A Novel Approach to Continuous Glucose Analysis Utilizing Glycemic Variation

McDonnell, Ciara M.; Donath, Susan; Vidmar, Suzanna; Werther, G; Cameron, Fergus

doi:10.1089/dia.2005.7.253

Cited by 314 publications

(266 citation statements)

References 25 publications

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“…So, to account for the influence of the method chosen, we calculated various different markers for glucose variability. The mean MODD and CONGA-1 for the 25 patients in our study appeared to be similar to values reported for ten patients with type 1 diabetes in an earlier study [19] (MODD, 4.0 mmol/l vs 4.3 mmol/l and CONGA-1, 2.5 mmol/l, in that and our studies). The mean MAGE in our study was higher than previously reported for 21 patients with type 2 diabetes (8.3 vs 4.2 mmol/l) [11], confirming that glucose variability in patients with type 1 diabetes is higher than in those with type 2 diabetes.…”

Section: Discussionsupporting

confidence: 91%

“…Inter-day glycaemic variability The day-to-day variation of the glucose pattern was calculated with the mean of the daily differences (MODD), which is defined as the mean of the absolute differences between glucose values on day 2 and the corresponding values on day 1, at the same time [19,20].…”

Section: Assessment Of Glycaemic Variabilitymentioning

confidence: 99%

“…We used CONGA-1, indicating intra-day variability based on 1 h time differences, to allow for comparison of our results with those reported in other articles [19,22]. In normal non-diabetic participants, CONGA values vary between 0.4 and 1.2: values above 1.5 indicate glycaemic lability [19]. Furthermore, we calculated the within-hour glucose SD and range, as markers of rapid glucose variability.…”

Section: Assessment Of Glycaemic Variabilitymentioning

confidence: 99%

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Glucose fluctuations and activation of oxidative stress in patients with type 1 diabetes

et al. 2007

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Aims/hypothesis Glucose fluctuations may help predict diabetic complications. We evaluated the relation between glucose variability and oxidative stress in patients with type 1 diabetes. Methods Continuous glucose monitors were inserted subcutaneously in 25 patients. During the measurement, patients collected two 24 h urine samples, while 24 healthy controls collected one 24 h urine sample for determination of 15(S)-8-iso-prostaglandin F 2a PGF 2a ð Þ using HPLC tandem mass spectrometry. Mean of the daily differences (MODD), mean amplitude of glycaemic excursions (MAGE) and continuous overlapping net glycaemic action calculated with n hour time-intervals (CONGA-n) were calculated as markers for glucose variability and correlation with 15 S ð Þ-8-iso-PGF 2α excretion was calculated. Results Median [interquartile range (IQR)] urinary 15 S ð Þ-8-iso-PGF 2α was higher in patients than healthy controls: 161 (140-217) pg/mg creatinine vs 118 (101-146) pg/mg creatinine (p=0.001). Median (IQR) MODD was 3.7 (3.2-5.0) mmol/l, MAGE 7.6 (6.4-9.0) mmol/l and CONGA-1 2.3 (2.1-2.8) mmol/l. Univariate regression did not reveal an association for MODD (r 2 =0.01), MAGE (0.08) or CONGA-1 (0.07) with 15 S ð Þ-8-iso-PGF 2α excretion, nor was an association revealed when corrected for HbA 1c , age, sex and smoking. Spearman correlation coefficients (r) between 15 S ð Þ-8-iso-PGF 2α excretion and MODD, MAGE and CONGA-1 were non-significant: −0.112, −0.381 and −0.177. Conclusions/interpretation We report that there is no relationship between glucose variability and urinary 15 S ð Þ-8-iso-PGF 2α . We also confirm that patients with type 1 diabetes have higher levels of urinary 15 S ð Þ-8-iso-PGF 2α than healthy controls, suggesting that in addition to glucose variability, other factors favouring oxidative stress may exist. We did not see a relation between high glucose variability and elevated levels of oxidative stress in patients with type 1 diabetes.

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Section: Discussionsupporting

confidence: 91%

Section: Assessment Of Glycaemic Variabilitymentioning

confidence: 99%

Section: Assessment Of Glycaemic Variabilitymentioning

confidence: 99%

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Glucose fluctuations and activation of oxidative stress in patients with type 1 diabetes

et al. 2007

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“…The HBGI and LBGI, as described by Clarke and Kovatchev [20], are summary indices reflecting the risk for hyperglycemia and hypoglycemia, respectively. Glycemic variability, which reflects acute glucose fluctuations, was assessed by the standard deviation of the average 24 h glucose concentration (SD) and by continuous overlapping net glycemic action (CONGA) as described by McDonnell et al [21]. With this method, the difference between each glucose reading and the glucose reading n hours previously is calculated.…”

Section: Continuous Glucose Monitoringmentioning

confidence: 99%

Glycemic control during consecutive days with prolonged walking exercise in individuals with type 1 diabetes mellitus

Dijk

Eijsvogels

Nyakayiru

et al. 2016

Diabetes Research and Clinical Practice

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Running title: prolonged physical activity in type 1 diabetes Word count abstract: 239Word count main text: 3182 Tables: 1 Figures: 4 Trial number: NTR4061, www.trialregister.nl ABSTRACT Aims: Despite its general benefits for health, exercise complicates the maintenance of stable blood glucose concentrations in individuals with type 1 diabetes. The aim of the current study was to examine changes in food intake, insulin administration, and 24-h glycemic control in response to consecutive days with prolonged walking exercise (~8 h daily) in individuals with type 1 diabetes. Methods:Ten individuals with type 1 diabetes participating in the worlds' largest walking event were recruited for this observational study. Simultaneous measurements of 24-h glycemic control (continuous glucose monitoring), insulin administration and food intake were performed during a non-walking day (control) and during three subsequent days with prolonged walking exercise (daily distance 40 or 50 km). Results:Despite an increase in daily energy (31±18%; p<0.01) and carbohydrate (82±71g; p<0.01) intake during walking days, subjects lowered their insulin administration by 26±16% relative to the control day (p<0.01). Average 24-h blood glucose concentrations, the prevalence of hyperglycemia (blood glucose >10 mmol/L) and hypoglycemia (blood glucose <3.9 mmol/L) did not differ between the control day and walking days (p>0.05 for all variables). The prolonged walking exercise was associated with a modest increase in glycemic variability compared with the control day (p<0.05). Conclusion:Prolonged walking exercise allows for profound reductions in daily insulin administration in persons with type 1 diabetes, despite large increments in energy and carbohydrate intake. When taking such adjustments into account, prolonged moderate-intensity exercise does not necessarily impair 24-h glycemic control.

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“…2,3 Currently, the statistics and metrics used to reflect glucose dynamics include, but are not limited to, the overall SD from a mean glucose value, percentage of values within, above, or below specified thresholds, area under the curve, mean amplitude of glycemic excursion (MAGE), 4 mean of daily differences (MODD), 5 and continuous overall net glycemic action (CONGA) (for the indicated n hours). 6 Additional metrics include the M-value, 7 average daily risk range, 8 Glycemic Risk Assessment Diabetes Equation scores, 9 and J-index. 10 Inconsistencies, however, can arise from the miscalculation, misinterpretation, and misuse of these metrics in disparate data types and applications.…”

Section: Introductionmentioning

confidence: 99%

Translating Glucose Variability Metrics into the Clinic viaContinuousGlucoseMonitoring: AGraphicalUserInterface forDiabetesEvaluation (CGM-GUIDE^©)

Rawlings

Shi

Yuan

et al. 2011

Diabetes Technology & Therapeutics

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Background: Several metrics of glucose variability have been proposed to date, but an integrated approach that provides a complete and consistent assessment of glycemic variation is missing. As a consequence, and because of the tedious coding necessary during quantification, most investigators and clinicians have not yet adopted the use of multiple glucose variability metrics to evaluate glycemic variation. Methods: We compiled the most extensively used statistical techniques and glucose variability metrics, with adjustable hyper-and hypoglycemic limits and metric parameters, to create a user-friendly Continuous Glucose

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A Novel Approach to Continuous Glucose Analysis Utilizing Glycemic Variation

Cited by 314 publications

References 25 publications

Glucose fluctuations and activation of oxidative stress in patients with type 1 diabetes

Glucose fluctuations and activation of oxidative stress in patients with type 1 diabetes

Glycemic control during consecutive days with prolonged walking exercise in individuals with type 1 diabetes mellitus

Translating Glucose Variability Metrics into the Clinic viaContinuousGlucoseMonitoring: AGraphicalUserInterface forDiabetesEvaluation (CGM-GUIDE^©)

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A Novel Approach to Continuous Glucose Analysis Utilizing Glycemic Variation

Cited by 314 publications

References 25 publications

Glucose fluctuations and activation of oxidative stress in patients with type 1 diabetes

Glucose fluctuations and activation of oxidative stress in patients with type 1 diabetes

Glycemic control during consecutive days with prolonged walking exercise in individuals with type 1 diabetes mellitus

Translating Glucose Variability Metrics into the Clinic viaContinuousGlucoseMonitoring: AGraphicalUserInterface forDiabetesEvaluation (CGM-GUIDE©)

Contact Info

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Translating Glucose Variability Metrics into the Clinic viaContinuousGlucoseMonitoring: AGraphicalUserInterface forDiabetesEvaluation (CGM-GUIDE^©)