, basal and bolus insulin doses than those of control group. CGM data showed that patients in Met + CSII group exhibited significant reduction in the 24-hr mean amplitude of glycemic excursions (MAGE), the standard deviation, and the coefficient of variation compared to those of control group. Our data suggest that metformin add-on to CSII therapy leads to a significant reduction in insulin doses required by T2D patients to control glycemic variations.

Healthcare Resource Waste Associated with Patient Nonadherence and Early Discontinuation of Traditional Continuous Glucose Monitoring in Real-World Settings: A Multicountry Analysis

BACKGROUND

Traditional continuous glucose monitoring (CGM) provides detailed information on glucose patterns and trends to inform daily diabetes management decisions, which is particularly beneficial for patients with a history of hypoglycemia unawareness. However, a high level of patient adherence (≥70%) is required to achieve clinical benefits. The aim of this study was to assess the impact of real-world patient nonadherence and early discontinuation on healthcare resource use.

METHODS

A cost calculator was designed to evaluate monthly healthcare resource waste within the first year of traditional CGM initiation by combining estimates of real-world nonadherence and early discontinuation from the literature with the wholesale acquisition costs of the current technology in the United States (for a commercial payer and for Medicare), or its equivalent in Sweden, Germany, or the Netherlands.

RESULTS

Based on an early discontinuation rate of 27% and nonadherence rates of 13.9%-31.1% over the 12 months following initiation, the healthcare resource waste associated with nonadherence and early discontinuation was $220,289 and $21,775, respectively, for every 100 patients initiating CGM in the U.S. commercial payer scenario. In the Medicare scenario, the corresponding figures were $72,648 and $5,675, respectively. In both scenarios, nonadherence and early discontinuation accounted for ∼24% of resources being wasted within the first year of CGM initiation. Similar results were observed using the local costs in the other countries analyzed.

CONCLUSIONS

The healthcare resource waste associated with traditional CGM nonadherence and early discontinuation warrants deliberate consideration when selecting suitable patients for this technology.

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.

Management of glycemic variation in diabetic patients receiving parenteral nutrition by continuous subcutaneous insulin infusion (CSII) therapy

To compare the continuous subcutaneous insulin infusion (CSII) or insulin glargine based multiple injections (MDI) therapy on glycemic variations in diabetic patients receiving PN outside of intensive care settings. This was a single-center, randomized, open-label trial. Patients with type 2 diabetes (T2D) who were receiving parenteral nutrition (PN) were recruited. After baseline data were collected, recruited patients were then randomized 1:1 to a CSII group or a MDI group. All patients were subjected to a 4-day retrospective Continuous Glucose Monitoring (CGM). The primary endpoint was the differences of the 24-hrs mean amplitude of glycemic excursion (MAGE) in patients receiving the PN therapy between the two groups. A total of 102 patients with T2D receiving PN were recruited. Patients in the CSII group had a significantly decreased mean glucose level (MBG), the standard deviation of MG (SDBG), MAGE, and the coefficient of variation (CV%) compared to those in MDI group (all P < 0.01). Furthermore, we found that the patients who received a bolus insulin dose required maintaining euglycemic control was gradually decreased during the PN period in both groups at the endpoint. The administration of insulin via CSII led to a significant decrease in glycemic variations in patients receiving PN.

Calculating the Mean Amplitude of Glycemic Excursions from Continuous Glucose Data Using an Open-Code Programmable Algorithm Based on the Integer Nonlinear Method

The mean amplitude of glycemic excursions (MAGE) is an essential index for glycemic variability assessment, which is treated as a key reference for blood glucose controlling at clinic. However, the traditional “ruler and pencil” manual method for the calculation of MAGE is time-consuming and prone to error due to the huge data size, making the development of robust computer-aided program an urgent requirement. Although several software products are available instead of manual calculation, poor agreement among them is reported. Therefore, more studies are required in this field. In this paper, we developed a mathematical algorithm based on integer nonlinear programming. Following the proposed mathematical method, an open-code computer program named MAGECAA v1.0 was developed and validated. The results of the statistical analysis indicated that the developed program was robust compared to the manual method. The agreement among the developed program and currently available popular software is satisfied, indicating that the worry about the disagreement among different software products is not necessary. The open-code programmable algorithm is an extra resource for those peers who are interested in the related study on methodology in the future.

Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data

BACKGROUND

Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach.

METHODS

The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D.

RESULTS

Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy.

CONCLUSIONS

Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.

Application of Glycemic Variability Percentage: Implications for Continuous Glucose Monitor Utilization and Analysis of Artificial Pancreas Data

BACKGROUND

The problem of glycemic variability has been widely acknowledged in patients with diabetes with severe insulin deficiency. In a companion article, we proposed a novel metric, the glycemic variability percentage (GVP), for assessing glycemic variability that accounts for both the amplitude and frequency of glycemic fluctuations.

METHOD

We applied the new metric, the GVP, to a previously reported case of a subject using an earlier generation continuous glucose monitoring (CGM) device, in which successive periods of use were associated with an apparent decrease in glycemic variability. Results were compared with histogram distributions for the rate of change of glucose as well. The GVP was also applied to data from a published study of a bihormonal artificial pancreas system comparing results from open loop and closed loop in adolescents and in adults.

RESULTS

The GVP was able to quantify the changes in glycemic variability during successive periods of CGM use. Application of the GVP to a published study of a bihormonal artificial pancreas found an increase in glycemic variability compared with other accepted metrics which suggested a decrease in glycemic variability.

CONCLUSION

The GVP may be a clinically useful tool in characterizing the change in glycemic variability in subjects using CGM devices. Compared with metrics, such as the standard deviation, that focus solely on the amplitude of oscillations, the GVP, which measures both frequency and amplitude, may also be a more useful tool in assessing the true level of glycemic variability in artificial pancreas studies.

Effects of Vildagliptin Add-on Insulin Therapy on Nocturnal Glycemic Variations in Uncontrolled Type 2 Diabetes

INTRODUCTION

To investigate whether vildagliptin add-on insulin therapy improves glycemic variations in patients with uncontrolled type 2 diabetes (T2D) compared to patients with placebo therapy.

METHODS

This was a 24-week, single-center, double-blind, placebo-controlled trial. Inadequately controlled T2D patients treated with insulin therapy were recruited between June 2012 and April 2013. The trial included a 2-week screening period and a 24-week randomized period. Subjects were randomly assigned to a vildagliptin add-on insulin therapy group (n = 17) or a matched placebo group (n = 16). Scheduled visits occurred at weeks 4, 8, 12, 16, 20, and 24. Continuous glucose monitoring (CGM) was performed before and at the endpoint of the study.

RESULTS

A total of 33 subjects were admitted, with 1 patient withdrawing from the placebo group. After 24 weeks of therapy, HbA1c values were significantly reduced at the endpoint in the vildagliptin add-on group. CGM data showed that patients with vildagliptin add-on therapy had a significantly lower 24-h mean glucose concentration and mean amplitude of glycemic excursion (MAGE). At the endpoint of the study, patients in the vildagliptin add-on group had a significantly lower MAGE and standard deviation compared to the control patients during the nocturnal period (0000-0600). A severe hypoglycemic episode was not observed in either group.

CONCLUSION

Vildagliptin add-on therapy to insulin has the ability to improve glycemic variations, especially during the nocturnal time period, in patients with uncontrolled T2D.

Effect of Oral Pre-Meal Administration of Betaglucans on Glycaemic Control and Variability in Subjects with Type 1 Diabetes

We conducted a double-blind placebo-controlled crossover pilot study to investigate the effect of oat betaglucans (β-glucan) on glycaemic control and variability in adults with type 1 diabetes (T1D; n = 14). Stomacol^® tablets (1.53 g of β-glucan) or placebo (Plac) were administered three times daily before meals for two weeks. Glucose levels were monitored during the second week by continuous glucose monitoring (CGM). There was an increase in basic measures of glycaemic control (maximal glucose value 341 ± 15 vs. 378 ± 13 mg/dL for Plac and β-glucan, p = 0.004), and average daily risk range (62 ± 5 vs. 79 ± 4 mg/dL for Plac and β-glucan, p = 0.003) favouring Plac over β-glucan, but no increase in the M-value (the weighted average of the glucose values) or other more complex measures. Basic measures of glucose variability were also slightly increased during β-glucan treatment, with no difference in more complex measures. However, glycaemic variability increased between the first and last two CGM days on Plac, but remained unchanged on β-glucan. In conclusion, in this pilot study we were unable to demonstrate a general positive effect of β-glucan before meals on glucose control or variability in T1D.

Assessing the Therapeutic Utility of Professional Continuous Glucose Monitoring in Type 2 Diabetes Across Various Therapies: A Retrospective Evaluation

BACKGROUND

There have been few large studies that have analyzed the effect of professional (masked) continuous glucose monitoring (P-CGM) on glycemic control in patients with type 2 diabetes (T2DM) who were on a broad spectrum of baseline therapies.

METHODS

We performed a retrospective, blinded evaluation of glycemic control in 296 T2DM adults for 6 months following a 6- to 7-day study of their glycemic profile using masked P-CGM. At baseline, 91% of the patients were on some form of insulin treatment with oral hypoglycemic agents (OHA), while 7% were on one or more OHAs without insulin, and the remaining 2% were on GLP-1RAs. On the basis of the masked CGM profile, patients were counselled on diet and exercise change(s) in their baseline diabetes therapy by our professionally trained diabetes team. They also continued to receive regular treatment advice and dose titrations through our Diabetes Tele-Management System (DTMS^®). The baseline changes in hemoglobin A1C (A1C) observed in these patients after 6 months of undergoing P-CGM was compared to a matched control group.

RESULTS

P-CGM revealed that the predominant pattern of hyperglycemia was postprandial while previously unknown hypoglycemia was found in 38% of the patients; over half of the cases of hypoglycemia were nocturnal. The mean A1C of the P-CGM group dropped from 7.5 ± 1.4% at baseline vs. 7.0 ± 0.9% at 6 months (p < 0.0001). The frequency of performing self-monitoring of blood glucose (SMBG) was also found to be significantly increased in these patients from the baseline. Meanwhile, no significant improvement in A1C was noted in the control group during the same time frame (7.7 ± 1.1% at baseline vs. 7.4 ± 1.1% at 6 months; p = 0.0663) and frequency of SMBG remained almost unchanged.

CONCLUSIONS

P-CGM can provide actionable data and motivate patients for diabetes self-care practices, resulting in an improvement in glycemic control over a wide range of baseline therapies.

Does the ingestion of a 24 hour low glycaemic index Asian mixed meal diet improve glycaemic response and promote fat oxidation? A controlled, randomized cross-over study

BACKGROUND

The health benefits of consuming a low glycaemic index (GI) diet to reduce the risk of type 2 Diabetes are well recognized. In recent years the GI values of various foods have been determined. Their efficacy in constructing and consuming a low GI diet over 24 h in modulating glycaemic response has not been fully documented. The translation of using single-point GI values of foods to develop a 24 h mixed meal diet can provide valuable information to consumers, researchers and dietitians to optimize food choice for glycaemic control. By using GI values of foods to develop mixed meals, our study is the first to determine how both blood glucose and substrate oxidation may be modulated over 24 h.

METHODS

The study included 11 Asian men with a BMI between 17-24 kg/m² who followed both a 1-day low GI and 1-day high GI diet in a randomized, controlled cross-over design. Test meals included breakfast, lunch, snack and dinner. Glycaemic response was measured continuously for over 24 h and postprandial substrate oxidation for 10 h inside a whole body calorimeter.

RESULTS

The low GI diet resulted in lower 24 h glucose iAUC (860 ± 440 vs 1329 ± 614 mmol/L.min; p = 0.014) with lower postprandial glucose iAUC after breakfast (p < 0.001), lunch (p = 0.009), snack (p = 0.012) and dinner (p = 0.003). Moreover, 24 h mean amplitude of glycaemic excursion was lower during the low GI vs high GI diet (1.44 ± 0.63 vs 2.33 ± 0.82 mmol/L; p < 0.001). Simultaneously, decrease in 10 h fat oxidation was less during the low vs high GI diet (-0.033 ± 0.021 vs -0.050 ± 0.017 g/min; p < 0.001), specifically after breakfast (p < 0.001) and lunch (p < 0.001).

CONCLUSIONS

Our study corroborates that using low GI local foods to construct a 24 h low GI diet, is able to reduce glycaemic response and variability as recorded by continuous glucose monitoring. Our observations also confirm that a low GI diet promotes fat oxidation over carbohydrate oxidation when compared to a high GI diet. These observations provide public health support for the encouragement of healthier nutrition choices by consuming low GI foods.

TRIAL REGISTRATION

NCT 02631083 (Clinicaltrials.gov).

A Low Glycaemic Index Diet Incorporating Isomaltulose Is Associated with Lower Glycaemic Response and Variability, and Promotes Fat Oxidation in Asians

Low glycaemic index (GI) foods minimize large blood glucose fluctuations and have been advocated to enhance fat oxidation and may contribute to weight management. We determined whether the inclusion of isomaltulose compared to sucrose in a low/high GI meal sequence can modulate the glycaemic response and substrate oxidation in an Asian population. Twenty Chinese men (body mass index (BMI): 17–28 kg/m²) followed a 24 h low GI (isomaltulose, Palatinose^TM) or high GI (sucrose) diet in a randomized double-blind, controlled cross-over design. Treatment meals included dinner (day 1), breakfast, lunch, and snack (day 2). Continuous glucose monitoring provided incremental area under the curve (iAUC) and mean amplitude of glycaemic excursion (MAGE) and 10 h indirect calorimetry (whole body calorimeter) (day 2) provided energy expenditure and substrate oxidation. Our results demonstrated that the low GI diet resulted in lower 24 h glucose iAUC (502.5 ± 231.4 vs. 872.6 ± 493.1 mmol/L; p = 0.002) and lower 24 h glycaemic variability (MAGE: 1.67 ± 0.53 vs. 2.68 ± 1.13 mmol/L; p < 0.001). Simultaneously, 10 h respiratory quotient increased more during high GI (p = 0.014) and fat oxidation was higher after low GI breakfast (p = 0.026), lunch (p < 0.001) and snack (p = 0.013). This indicates that lower GI mixed meals incorporating isomaltulose are able to acutely reduce the glycaemic response and variability and promote fat oxidation.

Features of glycemic variations in drug naïve type 2 diabetic patients with different HbA1c values

To define the features of glycemic variations in drug naïve type 2 diabetic (T2D) patients with different HbA_1c values using continuous glucose monitoring (CGM), a total of 195 drug naïve T2D patients were admitted. The subjects were divided into the following groups: lower HbA_1c values (≤8%), moderate HbA_1c values (>8% and ≤10%), and higher HbA_1c values (>10%). The patients underwent oral glucose tolerance tests and were then subjected to 3-day CGM. The primary endpoint was the differences in the 24-hr mean amplitude of glycemic excursions (MAGE) in patients with different HbA_1c values. Patients with higher HbA_1c values had larger MAGEs than those in the moderate and lower groups (7.44 ± 3.00 vs. 6.30 ± 2.38, P < 0.05, 7.44 ± 3.00 vs. 5.20 ± 2.35, P < 0.01, respectively). The 24-hr mean glucose concentrations increased incrementally in the patients with lower, moderate and higher HbA_1c values. Moreover, the patients with higher HbA_1c values exhibited higher peak glucose concentrations and prolongation in the time to peak glucose. Patients with higher HbA_1c values had larger MAGE compared with those with lower and moderate HbA_1c values. Our data indicated patients with higher HbA_1c values should receive special therapy aimed at reducing the larger glycemic variations.

Metrics Beyond Hemoglobin A1C in Diabetes Management: Time in Range, Hypoglycemia, and Other Parameters

We review clinical instances in which A1C should not be used and reflect on the use of other glucose metrics that can be used, in substitution of or in combination with A1C and SMBG, to tailor an individualized approach that will result in better outcomes and patient empowerment.

Measures of Glycemic Variability in Type 1 Diabetes and the Effect of Real-Time Continuous Glucose Monitoring

OBJECTIVE

To report the impact of continuous glucose monitoring (CGM) on glycemic variability (GV) indices, factors predictive of change, and to correlate variability with conventional markers of glycemia.

METHODS

Data from the JDRF study of CGM in participants with type 1 diabetes were used. Participants were randomized to CGM or self-monitored blood glucose (SMBG). GV indices at baseline, at 26 weeks in both groups, and at 52 weeks in the control group were analyzed. The associations of demographic and clinical factors with change in GV indices from baseline to 26 weeks were evaluated.

RESULTS

Baseline data were available for 448 subjects. GV indices were all outside normative ranges (P < 0.001). Intercorrelation between GV indices was common and, apart from coefficient of variation (CV), low blood glucose index (LBGI), and percentage of glycemic risk assessment diabetes equation score attributable to hypoglycemia (%GRADE_hypoglycemia), all indices correlate positively with HbA1c. There was strong correlation between time spent in hypoglycemia, and CV, LBGI, and %GRADE_hypoglycemia, but not with HbA1c. A significant reduction in all GV indices, except lability index and mean absolute glucose change per unit time (MAG), was demonstrated in the intervention group at 26 weeks compared with the control group. Baseline factors predicting a change in GV with CGM include baseline HbA1c, baseline GV, frequency of daily SMBG, and insulin pump use.

CONCLUSIONS

CGM reduces most GV indices compared with SMBG in people with type 1 diabetes. The strong correlation between time spent in hypoglycemia and CV, LBGI, and %GRADE_hypoglycemia highlights the value of these metrics in assessing hypoglycemia as an adjunct to HbA1c in the overall assessment of glycemia.

Glycemic Control Indices and Their Aggregation in the Prediction of Nocturnal Hypoglycemia From Intermittent Blood Glucose Measurements

BACKGROUND

Despite the risk associated with nocturnal hypoglycemia (NH) there are only a few methods aiming at the prediction of such events based on intermittent blood glucose monitoring data. One of the first methods that potentially can be used for NH prediction is based on the low blood glucose index (LBGI) and suggested, for example, in Accu-Chek® Connect as a hypoglycemia risk indicator. On the other hand, nowadays there are other glucose control indices (GCI), which could be used for NH prediction in the same spirit as LBGI. In the present study we propose a general approach of combining NH predictors constructed from different GCI.

METHODS

The approach is based on a recently developed strategy for aggregating ranking algorithms in machine learning. NH predictors have been calibrated and tested on data extracted from clinical trials, performed in EU FP7-funded project DIAdvisor. Then, to show a portability of the method we have tested it on another dataset that was received from EU Horizon 2020-funded project AMMODIT.

RESULTS

We exemplify the proposed approach by aggregating NH predictors that have been constructed based on 4 GCI associated with hypoglycemia. Even though these predictors have been preliminary optimized to exhibit better performance on the considered dataset, our aggregation approach allows a further performance improvement. On the dataset, where a portability of the proposed approach has been demonstrated, the aggregating predictor has exhibited the following performance: sensitivity 77%, specificity 83.4%, positive predictive value 80.2%, negative predictive value 80.6%, which is higher than conventionally considered as acceptable.

CONCLUSION

The proposed approach shows potential to be used in telemedicine systems for NH prediction.

Sensor and software use for the glycaemic management of insulin-treated type 1 and type 2 diabetes patients

Lowering glucose levels, while avoiding hypoglycaemia, can be challenging in insulin-treated patients with diabetes. We evaluated the role of ambulatory glucose profile in optimising glycaemic control in this population. Insulin-treated patients with type 1 and type 2 diabetes were recruited into a prospective, multicentre, 100-day study and randomised to control (n = 28) or intervention (n = 59) groups. The intervention group used ambulatory glucose profile, generated by continuous glucose monitoring, to assess daily glucose levels, whereas the controls relied on capillary glucose testing. Patients were reviewed at days 30 and 45 by the health care professional to adjust insulin therapy. Comparing first and last 2 weeks of the study, ambulatory glucose profile-monitored type 2 diabetes patients (n = 28) showed increased time in euglycaemia (mean ± standard deviation) by 1.4 ± 3.5 h/day (p = 0.0427) associated with reduction in HbA1c from 77 ± 15 to 67 ± 13 mmol/mol (p = 0.0002) without increased hypoglycaemia. Type 1 diabetes patients (n = 25) showed reduction in hypoglycaemia from 1.4 ± 1.7 to 0.8 ± 0.8 h/day (p = 0.0472) associated with a marginal HbA1c decrease from 75 ± 10 to 72 ± 8 mmol/mol (p = 0.0508). Largely similar findings were observed comparing intervention and control groups at end of study. In conclusion, ambulatory glucose profile helps glycaemic management in insulin-treated diabetes patients by increasing time spent in euglycaemia and decreasing HbA1c in type 2 diabetes patients, while reducing hypoglycaemia in type 1 diabetes patients.

Glycemic Variability Is Associated with Markers of Vascular Stress in Adolescents

OBJECTIVES

We used continuous glucose monitoring to test the hypothesis that mean amplitude of glycemic excursions (MAGE) is associated with circulating markers of oxidative and vascular stress in adolescents with habitually low physical activity classified as healthy weight, healthy obese, or obese with type 2 diabetes mellitus (T2DM).

STUDY DESIGN

A group of 13- to 21-year-olds (healthy weight = 12, healthy obese = 10, T2DM = 12) wore a continuous glucose monitor and step activity monitor for 5 days.

RESULTS

Physical activity was similar among groups (6551 ± 401 steps/d), but aerobic fitness (peak rate of oxygen consumption) was lower (P < .05) in T2DM (15.6 ± 1.8 mL/kg/min) than either healthy weight (26.2 ± 2.2) or healthy obese (24.4 ± 2.5). MAGE (mg/dL) was higher (P < .01) in T2DM (82 ± 10) vs healthy obese (33 ± 3) and healthy weight (30 ± 3). Average glucose followed a similar pattern as MAGE. Oxidized low density lipoprotein was higher (P < .05) in T2DM (70.3 ± 5.0 U/L) and healthy obese (58.1 ± 3.8) than healthy weight (48.4 ± 2) and positively correlated with MAGE (r = 0.77). Other stress markers that were both elevated in T2DM and correlated with MAGE included E-selectin (r = 0.50), intercellular adhesion molecule 1 (r = 0.35), and C-reactive protein (r = 0.52); soluble receptor for advanced glycosylation end product was lower in T2DM and inversely correlated with MAGE (r = -0.38).

CONCLUSIONS

MAGE is highest in obese youth with T2DM. The associations between MAGE and oxidative stress markers support the proposed contribution of glycemic variability to risk for future cardiovascular disease.

Use of a real time continuous glucose monitoring system as an educational tool for patients with gestational diabetes

BACKGROUND

Women with gestational diabetes mellitus (GDM) are required to control their blood glucose shortly after GDM diagnosis to minimize adverse pregnancy outcomes. A real time-continuous glucose monitoring system (RT-CGMS) provides the patient with continuous information about the alterations in levels of the blood glucose. This visibility may empower the patient to modify her lifestyle and engage in therapeutic management. The aim of this study was to determine whether a single application of RT-CGMS to pregnant women shortly after GDM diagnosis is useful as an educational and motivational tool.

METHODS

This study was a prospective open label randomized controlled study conducted at Maternity and Children Hospital, Medina, Saudi Arabia. A total of 130 pregnant women with GDM were randomised to either blood glucose self-monitor alone (SMBG group) (n = 62) or in addition to SMBG, patients wore a Guardian(®) REAL-Time Continuous Glucose Monitoring System (Medtronic MiniMed) once for 3-7 days, within 2 weeks of GDM diagnosis (RT-CGMS group) (n = 68). The primary outcomes were maternal glycemic control and pregnancy outcomes. Secondary outcomes were the changes in parameters of glucose variability, which includes mean sensor readings, standard deviation (SD) of blood glucose, and area under the curve for hyper and hypoglycaemia at the end of the RT-CGMS application.

RESULTS

HbA1c, mean fasting and postprandial glucose levels were similar in both groups at the end of the pregnancy. Pregnancy outcomes were comparable. However, there was significant improvement in the parameters of glucose variability on the last day of sensor application; both mean glucose and the SD of mean glycaemia were reduced significantly; P = 0.016 and P = 0.034, respectively. The area under the curve for hyper and hypoglycaemia were improved, however, the results were not statistically significant.

CONCLUSION

Although a single application of RT-CGMS shortly after GDM diagnosis is helpful as an educational tool, it was not associated with improvement in glycemic control or pregnancy outcomes.

How well do glucose variability measures predict patient glycaemic outcomes during treatment intensification in type 2 diabetes?

AIM

Despite links to clinical outcomes in patients with type 2 diabetes mellitus (T2DM), the clinical utility of glycaemic variability (GV) measures is unknown. We evaluated the correlation between baseline GV, and glycated haemoglobin (HbA1c) attainment and hypoglycaemic events during treatment intensification in a large group of patients.

METHODS

Patient-level data from six 24-week clinical trials of T2DM patients undergoing treatment intensification with basal insulin or comparators (N = 1699) were pooled. Baseline GV measures included standard deviation (SD), mean amplitude of glycaemic excursions (MAGE), mean absolute glucose (MAG), coefficient of variation (CV), high blood glucose index (HBGI), and low blood glucose index (LBGI) and were correlated with HbA1c change and hypoglycaemic events.

RESULTS

All mean GV measures, excluding CV which worsened, improved significantly from baseline to Week 24, with the largest proportional reduction obtained for HBGI (-65.5%). When assessed as mean individual percentage changes, only HBGI improved significantly. Baseline GV correlated positively with Week 24 HbA1c for SD, MAGE, and HBGI. Baseline HBGI and CV correlated negatively and positively, respectively, with Week 24 HbA1c change. Correlations also existed between most baseline GV measures and age, body mass index, Week 24 fasting plasma glucose, Week 24 postprandial plasma glucose, and hypoglycaemic events; statistical significance depended on the specific measure.

CONCLUSIONS

Pre-treatment GV is associated with glycaemic outcomes in T2DM patients undergoing treatment intensification over 24 weeks. HBGI might be the most robust predictor, warranting validation in dedicated prospective studies or randomized trials to assess the predictive value of measuring GV.

Design and rationale of the IN CONTROL trial: the effects of real-time continuous glucose monitoring on glycemia and quality of life in patients with type 1 diabetes mellitus and impaired awareness of hypoglycemia

BACKGROUND

Hypoglycemia is the main side effect of intensified insulin therapy in type 1 diabetes and recognized as a limitation in achieving glycemic targets. Patients with impaired awareness of hypoglycemia have a threefold to sixfold increased risk of severe hypoglycemia. Real-time continuous glucose monitoring may help patients with type 1 diabetes to achieve better glycemic control with less hypoglycemic episodes. Accordingly, one may hypothesize that particularly type 1 diabetes mellitus patients with impaired awareness of hypoglycemia will profit most from this technology with improvements in their quality of life. However, this has not yet been established. This trial aims to study the effect of real-time continuous glucose monitoring on glycemia and quality of life specifically in type 1 diabetes mellitus patients with established impaired awareness of hypoglycemia.

METHODS/DESIGN

This is a two-center, randomized, cross-over trial with a 12-week wash-out period in between intervention periods. A total of 52 type 1 diabetes mellitus patients with impaired awareness of hypoglycemia according to Gold et al. criteria will be randomized to receive real-time continuous glucose monitoring or blinded continuous glucose monitoring for 16 weeks. After a wash-out period, patients will cross over to the other intervention. The primary outcome measure is time spent in euglycemia. Secondary outcomes include (diabetes-specific) markers of quality of life and other glycemic variables.

DISCUSSION

It remains unclear whether patients with type 1 diabetes and impaired awareness of hypoglycemia benefit from real-time continuous glucose monitoring in real-life. This study will provide insight into the potential benefits of real-time continuous glucose monitoring in this patient population.

TRIAL REGISTRATION

Clinicaltrials.gov: NCT01787903.

Parsimonious Description of Glucose Variability in Type 2 Diabetes by Sparse Principal Component Analysis

BACKGROUND

Abnormal glucose variability (GV) is a risk factor for diabetes complications, and tens of indices for its quantification from continuous glucose monitoring (CGM) time series have been proposed. However, the information carried by these indices is redundant, and a parsimonious description of GV can be obtained through sparse principal component analysis (SPCA). We have recently shown that a set of 10 metrics selected by SPCA is able to describe more than 60% of the variance of 25 GV indicators in type 1 diabetes (T1D). Here, we want to extend the application of SPCA to type 2 diabetes (T2D).

METHODS

A data set of CGM time series collected in 13 T2D subjects was considered. The 25 GV indices considered for T1D were evaluated. SPCA was used to select a subset of indices able to describe the majority of the original variance.

RESULTS

A subset of 10 indicators was selected and allowed to describe 83% of the variance of the original pool of 25 indices. Four metrics sufficient to describe 67% of the original variance turned out to be shared by the parsimonious sets of indices in T1D and T2D.

CONCLUSIONS

Starting from a pool of 25 indices assessed from CGM time series in T2D subjects, reduced subsets of metrics virtually providing the same information content can be determined by SPCA. The fact that these indices also appear in the parsimonious description of GV in T1D may indicate that they could be particularly informative of GV in diabetes, regardless of the specific type of disease.

Carbohydrate intake is associated with time spent in the euglycemic range in patients with type 1 diabetes

AIMS/INTRODUCTION

Greater glycemic variability and lack of predictability are important issues for patients with type 1 diabetes. Dietary factors are one of the contributors to this variability, but how closely diet is linked to glycemic fluctuation on a daily basis has not been investigated. We examined the association between carbohydrate intake and glycemic excursion in outpatients.

MATERIALS AND METHODS

A total of 33 patients with type 1 diabetes were included in the analyses (age 44.5 ± 14.7 years, diabetes duration 15.1 ± 8.3 years, 64% female, 30% using insulin pump, glycated hemoglobin 8.1 ± 1.3%). Time spent in euglycemia (70-180 mg/dL), hyperglycemia (>180 mg/dL) and hypoglycemia (<70 mg/dL) of consecutive 48-h periods of continuous glucose monitoring data were collected together with simultaneous records of dietary intake, insulin dose and physical activity. Correlation analyses and multiple regression analyses were used to evaluate the contribution of carbohydrate intake to time spent in the target glycemic range.

RESULTS

In multiple regression analyses, carbohydrate intake (β = 0.53, P = 0.001), basal insulin dose per kg per day (β = -0.31, P = 0.034) and diabetes duration (β = 0.30, P = 0.042) were independent predictors of time spent in euglycemia. Carbohydrate intake (β = -0.51, P = 0.001) and insulin pump use (β = -0.34, P = 0.024) were independent predictors of time spent in hyperglycemia. Insulin pump use (β = 0.52, P < 0.001) and bolus insulin dose per kg per day (β = 0.46, P = 0.001) were independent predictors of time spent in hypoglycemia.

CONCLUSIONS

Carbohydrate intake is associated with time spent in euglycemia in patients with type 1 diabetes.

Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies

BACKGROUND

Continuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic 'weak points'. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored recommendations has not yet been developed. We aimed to develop a new patient-tailored approach for the routine clinical evaluation of CGM profiles. We developed a metric allowing screening for profiles that require therapeutic action and a method to identify the individual CGM parameters with improvement potential.

METHODS

Fifteen parameters frequently used to assess CGM profiles were calculated for 1,562 historic CGM profiles from subjects with type 1 or type 2 diabetes. Factor analysis and varimax rotation was performed to identify factors that accounted for the quality of the profiles.

RESULTS

We identified five primary factors that determined CGM profiles (central tendency, hyperglycaemia, hypoglycaemia, intra- and inter-daily variations). One parameter from each factor was selected for constructing the formula for the screening metric, (the 'Q-Score'). To derive Q-Score classifications, three diabetes specialists independently categorised 766 CGM profiles into groups of 'very good', 'good', 'satisfactory', 'fair', and 'poor' metabolic control. The Q-Score was then calculated for all profiles, and limits were defined based on the categorised groups (<4.0, very good; 4.0-5.9, good; 6.0-8.4, satisfactory; 8.5-11.9, fair; and ≥12.0, poor). Q-Scores increased significantly (P <0.01) with increasing antihyperglycaemic therapy complexity. Accordingly, the percentage of fair and poor profiles was higher in insulin-treated compared with diet-treated subjects (58.4% vs. 9.3%). In total, 90% of profiles categorised as fair or poor had at least three parameters that could potentially be optimised. The improvement potential of those parameters can be categorised as 'low', 'moderate' and 'high'.

CONCLUSIONS

The Q-Score is a new metric suitable to screen for CGM profiles that require therapeutic action. Moreover, because single components of the Q-Score formula respond to individual weak points in glycaemic control, parameters with improvement potential can be identified and used as targets for optimising patient-tailored therapies.