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

Perceived Cognitive Function and Glycemic Variability: Baseline Results From a Cognitive Rehabilitation Intervention

PURPOSE

The purpose of this study was to examine the association between glucose variability, diabetes self-management, and cognitive function in participants enrolled in a cognitive rehabilitation intervention for people with type 2 diabetes.

METHODS

Baseline data from the Memory, Attention, and Problem-Solving Skills for Diabetes randomized controlled trial (n = 95; mean age 65.6 years, SD 5.99; 59.3% female; 59% non-Hispanic White) were analyzed and included scores from the PROMIS Cognitive Function version 2, a measure of perceived cognitive function; glucose variability measurements from continuous glucose monitors; and scores on the Summary of Diabetes Self-Care Activities Survey.

RESULTS

Participants had higher levels of perceived cognitive dysfunction than the US average. Lower PROMIS scores were associated with higher levels of glucose variability. Better perceived cognitive health was related to better diabetes self-management. Glucose variability, measured by the coefficient of variation, was a significant predictor of perceived cognitive function.

CONCLUSIONS

Perceived cognitive function was associated with diabetes self-management and glucose variability. Understanding this association can support the development of interventions to mitigate effects associated with glucose variability and changes in cognitive function. Including measurements of perceived cognitive function in assessments has the potential to alert health care providers about the need for additional support in diabetes management and the possibility of cognitive impairment that may need further objective assessment.

Current landscape and comprehensive management of glycemic variability in diabetic retinopathy

Diabetic retinopathy (DR), a well-known microvascular complication of diabetes mellitus, remains the main cause of vision loss in working-age adults worldwide. Up to now, there is a shortage of information in the study regarding the contributing factors of DR in diabetes. Accumulating evidence has identified glycemic variability (GV), referred to fluctuations of blood glucose levels, as a risk factor for diabetes-related complications. Recent reports demonstrate that GV plays an important role in accounting for the susceptibility to DR development. However, its exact role in the pathogenesis of DR is still not fully understood. In this review, we highlight the current landscape and relevant mechanisms of GV in DR, as well as address the mechanism-based therapeutic strategies, aiming at better improving the quality of DR management in clinical practice.

Enhancement of Ambulatory Glucose Profile for Decision Assistance and Treatment Adjustments

The ambulatory glucose profile (AGP) lacks sufficient statistical metrics and insightful graphs; indeed, it is missing important information on the temporal patterns of glucose variations. The AGP graph is difficult to interpret due to the overlapping metrics and fluctuations in glucose levels over 14 days. The objective of this proposed work is to overcome these challenges, specifically the lack of insightful information and difficulty in interpreting AGP graphs, to create a platform for decision assistance. The present work proposes 20 findings built from decision rules that were developed from a combination of AGP metrics and additional statistical metrics, which have the potential to identify patterns and insightful information on hyperglycemia and hypoglycemia. The "CGM Trace" webpage was developed, in which insightful metrics and graphical representations can be used to make inferences regarding the glucose data of any user. However, doctors (endocrinologists) can access the "Findings" tab for a summarized presentation of their patients' glycemic control. The findings were implemented for 67 patients' data, in which the data of 15 patients were collected from a clinical study and the data of 52 patients were gathered from a public dataset. The findings were validated by means of MANOVA (multivariate analysis of variance), wherein a p value of < 0.05 was obtained, depicting a strong significant correlation between the findings and the metrics. The proposed work from "CGM Trace" offers a deeper understanding of the CGM data, enhancing AGP reports for doctors to make treatment adjustments based on insightful information and hidden patterns for better diabetic management.

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

BACKGROUND

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

METHODS

A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

RESULTS

A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

CONCLUSION

This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

7. Diabetes Technology: Standards of Care in Diabetes-2024

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Marjorie: Visualizing Type 1 Diabetes Data to Support Pattern Exploration

In this work we propose Marjorie, a visual analytics approach to address the challenge of analyzing patients' diabetes data during brief regular appointments with their diabetologists. Designed in consultation with diabetologists, Marjorie uses a combination of visual and algorithmic methods to support the exploration of patterns in the data. Patterns of interest include seasonal variations of the glucose profiles, and non-periodic patterns such as fluctuations around mealtimes or periods of hypoglycemia (i.e., glucose levels below the normal range). We introduce a unique representation of glucose data based on modified horizon graphs and hierarchical clustering of adjacent carbohydrate or insulin entries. Semantic zooming allows the exploration of patterns on different levels of temporal detail. We evaluated our solution in a case study, which demonstrated Marjorie's potential to provide valuable insights into therapy parameters and unfavorable eating habits, among others. The study results and informal feedback collected from target users suggest that Marjorie effectively supports patients and diabetologists in the joint exploration of patterns in diabetes data, potentially enabling more informed treatment decisions. A free copy of this paper and all supplemental materials are available at https://osf.io/34t8c/.

Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter?

Over the past two decades, there has been continuous advancement in the accuracy and complexity of continuous glucose monitoring devices. Continuous glucose monitoring provides valuable insights into blood glucose dynamics, and can record glucose fluctuations accurately and completely. Glycemic variability (GV) is a straightforward measure of the extent to which a patient's blood glucose levels fluctuate between high peaks and low nadirs. Many studies have investigated the relationship between GV and complications, primarily in the context of type 2 diabetes. Nevertheless, the exact contribution of GV to the development of diabetes complications remains unclear. In this literature review, we aimed to summarize the existing evidence regarding the measurement, target level, pathophysiological mechanisms relating GV and tissue damage, and population-based studies of GV and diabetes complications. Additionally, we introduce novel methods for measuring GV, and discuss several unresolved issues of GV. In the future, more longitudinal studies and trials are required to confirm the exact role of GV in the development of diabetes complications.

Roadmap to the Effective Use of Continuous Glucose Monitoring: Innovation, Investigation, and Implementation

For 25 years, continuous glucose monitoring (CGM) has been evolving into what it is now: a key tool to both measure individuals' glycemic status and to help guide their day-to-day management of diabetes. Through a series of engineering innovations, clinical investigations, and efforts to optimize workflow implementation, the use of CGM is helping to transform diabetes care. This article presents a roadmap to the effective use of CGM that outlines past, present, and possible future advances in harnessing the potential of CGM to improve the lives of many people with diabetes, with an emphasis on ensuring that CGM technology is available to all who could benefit from its use.

Roadmap to the Effective Use of Continuous Glucose Monitoring in Primary Care

Diabetes technology has undergone a remarkable evolution in the past decade, with dramatic improvements in accuracy and ease of use. Continuous glucose monitor (CGM) technology, in particular, has evolved, and coevolved with widely available consumer smartphone technology, to provide a unique opportunity to both improve management and decrease the burden of management for populations across nearly the entire spectrum of people living with diabetes. Capitalizing on that opportunity, however, will require both adoption of and adaptations to the use of CGM technology in the broader world of primary care. This article focuses on mechanisms to expand pathways to optimized glycemic management, thereby creating a robust roadway capable of improving care across broad populations managed in primary care settings. Recent expansions in access to devices combined with improved mechanisms for data access at the time of primary care visits and improved training and evolving systems of support within primary care, hold potential to improve glycemic management in diabetes across the health care spectrum.

Expert Recommendations for Using Time-in-Range and Other Continuous Glucose Monitoring Metrics to Achieve Patient-Centered Glycemic Control in People With Diabetes

New metrics for assessing glycemic control beyond HbA1c have recently emerged due to the increasing use of continuous glucose monitoring (CGM) in diabetes clinical practice. Among them, time in range (TIR) has appeared as a simple and intuitive metric that correlates inversely with HbA1c and has also been newly linked to the risk of long-term diabetes complications. The International Consensus on Time in Range established a series of target glucose ranges (TIR, time below range and time above range) and recommendations for time spent within these ranges for different diabetes populations. These parameters should be evaluated together with the ambulatory glucose profile (AGP). Using standardized visual reporting may help people with diabetes and healthcare professionals in the evaluation of glucose control in frequent clinical situations. The objective of the present review is to provide practical insights to quick interpretation of patient-centered metrics based on flash glucose monitoring data, as well as showing some visual examples of common clinical situations and giving practical recommendations for their management.

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling

Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR), and heart rate variability (HRV) in 25 healthy participants over 14 days. We develop a Bayesian inference framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modeling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm for glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40%-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals. A more complex model incorporating activity, HR, and HRV explains up to 15% of additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.

The Association of Glucose Variability and Dementia Incidence in Latinx Adults with Type 2 Diabetes: A Retrospective Study

Latinx adults with both cognitive dysfunction and type 2 diabetes mellitus (T2DM) are significantly more likely than Latinx adults with diabetes alone to have complications such as cardiovascular disease. Glucose variability may be a risk for dementia, but the course of glucose variability in the time before a dementia diagnosis for Latinx adults with T2DM has not been examined. We used a 10-year retrospective cohort of medical records of Latinx patients with T2DM who had at least one use of a continuous glucose monitor. The objective was to examine how glucose variability was associated with future dementia diagnoses. A total of 116 charts were included. Mean of daily differences and mean amplitude of glycemic excursions were more strongly associated with dementia diagnoses than other variability indices (p < .01). Understanding the relationships between cognitive function, glucose variability, and barriers to health care can translate into improved interventions to enhance diabetes care.

Acceptability and feasibility of continuous glucose monitoring in people with diabetes: protocol for a mixed-methods systematic review of quantitative and qualitative evidence

BACKGROUND

Good glycaemic control is a crucial part of diabetes management. Traditional assessment methods, including HbA1c checks and self-monitoring of blood glucose, can be unreliable and inaccurate. Continuous glucose monitoring (CGM) offers a non-invasive and more detailed alternative. Availability of this technology is increasing worldwide. However, there is no current comprehensive evidence on the acceptability and feasibility of these devices. This is a protocol for a mixed-methods systematic review of qualitative and quantitative evidence about acceptability and feasibility of CGM in people with diabetes.

METHODS

We will search MEDLINE, Embase, CINAHL, and CENTRAL for qualitative and quantitative evidence about the feasibility and acceptability of CGM in all populations with diabetes (any type) using search terms for "continuous glucose monitoring" and "diabetes". We will not apply any study-type filters. Searches will be restricted to studies conducted in humans and those published from 2011 onwards. We will not restrict the search by language. Study selection and data extraction will be carried out by two reviewers independently using Rayyan and Eppi-Reviewer, respectively, with disagreements resolved by discussion. Data extraction will include key information about each study, as well as qualitative evidence in the form of participant quotes from primary studies and themes and subthemes based on the authors' analysis. Quantitative data relating to acceptability and feasibility including data loss, adherence, and quantitative ratings of acceptability will be extracted as means and standard deviations or n/N as appropriate. Qualitative evidence will be analysed using framework analysis informed by the Theoretical Framework of Acceptability. Where possible, quantitative evidence will be combined using random-effects meta-analysis; otherwise, a narrative synthesis will be performed. The most appropriate method for integrating qualitative and quantitative findings will be selected based on the data available.

DISCUSSION

Ongoing assessment of the acceptability of interventions has been identified as crucially important to scale-up and implementation. This review will provide new knowledge with the potential to inform a programme theory of CGM as well as future roll-out to potentially vulnerable populations, including those with severe mental illness.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021255141.

Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults

It is often assumed that healthy people have the genuine ability to maintain tight blood glucose regulation. However, a few recent studies revealed that glucose dysregulation such as hyperglycemia may occur even in people who are considered normoglycemic by standard measures and were more prevalent than initially thought, suggesting that more investigations are needed to fully understand the within-day glucose dynamics of healthy people. In this paper, we conducted an analysis on a multi-modal dataset to examine the relationships between glycemic variability when people were awake and that when they were sleeping. The interstitial glucose levels were measured with a wearable continuous glucose monitoring (CGM) technology FreeStyle Libre 2 at every 15 min interval. In contrast to the traditional single-time-point measurements, the CGM data allow the investigation into the temporal patterns of glucose dynamics at high granularity. Sleep onset and offset timestamps were recorded daily with a Fitbit Charge 3 wristband. Our analysis leveraged the sleep data to split the glucose readings into segments of awake-time and in-sleep, instead of using fixed cut-off time points as has been done in existing literature. We combined repeated measure correlation analysis and quantitative association rules mining, together with an original post-filtering method, to identify significant and most relevant associations. Our results showed that low overall glucose in awake time was strongly correlated to low glucose in subsequent sleep, which in turn correlated to overall low glucose in the next day. Moreover, both analysis techniques identified significant associations between the minimal glucose reading in sleep and the low blood glucose index the next day. In addition, the association rules discovered in this study achieved high confidence (0.75–0.88) and lift (4.1–11.5), which implies that the proposed post-filtering method was effective in selecting quality rules.

An Integrated Mobile Platform for Automated Data Collection and Real-Time Patient Monitoring in Diabetes Clinical Trials

We present a new mobile platform to be used in clinical trials aimed at both collecting data and assessing new technologies and treatments for diabetes care. The main components of the platform are a mobile app, that automatically collects data from continuous glucose monitoring sensors and activity trackers, and also allows users to manually log daily events; a cloud database for safe data storage; a web interface, which allows clinicians to monitor patients' status in real-time. The platform is modular and highly customizable for a multitude of purposes in clinical research. Preliminary tests performed for daily-life data gathering by both clinicians and users are extremely encouraging.

Clinical application guidelines for blood glucose monitoring in China (2022 edition)

Glucose monitoring is an important component of diabetes management. The Chinese Diabetes Society (CDS) has been producing evidence-based guidelines on the optimal use of glucose monitoring since 2011. In recent years, new technologies in glucose monitoring and more clinical evidence, especially those derived from Chinese populations, have emerged. In this context, the CDS organised experts to revise the Clinical application guidelines for blood glucose monitoring in China in 2021. In this guideline, we focus on four methods of glucose monitoring that are commonly used in clinical practice, including capillary glucose monitoring, glycated haemoglobin A1c, glycated albumin, and continuous glucose monitoring. We describe the definitions and technical characteristics of these methods, the factor that may interfere with the measurement, the advantages and caveats in clinical practice, the interpretation of glucose metrics, and the relevant supporting evidence. The recommendations for the use of these methods are also provided. The various methods of glucose monitoring have their strengths and limitations and cannot be replaced by one another. We hope that these guidelines could aid in the optimal application of common methods of glucose monitoring in clinical practice for better diabetes care.

Comparing Patch vs Pen Bolus Insulin Delivery in Type 2 Diabetes Using Continuous Glucose Monitoring Metrics and Profiles

OBJECTIVE

CeQur Simplicity™ (CeQur, Marlborough, MA) is a 3-day insulin delivery patch designed to meet mealtime insulin requirements. A recently reported 48-week, randomized, multicenter, interventional trial compared efficacy, safety and self-reported outcomes in 278 adults with type 2 diabetes (T2D) on basal insulin therapy who initiated and managed mealtime insulin therapy with a patch pump versus insulin pen. We assessed changes in key glycemic metrics among a subset of patients who wore a continuous glucose monitoring (CGM) device.

METHODS

Study participants (patch, n = 49; pen, n = 48) wore a CGM device in masked setting during the baseline period and prior to week 24. Glycemic control was assessed using international consensus guidelines for percentage of Time In Range (%TIR: >70% at 70-180 mg/dL), Time Below Range (%TBR: <4% at <70 mg/dL; <1% at <54 mg/dL), and Time Above Range (%TAR: <25% at >180 mg/dL; <5% at >250 mg/dL).

RESULTS

Both the patch and pen groups achieved recommended targets in %TIR (74.1% ± 18.7%, 75.2 ± 16.1%, respectively) and marked reductions in %TAR >180 mg/dL (21.1% ± 19.9%, 19.7% ± 17.5%, respectively) but with increased %TBR <70 mg/dL (4.7% ± 5.2%, 5.1 ± 5.8, respectively), all P < .0001. No significant between-group differences in glycemic improvements or adverse events were observed.

CONCLUSIONS

CGM confirmed that the patch or pen can be used to safely initiate and optimize basal-bolus therapy using a simple insulin adjustment algorithm with SMBG. Preference data suggest that use of the patch vs pen may enhance treatment adherence.

Short Term Glucose Prediction in Patients with Type 1 Diabetes Mellitus

Glucose prediction is used in diabetes self-management as it allows to take suitable actions for proper glycemic regulation of the patient. The aim of this work is the short-term personalized glucose prediction in patients with Type 1 diabetes mellitus (T1DM). In this scope, we compared two different models, an autoregressive moving average (ARMA) model and a long short-term memory (LSTM) model for different prediction horizons. The comparison of two models was performed using the evaluation metrics of root mean square error (RMSE) and mean absolute error (MAE). The models were trained and tested in 29 real patients. The results shown that the LSTM model had better performance than ARMA with RMSE 3.13, 6.41 and 8.81 mg/dL and MAE 1.98, 5.06 and 6.47 mg/dL for 5-, 15- and 30-minutes prediction horizon.

Level of Agreement and Correlation Between the Estimated Hemoglobin A1c Results Derived by Continuous or Conventional Glucose Monitoring Systems Compared with the Point-of-Care or Laboratory-Based Measurements: An Observational Study

INTRODUCTION

Hemoglobin A1C (HbA1c) is an important marker for diabetes care management. With the increasing use of new technologies such as continuous glucose monitoring (CGM) and point-of-care testing (POCT), patients and their physicians have been able to monitor and continuously check their blood glucose levels in an efficient and timely manner. This study aimed to investigate the level of agreement between the standard laboratory test for HbA1c (Lab-HbA1c) with point-of-care testing (POCT-HbA1c) and glucose monitoring index (GMI) derived by intermittently scanned CGM (isCGM) or estimated average glucose (eAG) derived by conventional self-monitored blood glucose (SMBG) devices.

METHODS

A cross-sectional study was conducted at the Diabetes Treatment Center, Prince Sultan Military Medical City, Saudi Arabia, between May and December 2020 with 81 patients with diabetes who used the isCGM system (n = 30) or conventional finger-pricking SMBG system (n = 51). At the same visit, venous and capillary blood samples were taken for routine HbA1c analysis by the standard laboratory and POCT methods, respectively. Also, for isCGM users, the GMI data for 28 days (GMI-28) and 90 days (GMI-90) were obtained, while for SMBG users, eAG data for 30 days (eAG-30) and 90 days (eAG-90) were calculated. The limits of agreement in different HbA1c measurements were evaluated using a Bland-Altman analysis. Pearson correlation and multivariate linear regression analyses were also performed.

RESULTS

Based on the Bland-Altman analysis, HbA1c levels for 96.7% and 96.1% of the patients analyzed by the POCT and the standard laboratory methods were within the range of the 95% limit of agreement in both isCGM and conventional SMBG users, respectively. About 93.3% of the GMI measurements were within the 95% limit of agreement. Also, about 94.12% of the eAG-30 and 90.2% of the eAG-90 measurements were within the 95% limit of agreement. Moreover, the correlation analysis revealed a statistically significant positive correlation and linear regression among Lab-HbA1c, POCT-HbA1c, GMI, and eAG in both conventional SMBG and isCGM users (all p < 0.001). These positive results persisted significantly after adjusting for different factors (all p < 0.001).

CONCLUSION

GMI derived by isCGM or eAG derived by conventional SMBG systems, as well as the POCT-HbA1c measurements, showed a high level of agreement; therefore, we recommend them as potential methods for diabetes monitoring, especially when a rapid result is needed or with patients with uncontrolled diabetes or on intensive insulin therapy.

Expert Roundtable on Continuous Glucose Monitoring

OBJECTIVE

The objective of this study was to define expert opinion on Continuous Glucose Monitoring (CGM) in persons with type 2 diabetes mellitus (PWDM2), including its advantages, barriers, and best clinical practices for initiation, patient-clinician communication, and data management.

METHODS

A series of virtual discussions was held to recommend improvements to clinical practice and design clinical tools for primary care clinicians. Participants included endocrinologists, primary care physicians, physician assistants (PAs), advanced practice nurses (APNs), and diabetes care and education specialists.

RESULTS

The expert panels recommended CGM as a supplement to blood glucose monitoring (BGM) and hemoglobin A1c for managing persons with diabetes (PWD). CGM can help predict potential pitfalls in glycemic management--including hypo and hyperglycemic excursions--which directly influence lifestyle changes, medication initiation, and dosing decisions. A toolkit was designed with practical guidance on integration of CGM into clinical practice, interpretation of results, clinical guidelines, a patient action plan, and other useful management tools.

CONCLUSION

This review summarizes findings from a roundtable discussion with endocrinology and primary care clinicians, a discussion of the advantages and challenges with CGM, and clinical approaches to improving care of persons with diabetes. CGM offers more detailed tracking of glucose than BGM or A1c and it can detect asymptomatic hypoglycemia. Specialized education of providers, cost to patient and providers, and data management remain barriers to widespread adoption of CGM for PWD.

Glycaemia dynamics in gestational diabetes mellitus

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

Randomized comparison of self-monitored blood glucose (BGM) versus continuous glucose monitoring (CGM) data to optimize glucose control in type 2 diabetes

AIMS

Evaluate whether structured BGM testing (BGM) or real-time CGM (CGM) lead to improved glucose control (A1c). Determine which approach optimized glucose control more effectively. METHODS-MULTI-ARM PARALLEL: trial of three type 2 diabetes (T2D) therapies ± metformin: (1) sulfonylurea (SU), (2) incretin (DPP4 inhibitor or GLP-1 agonist), or (3) insulin. After a baseline CGM, 114 adult subjects were randomized to either BGM (4 times daily) or CGM (24/7) for 16 weeks with therapies adjusted every 4 weeks.

RESULTS

A1c means decreased from 8.19 to 7.07 (1.12% difference) with CGM (n = 59) and 7.85 to 7.03 (0.82% difference) with BGM (n = 55) (p < 0.001). BGM and CGM groups showed significant improvements in time in range and glucose variability-with no significant difference between the two groups. Clinically important hypoglycemia (<50 mg/dL) was significantly reduced for the CGM group compared with BGM (p < 0.01), particularly in subjects taking insulin or therapies with higher hypoglycemic risk (SU).

CONCLUSION

In T2D, structured, consistent use of glucose data regardless of device (structured BGM or CGM) leads to improvements in A1c control. CGM is more effective than BGM in minimizing hypoglycemia particularly for those using higher hypoglycemic risk therapies.

Efficacy of Flash Glucose Monitoring in Type 1 and Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

Objective

Flash glucose monitoring (FlashGM) is a sensor-based technology that displays glucose readings and trends to people with diabetes. In this meta-analysis, we assessed the effect of FlashGM on glycaemic outcomes including HbA1c, time in range, frequency of hypoglycaemic episodes and time in hypo/hyperglycaemia compared to self-monitoring of blood glucose, using data from randomised controlled trials.

Methods

A systematic search was conducted on MEDLINE, EMBASE and CENTRAL for articles published between 2014 and 2021. We selected randomised controlled trials comparing flash glucose monitoring to self-monitoring of blood glucose that reported change in HbA1c and at least one other glycaemic outcome in adults with type 1 or type 2 diabetes. Two independent reviewers extracted data from each study using a piloted form. Meta-analyses using a random-effects model was conducted to obtain a pooled estimate of the treatment effect. Heterogeneity was assessed using forest plots and the I2 statistic.

Results

We identified 5 randomised controlled trials lasting 10 – 24 weeks and involving 719 participants. Flash glucose monitoring did not lead to a significant reduction in HbA1c. However, it resulted in increased time in range (mean difference 1.16 hr, 95% CI 0.13 to 2.19, I2 = 71.7%) and decreased frequency of hypoglycaemic episodes (mean difference -0.28 episodes per 24 hours, 95% CI -0.53 to -0.04, I2 = 71.4%).

Conclusions

Flash glucose monitoring did not lead to a significant reduction in HbA1c compared to self-monitoring of blood glucose, however, it improved glycaemic management through increased time in range and decreased frequency of hypoglycaemic episodes.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, identifier PROSPERO (CRD42020165688).

Continuous glucose monitoring in primary care - are we there?

PURPOSE OF REVIEW

In this review, we examine the expanding role of continuous glucose monitoring in glycaemic management in primary care.

RECENT FINDINGS

Improving technology and decreasing cost have increased the uptake of use of continuous glucose monitoring (CGM) for glycaemic management in primary care, wherein most diabetes is managed. Optimized use of this technology, however, will require a convergence of several factors. Availability of devices for people with diabetes, availability of data at the time of clinical interactions, and expertise in interpretation of CGM and ambulatory glucose profile (AGP) data, as well as optimization of therapies, will be required. Significant progress has been made in all three areas in recent years, yet creating systems of support for widespread use of CGM in primary care remains an area of active investigation.

SUMMARY

There has been significant uptake in the use of CGM in the management of diabetes in primary care. Optimized use, however, requires both access to CGM data and the expertise to use the data. Although promising strategies have emerged, the task of generalizing these strategies to the broad population of primary care in America is ongoing. CGM technology holds significant potential for improving glycaemic management in primary care, yet important work remains to leverage the full potential of this promising technology.

Relationship between blood glucose variability in ambulatory glucose profile and standardized continuous glucose monitoring metrics: Subanalysis of a prospective cohort study

AIM

To clarify the relationship between ambulatory glucose profile (AGP) indexes and standardized continuous glucose monitoring (CGM) metrics in patients with type 2 diabetes (T2D).

METHODS

This is an exploratory, cross-sectional analysis of baseline data collected from a prospective, multicentre, 5-year follow-up observational study conducted and published previously by our group. The study participants were 999 outpatients with T2D who used CGM at baseline, and had no apparent history of cardiovascular disease. We investigated the relationship between average interquartile range (IQR) and time in range (TIR). We also calculated, for the first time, the cutoff values to achieve the TIR target values.

RESULTS

In both the TIR more than 70% and TIR more than 90% achievement groups, the average IQR was notably small compared with the non-achievement groups. Particularly in comparison of the TIR quartiles, the average IQR became significantly smaller as the TIR became larger. The average IQR correlated negatively with TIR, and the cutoff values for TIR of more than 70% achievement and TIR of more than 90% achievement were an average IQR (>70%/>90%) of 2.13/1.85 mmol/L.

CONCLUSION

Our results showed a negative correlation between TIR and the range of blood glucose variations visually represented in AGP. The results also showed that the range of blood glucose variations in AGP is associated with indices of intraday and interday blood glucose variations and also with hypoglycaemia. Our results may provide new perspectives in the assessment and application of AGP in the clinical setting.

Time in range: new parameter to evaluate blood glucose control

Continuous glucose monitoring became more common in everyday clinical practice. New parameters have been created as a standard for assessing the degree of control for people with diabetes and can be used more clearly than glycated haemoglobin. The new parameter time in range represents a significant benefit not only for diabetologists, but also an important tool to help the patients in their daily lives with diabetes.

Population-level management of type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health

OBJECTIVE

To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review.

RESEARCH DESIGN AND METHODS

We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review.

RESULTS

The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2 ± 0.20 to 1.3 ± 0.24 min per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n = 58) have associated 8.8 percentage points (pp) (95% CI = 0.6-16.9 pp) greater time-in-range (70-180 mg/dl) glucoses compared to 25 control patients who did not qualify at 12 months after T1D onset.

CONCLUSIONS

An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range.

Diabetes Self-Care in Older Adults With Type 1 Diabetes Mellitus: How Does Cognition Influence Self-Management

With increasing knowledge and improvements in options for glycemic control, the life-expectancy of patients with type 1 diabetes mellitus (T1DM) has increased considerably over the past decades. Whereas this is undeniably positive for patients, aging is related to natural decline in cognitive functions. As patients with T1DM across the life-span are susceptible to cognitive deterioration, an interaction with aging may be expected and the risk of development of dementia might be increased. As achieving glycemic control depends on a set of diabetes self-management behaviors, it is imperative to understand how cognitive functions are involved in the upkeep of these behaviors and how cognitive impairment may affect them. In this narrative review, we set out to understand the relationship between cognition and T1DM self-care by first reviewing the glycemic targets in older adults, what treatment options are available, and what cognitive functions they draw upon. We will then review the cognitive literature in older adults that is available and then link both together. Lastly, we finish with clinical recommendations and suggestions for future research.

Reduction of hypoglycaemia, lifestyle modifications and psychological distress during lockdown following SARS-CoV-2 outbreak in type 1 diabetes

AIMS

To assess changes in glucose metrics and their association with psychological distress and lifestyle changes in patients with type 1 diabetes (T1D) using flash glucose monitoring (FGM) during lockdown following severe acute respiratory syndrome coronavirus 2 outbreak.

MATERIALS AND METHODS

Single-centre, observational, retrospective study enrolling T1D patients who attended a remote visit on April 2020 at the Endocrinology division of the University Hospital Policlinico Consorziale, Bari, Italy. Lockdown-related changes in physical activity level and dietary habits were assessed on a semi-quantitative basis. Changes in general well-being were assessed by the General Health Questionnaire-12 items with a binary scoring system. Glucose metrics were obtained from the Libreview platform for the first 2 weeks of February 2020 (T0) and the last 2 weeks before the phone visit (T1).

RESULTS

Out of 84 patients assessed for eligibility, 48 had sufficient FGM data to be included in the analysis. FGM data analysis revealed significant reductions in coefficient of variation, number of hypoglycaemic events, and time below range, while no changes were found in time in range, time above range, mean sensor glucose, and glucose management indicator. Moreover, the frequency of sweets consumption was inversely related to the occurrence of hypoglycaemic events during lockdown.

CONCLUSIONS

Lockdown-related lifestyle changes, albeit unhealthy, may lead to reduction in FGM-derived measures of hypoglycaemia and glycaemic variability in patients with T1D.

Impact of quarterly professional-mode flash glucose monitoring in adults with type 2 diabetes in general practice (GP-OSMOTIC): Secondary psychological and self-care outcomes of a pragmatic, open-label, 12-month, randomised controlled trial

AIMS

To investigate the impact of quarterly professional-mode flash glucose monitoring on psychological outcomes in adults with type 2 diabetes in primary care.

METHODS

The GP-OSMOTIC trial randomised 299 adults with type 2 diabetes in 25 general practices to quarterly use of professional-mode flash glucose monitoring (sensor worn for 14 days; data discussed at clinic visit) or usual care. At baseline and 12 months, participants completed validated measures: general emotional well-being (WHO-5), diabetes-specific quality of life (DIDP), satisfaction with glucose monitoring (GME-Q), self-care activities (SDSCA) and perceived involvement in clinical care (PICS). Linear mixed-effects models examined between-group differences at 12 months.

RESULTS

At 12 months, there were no clinically important between-arm differences in any secondary psychological or self-care outcomes. Per protocol analyses showed no clinically significant between-group differences.

CONCLUSIONS

The GP-OSMOTIC intervention had no significant impact, at 12 months, on general emotional well-being, diabetes-specific quality of life or satisfaction with glucose monitoring, suggesting no added psychological burden. Lack of positive impact on self-reported self-care activities or perceived involvement in clinical care may warrant closer attention to the fidelity of intervention delivery, the context (e.g. the nature of clinician-patient interactions) and/or the sensitivity of the measures, as these will help plan future studies.

Time-in-range for monitoring glucose control: Is it time for a change?

The HbA1c value has been the gold standard for evaluating glucose control for decades. However, it has limitations such as the lack of information on glycemic variability or the risk of hypoglycemia. The increasing use of continuous glucose monitoring has provided patients and healthcare professionals with a range of useful metrics for the management of diabetes. Among them, Time in Range (TIR) is a simple and intuitive metric that gives information regarding the quality of glucose control. It is defined as the time spent in an individual's target glucose range. TIR is strongly correlated with HbA1c, and it has been linked to the risk of developing microvascular and macrovascular complications. The International Consensus on Time in Range has recently set targets for different diabetes populations. For the majority of people with type 1 or type 2 diabetes, a TIR (70-180 mg/dL or 3.9-10.0 mmol/L) of >70%, a time below range (TBR) <70 mg/dL (<3.9 mmol/L) of <4% and a TBR <54 (<3.0 mmol/L) of <1% are recommended. In this review, we address the latest evidence for the use of TIR as an essential parameter in the management of diabetes.

Diabetes Technology Meeting 2020

Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.

Economic burden of diabetes-related hypoglycemia on patients, payors, and employers

The economic and psychological consequences of diabetes-related hypoglycemic events are multifold and shared across various parties, including patients and their family or caregivers, payors, and employers. Hypoglycemic events contribute to increased morbidity, mortality, and a substantial portion of diabetes economic burden. Both severe and non-severe hypoglycemic episodes contribute to economic and psychological burden, and can have short-term consequences, such as emergency services, hospitalization, clinic visits, and increased use of diabetes supplies. Severe hypoglycemic events also generate additional follow-up costs, and are likely to occur again. Left untreated, hypoglycemia can have long-term consequences including, death, cardiovascular events, and cognitive issues. Costs vary geographically based on the treatment protocols which focus on outpatient treatment versus increased in-patient hospitalization. Certain types of medications are also associated with increased hypoglycemia, which requires closer monitoring of the patient, such as with basal insulin initiation. Some individuals with diabetes may be more vulnerable to hypoglycemia, such as the elderly, postoperative bariatric patients, and adolescent females. Measures to mitigate hypoglycemia are essential to ease the economic burden of these events. Medication management, optimal glucose control, lifestyle modifications and frequent glucose monitoring are some interventions which may help prevent hypoglycemia.

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

OBJECTIVE

To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders.

METHODS

The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development.

MAIN OUTCOME MEASURES

Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range.

RESULTS

This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base.

RECOMMENDATIONS

Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology.

CONCLUSIONS

Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.

Effect of Professional CGM (pCGM) on Glucose Management in Type 2 Diabetes Patients in Primary Care

BACKGROUND

Little data exists regarding the impact of continuous glucose monitoring (CGM) in the primary care management of type 2 diabetes (T2D). We initiated a quality improvement (QI) project in a large healthcare system to determine the effect of professional CGM (pCGM) on glucose management. We evaluated both an MD and RN/Certified Diabetes Care and Education Specialist (CDCES) Care Model.

METHODS

Participants with T2D for >1 yr., A1C ≥7.0% to <11.0%, managed with any T2D regimen and willing to use pCGM were included. Baseline A1C was collected and participants wore a pCGM (Libre Pro) for up to 2 weeks, followed by a visit with an MD or RN/CDCES to review CGM data including Ambulatory Glucose Profile (AGP) Report. Shared-decision making was used to modify lifestyle and medications. Clinic follow-up in 3 to 6 months included an A1C and, in a subset, a repeat pCGM.

RESULTS

Sixty-eight participants average age 61.6 years, average duration of T2D 15 years, mean A1C 8.8%, were identified. Pre to post pCGM lowered A1C from 8.8% ± 1.2% to 8.2% ± 1.3% (n=68, P=0.006). The time in range (TIR) and time in hyperglycemia improved along with more hypoglycemia in the subset of 37 participants who wore a second pCGM. Glycemic improvement was due to lifestyle counseling (68% of participants) and intensification of therapy (65% of participants), rather than addition of medications.

CONCLUSIONS

Using pCGM in primary care, with an MD or RN/CDCES Care Model, is effective at lowering A1C, increasing TIR and reducing time in hyperglycemia without necessarily requiring additional medications.

Continuous glucose monitoring: A review of the evidence in type 1 and 2 diabetes mellitus

CONTEXT AND AIM

Continuous glucose monitoring (CGM) is becoming widely accepted as an adjunct to diabetes management. Compared to standard care, CGM can provide detailed information about glycaemic variability in an internationally standardised ambulatory glucose profile, enabling more informed user and clinician decision making. We aimed to review the evidence, user experience and cost-effectiveness of CGM.

METHODS

A literature search was conducted by combining subject headings 'CGM' and 'flash glucose monitoring', with key words 'type 1 diabetes' and 'type 2 diabetes', limited to '1999 to current'. Further evidence was obtained from relevant references of retrieved articles.

RESULTS

There is a strong evidence for CGM use in people with type 1 diabetes, with benefits of reduced glycated haemoglobin and hypoglycaemia, and increased time in range. While the evidence for CGM use in type 2 diabetes is less robust, similar benefits have been demonstrated. CGM can improve diabetes-related satisfaction in people with diabetes (PWD) and parents of children with diabetes, as well as the clinician experience. However, CGM does have limitations including cost, accuracy and perceived inconvenience. Cost-effectiveness analyses have indicated that CGM is a cost-effective adjunct to type 1 diabetes management that is associated with reduced diabetes-related complications and hospitalisation.

CONCLUSIONS

Continuous glucose monitoring is revolutionising diabetes management. It is a cost-effective adjunct to diabetes management that has the potential to improve glycaemic outcomes and quality of life in PWD, especially type 1 diabetes.

Standardized Hybrid Closed-Loop System Reporting

The hybrid closed-loop (HCL) system has been shown to improve glycemic control and reduce hypoglycemia. Optimization of HCL settings requires interpretation of the glucose, insulin, and factors affecting glucose such as food intake and exercise. To the best of our knowledge, there is no published guidance on the standardized reporting of HCL systems. Standardization of HCL reporting would make interpretation of data easy across different systems. We reviewed the literature on patient and provider perspectives on downloading and reporting glucose metric preferences. We also incorporated international consensus on standardized reporting for glucose metrics. We describe a single-page HCL data reporting, referred to here as "artificial pancreas (AP) Dashboard." We propose seven components in the AP Dashboard that can provide detailed information and visualization of glucose, insulin, and HCL-specific metrics. The seven components include (A) glucose metrics, (B) hypoglycemia, (C) insulin, (D) user experience, (E) hyperglycemia, (F) glucose modal-day profile, and (G) insight. A single-page report similar to an electrocardiogram can help providers and patients interpret HCL data easily and take the necessary steps to improve glycemic outcomes. We also describe the optimal sampling duration for HCL data download and color coding for visualization ease. We believe that this is a first step in creating a standardized HCL reporting, which may result in better uptake of the systems. For increased adoption, standardized reporting will require input from providers, patients, diabetes device manufacturers, and regulators.

The Ambulatory Glucose Profile: Opportunities for Enhancement

The ambulatory glucose profile (AGP) and the frequency distribution for glucose by ranges are well established as standard methods for display, analysis, and interpretation of glucose data arising from self-monitoring, continuous glucose monitoring, and automated insulin delivery systems. In this review, we consider several refinements that may further improve the utility of the AGP. These include (1) display of the AGP together with information regarding dietary intake, medication administration (e.g., insulin), glucose lowering (pharmacodynamic) activity of medications, and physical activity measured by accelerometers or heart rate; (2) display of average time below range (%TBR), time above range (%TAR), and time in range (%TIR) by time of day to indicate timing of hypoglycemic and hyperglycemic episodes; (3) detailed analysis of postprandial excursions for each of the major meals after synchronizing by onset of meals and adjusting for the premeal glucose levels, enabling comparisons of magnitude, shape, and patterns; (4) methods to characterize distinct patterns on different days of the week; (5) display of glucose on a nonlinear scale to improve the balance between deviations associated with hypoglycemia versus hyperglycemia; (6) use of time scales other than midnight-to-midnight to facilitate analysis of time segments of particular interest (e.g., overnight); (7) options to display individual glucose values to assist in the identification of dates and times of outliers and episodes of hypoglycemia and hyperglycemia; and (8) methods to compare AGPs obtained from different individuals or groups receiving alternative interventions in terms of therapy or technology. These refinements, individually or collectively, can potentially further enhance the effectiveness of the AGP for assessment of glucose levels, patterns, and variability. We discuss several questions regarding implementation and optimization of these methods.

Clinical Application of Time in Range and Other Metrics

Time in range (TIR) and other continuous glucose monitoring (CGM)-derived metrics have been standardized in international consensus conferences. TIR correlates closely with A1C; a TIR of 70% correlates to an A1C of 6.7-7%. Evidence is emerging on the association of TIR with long-term diabetes complications, and each 10% increase in TIR shows a substantial decrease in risk for long-term complications. Application of TIR to clinical practice can be easily done with a stepped approach to the analysis and interpretation of CGM-derived metrics and the ambulatory glucose profile report. Clinician education and partnership with patients are crucial for successful implementation of TIR and all CGM-derived metrics in clinical practice.

Impact of blinded retrospective continuous glucose monitoring on clinical decision making and glycemic control in persons with type 2 diabetes on insulin therapy

BACKGROUND AND AIMS

Blinded retrospective continuous glucose monitoring (rCGM) provides detailed information about real-life glycaemic profile. In persons with type 2 diabetes without adequate glycaemic control, the structured introduction of rCGM may be beneficial to sustain improvements in diabetes management.

METHODS AND RESULTS

102 individuals with insulin-treated type 2 diabetes, age less than 66 years old and HbA1c >7.5%, were recruited. Participants performed a 7-day blinded rCGM (iPro2) every four months for one year. Biochemical, anthropometric, and rCGM data was collected. Participants' and healthcare professionals' perceptions were assessed. 90 participants completed the protocol. HbA1c was 9.1 ± 0.1% one year prior to enrolment and 9.4 ± 0.1% at enrolment (p < 0.01). With the rCGM-based intervention, a decrease in HbA1c was achieved at 4 months (8.4 ± 0.1%, p < 0.0001), and 12 months (8.1 ± 0.1%, p < 0.0001). A significant increase in time-in-range was observed (50.8 ± 2.4 at baseline vs 61.5 ± 2.2% at 12 months, for 70-180 mg/dL, p < 0.001), with no difference in exposure time to hypoglycaemia. After 12 months, there was an increase in self-reported diabetes treatment satisfaction (p < 0.05).

CONCLUSION

In persons with type 2 diabetes and poor metabolic control, specific data from blinded rCGM informed therapeutic changes and referral to targeted education consultations on nutrition and insulin administration technique. Therapeutic changes were made more frequently and targeted to changes in medication dose, timing, and/or type, as well as to lifestyle. Together, these brought significant improvements in clinical outcomes, effective shared decision-making, and satisfaction with treatment.

REGISTRATION NUMBER

NCT04141111.

Interpreting blood GLUcose data with R package iglu

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

Use of ambulatory glucose monitoring and analysis of ambulatory glucose profile in clinical practice for diabetes management; a position statement of the Arab Society of Paediatric Endocrinology and diabetes

BACKGROUND AND AIM

Diabetes mellitus imposes a significant burden around the world generally and in the Middle East and North Africa specifically. Glucose monitoring is a cornerstone of diabetes management.

METHODS

Glycated haemoglobin has always been the main metric for assessing glycaemic control, but its use is linked with multiple pitfalls. As an alternative, continuous glucose monitoring is becoming a standard of care in many countries. Intermittent scanning glucose monitoring (isCGM) has acquired a worldwide popularity and has been proven to improve glycaemic control, hypoglycaemia detection and prevention, and quality of life.

RESULTS

The most recent International Society of Paediatric and Adolescent Diabetes practice. guidelines recommended its use in young people with diabetes observing Ramadan to ensure safe fasting. At a meeting in Abu Dhabi in November 2019, the Arab Society for Paediatric. Endocrinology and Diabetes brought together a number of regional diabetes experts, patient. representatives and international expert advisors to review the evidence for isCGM and propose. guidelines for its use in the Middle East and North Africa region.

CONCLUSION

In this paper, the authors strongly recommend the use of isCGM for patients in MENA and present general recommendations and compressive specific guidance for physicians and patients, which they believe will also have wider resonance.

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.