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Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are 300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.

BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Aims: To explore the feasibility and potential benefits of a peer support programme for adults with insulin‐treated type 2 diabetes (T2D) starting continuous glucose monitoring (CGM). Methods: This part of the Steno2tech study is an exploratory, single‐centre, open‐labelled, prospective, randomised controlled trial (RCT). A total of 60 participants were randomised 2:1 to 12 months of CGM with or without peer support. All participants received a 3‐h diabetes self‐management education course including a CGM part on how to use the CGM and interpret the CGM‐derived data. Peer support consisted of three 3‐h peer support meetings over the first 6 months of the study period with groups of three to six people. The exploratory outcomes included the acceptability and feasibility of the peer support intervention, and the between‐group difference in change in several glycaemic, metabolic and participant‐reported outcomes measured at baseline, 6 and 12 months. Results: The peer support intervention was found acceptable and feasible. Participants shared their experiences of using and interpreting CGM data and its association with health behaviour. While both groups had improvements in glycaemic, metabolic and participant‐reported outcomes, there were no significant between‐group differences. Conclusions: Although feasible, we found no measured additional benefits when adding a peer support programme after starting CGM in this exploratory RCT including adults with insulin‐treated T2D. Understanding the perceived effect of and preferences for a peer support intervention from the participants' points of view, including why individuals declined to participate, would be of value for future research. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To compare the 12-month effects of continuous glucose monitoring (CGM) versus blood glucose monitoring (BGM) in adults with insulin-treated type 2 diabetes. RESEARCH DESIGN AND METHODS: This is a single-center, parallel, open-label, randomized controlled trial including adults with inadequately controlled, insulin-treated type 2 diabetes from the outpatient clinic at Steno Diabetes Center Copenhagen, Denmark. Inclusion criteria were ≥18 years of age, insulin-treated type 2 diabetes, and HbA1c ≥7.5% (58 mmol/mol). Participants were randomly assigned (1:1) to 12 months of either CGM or BGM. All participants received a diabetes self-management education course and were followed by their usual health care providers. Primary outcome was between-group differences in change in time in range (TIR) 3.9–10.0 mmol/L, assessed at baseline, after 6 and 12 months by blinded CGM. The prespecified secondary outcomes were differences in change in several other glycemic, metabolic, and participant-reported outcomes. RESULTS: The 76 participants had a median baseline HbA1c of 8.3 (7.8, 9.1)% (67 [62–76] mmol/mol), and 61.8% were male. Compared with BGM, CGM usage was associated with significantly greater improvements in TIR (between-group difference 15.2%, 95% CI 4.6; 25.9), HbA1c (−0.9%, −1.4; −0.3 [−9.4 mmol/mol, −15.2; −3.5]), total daily insulin dose (−10.6 units/day, −19.9; −1.3), weight (−3.3 kg, −5.5; −1.1), and BMI (−1.1 kg/m2, −1.8; −0.3) and greater self-rated diabetes-related health, well-being, satisfaction, and health behavior. CONCLUSIONS: In adults with inadequately controlled insulin-treated type 2 diabetes, the 12-month impact of CGM was superior to BGM in improving glucose control and other crucial health parameters. The findings support the use of CGM in the insulin-treated subgroup of type 2 diabetes. [ABSTRACT FROM AUTHOR]

Aims: The study objective was to explore how upper extremity impairments (UEIs) affect the everyday life and work‐life of people with type 1 diabetes (T1D) and to compare them to a control group without T1D to determine if there are diabetes‐specific consequences of UEIs. Methods: In a controlled cross‐sectional study, a survey was distributed across all regions of Denmark. A total of 2174 people with T1D and 827 controls were included in the study population. The survey addressed UEI symptoms, employment status, functional disability, mental well‐being and diabetes distress. Data were analysed using multivariable logistic and linear regression. Results: Upper extremity impairments were associated with a higher rate of work absence and modification, but no more so for people with T1D than for the control group. Among people with T1D, UEIs were significantly associated with worse mental well‐being and diabetes distress, and across all outcomes including functional disability, additive effects were found with an increasing number of coexisting impairments. The impact of UEIs on functional disability was more severe for the T1D group than the control group, but this was primarily due to differences in the number of coexisting impairments. Conclusions: Upper extremity impairments have significant negative implications for the work‐life and everyday life of people with T1D, and interventions to reduce UEIs and their impact among this group are highly relevant. [ABSTRACT FROM AUTHOR]

Context Current guidelines for exercise-related glucose management focus on reducing bolus and/or basal insulin doses and considering carbohydrate intake. Yet far less attention has been paid to the potential role of other macronutrients alongside carbohydrates on glucose dynamics around exercise. Objective To investigate the effects of a low-carbohydrate-high-protein (LCHP) compared with a high-carbohydrate-low-protein (HCLP) pre-exercise meal on the metabolic, hormonal, and physiological responses to exercise in adults with insulin pump–treated type 1 diabetes. Methods Fourteen adults (11 women, 3 men) with insulin pump–treated type 1 diabetes (median [range] HbA1c of 50 [43-59] mmol/mol (6.7% [6.1%-7.5%]), age of 49 [25-65] years, and body mass index of 24.0 [19.3-27.1] kg/m2) completed an unblinded, 2-arm, randomized, crossover study. Participants ingested isocaloric meals that were either LCHP (carbohydrate 21%, protein 52%, fat 27%) or HCLP (carbohydrate 52%, protein 21%, fat 27%) 90 minutes prior to undertaking 45 minutes of cycling at moderate intensity. Meal insulin bolus was dosed according to meal carbohydrate content but reduced by 25%. Basal insulin rates were reduced by 35% from meal ingestion to end of exercise. Results Around exercise the coefficient of variability was lower during LCHP (LCHP: 14.5 ± 5.3 vs HCLP: 24.9 ± 7.7%, P =.001). Over exercise, LCHP was associated with a lesser drop (LCHP: Δ−1.49 ± 1.89 vs HCLP: Δ−3.78 ± 1.95 mmol/L, P =.001). Mean insulin concentration was 30% lower during exercise for LCHP compared with HCLP (LCHP: 25.5 ± 11.0 vs HCLP: 36.5 ± 15.9 mU/L, P <.001). Conclusion Ingesting a LCHP pre-exercise meal lowered plasma glucose variability around exercise and diminished the drop in plasma glucose over exercise. [ABSTRACT FROM AUTHOR]