Characterizing Glucose Exposure for Individuals with Normal Glucose Tolerance Using Continuous Glucose Monitoring and Ambulatory Glucose Profile Analysis

Mazze, Roger S.; Strock, Ellie S.; Wesley, David; Borgman, Sarah; Morgan, Blaine; Bergenstal, Richard M.; Cuddihy, Robert

doi:10.1089/dia.2007.0293

Cited by 163 publications

(184 citation statements)

References 32 publications

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“…Their use in predicting diabetes will, however, require studies of larger cohorts for longer periods of time in order to establish robust and harmonised cut-off values. The observed normal ranges for various CGM variables were in line with some previous studies [27][28][29], but lower than in others [14,30,31]. Apart from the limited number of participants in most studies, including the current study, these discrepancies might relate to differences in measuring devices, age ranges, ethnicities, dietary restrictions, risk levels for diabetes or criteria to ascertain normoglycaemia.…”

Section: Correlation Of Gv With Beta Cell Function and Glucose Dispossupporting

confidence: 88%

Relationship between glycaemic variability and hyperglycaemic clamp-derived functional variables in (impending) type 1 diabetes

et al. 2015

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Aims/hypothesis We examined whether measures of glycaemic variability (GV), assessed by continuous glucose monitoring (CGM) and self-monitoring of blood glucose (SMBG), can complement or replace measures of beta cell function and insulin action in detecting the progression of preclinical disease to type 1 diabetes. Methods Twenty-two autoantibody-positive (autoAb + ) firstdegree relatives (FDRs) of patients with type 1 diabetes who were themselves at high 5-year risk (50%) for type 1 diabetes underwent CGM, a hyperglycaemic clamp test and OGTT, and were followed for up to 31 months. Clamp variables were used to estimate beta cell function (first-phase [AUC 5-10 min ] and second-phase [AUC 120-150 min ] C-peptide release) combined with insulin resistance (glucose disposal rate; M 120-150 min ). Age-matched healthy volunteers (n =20) and individuals with recent-onset type 1 diabetes (n=9) served as control groups. Results In autoAb + FDRs, M 120-150 min below the 10th percentile (P10) of controls achieved 86% diagnostic efficiency in discriminating between normoglycaemic FDRs and individuals with (impending) dysglycaemia. M 120-150 min outperformed AUC 5-10 min and AUC 120-150 min C-peptide below P10 of controls, which were only 59-68% effective. Among GV variables, CGM above the reference range was better at detecting (impending) dysglycaemia than elevated SMBG (77-82% vs 73% efficiency). Combined CGM measures were equally efficient as M 120-150 min (86%). Daytime GV variables were inversely correlated with clamp variables, and more strongly with M 120-150 min than with AUC 5-10 min or AUC 120-150 min C-peptide. Conclusions/interpretation CGM-derived GV and the glucose disposal rate, reflecting both insulin secretion and action, outperformed SMBG and first-or second-phase AUC C-peptide in identifying FDRs with (impending) dysglycaemia or diabetes. Our results indicate the feasibility of developing minimally invasive CGM-based criteria for close metabolic monitoring and as outcome measures in trials.

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Section: Correlation Of Gv With Beta Cell Function and Glucose Dispossupporting

confidence: 88%

Relationship between glycaemic variability and hyperglycaemic clamp-derived functional variables in (impending) type 1 diabetes

et al. 2015

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“…The AGP was introduced by Mazze et al (1987) for BGM and subsequently applied with further enhancements (display of the smoothed curves for the 10 th and 90 th percentiles) to CGM data by Mazze (2008) and Bergenstal and colleagues (2013). The AGP provides an excellent starting point for a standardized computerized display of BGM and/or CGM data by time of day (173,178,179).…”

Section: Agpmentioning

confidence: 99%

“…The result is a standardized software report that can be displayed graphically. Examples of graphic AGP displays for patients with normal glucose tolerance, T1DM, and T2DM are shown in Table 9 (images 2A-C) (173,181). AGP has been proposed as a standardized method for glucose reporting and analysis (173,178,181).…”

Section: Agpmentioning

confidence: 99%

American Association Of Clinical Endocrinologists And American College Of Endocrinology 2016 Outpatient Glucose Monitoring Consensus Statement

et al. 2016

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“…While treatment in diabetic patients would help to minimise daily marked oscillations in plasma glucose values, in non-diabetic people glucose plasma concentrations are always within a narrow range [34]. This has raised the hypothesis that glucose variability, particularly after meal ingestion, may favour the development of complications in diabetes [35,36].…”

Section: Discussionmentioning

confidence: 99%

Glucose oscillations, more than constant high glucose, induce p53 activation and a metabolic memory in human endothelial cells

et al. 2011

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Aims/hypothesis Damage persists in HUVECs exposed to a constant high glucose concentration long after glucose normalisation, a phenomenon termed 'metabolic memory'. Evaluation of the effects of exposure of HUVECs to oscillating high glucose on the induction of markers of oxidative stress and DNA damage (phospho-γ-histone H2AX and PKCδ) and onset of metabolic memory, and the possible role of the tumour suppressor transcriptional factor p53 is of pivotal interest. Methods HUVECs were incubated for 3 weeks in 5 or 25 mmol/l glucose or oscillating glucose (24 h in 5 mmol/l glucose followed by 24 h in 25 mmol/l glucose) or for 1 week in constant 5 mmol/l glucose after being exposed for 2 weeks to continuous 25 mmol/l high glucose or oscillating glucose. Transcriptional activity of p53 was also evaluated in the first 24 h after high glucose exposure. Results High constant glucose upregulated phospho-γ-histone H2AX and protein kinase C (PKC)δ compared with control. Oscillating glucose was even more effective than both normal and constant high glucose. Both constant and oscillating glucose resulted in a memory effect, which was more pronounced in the oscillating condition. Transcriptional activity of p53 peaked 6 h after glucose exposure, showing a predicted oscillatory behaviour. Conclusions/interpretation Exposure to oscillating glucose was more deleterious than constant high glucose and induced a metabolic memory after glucose normalisation. Hyperactivation of p53 during glucose oscillation might be due to the absence of consistent feedback inhibition during each glucose spike and might account for the worse outcome of this condition.

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Characterizing Glucose Exposure for Individuals with Normal Glucose Tolerance Using Continuous Glucose Monitoring and Ambulatory Glucose Profile Analysis

Cited by 163 publications

References 32 publications

Relationship between glycaemic variability and hyperglycaemic clamp-derived functional variables in (impending) type 1 diabetes

Relationship between glycaemic variability and hyperglycaemic clamp-derived functional variables in (impending) type 1 diabetes

American Association Of Clinical Endocrinologists And American College Of Endocrinology 2016 Outpatient Glucose Monitoring Consensus Statement

Glucose oscillations, more than constant high glucose, induce p53 activation and a metabolic memory in human endothelial cells

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