Utilizing the Ambulatory Glucose Profile to Standardize and Implement Continuous Glucose Monitoring in Clinical Practice

Standardizing Reporting of Glucose and Insulin Data for Patients on Multiple Daily Injections Using Connected Insulin Pens and Continuous Glucose Monitoring

Background: Recent development and availability of several connected insulin pens with digital memory are likely to expand the availability of glucose and insulin metrics that previously had been available only for the much smaller number of people using insulin pumps. It would be highly desirable to standardize data presentations to avoid the chaotic emergence of multiple formats that might reduce the clinical utility of connected pens. Methods: We reviewed the literature and analyzed data displays from multiple blood glucose monitoring, continuous glucose monitoring (CGM), insulin pump, and automated insulin delivery systems, and methods for combination of glucose and insulin data. We examined multiple forms of presentation and now propose a prototype for a standardized method for data analysis and display, focusing on the content and format of a one-page dashboard summary for patients on multiple daily injection (MDI) insulin regimens. Results: We propose the following metrics to be included in the one-page report: (A) glucose metrics: simplified glucose distribution in the form of a stacked bar chart showing percentages of time below-, above-, or within-target ranges overall and (optionally) by date, by time of day, or day of the week; (B) insulin metrics: types and doses, and timing of basal and bolus insulin; (C) an enhanced ambulatory glucose profile or "AGP+" showing glucose data points and/or distributions (10th to 90th percentiles), dosages and timing of basal and bolus insulins and (optionally) graphical display of risk of hypoglycemia and hyperglycemia; and (D) user experience regarding technology usage, frequency of alerts for hypo- and hyperglycemia, and information regarding lifestyle, meals, exercise, and sleep, if available; and (E) clinical insights and interpretation. Conclusion: We propose a prototype for a dashboard summary report of glucose, insulin, meals, and activity data intended for providers and patients on MDI using connected pens and CGM. Our goal is to stimulate development of a standardized approach.

Mitigation of hypoglycemia during Ramadan using the flash glucose monitoring system following dose adjustment of insulin and sulphonylurea in patients taking multiple glucose-lowering therapies (The PROFAST-IT Study)

BACKGROUND AND HYPOTHESIS

Patients with type-2 diabetes mellitus (T2DM) on multiple glucose-lowering therapies who fast during Ramadan are at increased risk of hypoglycemia. We have assessed the utility of the flash glucose monitoring system after adjusting the dose of insulin and sulphonylureas to mitigate the risk of hypoglycemia in patients with T2DM who fast during Ramadan.

PATIENTS AND METHODS

Patients with T2DM on either basal insulin or a sulphonylurea and at least 2 other glucose-lowering agents received structured education and adjustment of insulin or sulphonylurea dose according to the PROFAST Ramadan protocol. Glucose variability and episodes of hypoglycemia were assessed using the flash glucose monitoring system (Free Style Libre) before and during Ramadan.

RESULTS

A total of 33 patients with T2DM (on sulphonylurea (SU+) (n = 21), on basal insulin (BI+) (n = 12) aged 50.8 ± 1.6 years with a diabetes duration of 13.1 ± 6.5 years were studied. The average sensor glucose was 154 ± 34 mg/dl (8.5 ± 1.88 mmol/l) with 65.2% in the target range before Ramadan and the average sensor glucose was 156 ± 36 mg/dl (8.6 ± 2.0 mmol/l) with 67.1% in the target range during Ramadan. The incidence of hypoglycemia in the whole group (2.9 v 2.9) and in the SU+ (3.7 vs 3.0) and BI+ (1.7 vs 2.9) groups and eHbA1c (P = 0.56, P = 0.93), average glucose (P = 0.56, P = 0.92) and time within range (P = 0.63, P = 0.73) did not change in the SU+ and BI+ groups, respectively, before and during Ramadan.

CONCLUSION

Structured education with adjustment of the dose of glucose lowering medication alongside use of the FGMS can effectively mitigate the increased risk of hypoglycemia in patients with T2DM on multiple glucose-lowering therapies who fast during Ramadan.

Time in range-A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study

INTRODUCTION

The availability of easily accessible continuous glucose monitoring (CGM) metrics can improve glycemic control in diabetes, and they may even become a viable alternative to hemoglobin A1c (HbA1c) laboratory tests in the next years. The REALISM-T1D study (REAl-Life glucoSe Monitoring in Type 1 Diabetes) was aimed at contributing, with real-world data, to a deeper understanding of these metrics, including the time in range (TIR)-HbA1c relationship, to facilitate their adoption by diabetologists in everyday practice.

RESEARCH DESIGN AND METHODS

70 adults affected by type 1 diabetes were monitored for 1 year by means of either flash (FGM) or real-time (rtCGM) glucose monitoring devices. Follow-up visits were performed after 90, 180 and 365 days from baseline and percentage TIR^70-180 evaluated for the 90-day time period preceding each visit. HbA1c tests were also carried out in the same occasions and measured values paired with the corresponding TIR data.

RESULTS

A monovariate linear regression analysis confirms a strong correlation between TIR and HbA1c as found in previous studies, but leveraging more homogeneous data (n=146) collected in real-life conditions. Differences were determined between FGM and rtCGM devices in Pearson's correlation (r_FGM=0.703, r_rtCGM=0.739), slope (β_1,FGM=-11.77, β_1,rtCGM=-10.74) and intercept (β_0,FGM=141.19, β_0,rtCGM=140.77) coefficients. Normality of residuals and homoscedasticity were successfully verified in both cases.

CONCLUSIONS

Regression lines for two patient groups monitored through FGM and rtCGM devices, respectively, while confirming a linear relationship between TIR and A1c hemoglobin (A1C) in good accordance with previous studies, also show a statistically significant difference in the regression intercept, thus suggesting the need for different models tailored to device characteristics. The predictive power of A1C as a TIR estimator also deserves further investigations.

Making sense of glucose metrics in diabetes: linkage between postprandial glucose (PPG), time in range (TIR) & hemoglobin A1c (A1C)

While A1C is the standard diagnostic test for evaluating long-term glucose management, additional glucose data, either from fingerstick blood glucose testing, or more recently, continuous glucose monitoring (CGM), is necessary for safe and effective management of diabetes, especially for individuals treated with insulin. CGM technology and retrospective pattern-based management using various CGM reports have the potential to improve glycemic management beyond what is possible with fingerstick blood glucose monitoring. CGM software can provide valuable retrospective data on Time-in-Ranges (above, below, within) metrics, the Ambulatory Glucose Profile (AGP), overlay reports, and daily views for persons with diabetes and their healthcare providers. This data can aid in glycemic pattern identification and evaluation of the impact of lifestyle factors on these patterns. Time-in-Ranges data provide an easy-to-define metric that can facilitate goal setting discussions between clinicians and persons with diabetes to improve glycemic management and can empower persons with diabetes in self-management between clinic consultation visits. Here we discuss multiple real-life scenarios from a primary care clinic for the application of CGM in persons with diabetes. Optimizing the use of the reports generated by CGM software, with attention to time in range, time below range, and postprandial glucose-induced time above range, can improve the safety and efficacy of ongoing glucose management.

Continuous glucose monitoring: The achievement of 100 years of innovation in diabetes technology

Monitoring of glucose levels is essential to effective diabetes management. Over the past 100 years, there have been numerous innovations in glucose monitoring methods. The most recent advances have centered on continuous glucose monitoring (CGM) technologies. Numerous studies have demonstrated that use of continuous glucose monitoring confers significant glycemic benefits on individuals with type 1 diabetes (T1DM) and type 2 diabetes (T2DM). Ongoing improvements in accuracy and convenience of CGM devices have prompted increasing adoption of this technology. The development of standardized metrics for assessing CGM data has greatly improved and streamlined analysis and interpretation, enabling clinicians and patients to make more informed therapy modifications. However, many clinicians many be unfamiliar with current CGM and how use of these devices may help individuals with T1DM and T2DM achieve their glycemic targets. The purpose of this review is to present an overview of current CGM systems and provide guidance to clinicians for initiating and utilizing CGM in their practice settings.

Time in Range: How to Measure It, How to Report It, and Its Practical Application in Clinical Decision-Making

The A1C metric has been the gold standard for assessing glycemia for decades. This biologic assay, based on averaging, is fraught with limitations and may be giving way to more holistic approaches. This article reviews glycemic time in range as the new standard for assessing patients with continuous glucose monitoring data. Information from the International Consensus Group on Time in Range will be summarized.

Glycemic variability in patients with gastrointestinal cancer: An integrative review

PURPOSE

Glycemic variability is associated with risks for adverse events in patients with cancer. Several studies have evaluated the presence and impact of hyperglycemia and/or hypoglycemia in patients with cancer; however, few studies have evaluated glycemic variability. The purpose of this integrative review of studies in patients with gastrointestinal cancers was to investigate the presence and methods of reporting glycemic variability during and following treatments.

METHODS

A comprehensive review of the literature was conducted. PubMed, CINAHL, EMBASE, and Cochrane databases were searched for publications between 1/1/1969 and 7/24/2019. Studies of patients with gastrointestinal cancer following surgery, during treatment, and <5 years following treatment were included and evaluated by cancer type and method of glucose and glycemic variability measurement.

RESULTS

Among 1526 patients with gastrointestinal cancer across 19 studies, gastric and pancreatic cancers were most prevalent. Timing of glucose testing and methods of analyzing glycemic variability varied. Most analyses used the standard deviation or interquartile range. Glycemic variability was more prevalent among patients with Type 2 Diabetes and among those with pancreatic cancer. In some patients glycemic variability remained notable > one year following surgery despite improvements in glycemic control.

CONCLUSION

Patients with gastrointestinal cancer experience glycemic variability during and up to one year following treatment. There was heterogeneity in methods related to timing of testing and reporting glycemic variability among the 19 studies in this review. Future investigations need to identify the presence and define the methods of measuring glycemic variability in patients with gastrointestinal cancer.

Managing New-Onset Type 1 Diabetes During the COVID-19 Pandemic: Challenges and Opportunities

Background: The current COVID-19 pandemic provides an incentive to expand considerably the use of telemedicine for high-risk patients with diabetes, and especially for the management of type 1 diabetes (T1D). Telemedicine and digital medicine also offer critically important approaches to improve access, efficacy, efficiency, and cost-effectiveness of medical care for people with diabetes. Methods: Two case reports are presented where telemedicine was used effectively and safely after day 1 in person patient education. These aspects of the management of new-onset T1D patients (adult and pediatric) included ongoing diabetes education of the patient and family digitally. The patients used continuous glucose monitoring with commercially available analysis software (Dexcom Clarity and Glooko) to generate ambulatory glucose profiles and interpretive summary reports. The adult subject used multiple daily insulin injections; the pediatric patient used an insulin pump. The subjects were managed using a combination of e-mail, Internet via Zoom, and telephone calls. Results: These two cases show the feasibility and effectiveness of use of telemedicine in applications in which we had not used it previously: new-onset diabetes education and insulin dosage management. Conclusions: The present case reports illustrate how telemedicine can be used safely and effectively for new-onset T1D training and education for both pediatric and adult patients and their families. The COVID-19 pandemic has acutely stimulated the expansion of the use of telemedicine and digital medicine. We conclude that telemedicine is an effective approach for the management of patients with new-onset T1D.

Impacts of glycemic variability on the relationship between glucose management indicator from iPro™2 and laboratory hemoglobin A1c in adult patients with type 1 diabetes mellitus

AIMS

Our aim was to investigate the impact of glycemic variability (GV) on the relationship between glucose management indicator (GMI) and laboratory glycated hemoglobin A1c (HbA1c).

METHODS

Adult patients with type 1 diabetes mellitus (T1D) were enrolled from five hospitals in China. All subjects wore the iPro^™2 system for 14 days before HbA1c was measured at baseline, 3 months and 6 months. Data derived from iPro^™2 sensor was used to calculate GMI and GV parameters [standard deviation (SD), glucose coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE)]. Differences between GMI and laboratory HbA1c were assessed by the absolute value of the hemoglobin glycation index (HGI).

RESULTS

A total of 91 sensor data and corresponding laboratory HbA1c, as well as demographic and clinical characteristics were analyzed. GMI and HbA1c were 7.20 ± 0.67% and 7.52 ± 0.73%, respectively. The percentage of subjects with absolute HGI 0 to lower than 0.1% was 21%. GMI was significantly associated with laboratory HbA1c after basic adjustment (standardized β = 0.83, p < 0.001). Further adjustment for SD or MAGE reduced the standardized β for laboratory HbA1c from 0.83 to 0.71 and 0.73, respectively (both p < 0.001). In contrast, the β remained relatively constant when further adjusting for CV. Spearman correlation analysis showed that GMI and laboratory HbA1c were correlated for each quartile of SD and MAGE (all p < 0.05), with the corresponding correlation coefficients decreased across ascending quartiles.

CONCLUSIONS

This study validated the GMI formula using the iPro^™2 sensor in adult patients with T1D. GV influenced the relationship between GMI and laboratory HbA1c.