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

Montaser, Eslam; Fabris, Chiara; Kovatchev, Boris

doi:10.1089/dia.2022.0104

Cited by 24 publications

(15 citation statements)

References 37 publications

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“…For glycaemic control we recommend using TBR, TIR (and by extension, time above range), whether the targets for these ranges are met, and a discrete description of mean SG (and mean GMI) and SD of SG values. Our recommendations align with those of other authors 27 …”

Section: Discussionsupporting

confidence: 88%

The significance of coefficient of variation as a measure of hypoglycaemia risk and glycaemic control in real world users of the automated insulin delivery MiniMed 780G system

Castañeda

Arrieta

Heuvel

et al. 2023

Diabetes Obesity Metabolism

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Aim: Use of the MiniMed 780G system (MM780G) can result in a reduction in mean and standard deviation (SD) of sensor glucose (SG) values. We assessed the significance of the coefficient of variation (CV) as a measure of hypoglycaemia risk and glycaemic control.Materials and Methods: Data from 10 404 MM780G users were analysed using multivariable logistic regression to assess the contribution of CV to (a) hypoglycaemia risk, measured as not reaching target <1% for time below range (TBR), and (b) achieving targets of time-in-range (TIR) >70% and glucose management indicator <7%. CV was compared with SD and low blood glucose index. To assess the relevance of CV <36% as a therapeutic threshold, we identified the CV cut-off point that optimally discriminated users at risk of hypoglycaemia.Results: The contribution of CV was the smallest in terms of risk of hypoglycaemia (vs. low blood glucose index and SD) and TIR and glucose management indicator targets (vs. SD). In all cases the models with SD showed the best fit. A CV <43.4% (95% CI: 42.9-43.9) was the optimal cut-off point with a correct classification rate of 87.2% (vs. 72.9% for CV <36%). Conclusion:For MM780G users, CV is a poor marker for hypoglycaemia risk and glycaemic control. We recommend using, for the former, TBR and whether the TBR target is met (and not using CV <36% as a therapeutic threshold for hypoglycaemia); for the latter, TIR, time above range, whether targets are met and a discrete description of mean SG and SD of SG values.coefficient of variation, hypoglycaemia, insulin infusion system, type 1 diabetes | INTRODUCTIONGlycaemic variability is considered among the factors contributing to the risk for long-term diabetes-related complications, and in the short-term, it is also associated with episodes of hypoglycaemia and hyperglycaemia. 1-3 However, the measurement of glycaemic variability is not straightforward, with various measures available, which reflects a lack of a standard acceptable and clinically usable measure. 1 Recently,

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Section: Discussionsupporting

confidence: 88%

The significance of coefficient of variation as a measure of hypoglycaemia risk and glycaemic control in real world users of the automated insulin delivery MiniMed 780G system

Castañeda

Arrieta

Heuvel

et al. 2023

Diabetes Obesity Metabolism

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“…Finally, classification metrics revealed that the geometry of diabetes optimization depends on two categories of variables, namely, metrics of hyperglycemia exposure (which include mean glucose, % time >180 mg/dL, and % time >250 mg/dL) as well as the risk of hypoglycemia (which is based on % time <70 mg/dL, % time <54 mg/dL, and coefficient of variation). 13 These two metrics together can accurately predict ~90% of the variance in training and data sets. 13 While the metrics described above are of great interest, it is important to consider how clinicians think about CGM data.…”

Section: Continuous Glucose Monitor Data In People Without Diabetesmentioning

confidence: 99%

“…13 These two metrics together can accurately predict ~90% of the variance in training and data sets. 13 While the metrics described above are of great interest, it is important to consider how clinicians think about CGM data. Working on the premise that an item's quality has many dimensions which can be captured by listing their performances individually or by using a composite score to summarize their features, Dr David Klonoff led an international group of 90 experts in the creation of the GRI.…”

Section: Continuous Glucose Monitor Data In People Without Diabetesmentioning

confidence: 99%

Diabetes Technology Meeting 2022

Huang

Yeung

DuBord

et al. 2023

J Diabetes Sci Technol

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Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 3 to November 5, 2022. Meeting topics included (1) the measurement of glucose, insulin, and ketones; (2) virtual diabetes care; (3) metrics for managing diabetes and predicting outcomes; (4) integration of continuous glucose monitor data into the electronic health record; (5) regulation of diabetes technology; (6) digital health to nudge behavior; (7) estimating carbohydrates; (8) fully automated insulin delivery systems; (9) hypoglycemia; (10) novel insulins; (11) insulin delivery; (12) on-body sensors; (13) continuous glucose monitoring; (14) diabetic foot ulcers; (15) the environmental impact of diabetes technology; and (16) spinal cord stimulation for painful diabetic neuropathy. A live demonstration of a device that can allow for the recycling of used insulin pens was also presented.

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“…A previous study found that two essential metrics, quantifying risk for hyperglycaemia and hypoglycaemia, can explain approximately 90% of the total variance in CGM data, and hence are necessary and sufficient to characterize glycaemic control. 17 In a recent study, 330 expert diabetologists from six continents were invited to rank a dataset of 14-day CGM tracings from 225 adults with diabetes, based on whch a novel composite metric was proposed, the glycaemia risk index (GRI). 18,19 With a simple equation weighting standard metrics in the AGP including TAR (10.1-13.9 mmol/L; >13.9mmol/L) and TBR (3-3.8mmol/L; <3mmol/L), GRI can be presented as a single score ranging from 0 to 100, reflecting both the risk of hyperglycaemia and the risk of hypoglycaemia.…”

Section: Introductionmentioning

confidence: 99%

“…The clinical management of diabetes has been recognized as a classic two‐sided optimization task to minimize exposure to both hyperglycaemia and hypoglycaemia, which reflect treatment efficacy and safety, respectively. A previous study found that two essential metrics, quantifying risk for hyperglycaemia and hypoglycaemia, can explain approximately 90% of the total variance in CGM data, and hence are necessary and sufficient to characterize glycaemic control 17 . In a recent study, 330 expert diabetologists from six continents were invited to rank a dataset of 14‐day CGM tracings from 225 adults with diabetes, based on whch a novel composite metric was proposed, the glycaemia risk index (GRI) 18,19 .…”

Section: Introductionmentioning

confidence: 99%

Association between glycaemia risk index (GRI) and diabetic retinopathy in type 2 diabetes: A cohort study

Wang

et al. 2023

Diabetes Obesity Metabolism

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Aim: To investigate the association between a new composite metric, glycaemia risk index (GRI), and incident diabetic retinopathy (DR).Methods: A total of 1204 adults with type 2 diabetes without DR at baseline were included between 2005 and 2019 from a single centre in Shanghai, China. GRI was obtained from continuous glucose monitoring data at baseline. Cox proportion hazard regression analysis was used to assess the association between GRI and the risk of incident DR.Results: During a median follow-up of 8.4 years, 301 patients developed DR. The multivariable-adjusted hazard ratios (HRs) for incident DR across ascending GRI quartiles (≤14 [reference], 15 $ 28, 29 $ 47 and > 47) were 1.00, 1.05 (95% CI 0.74-1.48), 1.33 (95% confidence interval [CI] 0.96-1.84) and 1.53 (95% CI 1.11-2.11), respectively. For each 1-SD increase in GRI, the risk of DR was increased by 20% (HR 1.20, 95% CI 1.07-1.33) after adjustment for confounders. Conclusions:In patients with type 2 diabetes, higher GRI is associated with an increased risk of incident DR. GRI has the potential to be a valuable clinical measure, which needs to be further explored in future studies.

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

Cited by 24 publications

References 37 publications

The significance of coefficient of variation as a measure of hypoglycaemia risk and glycaemic control in real world users of the automated insulin delivery MiniMed 780G system

The significance of coefficient of variation as a measure of hypoglycaemia risk and glycaemic control in real world users of the automated insulin delivery MiniMed 780G system

Diabetes Technology Meeting 2022

Association between glycaemia risk index (GRI) and diabetic retinopathy in type 2 diabetes: A cohort study

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