Measures of Glycemic Variability In Type 1 Diabetes and the Effect of Real-Time Continuous Glucose Monitoring
Author(s)
El-Laboudi, AH
Godsland, I
Johnston, D
Oliver, N
Type
Journal Article
Abstract
Objective: To report the impact of continuous glucose monitoring (CGM) on
glycemic variability (GV) indices, factors predictive of change and to correlate
variability with conventional markers of glycaemia.
Methods: Data from the JDRF study of CGM in participants with type 1 diabetes
were used. Participants were randomised to CGM or self-monitored blood glucose
(SMBG). GV indices at baseline, at 26 weeks in both groups, and at 52 weeks in the
control group were analysed. The associations of demographic and clinical factors
with change in GV indices from baseline to 26 weeks were evaluated.
Results: Baseline data were available for 448 subjects. GV indices were all outside
normative ranges (P<0.001). Inter-correlation between GV indices was common and,
apart from coefficient of variation (CV), low blood glucose index (LBGI) and
percentage of glycemic risk assessment diabetes equation score attributable to
hypoglycaemia (%GRADEhypoglycaemia), all indices correlate positively with HbA1c.
There was strong correlation between time spent in hypoglycaemia, and CV, LBGI
and %GRADEhypoglycaemia, but not with HbA1c. A significant reduction in all GV
indices, except lability index and mean absolute glucose change per unit time (MAG),
was demonstrated in the intervention group at 26 weeks compared with the control
group. Baseline factors predicting a change in GV with CGM include baseline
HbA1c, baseline GV, frequency of daily SMBG and insulin pump use.
Conclusions: CGM reduces most GV indices compared with SMBG in people with
type 1 diabetes. The strong correlation between time spent in hypoglycaemia and CV,
LBGI and %GRADEhypoglycaemia highlights the value of these metrics in assessing
hypoglycaemia as an adjunct to HbA1c in overall assessment of glycaemia.
glycemic variability (GV) indices, factors predictive of change and to correlate
variability with conventional markers of glycaemia.
Methods: Data from the JDRF study of CGM in participants with type 1 diabetes
were used. Participants were randomised to CGM or self-monitored blood glucose
(SMBG). GV indices at baseline, at 26 weeks in both groups, and at 52 weeks in the
control group were analysed. The associations of demographic and clinical factors
with change in GV indices from baseline to 26 weeks were evaluated.
Results: Baseline data were available for 448 subjects. GV indices were all outside
normative ranges (P<0.001). Inter-correlation between GV indices was common and,
apart from coefficient of variation (CV), low blood glucose index (LBGI) and
percentage of glycemic risk assessment diabetes equation score attributable to
hypoglycaemia (%GRADEhypoglycaemia), all indices correlate positively with HbA1c.
There was strong correlation between time spent in hypoglycaemia, and CV, LBGI
and %GRADEhypoglycaemia, but not with HbA1c. A significant reduction in all GV
indices, except lability index and mean absolute glucose change per unit time (MAG),
was demonstrated in the intervention group at 26 weeks compared with the control
group. Baseline factors predicting a change in GV with CGM include baseline
HbA1c, baseline GV, frequency of daily SMBG and insulin pump use.
Conclusions: CGM reduces most GV indices compared with SMBG in people with
type 1 diabetes. The strong correlation between time spent in hypoglycaemia and CV,
LBGI and %GRADEhypoglycaemia highlights the value of these metrics in assessing
hypoglycaemia as an adjunct to HbA1c in overall assessment of glycaemia.
Date Issued
2016-12-01
Date Acceptance
2016-09-28
Citation
Diabetes Technology & Therapeutics, 2016, 18 (12), pp.806-812
ISSN
1557-8593
Publisher
Mary Ann Liebert
Start Page
806
End Page
812
Journal / Book Title
Diabetes Technology & Therapeutics
Volume
18
Issue
12
Copyright Statement
Final publication is available from Mary Ann Liebert, Inc., publishers https://doi.org/10.1089/dia.2016.0146
Subjects
Science & Technology
Life Sciences & Biomedicine
Endocrinology & Metabolism
Glycemic variability
Continuous glucose monitoring
HbA1c
Hypoglycemia
Type 1 diabetes
BLOOD-GLUCOSE
OXIDATIVE STRESS
MEAN AMPLITUDE
RISK
HYPERGLYCEMIA
HYPOGLYCEMIA
INSTABILITY
EXCURSIONS
MORTALITY
MELLITUS
Publication Status
Published