Normal Reference Range for Mean Tissue Glucose and Glycemic Variability Derived from Continuous Glucose Monitoring for Subjects Without Diabetes in Different Ethnic Groups

Hill, Nathan R.; Oliver, Nick; Choudhary, Pratik; Levy, Jonathan C.; Hindmarsh, Peter C.; Matthews, David R.

doi:10.1089/dia.2010.0247

Cited by 311 publications

(297 citation statements)

References 31 publications

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“…There is a lack of studies examining postprandial hyperglycemia and glycemic variability in healthy Koreans; however, we can compare our results with data for normal Asian subjects. 24,25 Compared with normal Asian subjects, our results showed not only high mean blood glucose levels, but also high MPMG and MAGE values. These results indicate the presence of postprandial hyperglycemia and glycemic variability in well-controlled diabetes.…”

Section: Discussionmentioning

confidence: 54%

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Evaluation of Glycemic Variability in Well-Controlled Type 2 Diabetes Mellitus

Chon

Lee²,

Fraterrigo³

et al. 2013

Diabetes Technology & Therapeutics

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Aims: It is necessary to evaluate glucose variability and postprandial hyperglycemia in patients with well-controlled type 2 diabetes mellitus because of the limitations associated with hemoglobin A1c (HbA1c) measurements. We evaluated parameters reflecting postprandial hyperglycemia and glycemic variability in patients with optimal HbA1c. Patients and Methods: Thirty-nine patients with HbA1c levels below 7% were recruited to the study. A continuous glucose monitoring system (CGMS) was applied for two 72-h periods. 1,5-Anhydroglucitol (1,5-AG) and fructosamine (FA) were measured as parameters for postprandial hyperglycemia and glucose variability. Using CGMS data, the following postprandial hyperglycemia parameters were calculated: mean postprandial maximum glucose (MPMG) and area under the curve for glucose above 180 mg/dL (AUC-180). To measure glycemic variability, we calculated mean amplitude of glucose excursion (MAGE) using a classical (MAGE c ) and new method (MAGE group of sign [MAGE gos ]). Results: The baseline HbA1c level was 6.3 -0.3%. The mean MPMG was 10.34 -1.84 mmol/L, and the mean AUC-180 was 0.17 -0.23 mmol/L/day. The mean MAGE gos was 3.27 -1.29 mmol/L, and MAGE c was 4.30 -1.43 mmol/L, indicating glycemic variability in our patients. The mean levels of 1,5-AG and FA were 16.7 -7.4 lg/mL and 273.0 -22.5 lmol/L, respectively. In a correlation analysis, FA was significantly correlated with MPMG, AUC-180, MAGE gos , and MAGE c . In contrast, 1,5-AG was only correlated with AUC-180. Conclusions: This study demonstrated postprandial hyperglycemia and glycemic variability in subjects with well-controlled diabetes. FA may reflect postprandial hyperglycemia and glycemic variability, but 1,5-AG may be of limited value for assessing glucose variability in patients with well-controlled type 2 diabetes mellitus.

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Section: Discussionmentioning

confidence: 54%

“…These results indicate the presence of postprandial hyperglycemia and glycemic variability in well-controlled diabetes. 24,25 This suggests that it is important to recognize that despite having ''normal'' HbA1c values, patients may not have optimal control of their diabetes.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Glycemic Variability in Well-Controlled Type 2 Diabetes Mellitus

Chon

Lee²,

Fraterrigo³

et al. 2013

Diabetes Technology & Therapeutics

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“…16 Easy GV version 6.0 software (available free for non-commercial use at www.easygv.co.uk) was used to calculate parameters of glucose fluctuations. 19 The incremental area under the curve (iAUC) of glucose concentration, time spent reaching the postprandial glucose spike (PGS), and the time required for glucose concentrations to decrease to baseline were calculated to assess glucose responses to the different types of meals.…”

Section: Parameters On Evaluation Of Glucose Fluctuationsmentioning

confidence: 99%

Effects of Different Proportion of Carbohydrate in Breakfast on Postprandial Glucose Excursion in Normal Glucose Tolerance and Impaired Glucose Regulation Subjects

Kang

Wang

et al. 2013

Diabetes Technology & Therapeutics

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Background: The variability of postprandial plasma glucose is an independent risk factor for diabetes. The type and amount of carbohydrate may be important determinants of glycemic control. The aim of the study was to compare the effects of different proportions of carbohydrate in breakfast on postprandial blood glucose fluctuations in impaired glucose regulation (IGR) and normal glucose tolerance (NGT) subjects. Subjects and Methods: This is a cross-sectional study of two groups including 55 subjects with IGR and 78 individuals with NGT. Their recorded breakfast was sorted into low-carbohydrate (LC) (carbohydrate < 45%), medium-carbohydrate (MC) (carbohydrate 45-65%), and high-carbohydrate (HC) (carbohydrate > 65%) meals according to the proportion of carbohydrate. Glucose concentrations were continuously measured with a continuous glucose monitoring system, and parameters such as the incremental area under the curve (iAUC) of glucose and postprandial glucose excursion (PPGE) were calculated to evaluate postprandial glucose fluctuations. Results: The postprandial fluctuations of glucose increased gradually with increased proportions of carbohydrate in breakfast in both IGR and NGT subjects. For the MC and HC meals, iAUC, PPGE, postprandial glucose spike (PGS), and mean blood glucose were significantly greater than those in the NGT group (P < 0.05), respectively. The median time to PGS and the time period in which glucose concentrations decreased to baseline after the MC and HC meals in the IGR group were significantly longer than those in the NGT group (P < 0.01), respectively. Compared with the NGT subjects for the HC meal, the IGR subjects consuming the MC meal had greater PGS, range of glucose concentrations, SD, and PPGE (P < 0.05). Conclusions: The proportion of carbohydrate in breakfast contributes to glucose excursions in the NGT and IGR subjects. In the IGR subjects, a HC meal should be avoided and a LC meal should be recommended to prevent development of diabetes.

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“…The measures were mean glucose, SD, MAGE, CONGA, mean of daily difference (MODD) proportion of readings < 70 mg/dl and proportion of readings > 240 mg/dl. The measures were calculated using EasyGV © Version 8.6 (N. R. Hill, University of Oxford, Oxford, UK) 12 or SAS version 9.2.…”

Section: Analysis Of the Gap-fill Processmentioning

confidence: 99%

Minding the Gaps in Continuous Glucose Monitoring: A Method to Repair Gaps to Achieve More Accurate Glucometrics

Fonda

Lewis²,

Vigersky

2013

J Diabetes Sci Technol

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Estimation of glycemic variability requires frequent measures of glucose and is greatly aided by continuous glucose monitoring (CGM); however, under real-world conditions, missing data or “gaps” of ≥ 10 minutes can occur in CGM data, affecting the reliability of certain estimates. Thus, we determined the magnitude of the gap problem as observed in a cohort of patients with type 2 diabetes and demonstrated an approach to fill the gaps. The approach takes the difference between readings before and after a gap and distributes the difference equally across the number of missing readings, as determined by the sensor's setting for reading frequency. The approach is easy to implement, conservative, and improves estimation of variability measures that reference time, namely, mean of daily differences and continuous overlapping net glycemic action.

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Normal Reference Range for Mean Tissue Glucose and Glycemic Variability Derived from Continuous Glucose Monitoring for Subjects Without Diabetes in Different Ethnic Groups

Cited by 311 publications

References 31 publications

Evaluation of Glycemic Variability in Well-Controlled Type 2 Diabetes Mellitus

Evaluation of Glycemic Variability in Well-Controlled Type 2 Diabetes Mellitus

Effects of Different Proportion of Carbohydrate in Breakfast on Postprandial Glucose Excursion in Normal Glucose Tolerance and Impaired Glucose Regulation Subjects

Minding the Gaps in Continuous Glucose Monitoring: A Method to Repair Gaps to Achieve More Accurate Glucometrics

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