Glycemic Metrics Derived From Intermittently Scanned Continuous Glucose Monitoring

2020 ◽  
pp. 193229682097582
Author(s):  
Klavs Würgler Hansen ◽  
Bo Martin Bibby
Keyword(s):  
Continuous Glucose Monitoring ◽  
Regression Line ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Lack Of Information ◽  
Time In Range ◽  
The Difference ◽  
The Individual ◽  
Glucose Management ◽  
Mean Glucose

Background: Glucose data from intermittently scanned continuous glucose monitoring (isCGM) is a combination of scanned and imported glucose values. The present knowledge of glycemic metrics originate mostly from glucose data from real-time CGM sampled every five minutes with a lack of information derived from isCGM. Methods: Glucose data obtained with isCGM and hemoglobin A1c (HbA1c) were obtained from 169 patients with type 1 diabetes. Sixty-one patients had two observations with an interval of more than three months. Results: The best regression line of HbA1c against mean glucose was observed from 60 days prior to HbA1c measurement as compared to 14, 30, and 90 days. The difference between HbA1c and estimated HbA1c (=glucose management indicator [GMI]) first observed correlated with the second observation (R2 0.61, P < .001). Time in range (TIR, glucose between 3.9 and 10 mmol/L) was significantly related to GMI (R2 0.87, P < .001). A TIR of 70% corresponded to a GMI of 6.8% (95% confidence interval, 6.3-7.4). The fraction of patients with the optimal combination of TIR >70% and time below range (TBR) <4% was 3.6%. The fraction of patients with TBR>4% was four times higher for those with high glycemic variability (coefficient of variation [CV] >36%) than for those with lower CV. Conclusion: The individual difference between HbA1c and GMI was reproducible. High glycemic variability was related to increased TBR. A combination of TIR and TBR is suggested as a new composite quality indicator.

scholarly journals Enlarged glycemic variability in sulfonylurea-treated well-controlled type 2 diabetics identified using continuous glucose monitoring

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fumi Uemura ◽  
Yosuke Okada ◽  
Keiichi Torimoto ◽  
Yoshiya Tanaka
Keyword(s):  
Glucose Level ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
High Dose ◽  
Type 2 Diabetics ◽  
Time In Range ◽  
The Difference ◽  
Glycated Hemoglobin Level

AbstractTime in range (TIR) is an index of glycemic control obtained from continuous glucose monitoring (CGM). The aim was to compare the glycemic variability of treatment with sulfonylureas (SUs) in type 2 diabetes mellitus (T2DM) with well-controlled glucose level (TIR > 70%). The study subjects were 123 patients selected T2DM who underwent CGM more than 24 h on admission without changing treatment. The primary endpoint was the difference in glycemic variability, while the secondary endpoint was the difference in time below range < 54 mg/dL; TBR < 54, between the SU (n = 63) and non-SU (n = 60) groups. The standard deviation, percentage coefficient of variation (%CV), and maximum glucose level were higher in the SU group than in the non-SU group, and TBR < 54 was longer in the high-dose SU patients. SU treatment was identified as a significant factor that affected %CV (β: 2.678, p = 0.034). High-dose SU use contributed to prolonged TBR < 54 (β: 0.487, p = 0.028). Our study identified enlarged glycemic variability in sulfonylurea-treated well-controlled T2DM patients and high-dose SU use was associated with TBR < 54. The results highlight the need for careful adjustment of the SU dose, irrespective of glycated hemoglobin level or TIR value.

scholarly journals The Importance of Time in Range (TIR) for Continuous Glucose Monitoring (CGM) in the Clinical Practice for Diabetes

2021 ◽  
pp. 12-13
Author(s):  
Bando Hiroshi
Keyword(s):  
Clinical Practice ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Coefficient Of Variation ◽  
Hba1c Level ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Glucose Profile ◽  
Time In Range ◽  
Glucose Management

As to the development of treatment for diabetes, Continuous Glucose Monitoring (CGM) has been recently prevalent rapidly. By the analysis of real-time CGM, Ambulatory Glucose Profile (AGP) has been used. It includes time in range (TIR, 70-180 mg/dL), time above range (TAR, >181mg/dL), time below range (TBR, <69 mg/dL), Glycemic Variability (GV), Glucose Management Indicator (GMI), Glycemic variability, Coefficient Of Variation (CV%) and so on. TIR value indicating approximately 70% seems to correlate closely with the HbA1c level of 6.77.0%. Marked discordance of HbA1c values has been found between laboratory HbA1c and estimated HbA1c (eA1c) using GMI from CGM.

scholarly journals Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management

2019 ◽  
Vol 14 (2) ◽  
pp. 271-276 ◽  
Author(s):  
Tong Sheng ◽  
Reid Offringa ◽  
David Kerr ◽  
Mark Clements ◽  
Jerome Fischer ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Healthcare Professionals ◽  
Rank Order ◽  
Glucose Monitoring ◽  
Absolute Error ◽  
Multiple Data ◽  
Time In Range ◽  
Glucose Management ◽  
Diabetes Educators ◽  
Glycemic Management

Background: Continuous glucose monitoring (CGM) offers multiple data features that can be leveraged to assess glucose management. However, how diabetes healthcare professionals (HCPs) actually assess CGM data and the extent to which they agree in assessing glycemic management are not well understood. Methods: We asked HCPs to assess ten de-identified CGM datasets (each spanning seven days) and rank order each day by relative glycemic management (from “best” to “worst”). We also asked HCPs to endorse features of CGM data that were important in making such assessments. Results: In the study, 57 HCPs (29 endocrinologists; 28 diabetes educators) participated. Hypoglycemia and glycemic variance were endorsed by nearly all HCPs to be important (91% and 88%, respectively). Time in range and daily lows and highs were endorsed more frequently by educators (all Ps < .05). On average, HCPs endorsed 3.7 of eight data features. Overall, HCPs demonstrated agreement in ranking days by relative glycemic control (Kendall’s W = .52, P < .001). Rankings were similar between endocrinologists and educators ( R2 = .90, Cohen’s kappa = .95, mean absolute error = .4 [all Ps < .05]; Mann-Whitney U = 41, P = .53). Conclusions: Consensus in the endorsement of certain data features and agreement in assessing glycemic management were observed. While some practice-specific differences in feature endorsement were found, no differences between educators and endocrinologists were observed in assessing glycemic management. Overall, HCPs tended to consider CGM data holistically, in alignment with published recommendations, and made converging assessments regardless of practice.

scholarly journals Time in Specific Glucose Ranges, Glucose Management Indicator, and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics

2020 ◽  
pp. 193229682093182
Author(s):  
Stefan Pleus ◽  
Ulrike Kamecke ◽  
Delia Waldenmaier ◽  
Manuela Link ◽  
Eva Zschornack ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
International Consensus ◽  
Freestyle Libre ◽  
Pairwise Differences ◽  
Assess Quality ◽  
Glucose Management ◽  
Post Hoc ◽  
The Impact

Background: International consensus recommends a set of continuous glucose monitoring (CGM) metrics to assess quality of diabetes therapy. The impact of individual CGM sensors on these metrics has not been thoroughly studied yet. This post hoc analysis aimed at comparing time in specific glucose ranges, coefficient of variation (CV) of glucose concentrations, and glucose management indicator (GMI) between different CGM systems and different sensors of the same system. Method: A total of 20 subjects each wore two Dexcom G5 (G5) sensors and two FreeStyle Libre (FL) sensors for 14 days in parallel. Times in ranges, GMI, and CV were calculated for each 14-day sensor experiment, with up to four sensor experiments per subject. Pairwise differences between different sensors of the same CGM system as well as between sensors of different CGM system were calculated for these metrics. Results: Pairwise differences between sensors of the same model showed larger differences and larger variability for FL than for G5, with some subjects showing considerable differences between the two sensors. When pairwise differences between sensors of different CGM models were calculated, substantial differences were found in some subjects (75th percentiles of differences of time spent <70 mg/dL: 5.0%, time spent >180 mg/dL: 9.2%, and GMI: 0.42%). Conclusion: Relevant differences in CGM metrics between different models of CGM systems, and between different sensors of the same model, worn by the same study subjects were found. Such differences should be taken into consideration when these metrics are used in the treatment of diabetes.

Continuous Glucose Monitoring and HbA1c in Cystic Fibrosis: Clinical Correlations and Implications for CFRD Diagnosis

2021 ◽  
Author(s):  
Kevin J Scully ◽  
Jordan S Sherwood ◽  
Kimberly Martin ◽  
Melanie Ruazol ◽  
Peter Marchetti ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Glucose Tolerance ◽  
Continuous Glucose Monitoring ◽  
Regression Line ◽  
Glucose Monitoring ◽  
Area Under The Curve ◽  
Glycemic Variability ◽  
Oral Glucose ◽  
Oral Glucose Tolerance Testing ◽  
Significant Difference

Abstract Context The clinical utility and implications of continuous glucose monitoring (CGM) in cystic fibrosis (CF) are unclear. Objective We examined the correlation between CGM measures and clinical outcomes in adults with CF, investigated the relationship between hemoglobin A1c (HbA1c) and CGM-derived average glucose (AG), and explored CGM measures that distinguish CFRD from normal and abnormal glucose tolerance. Design Prospective observational study. Participants 77 adults with CF. Main outcomes CGM and HbA1c measured at 2-3 time-points three months apart. Results Thirty-one of the 77 participants met American Diabetes Association-recommended diagnostic criteria for CFRD by oral glucose tolerance testing and/or HbA1c. In all participants, CGM measures of hyperglycemia and glycemic variability correlated with nutritional status and pulmonary function. HbA1c was correlated with AG (R 2=0.71, p=&lt;0.001), with no significant difference between this regression line and that previously established in type 1 and type 2 diabetes and healthy volunteers. Cutoffs of 17.5% time &gt;140 mg/dL and 3.4% time &gt;180 mg/dL had sensitivities of 87% and 90%, respectively, and specificities of 95%, for identifying CFRD. Area under the curve and percent of participants correctly classified with CFRD were higher for AG, standard deviation, % time &gt;140, &gt;180, and &gt;250 mg/dL than HbA1c. Conclusions CGM measures of hyperglycemia and glycemic variability are superior to HbA1c in distinguishing those with and without CFRD. CGM-derived AG is strongly correlated with HbA1c in adults with CF, with a similar relationship to other diabetes populations. Future studies are needed to investigate CGM as a diagnostic and screening tool for CFRD.

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