Optimal sampling intervals to assess long-term glycemic control using continuous glucose monitoring

Glucose metrics improvement in youths with type 1 diabetes using the Ambulatory Glucose Profile report: A real-world study

AIMS

In this study, we aimed to analyze the possible change in Time In Range (TIR) in subjects with type 1 diabetes (T1D) using the Ambulatory Glucose Profile (AGP) and to identify the main socio-demographic and clinical predictors of sustained use.

METHODS

143 youths wearing instant-scanning CGM received structured counseling on the AGP report interpretation, and who were able to use AGP at least every 14 days were enrolled in group A (n = 100), whereas no users were considered as group B (n = 43). Socio-demographic data at the enrollment, clinical data, and glucose metrics were collected at baseline and during quarterly consultations. Metabolic outcomes were evaluated during follow-up, and a comparison between groups A and B was performed.

RESULTS

In group A compared to group B, at 12 months, the percentage of sensor usage and TIR were higher (p = 0.04 and p = 0.02), and Time Above Range and HbA1c were lower (p = 0.0004, p < 0.0001, respectively). Multiple logistic regression analysis did not show a significant relationship between sustained AGP software usage and the variables analyzed.

CONCLUSIONS

Systematic use of the AGP software was feasible and showed improved metabolic control in youths with T1D. This may be related to increased sensor usage and more informed decisions.

The Relationship Between Glycated Hemoglobin and Time in Range in a Pediatric Population

In adults with type 1 diabetes (T1D), time in range (TIR) [70-180 mg/dL] has been proposed as an additional metric besides glycated hemoglobin (HbA1c). This retrospective monocentric cohort study determined the correlation between HbA1c and TIR during the 2, 4, and 12 weeks (TIR2w, TIR4w, and TIR12w) before consultation in a pediatric T1D population. A total of 168 children with T1D were included. Continuous glucose monitoring data, HbA1c, and demographic variables were collected. We found strong linear correlations between HbA1c and TIR2w (R = -0.571), HbA1c and TIR4w (R = -0.603), and between HbA1c and TIR12w (R = -0.624). A strong correlation exists between TIR2w and TIR12w, HbA1c and time above range (TAR), and between TIR and TAR at different time points. In conclusion, a strong correlation was found between HbA1c and TIR, making TIR a potentially complementary metric to HbA1c. TIR2w seems a viable alternative to TIR12w. TAR also seems promising in assessing glycemic control.

Impact of short-term application of continuous glucose monitoring system(CGMS) on long-term glycemic profile in adolescents and adults with type 1 diabetes mellitus: An open-label randomized control cross over study

AIMS

The use of Continuous Glucose Monitoring System (CGMS) improves glycemic parameters in Type 1 Diabetes Mellitus (T1D), but the cost is prohibitive. Here, we investigated the effect of short-term application of real-time and intermittently-scanned CGMS (rt and is-CGMS) in T1D individuals on change in HbA1c at the end of 3 months.

METHODS

T1D individuals were randomized into three groups in a ratio of 1:1:2 - Group A (rt-CGMS for 2 weeks initially, followed by is-CGMS for 2 weeks at 3 months), Group B (is-CGMS for 2 weeks initially followed by rt-CGMS for 2 weeks at 3 months) and Group C (only self-monitoring of blood glucose), respectively. HbA1c at baseline, 3, and 6 months were compared.

RESULTS

Out of a total 68 T1D patients, HbA1c decreased significantly in groups A and B at 6 months compared to the baseline, but not in group C. HbA1c was significantly lower in Group A compared to Group C at 3 and 6 months. Fructosamine levels significantly decreased in Group B before and after cross-over. Glycemic variability indices improved significantly after cross-over from is-CGMS to rt-CGMS.

CONCLUSION

Intermittent application of CGMS for 2 weeks improves short- and long-term blood glucose control in T1D.

Continuous glucose monitoring metrics in pregnancy with type 1 diabetes mellitus

Managing diabetes during pregnancy is challenging, given the significant risk it poses for both maternal and foetal health outcomes. While traditional methods involve capillary self-monitoring of blood glucose level monitoring and periodic HbA1c tests, the advent of continuous glucose monitoring (CGM) systems has revolutionized the approach. These devices offer a safe and reliable means of tracking glucose levels in real-time, benefiting both women with diabetes during pregnancy and the healthcare providers. Moreover, CGM systems have shown a low rate of side effects and high feasibility when used in pregnancies complicated by diabetes, especially when paired with continuous subcutaneous insulin infusion pump as hybrid closed loop device. Such a combined approach has been demonstrated to improve overall blood sugar control, lessen the occurrence of preeclampsia and neonatal hypoglycaemia, and minimize the duration of neonatal intensive care unit stays. This paper aims to offer a comprehensive evaluation of CGM metrics specifically tailored for pregnancies impacted by type 1 diabetes mellitus.

Determination of Time in Range Associated With HbA1c ≤7% in a Prospective Cohort of Patients With Type 1 Diabetes Using Continuous Glucose Monitoring for Three Months

INTRODUCTION

Finding a goal of time in range (%TIR) that defines good glycemic control is necessary. Previous retrospective studies suggest good concordance between HbA1c ≤7% with a TIR >70%; however, the studies that included the largest number of patients used blood glucose measurement data with a follow-up time of less than 90 days. This study defined the TIR value that best discriminates HbA1c ≤7%.

METHODS

We performed a prospective study of diagnostic tests based on a cohort of patients with type 1 diabetes (T1D) treated with a hybrid closed loop (HCL) followed for three months. The ability of %TIR to distinguish patients with HbA1c ≤7% was evaluated through receiver operating characteristic curve analysis. We determined the %TIR cutoff point with the best operating characteristics.

RESULTS

A total of 118 patients were included (58.1% women, 47% overweight or obese, and 33% with high glycemic variability). A moderate negative correlation (R = -.54, P < .001) was found between %TIR and HbA1c. The discrimination ability was moderate, with an area under the curve of 0.7485 (95% confidence interval = 0.6608-0.8363). The cutoff point that best predicted HbA1c ≤7% was %TIR ≥75.5 (sensitivity 70%, specificity 67%). The findings were similar among those with a coefficient of variation (CV%) ≥36%.

CONCLUSIONS

Our data suggest that the %TIR adequately identifies patients with HbA1c ≤7%. A target of TIR ≥75%, rather than the currently recommended TIR ≥70%, may be a more suitable value for optimal glycemic control.

Scans per day as predictors of optimal glycemic control in people with type 1 diabetes mellitus using flash glucose monitoring: what number of scans per day should raise a red flag?

AIMS

This study aimed to determine the minimum frequency of flash glucose monitoring (FGM) scans necessary for optimal glycemic control in patients with type 1 diabetes (T1D).

METHODS

Data were collected from 692 patients (47.5% female, with a median age of 47.4 years) who used FGM systems daily and recorded their clinical variables and device data.

RESULTS

Logistic regression models showed that performing more than 12 scans per day was associated with improved T1D control (OR = 4.22, p < 0.001) and a reduction in HbA1c (7.6 vs 7.0%, 60-53 mmol/mol p < 0.001). However, those performing less than 6 scans showed no improvement in HbA1c (7.9 vs 7.8%, 63-61 mmol/mol p = 0.514). Thirteen daily scans were determined as the optimal cutoff point for predicting optimal glycemic control using a maximally selected rank algorithm. Significant reductions were observed in mean glucose (< 0.001), coefficient of variation (< 0.001), HbA1c (< 0.001), and an increase in TIR (< 0.001) in patients who performed more than 12 daily scans.

CONCLUSIONS

The results suggest that a higher frequency of daily scans by T1D patients using FGM systems leads to improved chronic glycemic control. The minimum recommended frequency for optimal control is 13 scans per day, and more than 6 daily scans are needed to improve HbA1c.

Limitations of 14-Day Continuous Glucose Monitoring Sampling for Assessment of Hypoglycemia and Glycemic Variability in Type 1 Diabetes

Continuous glucose monitoring (CGM) has become the standard of care in diabetes management with the recent advances in technology and accessibility in the last decade. An International Consensus was established to define CGM metrics and its goals in diabetes care. The 2019 International Consensus suggested 14 days of CGM sampling for the assessment of CGM metrics stating the limitations that may occur for hypoglycemia and glycemic variability metrics. Since then, several studies assessed the correlation between CGM metrics and duration of the sampling period. This review summarized the studies that investigated the relationship between 14-day CGM sampling to 90-day CGM data in >70% CGM users for all CGM metrics and highlighted possible solutions for more accurate CGM sampling durations in type 1 diabetes (T1D). Accumulating evidence showed that 14-day CGM sampling correlates well with 90-day CGM data for mean glucose, time in 70-180 mg/dL, and hyperglycemia metrics; however, it correlates weakly for hypoglycemia and glycemic variability metrics. In the studies included in this review, in adults with T1D, minimum sampling duration was 14 days for mean glucose, time in 70-180 mg/dL, and time in hyperglycemia (>180 and >250 mg/dL); however, minimum sampling duration varied between 21 to 30 days for time <70 mg/dL, 30 to 35 days for time <54 mg/dL, and 28 to 35 days for coefficient of variation. Longer than 14 days of CGM, sampling was required to properly assess hypoglycemia and glycemic variability in T1D.

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.

Assessment of factors associated with improved glycemic control after switching from intermittently scanned to real-time continuous glucose monitoring in Japanese patients with type 1 diabetes

The advantages of real-time continuous glucose monitoring (rtCGM) over intermittently scanned CGM (isCGM) reportedly include lower glycated hemoglobin (HbA1c) levels as well as reduced glycemic variability. However, there have been few studies of the effect of switching from isCGM to rtCGM on glycemic control, as well as the specific factors underlying any observed improvements. To that end, all patients with type 1 diabetes mellitus who used the DEXCOM rtCGM device (Terumo Corporation, Tokyo, Japan) at our institution were reviewed, and 16 individuals with type 1 diabetes who switched from isCGM to rtCGM were investigated. The patients' HbA1c decreased in 75% of the cases (p = 0.02). On the other hand, GMI increased in 75% of the cases (p = 0.01). Intriguingly, the percentage of time below range and coefficient of variation were significantly improved with rtCGM compared to isCGM (2.9% vs. 7.6%, p = 0.016 and 35% vs. 40%, p = 0.0019, respectively). We also found that the discrepancy between HbA1c and GMI among users of isCGM was a key indicator that improved when switching to rtCGM. If discrepancies are observed between HbA1c and GMI when using isCGM, switching to rtCGM should be considered for improving glycemic control.

Minimum Sampling Duration for Continuous Glucose Monitoring Metrics to Achieve Representative Glycemic Outcomes in Suboptimal Continuous Glucose Monitor Use

BACKGROUND

Two weeks of continuous glucose monitoring (CGM) sampling with >70% CGM use is recommended to accurately reflect 90 days of glycemic metrics. However, minimum sampling duration for CGM use <70% is not well studied. We investigated the minimum duration of CGM sampling required for each CGM metric to achieve representative glycemic outcomes for <70% CGM use over 90 days.

METHODS

Ninety days of CGM data were collected in 336 real-life CGM users with type 1 diabetes. CGM data were grouped in 5% increments of CGM use (45%-95%) over 90 days. For each CGM metric and each CGM use category, the correlation between the summary statistic calculated using each sampling period and all 90 days of data was determined using the squared value of the Spearmen correlation coefficient (R2).

RESULTS

For CGM use 45% to 95% over 90 days, minimum sampling period is 14 days for mean glucose, time in range (70-180 mg/dL), time >180 mg/dL, and time >250 mg/dL; 28 days for coefficient of variation, and 35 days for time <54 mg/dL. For time <70 mg/dL, 28 days is sufficient between 45 and 80% CGM use, while 21 days is required >80% CGM use.

CONCLUSION

We defined minimum sampling durations for all CGM metrics in suboptimal CGM use. CGM sampling of at least 14 days is required for >45% CGM use over 90 days to sufficiently reflect most of the CGM metrics. Assessment of hypoglycemia and coefficient of variation require a longer sampling period regardless of CGM use duration.

Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: a functional data analysis

Continuous glucose monitoring systems (CGM) are a very useful tool to understand the behaviour of glucose in different situations and populations. Despite the widespread use of CGM systems in both clinical practice and research, our understanding of the reproducibility of CGM data remains limited. The present work examines the reproducibility of the results provided by a CGM system in a random sample of a free-living adult population, from a functional data analysis approach. Functional intraclass correlation coefficients (ICCs) and their 95% confidence intervals (CI) were calculated to assess the reproducibility of CGM results in 581 individuals. 62% were females 581 participants (62% women) mean age 48 years (range 18-87) were included, 12% had previously been diagnosed with diabetes. The inter-day reproducibility of the CGM results was greater for subjects with diabetes (ICC 0.46 [CI 0.39-0.55]) than for normoglycaemic subjects (ICC 0.30 [CI 0.27-0.33]); the value for prediabetic subjects was intermediate (ICC 0.37 [CI 0.31-0.42]). For normoglycaemic subjects, inter-day reproducibility was poorer among the younger (ICC 0.26 [CI 0.21-0.30]) than the older subjects (ICC 0.39 [CI 0.32-0.45]). Inter-day reproducibility was poorest among normoglycaemic subjects, especially younger normoglycaemic subjects, suggesting the need to monitor some patient groups more often than others.

Progress and indication for use of continuous glucose monitoring in patients with diabetes in pregnancy: a review

Gestational diabetes mellitus is one of the most common endocrine diseases that occur during pregnancy. Disorders of blood glucose metabolism during pregnancy can increase the risk of adverse pregnancy outcomes, such as pregnancy-related hypertension, preeclampsia, eclampsia, miscarriage, macrosomia, and neonatal hypoglycemia. Continuous glucose monitoring (CGM) can safely and effectively monitor blood glucose changes in patients with gestational hyperglycemia, thereby reducing adverse pregnancy outcomes. Hence, this article aimed to provide a comprehensive review of the progress and indications for using CGM in pregnant patients with diabetes. CGM can reduce blood glucose fluctuations and the occurrence of serious hypoglycemia and hyperglycemia events and can provide time in range (TIR). TIR is an important indicator of blood glucose level. Patients with a higher TIR during pregnancy have better gestational outcomes.

Use of Continuous Glucose Monitoring in Patients Following Bariatric Surgery: A Scoping Review

The objective of this scoping review was to summarize the emerging literature on the use of continuous glucose monitoring (CGM) in post-bariatric surgery patients, with a focus on its features (e.g., device, mode, and accuracy), as well as purposes and outcomes of utilization. Three databases (PubMed, EMBASE, and Web of Science) were searched to obtain relevant studies. Results suggested that most studies used CGM for 3-7 days under blinded mode. Accuracy data were available in only one study, which reported a mean absolute relative difference of 21.7% for Freestyle Libre. The primary applications of CGM were for elucidating glucose patterns and assessing glycemic treatment outcomes. No study has tested the effect of CGM as an intervention strategy to enhance glucose control.

Beyond A1C: exploring continuous glucose monitoring metrics in managing diabetes

Hemoglobin A1c (HbA1c) has long been considered a cornerstone of diabetes mellitus (DM) management, as both an indicator of average glycemia and a predictor of long-term complications among people with DM. However, HbA1c is subject to non-glycemic influences which confound interpretation and as a measure of average glycemia does not provide information regarding glucose trends or about the occurrence of hypoglycemia and/or hyperglycemia episodes. As such, solitary use of HbA1c, without accompanying glucose data, does not confer actionable information that can be harnessed to guide targeted therapy in many patients with DM. While conventional capillary blood glucose monitoring (BGM) sheds light on momentary glucose levels, in practical use the inherent infrequency of measurement precludes elucidation of glycemic trends or reliable detection of hypoglycemia or hyperglycemia episodes. In contrast, continuous glucose monitoring (CGM) data reveal glucose trends and potentially undetected hypo- and hyperglycemia patterns that can occur between discrete BGM measurements. The use of CGM has grown significantly over the past decades as an ever-expanding body of literature demonstrates a multitude of clinical benefits for people with DM. Continually improving CGM accuracy and ease of use have further fueled the widespread adoption of CGM. Furthermore, percent time in range correlates well with HbA1c, is accepted as a validated indicator of glycemia, and is associated with the risk of several DM complications. We explore the benefits and limitations of CGM use, the use of CGM in clinical practice, and the application of CGM to advanced diabetes technologies.

Research progress in the application of time in range: more than a percentage

ABSTRACT

Glucose monitoring is an important part of medical care in diabetes mellitus, which not only helps assess glycemic control and treatment safety, but also assists with treatment adjustment. With the development of continuous glucose monitoring (CGM), the use of CGM has increased rapidly. With the wealth of glucose data produced by CGM, new metrics are greatly needed to optimally evaluate glucose status and guide the treatment. One of the parameters that CGM provides, time in range (TIR), has been recognized as a key metric by the international consensus. Before the adoption of TIR in clinical practice, several issues including the minimum length of CGM use, the setting of the target range, and individualized TIR goals are summarized. Additionally, we discussed the mounting evidence supporting the association between TIR and diabetes-related outcomes. As a novel glucose metric, it is of interest to compare TIR with other conventional glucose markers such as glycated hemoglobin A1c. It is anticipated that the use of TIR may provide further information on the quality of glucose control and lead to improved diabetes management.

Potential cost savings in the United States from a reduction in sensor-detected severe hypoglycemia among users of the InPen smart insulin pen system

BACKGROUND: Severe hypoglycemia is a significant barrier to optimizing insulin therapy in both type 1 and type 2 diabetes and places a burden on the US health care system because of the high costs of hypoglycemia-related health care utilization. OBJECTIVE: To compare the frequency of sensor-detected severe hypoglycemic events (SHEs) among a population of continuous glucose monitoring (CGM) users on insulin therapy after initiation of the InPen smart insulin pen (SIP) system and to estimate the potential hypoglycemia-related medical cost savings across a population of SIP users. METHODS: SIP users of all ages with type 1 or type 2 diabetes were required to have at least 90 days of SIP use with a connected CGM device. The last 14 days of sensor glucose (SG) data within the 30-day period prior to the start of SIP use ("pre-SIP") and the last 14 days of SG data, along with the requirement of at least 1 bolus entry per day within the 61- to 90-day period after SIP start ("post-SIP"), were analyzed. Sensor-detected SHEs (defined as ≥10 minutes of consecutive SG readings at <54 mg/dL) were determined. Once factored, the expected medical intervention rates and associated costs were calculated. Intervention rates and costs were obtained from the literature. RESULTS: There were 1,681 SIP + CGM users from March 1, 2018, to April 30, 2021. The mean number of sensor-detected SHEs per week declined from 0.67 in the pre-SIP period to 0.58 in the post-SIP period (P = 0.008), which represented a 13% reduction. Assuming a range of 5%-25% of all sensor-detected SHEs resulted in a clinical event, the estimated cost reduction associated with reduced SHEs was $12-$59 and $110-$551 per SIP user per month and per year, respectively. For those aged at least 65 years, there were 166 SIP+CGM users and the reduction in the mean number of sensor-detected SHEs per week between the pre-SIP and post-SIP periods was 31%. CONCLUSIONS: Use of the SIP system with a connected CGM is associated with reduced sensor-detected severe hypoglycemia, which may result in significant cost savings. DISCLOSURES: Albert Chien, Glen Im, Kael Wherry, Janice MacLeod, and Robert A Vigersky are employees of Medtronic; Sneha Thanasekaran and Angela Gaetano were affiliated with Medtronic while doing this research. The submitted work did not involve study subject recruitment, enrollment, or participation in a trial and did not fall under human subject protection requirements (per the Department of Health and Human Services CFR Part 46) necessitating Internal Review Board approval or exemption.

Low-Dose Empagliflozin as Adjunct to Hybrid Closed-Loop Insulin Therapy in Adults With Suboptimally Controlled Type 1 Diabetes: A Randomized Crossover Controlled Trial

OBJECTIVE

To assess whether low doses of empagliflozin as adjunct to hybrid closed-loop therapy improve glycemia compared with placebo in adults with type 1 diabetes (T1D) who are not able to achieve targets with the system alone.

RESEARCH DESIGN AND METHODS

A double-blind crossover randomized controlled trial was performed in adults with suboptimally controlled T1D (HbA1c 7.0-10.5%) who were not able to achieve a target time in range (3.9-10.0 mmol/L) ≥70% after 14 days of hybrid closed-loop therapy. Three 14-day interventions were performed with placebo, 2.5 mg empagliflozin, or 5 mg empagliflozin as adjunct to the McGill artificial pancreas. Participants were assigned at a 1:1:1:1:1:1 ratio with blocked randomization. The primary outcome was time in range (3.9-10.0 mmol/L). Analysis was by intention to treat, and a P value <0.05 was regarded as significant.

RESULTS

A total of 24 participants completed the study (50% male; age 33 ± 14 years; HbA1c 8.1 ± 0.5%). The time in range was 59.0 ± 9.0% for placebo, 71.6 ± 9.7% for 2.5 mg empagliflozin, and 70.2 ± 8.0% for 5 mg empagliflozin (P < 0.0001 between 2.5 mg empagliflozin and placebo and between 5 mg empagliflozin and placebo). Mean daily capillary ketone levels were not different between arms. There were no serious adverse events or cases of diabetic ketoacidosis or severe hypoglycemia in any intervention.

CONCLUSIONS

Empagliflozin at 2.5 and 5 mg increased time in range during hybrid closed-loop therapy by 11-13 percentage points compared with placebo in those who otherwise were unable to attain glycemic targets. Future studies are required to assess long-term efficacy and safety.

Continuous glucose monitoring and metrics for clinical trials: an international consensus statement

Randomised controlled trials and other prospective clinical studies for novel medical interventions in people with diabetes have traditionally reported HbA1c as the measure of average blood glucose levels for the 3 months preceding the HbA1c test date. The use of this measure highlights the long-established correlation between HbA1c and relative risk of diabetes complications; the change in the measure, before and after the therapeutic intervention, is used by regulators for the approval of medications for diabetes. However, with the increasing use of continuous glucose monitoring (CGM) in clinical practice, prospective clinical studies are also increasingly using CGM devices to collect data and evaluate glucose profiles among study participants, complementing HbA1c findings, and further assess the effects of therapeutic interventions on HbA1c. Data is collected by CGM devices at 1-5 min intervals, which obtains data on glycaemic excursions and periods of asymptomatic hypoglycaemia or hyperglycaemia (ie, details of glycaemic control that are not provided by HbA1c concentrations alone that are measured continuously and can be analysed in daily, weekly, or monthly timeframes). These CGM-derived metrics are the subject of standardised, internationally agreed reporting formats and should, therefore, be considered for use in all clinical studies in diabetes. The purpose of this consensus statement is to recommend the ways CGM data might be used in prospective clinical studies, either as a specified study endpoint or as supportive complementary glucose metrics, to provide clinical information that can be considered by investigators, regulators, companies, clinicians, and individuals with diabetes who are stakeholders in trial outcomes. In this consensus statement, we provide recommendations on how to optimise CGM-derived glucose data collection in clinical studies, including the specific glucose metrics and specific glucose metrics that should be evaluated. These recommendations have been endorsed by the American Association of Clinical Endocrinologists, the American Diabetes Association, the Association of Diabetes Care and Education Specialists, DiabetesIndia, the European Association for the Study of Diabetes, the International Society for Pediatric and Adolescent Diabetes, the Japanese Diabetes Society, and the Juvenile Diabetes Research Foundation. A standardised approach to CGM data collection and reporting in clinical trials will encourage the use of these metrics and enhance the interpretability of CGM data, which could provide useful information other than HbA1c for informing therapeutic and treatment decisions, particularly related to hypoglycaemia, postprandial hyperglycaemia, and glucose variability.

Essential Continuous Glucose Monitoring Metrics: The Principal Dimensions of Glycemic Control in Diabetes

Background: With the proliferation of continuous glucose monitoring (CGM), a number of metrics were developed to assess quality of glycemic control. Many of them are highly correlated. Thus, we aim to identify the principal dimensions of glycemic control-a minimal set of metrics, necessary and sufficient for comprehensive assessment of diabetes management. Methods: Seventy-five thousand five hundred sixty-three 2-week CGM profiles recorded in six studies by 790 individuals with type 1 or type 2 diabetes were used to compute mean glucose (MG), percentage time >180 mg/dL (TAR), >250 mg/dL (TAR2), <70 mg/dL (TBR), <54 mg/dL (TBR2), and coefficient of variation (CV). The true dimensionality of the glycemic-metric space was identified in a training set (53,380 profiles) and validated in an independent test set (22,183 profiles). Results: Correlation analysis identified two blocks of metrics-(MG, TAR, TAR2) and (TBR, TBR2, CV)-each with high internal correlation, but insignificant between-block correlation, suggesting that the true dimensionality of the glycemic-metric space is 2. Principal component analysis confirmed two essential metrics quantifying exposure to hyperglycemia (i.e., treatment efficacy) and risk for hypoglycemia (i.e., treatment safety), and explaining ∼90% of the variance in the training and test data. Conclusion: Two essential metrics, treatment efficacy and treatment safety, are necessary and sufficient to characterize glycemic control in diabetes. Thus, quantitatively, diabetes treatment optimization is reduced to a two-dimensional problem, meaning that minimizing both exposure to hyperglycemia and risk for hypoglycemia will lead to improvement in any other metric of glycemic control.

An Examination of the Glucose Management Indicator in Young Children with Type 1 Diabetes

BACKGROUND

Previous studies utilizing glucose data from continuous glucose monitors (CGM) to estimate the Glucose Management Indicator (GMI) have not included young children or determined appropriate GMI formulas for young children with type 1 diabetes (T1D).

METHODS

We extracted CGM data for 215 children with T1D (0-6 years) from a repository. We defined sampling periods ranging from the 3-27 days prior to an HbA1c measurement and compared a previously established GMI formula to a young child-specific GMI equation based on the sample's CGM data. We examined associations between HbA1c, GMI values, and other CGM metrics for each sampling period.

RESULTS

The young child-specific GMI formula and the published GMI formula did not evidence significant differences when using 21-27 days of CGM data. The young child-specific GMI formula demonstrated higher correlations to laboratory HbA1c when using 18 or fewer days of CGM data. Overall, the GMI estimate and HbA1c values demonstrate a strong relationship in young children with T1D.

CONCLUSIONS

Future research studies may consider utilizing the young child-specific GMI formula if the data collection period for CGM values is under 18 days. Further, researchers and clinicians may consider changing the default number of days of data used to calculate glycemic metrics in order to maximize validity of CGM-derived metrics.

Impact of Different Types of Data Loss on Optimal Continuous Glucose Monitoring Sampling Duration

Aims: To determine if a longer duration of continuous glucose monitoring (CGM) sampling is needed to correctly assess the quality of glycemic control given different types of data loss. Materials and Methods: Data loss was generated in two different methods until the desired percentage of data loss (10-50%) was achieved with (1) eliminating random individual CGM values and (2) eliminating gaps of a predefined time length (1-5 h). For CGM metrics, days required to cross predetermined targets for median absolute percentage error (MdAPE) for the different data loss strategies were calculated and compared with current international consensus recommendation of >70% of optimal data sampling. Results: Up to 90 days of CGM data from 291 adults with type 1 diabetes were analyzed. MdAPE threshold crossing remained virtually constant for random CGM data loss up to 50% for all CGM metrics. However, the MdAPE crossing threshold increased when losing data with longer gaps. For all CGM metrics assessed in our study (%T70-180, %T < 70, %T < 54, %T > 180, and %T > 250), up to 50% data loss in a random manner did not cause any significant change on optimal sampling duration; however, >30% of data loss in gaps up to 5 h required longer optimal sampling duration. Conclusions: Optimal sampling duration for CGM metrics depends on percentage of data loss as well as duration of data loss. International consensus recommendation for 70% CGM data adequacy is sufficient to report %T70-180 with 2 weeks of data without large data gaps.

Identifying Continuous Glucose Monitoring Data Using Machine Learning

Background and Aims: The recent increase in wearable devices for diabetes care, and in particular the use of continuous glucose monitoring (CGM), generates large data sets and associated cybersecurity challenges. In this study, we demonstrate that it is possible to identify CGM data at an individual level by using standard machine learning techniques. Methods: The publicly available REPLACE-BG data set (NCT02258373) containing 226 adult participants with type 1 diabetes (T1D) wearing CGM over 6 months was used. A support vector machine (SVM) binary classifier aiming to determine if a CGM data stream belongs to an individual participant was trained and tested for each of the subjects in the data set. To generate the feature vector used for classification, 12 standard glycemic metrics were selected and evaluated at different time periods of the day (24 h, day, night, breakfast, lunch, and dinner). Different window lengths of CGM data (3, 7, 15, and 30 days) were chosen to evaluate their impact on the classification performance. A recursive feature selection method was employed to select the minimum subset of features that did not significantly degrade performance. Results: A total of 40 features were generated as a result of evaluating the glycemic metrics over the selected time periods (24 h, day, night, breakfast, lunch, and dinner). A window length of 15 days was found to perform the best in terms of accuracy (86.8% ± 12.8%) and F1 score (0.86 ± 0.16). The corresponding sensitivity and specificity were 85.7% ± 19.5% and 87.9% ± 17.5%, respectively. Through recursive feature selection, a subset of 9 features was shown to perform similarly to the 40 features. Conclusion: It is possible to determine with a relatively high accuracy if a CGM data stream belongs to an individual. The proposed approach can be used as a digital CGM "fingerprint" or for detecting glycemic changes within an individual, for example during intercurrent illness.

A guide for the use of LibreView digital diabetes platform in clinical practice: Expert paper of the Italian Working Group on Diabetes and Technology

Wider access to continuous glucose monitoring systems, including flash glucose monitoring, has enabled people with diabetes to achieve lower HbA1c levels and reduce the amount of time they spend in hypoglycaemia or hyperglycaemia, and has improved their quality of life. An International Consensus Panel proposed different target glucose ranges and recommendations according to different ages and situations (adults, young people and children with type 1 or type 2 diabetes, as well as elderly people who are at higher risk of hypoglycaemia, and women with diabetes during pregnancy). In this expert opinion, we interpret the international recommendations in the context of established clinical practice for diabetes care, and propose three different step-by-step algorithms to help the healthcare professionals use the most innovative glucose metrics, including time in glucose ranges, glucose management indicator, coefficient of variation, and ambulatory glucose profile. In detail, we focus on glucose metrics as measured by the FreeStyle Libre system and as visualized on the LibreView digital diabetes platform to support appropriate interpretation of flash glucose monitoring data. This is specifically structured for healthcare professionals and general practitioners who may have a low level of confidence with diabetes technology, with the aim of optimizing diabetes management, ensuring effective use of healthcare resources and to maximise outcomes for people with diabetes.

Choosing the duration of continuous glucose monitoring for reliable assessment of time in range: A new analytical approach to overcome the limitations of correlation-based methods

AIMS

Reliable estimation of the time spent in different glycaemic ranges (time-in-ranges) requires sufficiently long continuous glucose monitoring. In a 2019 paper (Battelino et al., Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42:1593-1603), an international panel of experts suggested using a correlation-based approach to obtain the minimum number of days for reliable time-in-ranges estimates. More recently (in Camerlingo et al., Design of clinical trials to assess diabetes treatment: minimum duration of continuous glucose monitoring data to estimate time-in-ranges with the desired precision. Diabetes Obes Metab. 2021;23:2446-2454) we presented a mathematical equation linking the number of monitoring days to the uncertainty around time-in-ranges estimates. In this work, we compare these two approaches, mainly focusing on time spent in (70-180) mg/dL range (TIR).

METHODS

The first 100 and 150 days of data were extracted from study A (148 subjects, ~180 days), and the first 100, 150, 200, 250 and 300 days of data from study B (45 subjects, ~365 days). For each of these data windows, the minimum monitoring duration was computed using correlation-based and equation-based approaches. The suggestions were compared for the windows of different durations extracted from the same study, and for the windows of equal duration extracted from different studies.

RESULTS

When changing the dataset duration, the correlation-based approach produces inconsistent results, ranging from 23 to 64 days, for TIR. The equation-based approach was found to be robust versus this issue, as it is affected only by the characteristics of the population being monitored. Indeed, to grant a confidence interval of 5% around TIR, it suggests 18 days for windows from study A, and 17 days for windows from study B. Similar considerations hold for other time-in-ranges.

CONCLUSIONS

The equation-based approach offers advantages for the design of clinical trials having time-in-ranges as final end points, with focus on trial duration.

Assessment of glycemia in chronic kidney disease

Reliable assessment of glycemia is central to the management of diabetes. The kidneys play a vital role in maintaining glucose homeostasis through glucose filtration, reabsorption, consumption, and generation. This review article highlights the role of the kidneys in glucose metabolism and discusses the benefits, pitfalls, and evidence behind the glycemic markers in patients with chronic kidney disease. We specifically highlight the role of continuous glucose monitoring as an emerging minimally invasive technique for glycemic assessment.

Continuous Glucose Monitoring in Adults With Type 1 Diabetes With 35 Years Duration From the DCCT/EDIC Study

OBJECTIVE

We evaluated blinded continuous glucose monitoring (CGM) profiles in a subset of adults with type 1 diabetes from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study to characterize the frequency of glycemic excursions and contributing factors.

RESEARCH DESIGN AND METHODS

CGM-derived metrics were compared for daytime and nighttime periods using blinded CGM for a minimum of 6.5 days (average 11.9 days) and correlated with HbA1c levels, routine use of diabetes devices, and other characteristics in 765 participants.

RESULTS

Participants were 58.9 ± 6.5 years of age with diabetes duration 36.8 ± 4.9 years and HbA1c 7.8 ± 1.2%; 58% used insulin pumps, and 27% used personal, unblinded CGM. Compared with daytime, nighttime mean sensor glucose was lower, percent time in range 70-180 mg/dL (TIR) was similar, and hypoglycemia was more common. Over the entire recording period, only 9% of the 765 participants achieved >70% TIR and only 28% achieved <1% of observations of <54 mg/dL. Indeed, participants with the highest percentage of hypoglycemia had the lowest HbA1c levels. However, use of insulin pumps and CGM decreased the percent time at <54 mg/dL.

CONCLUSIONS

In adults with long-standing type 1 diabetes, short-term blinded CGM profiles revealed frequent clinically significant hypoglycemia (<54 mg/dL) during the night and more time in hyperglycemia during the day. The small subset of participants using routine CGM and insulin pumps had fewer hypoglycemic and hyperglycemic excursions and lower HbA1c levels. Thus, strategies to lower meal-stimulated hyperglycemia during the day and prevent hypoglycemia at night are relevant clinical goals in older patients with type 1 diabetes.

Optimal Data Collection Period for Continuous Glucose Monitoring to Assess Long-Term Glycemic Control: Revisited

BACKGROUND AND OBJECTIVE

It is not clear how the short-term continuous glucose monitoring (CGM) sampling time could influence the bias in estimating long-term glycemic control. A large bias could, in the worst case, lead to incorrect classification of patients achieving glycemic targets, nonoptimal treatment, and false conclusions about the effect of new treatments. This study sought to investigate the relation between sampling time and bias in the estimates.

METHODS

We included a total of 329 type 1 patients (age 14-86 years) with long-term CGM (90 days) data from three studies. The analysis calculated the bias from estimating long-term glycemic control based on short-term sampling. Time in range (TIR), time above range (TAR), time below range (TBR), correlation, and glycemic target classification accuracy were assessed.

RESULTS

A sampling time of ten days is associated with a high bias of 10% to 47%, which can be reduced to 4.9% to 26.4% if a sampling time of 30 days is used (P < .001). Correct classification of patients archiving glycemic targets can also be improved from 81.5% to 91.9 to 90% to 95.2%.

CONCLUSIONS

Our results suggest that the proposed 10-14 day CGM sampling time may be associated with a high correlation with three-month CGM. However, these estimates are subject to large intersubject bias, which is clinically relevant. Clinicians and researchers should consider using assessments of longer durations of CGM data if possible, especially when assessing time in hypoglycemia or while testing a new treatment.

Can Faster Aspart Be Used to Optimize Glycemic Control With Insulin Pump Therapy? From Expectations to Lessons Learned After a Year of Use in the United States

Fast-acting insulin aspart (faster aspart) is an ultra-rapid-acting formulation of insulin aspart developed to more closely match the prandial endogenous insulin profile, and its accelerated absorption kinetics are expected to provide clinical benefits for patients using insulin pump therapy. A head-to-head trial versus the original insulin aspart formulation in pump therapy did not demonstrate superiority of faster aspart in terms of A1C reduction, but pump settings were not optimized for the pharmacokinetic/pharmacodynamic profile of faster aspart. Nevertheless, meal test and continuous glucose monitoring data suggest that faster aspart is beneficial for postprandial glucose control, and a case study is presented illustrating excellent results using this insulin in pump therapy. Frequent blood glucose monitoring and appropriate patient education are vital for success.

Remote Pharmacist-Assisted Flash Continuous Glucose Monitoring Improves Glycemic Outcomes in Patients With Poorly Controlled Diabetes: A Retrospective Case Series

This article reports on a retrospective case series evaluating glycemic outcomes using a flash continuous glucose monitoring (CGM) system in pharmacist-managed diabetes cases. The flash CGM system was used during initial assessment of patients' diabetes control and then continued throughout the intervention to ensure the safety and efficacy of the glycemic interventions. The Cloud-based CGM software was used to monitor patients remotely and assess their glycemic metrics. Action plans were created to address areas of most pressing concern, ensuring reduction or elimination of hypoglycemia, correction of hyperglycemia, and minimization of glycemic variability. In these complex cases, use of the flash CGM system in conjunction with lifestyle and medication interventions safely and effectively improved diabetes management and achieved targeted glucose outcomes.

Utilizing the New Glucometrics: A Practical Guide to Ambulatory Glucose Profile Interpretation

Traditional continuous glucose monitoring and flash glucose monitoring systems are proven to lower glycated haemoglobin levels, decrease the time and impact of hypoglycaemia or hyperglycaemia and, consequently, improve the quality of life for children and adults with type 1 diabetes mellitus (T1DM) and adults with type 2 diabetes mellitus (T2DM). These glucose-sensing devices can generate large amounts of glucose data that can be used to define a detailed glycaemic profile for each user, which can be compared with targets for glucose control set by an International Consensus Panel of diabetes experts. Targets have been agreed upon for adults, children and adolescents with T1DM and adults with T2DM; separate targets have been agreed upon for older adults with diabetes, who are at higher risk of hypoglycaemia, and women with pregestational T1DM during pregnancy. Along with the objective measures and targets identified by the International Consensus Panel, the dense glucose data delivered by traditional continuous glucose monitoring and flash glucose monitoring systems is used to generate an ambulatory glucose profile, which summarizes the data in a visually impactful format that can be used to identify patterns and trends in daily glucose control, including those that raise clinical concerns. In this article, we provide a practical guide on how to interpret these new glucometrics using a straightforward algorithm, and clear visual examples that demystify the process of reviewing the glycaemic health of people with T1DM or T2DM such that forward-looking goals for diabetes management can be agreed.

Early Initiation of Intermittently Scanned Continuous Glucose Monitoring in a Pediatric Population With Type 1 Diabetes: A Real World Study

BACKGROUND

Use of Continuous Glucose Monitoring (CGM) systems early in the course of diabetes has the potential to help glycemic management and to improve quality of life (QoL). No previous research has examined these outcomes in children-adolescents with type 1 diabetes (T1D) who use intermittently scanned CGM (isCGM) starting within the first month after diagnosis.

AIM

To evaluate the impact of isCGM early after T1D diagnosis, on metabolic control and QoL, comparing a group who started the use of the device within one month from the onset with another one who started at least one year later.

SUBJECTS AND METHODS

Patients who used isCGM within 1 month from T1D diagnosis were enrolled in group A; those who didn't have the device during the first year were considered as control group (group B). HbA1c and total daily insulin were evaluated at 3 (T1), 6 (T2) and 12 (T3) months post-baseline (T0, diabetes onset), QoL after 1 year. In group A, isCGM glucose metrics were also recorded.

RESULTS

85 patients were enrolled in group A and 67 patients in group B. In group A isCGM was well accepted during follow up: no patient dropped out; percentage of time with active sensor was in mean > 87%; number of scans/day remained stable. QoL was higher in group A than in group B both in children-adolescents (p<0.0001) and in parents (p 0.003). Group A presented lower HbA1c during the first year after diagnosis (p<0.001), and this data correlated with glucose management indicator (GMI), time in range (TIR) and mean glucose. The honeymoon period lasted more in group A than in B (p 0.028). Furthermore, the mean hypoglycemia duration decreased during follow-up (p 0.001) in group A.

CONCLUSIONS

Early use of isCGM, starting within the first month after diagnosis, improves metabolic control and QoL in pediatric patients with T1D.

Considerations to address missing data when deriving clinical trial endpoints from digital health technologies

Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs. Meanwhile, current approaches used to address missing data from DHTs are of limited sophistication and focus on the exclusion of data where the quantity of missing data exceeds a given threshold. High-frequency time series data collected by DHTs are often summarized to derive epoch-level data, which are then processed to compute daily summary measures. In this article, we discuss characteristics of missing data collected by DHT, review emerging statistical approaches for addressing missingness in epoch-level data including within-patient imputations across common time periods, functional data analysis, and deep learning methods, as well as imputation approaches and robust modeling appropriate for handling missing data in daily summary measures. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients' perspective in the study design. We believe that these approaches provide more insight into preventing missing data when deriving digital endpoints. We hope this article can serve as a starting point for further discussion among clinical trial stakeholders.

Pitfalls of intermittent continuous glucose monitoring in patients with steroid diabetes

Only a few studies of continuous glucose monitoring (CGM) in patients with steroid diabetes have been published. Therefore, we investigated all patients with type 2 diabetes (n = 121) and steroid diabetes (n = 40) who used the FreeStyle Libre Pro^® device (Abbott Japan) at Gunma University Hospital between 2017 and 2019. Glycated hemoglobin (HbA1c), mean sensor glucose (SG), and glucose management indicator values were similar in both groups. However, the indices for glycemic variabilities, expressed as standard deviations and percent coefficients of variation, were higher in patients with steroid diabetes than in those with type 2 diabetes. The associations between HbA1c, mean SG, and time in range (TIR) when glucose values were 70-180, <70, or >180 mg/dL were assessed using Pearson's product-moment correlation coefficient, which demonstrated good correlations in both patient groups. However, patients with steroid diabetes had a higher SG and lower TIR than did counterparts with type 2 diabetes who had similar HbA1c levels. To examine the effect of prednisolone on CGM data, we divided patients with steroid diabetes into 2 subgroups according to prednisolone dose (≤5 and >5 mg), and found that the dose of this steroid impacted the associations between HbA1c and CGM data, mean SG, and TIR. In summary, our data highlight the importance of cautiously interpreting CGM data and the optimal HbA1c level in patients with steroid diabetes to prevent diabetes-related complications. Further analyses using other CGM devices are necessary to further validate our findings.

The Optimal "Time in Range" and "Time below Range" are Difficult to Coordinate in Patients with Type 1 Diabetes

Achieving the optimal glucose level time in range (TIR), as recently proposed by the "International Consensus on Time in Range," is challenging. We retrospectively analyzed data from 192 patients, including 58 with type 1 diabetes, using the FreeStyle Libre Pro system. This device was used by physicians for continuous glucose monitoring (CGM) and for making therapeutic decisions based on unbiased data, as the patients were blinded to their blood glucose levels during monitoring. The desired 70% TIR among patients with type 2 diabetes corresponded to an HbA1c of 7.7%. Importantly, however, a 70% TIR for patients with type 1 diabetes corresponded to an HbA1c of 6.9%, which diverged markedly from the HbA1c of 7.9% that corresponded to the desired 4% time below range (TBR). Moreover, these dissociations were observed more in patients with type 1 diabetes with a higher % coefficient of variation (> 36%). Hence, while the TIR is strongly correlated with HbA1c, it is difficult to coordinate with the TBR in Japanese patients with type 1 diabetes. As these metrics (which are critical indicators in clinical practice) are rapidly gaining popularity globally, including in Japan, our data strongly support the cautious use of new CGM metrics such as TIR and TBR/time above range, and emphasize the importance of individualized treatment in achieving the optimal TIR and TBR, especially in patients with type 1 diabetes.

Hemoglobin A1c and Fructosamine Evaluated in Patients with Type 2 Diabetes Receiving Peritoneal Dialysis Using Long-Term Continuous Glucose Monitoring

INTRODUCTION

Shortened erythrocyte life span and erythropoietin-stimulating agents may affect hemoglobin A1c (HbA1c) levels in patients receiving peritoneal dialysis (PD). We compared HbA1c with interstitial glucose measured by continuous glucose monitoring (CGM) in patients with type 2 diabetes receiving PD.

METHODS

Fourteen days of CGM (Ipro2, Medtronic) were performed in 23 patients with type 2 diabetes receiving PD and in 23 controls with type 2 diabetes and an estimated glomerular filtration rate over 60 mL/min/1.73 m2. Patients were matched on gender and age (±5 years). HbA1c (mmol/mol), its derived estimate of mean plasma glucose (eMPGA1c) (mmol/L), and fructosamine (µmol/L) were measured at the end of the CGM period and compared with the mean sensor glucose (mmol/L) from CGM.

RESULTS

In the PD group, mean sensor glucose was 0.98 (95% con-fidence interval (CI): 0.43-1.54) mmol/L higher than the eMPGA1c compared with the control group (p = 0.002) where glucose levels were nearly identical (-0.05 (95% CI: -0.35-0.25) mmol/L). A significant association was found between fructosamine and mean sensor glucose using linear regression with no difference between slopes (p = 0.89) or y-intercepts (p = 0.28).

DISCUSSION/CONCLUSION

HbA1c underestimates mean plasma glucose levels in patients with type 2 diabetes receiving PD. However, the clinical significance of this finding is undetermined. Fructosamine seems to more accurately reflect glycemic status. CGM or fructosamine could complement HbA1c to increase the accuracy of glycemic monitoring in the PD population.

Design of clinical trials to assess diabetes treatment: Minimum duration of continuous glucose monitoring data to estimate time-in-ranges with the desired precision

AIM

To compute the uncertainty of time-in-ranges, such as time in range (TIR), time in tight range (TITR), time below range (TBR) and time above range (TAR), to evaluate glucose control and to determine the minimum duration of a trial to achieve the desired precision.

MATERIALS AND METHODS

Four formulas for the aforementioned time-in-ranges were obtained by estimating the equation's parameters on a training set extracted from study A (226 subjects, ~180 days, 5-minute Dexcom G4 Platinum sensor). The formulas were then validated on the remaining data. We also illustrate how to adjust the parameters for sensors with different sampling rates. Finally, we used study B (45 subjects, ~365 days, 15-minute Abbott Freestyle Libre sensor) to further validate our results.

RESULTS

Our approach was effective in predicting the uncertainty when time-in-ranges are estimated using n days of continuous glucose monitoring (CGM), matching the variability observed in the data. As an example, monitoring a population with TIR = 70%, TITR = 50%, TBR = 5% and TAR = 25% for 30 days warrants a precision of ±3.50%, ±3.68%, ±1.33% and ±3.66%, respectively.

CONCLUSIONS

The presented approach can be used to both compute the uncertainty of time-in-ranges and determine the minimum duration of a trial to achieve the desired precision. An online tool to facilitate its implementation is made freely available to the clinical investigator.

Early Glycemic Control Assessment Based on Consensus CGM Metrics. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:1269-1275. [PMID: 34891517 DOI: 10.1109/embc46164.2021.9631015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Continuous glucose monitoring (CGM) has revolutionized the world of diabetes and transformed the approach to diabetes care. In this context, an expert panel has reached consensus on clinical targets for CGM data interpretation based on eight CGM metrics. At least 70% of 14 consecutive CGM days (referred to as a period) are recommended to assess glycemic control based on the metrics. In clinical practice less CGM data may be available. Therefore, the primary aim of this study is to explore the ability to recover the consensus metrics utilizing less than 14 days of CGM data (intra-period). As a secondary aim, we investigate the recovery considering two consecutive periods (inter-period). The analyses are based on real-world CGM data from 484 diabetes users (4726 periods) acquired from the Cornerstones4Care® Powered by Glooko app. Using up to 14 accumulated days, the consensus metrics are calculated for each user and period, and compared to the fully 14 accumulated intra- and inter-period days. Relatively low deviations were observed for time in range (TIR) and average based metrics when using less than 14 days, however, we observed large deviations in metrics characterizing infrequent events such as time below range (TBR). Furthermore, the consensus metrics obtained in two consecutive 14 day periods have clear discrepancies (inter-period). Recovering consensus metrics using less than 14 days might still be valuable in terms of interpreting CGM data in certain clinical contexts. However, caution should be taken if treatment decisions would be made with less than 14 days of data on critical metrics such as TBR, since the metrics characterizing infrequent events deviate substantially when less data are available. Substantial deviation is also seen when comparing across two consecutive periods, which means that care should be taken not to over-generalize consensus metric based glycemic control conclusions from one period to subsequent periods.

The Accuracy of Hemoglobin A1c and Fructosamine Evaluated by Long-Term Continuous Glucose Monitoring in Patients with Type 2 Diabetes Undergoing Hemodialysis

INTRODUCTION

The accuracy of hemoglobin A1c (HbA1c) as a glycemic marker in patients with type 2 diabetes (T2D) receiving hemodialysis (HD) remains unknown. To assess accuracy, we compared HbA1c and fructosamine levels with interstitial glucose measured by continuous glucose monitoring (CGM) in patients with T2D receiving HD.

METHODS

Thirty patients in the HD group and 36 patients in the control group (T2D and an estimated glomerular filtration rate >60 mL/min/1.73 m2) completed the study period of 17 weeks. CGM (Ipro2®, Medtronic) was performed 5 times for periods of up to 7 days (with 4-week intervals) during a 16-week period. HbA1c (mmol/mol), the estimated mean plasma glucose from HbA1c (eMPGA1c [mmol/L]) and fructosamine (μmol/L) was measured at week 17 and compared with mean sensor glucose levels from CGM.

FINDINGS

In the HD group, mean sensor glucose was 1.4 mmol/L (95% confidence interval [CI]: 1.0-1.8) higher than the eMPGA1c, whereas the difference for controls was 0.1 mmol/L (95% CI: -0.1-[0.4]; p < 0.001). Adjusted for mean sensor glucose, HbA1c was lower in the HD group (-7.3 mmol/mol, 95% CI: -10.0-[-4.7]) than in the control group (p < 0.001), with no difference detected for fructosamine (p = 0.64).

DISCUSSION

HbA1c evaluated by CGM underestimates plasma glucose levels in patients receiving HD. The underestimation represents a clinical challenge in optimizing glycemic control in the HD population. Fructosamine is unaffected by the factors affecting HbA1c and appears to be more accurate for glycemic monitoring. CGM or fructosamine could thus complement HbA1c in obtaining more accurate glycemic control in this patient group.

Assessment of Hyperglycemia, Hypoglycemia and Inter-Day Glucose Variability Using Continuous Glucose Monitoring among Diabetic Patients on Chronic Hemodialysis

Continuous glucose monitoring (CGM) facilitates the assessment of short-term glucose variability and identification of acute excursions of hyper- and hypo-glycemia. Among 37 diabetic hemodialysis patients who underwent 7-day CGM with the iPRO2 device (Medtronic Diabetes, Northridge, CA, USA), we explored the accuracy of glycated albumin (GA) and hemoglobin A1c (HbA1c) in assessing glycemic control, using CGM-derived metrics as the reference standard. In receiver operating characteristic (ROC) analysis, the area under the curve (AUC) in diagnosing a time in the target glucose range of 70–180 mg/dL (TIR^70–180) in <50% of readings was higher for GA (AUC: 0.878; 95% confidence interval (CI): 0.728–0.962) as compared to HbA1c (AUC: 0.682; 95% CI: 0.508–0.825) (p < 0.01). The accuracy of GA (AUC: 0.939; 95% CI: 0.808–0.991) in detecting a time above the target glucose range > 250 mg/dL (TAR^>250) in >10% of readings did not differ from that of HbA1c (AUC: 0.854; 95% CI: 0.699–0.948) (p = 0.16). GA (AUC: 0.712; 95% CI: 0.539–0.848) and HbA1c (AUC: 0.740; 95% CI: 0.570–0.870) had a similarly lower efficiency in detecting a time below target glucose range < 70 mg/dL (TBR^<70) in >1% of readings (p = 0.71). Although the mean glucose levels were similar, the coefficient of variation of glucose recordings (39.2 ± 17.3% vs. 32.0 ± 7.8%, p < 0.001) and TBR^<70 (median (range): 5.6% (0, 25.8) vs. 2.8% (0, 17.9)) were higher during the dialysis-on than during the dialysis-off day. In conclusion, the present study shows that among diabetic hemodialysis patients, GA had higher accuracy than HbA1c in detecting a 7-day CGM-derived TIR^70–180 < 50%. However, both biomarkers provided an imprecise reflection of acute excursions of hypoglycemia and inter-day glucose variability.

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.

Insulin Metrics: Need for Development of Consensus Standards for Reporting of Insulin Dosing Data

The advent of connected insulin pens will generate an avalanche of digital insulin data, especially in the context of prandial- and multiple daily injection-insulin regimens. There is a need for the diabetes community to develop standards for such data, analogous to what has been achieved using the ambulatory glucose profile and associated metrics for glucose, permitting harmonization of data reporting for multiple devices and facilitating integration of glucose, insulin, food intake, and physical activity data. Several studies have estimated the timing of meals by analyses of glucose excursions but using diverse criteria. There is need for uniform criteria for multiple types of insulin boluses, including premeal, perimeal, delayed, missed, and correction boluses to facilitate research studies and patient care. This article contains a first preliminary proposal for standards regarding reporting of insulin dosing data. Clinical usage of these reports will require sensitive communication between health care providers and patients.

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.

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.

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.

Beyond A1C: A Practical Approach to Interpreting and Optimizing Continuous Glucose Data in Youth

Despite significant pharmacological and technological advances in the treatment of type 1 diabetes, the majority of youth in the United States do not meet the American Diabetes Association's recommended A1C goal. Understanding and managing glycemic variability is important in children and adolescents. Because A1C provides an incomplete picture of day-to-day glycemic fluctuations, continuous glucose monitoring (CGM)-derived metrics are a promising addition to address glycemic management challenges in youth with diabetes. In this article, we discuss how to develop practical strategies to optimize the use of CGM in the pediatric population, interpret the valuable data it provides, and develop personalized and actionable treatment goals.

Flash Glucose Monitoring Compared to Capillary Glucose Levels in Patients With Diabetic Ketoacidosis: Potential Clinical Applications

OBJECTIVE

Frequent, finger-prick capillary blood glucose measurement is standard care, used to drive insulin infusion rates for inpatients being resuscitated from diabetic ketoacidosis (DKA). Over recent years there has been a shift toward continuous interstitial glucose monitoring, allowing monitoring of glucose without repeated invasive testing. While continuous interstitial glucose monitoring has been safely and reliably utilized in the outpatient setting, it has yet to be studied in acutely unwell patients with DKA. The aim of this study, allowing for physiologically lower interstitial compared to capillary glucose, was to determine if interstitial flash glucose monitoring (FGM) would lead to insulin infusion rates that were similar to capillary blood glucose (CapBG) in DKA.

METHODS

In this study, 10 patients with diabetes mellitus, assessed to be in DKA, were enrolled. At the same time as standard DKA management commencement, simultaneous FGM measurements were obtained. Duplicate paired glucose readings were then analyzed for agreement.

RESULTS

Actual (CapBG-driven) and predicted (FGM determined) insulin infusion rates were similar. Minor differences in predicted insulin infusion rates were noted in 2/10 patients at higher glucose concentrations, which may relate to the lag in change in glucose in the interstitial space.

CONCLUSION

Based on our results, a trial of clinical outcomes in patients with DKA treated with insulin infusion rates driven by CapBG versus subcutaneous FGM appears justified. The FGM method of testing may improve patient comfort, obviate fatigue, improve staff time and direct patient contact, and potentially facilitate rapid discharge.

Practical Considerations for Initiating and Utilizing Flash Continuous Glucose Monitoring in Clinical Practice

Use of continuous glucose monitoring (CGM) has been shown to improve clinical outcomes in patients with type 1 diabetes (T1D) and type 2 diabetes (T2D), including improved glycemic control, better treatment adherence, and an increased understanding of their treatment regimens. Retrospective analysis of CGM data allows clinicians and patients to identify glycemic patterns that support and facilitate informed therapy adjustments. There are currently 2 types of CGM systems: real-time CGM (rtCGM) and flash CGM. The FreeStyle Libre 2 (FSL2) is the newest flash CGM system commercially available. Because the FSL2 system was only recently cleared for use in the US, many endocrinologists and diabetes specialists may be unfamiliar with the strengths, limitations, and potential of the FSL2 system. This article focuses on practical approaches and strategies for initiating and using flash CGM in endocrinology and diabetes specialty practices.

Robust Determination of the Optimal Continuous Glucose Monitoring Length of Intervention to Evaluate Long-Term Glycemic Control

Objective: Consensus continuous glucose monitoring (CGM) guidance includes a recommendation that a minimum of 14 days of CGM data are used to report times in ranges. The previously employed approaches to determine the optimal duration for CGM data have limitations. In this study, we present a robust approach to define the minimum duration of CGM data to report times in ranges, as well as other glycemic metrics. Methods: The approach is based on the median absolute percentage error and employs a sliding time window to reduce the impact of inter-time interval variability, hence allowing smaller data sets to be used. A 10% and 5% threshold were employed to assess the optimal duration of CGM data for a set of commonly employed metrics to assess quality of glycemic control and glycemic variability. To evaluate the impact of the data set size and type of intervention, data from two randomized controlled trials involving participants with type 1 diabetes were used (n = 236 and n = 25). Results: Results suggest that mean glucose reaches the 5% threshold for mean absolute percentage error within 2 weeks, whereas percentage time in target 70-180 mg/dL, mean absolute glucose, standard deviation, and coefficient of variation reach the same threshold within 4 weeks in both data sets, suggesting that these metrics can be robustly assessed from CGM data for a 4-week period, whereas some other metrics require much longer window lengths, especially those evaluating hypoglycemia. Conclusions: Our data suggest that there is no optimal duration for CGM data to robustly assess all outcomes and that the duration required for a robust outcome depends on the population being studied, the sampling frequency, and the primary outcomes selected.