Measures of Glycemic Variability In Type 1 Diabetes and the Effect

of Real-Time Continuous Glucose Monitoring.

(GV in Type 1 Diabetes & CGM Effect)

Dr Ahmed H El-Laboudi, PhD, MRCP, Specialist Registrar & Honorary Clinical

Lecturer in Diabetes, Endocrinology & Metabolism.

Dr Ian F Godsland, PhD, Wynn Reader in Human Metabolism

Professor Desmond G Johnston, FMedSci, Professor of Diabetes, Endocrinology &

Metabolism.

Dr Nick S Oliver, FRCP, Professor in Diabetes.

Authors’ Affiliations:

Diabetes, Endocrinology & Metabolism, Imperial College London, London, UK,

Corresponding author: Dr Ahmed El-Laboudi

Address:

Division of Diabetes, Endocrinology & Metabolic Medicine

Imperial College London

St Mary's campus

Norfolk Place

London W2 1PG

Tel: + 44 (0)20 7594 2460

Fax: + 44 (0)20 7594 2432

Email address: a.el-laboudi@imperial.ac.uk

Key words: glycemic variability, continuous glucose monitoring, HbA1c,

hypoglycaemia, type 1 diabetes.

Funding sources: Not aplicable

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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.

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Introduction

Clinical studies have demonstrated the relationship between HbA1c and the risk of

micro- and macro-vascular complications in type 1 diabetes (1, 2) and HbA1c is

routinely used as a treatment target, to monitor glucose control, and for assessment of

clinical response to therapeutic interventions. However, despite robust longitudinal

data and its role in clinical care, HbA1c may not provide complete information on

frequency, duration or magnitude of short-term fluctuations in glycaemia, and its

validity is compromised in people with haemoglobinopathies, renal disease, and iron

deficiency anaemia (3). Furthermore, HbA1c is a poor predictor of the risk of severe

hypoglycaemia (4) and the three fold increase in severe hypoglycaemia with intensive

insulin treatment in the Diabetes Control and Complications Trial was in excess of

that which could be explained by differences in HbA1c between the two groups (5).

Despite evidence supporting a role for glycemic variability (GV) (6-9), its

contribution to diabetes-related complications remains controversial and its clinical

value as an additional measure to HbA1c remains unclear (10, 11). A recent

systematic review has suggested that GV is associated with an increased risk of

microvascular complications in type 2 diabetes only and that the relationship between

GV and vascular complications in type 1 diabetes is less clear (12).

This analysis evaluates several indices of GV in a large cohort with type 1 diabetes

with reference to ranges previously defined in people without diabetes and explores

the primary hypothesis that the use of real time continuous glucose monitoring

(CGM) reduces glycemic variability. We have also explored the extent to which

different GV measures positively correlate with HbA1c and time spent in

hypoglycaemia, and whether baseline factors predict GV. Our aim is to identify GV

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measures that could be a useful adjunct to HbA1c for a better assessment of the

overall glycemic status of people with type 1 diabetes.

Method

Data source

Data from the JDRF CGM study were used (13-15). The publicly accessible data

were obtained from the Jaeb Center for Health Research

(http://diabetes.jaeb.org/Dataset.aspx) and were processed using Matlab (Mathworks).

The JDRF study protocol and clinical characteristics of enrolled subjects have been

previously described in detail (13-15). In summary, the study is a six-month

randomised, parallel group, efficacy and safety study designed to evaluate the impact

of CGM on glycemic control in children and adults with type 1 diabetes. Participants

underwent blinded CGM for one week before randomisation to either standard self-

monitored blood glucose (SMBG) (control) or use of unblinded CGM as a

supplement to SMBG (intervention). All trial participants were provided with written

instructions on how to use the data provided by CGM and capillary blood glucose

meters to make real-time adjustments to insulin, and on the use of computer software

to retrospectively review the glucose data to alter future insulin doses. Patients using

CGM received additional instructions for modifying insulin doses and treatment of

hypoglycemia on the basis of the glucose trend. Changes were made to diabetes

management, as needed, for all participants during scheduled contacts. The

randomised trial was followed by a six-month extension study in which unblinded

CGM was continued in the CGM group but with unblinding to CGM data in the

control group. CGM profiles were obtained at 26 weeks post-randomisation from both

control and intervention groups.

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Measures of glycemic variability:

We computed 13 measures of GV using EasyGV (v8.8.2.R2) software. Evaluated GV

measures included standard deviation (SD), coefficient of variation (CV), mean

amplitude of glycemic excursions (MAGE), continuous overall net glycemic action

(CONGA), mean of daily differences (MODD), lability index (LI), and mean absolute

glucose change per unit time (MAG), glycemic risk assessment diabetes equation

(GRADE), M-value, average daily risk range (ADRR), J-Index, low blood glucose

index (LBGI), and high blood glucose index (HBGI). GRADE score is also reported

as %GRADEhypoglycaemia, %GRADEeuglycaemia and %GRADEhyperglycaemia representing

percentages of GRADE scores attributable to glucose values <3.9 mmol/L, between

3.9 – 7.8 mmol/L and >7.8 mmol/L, respectively. EasyGV is a Microsoft Excel

workbook that has a number of options to define the sampling interval, CONGA

length, LI interval, reference value of M-value and whether SMBG or CGM is used

for MAGE calculation (16). A description of the GV measures, formulae used for

their calculations and a critical review of their limitations is described elsewhere (6,

17).

Analysis design

GV indices were assessed in comparison to previously published GV reference ranges

in people without diabetes (6). To evaluate the effect of CGM on GV, between-group

differences in GV indices were evaluated at 26 weeks and glucose profile data

collected at 52 weeks from subjects in the control group who crossed-over to CGM

(unblinded control) were compared with data collected at 26 weeks in the same group

(blinded control). The relationship between baseline GV indices and HbA1c was

explored. Finally, baseline predictors of change in GV indices from baseline to 26

weeks in the CGM group were evaluated.

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Statistical analysis:

Data were examined for normality; non-normally distributed variables were

logarithmically transformed with the use of geometric mean, SD and 95% CI for

descriptive statistics of baseline data (n=448).

An unpaired t-test was used to compare the effect of CGM on the change of GV

indices from baseline to 26 weeks in the control and intervention arms whilst a paired

t-test was used for analysis of within-group differences in each of those arms. To

study the effect on GV of unmasking CGM in the control group, a paired t-test was

used to compare GV indices in the 207 participants in the control group with

unblinded CGM at 52 weeks with their blinded GV indices at 26 weeks. Spearman

correlation was used to examine the relationship between HbA1c and GV indices at

baseline. The associations of baseline demographic and clinical factors with change in

GV indices from baseline to 26 weeks were evaluated in the CGM group (n=231)

using regression analysis. The analysis was constructed using the following predictor

variables: age, gender, race, education level of care giver, Insulin modality (pump or

multiple daily injections (MDI)), frequency of daily self-reported blood glucose

monitoring, occurrence of one or more episodes of severe hypoglycaemia in last six

months, diabetes duration, baseline HbA1c, and baseline GV. The analysis was

performed with change in GV measures and continuous variables expressed as z-

scores. Categorical variables were included as dummy variables. Baseline factors with

P≤0.2 in the univariate analysis were carried forward to multivariable analysis. Data

are presented as means (SD), unless otherwise stated. Statistical tests were two-tailed

and for descriptive and exploratory analyses, a significance level of P<0.05 was

adopted. Where significance levels were consistently <0.001, t-values were reported

to aid evaluation of relative differences in magnitude and scatter of the differences

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observed. For exploring our primary hypothesis that the use of real time continuous

glucose monitoring (CGM) reduces glycemic variability, 13 different measures of

glycaemic variability were tested. Therefore, to test our primary hypothesis, we have

adopted a significance level of p<0.004 (i.e. 0.05/13). Statistical tests were

performed using SPSS 21.0 for Mac (SPSS Inc., Chicago, IL).

Results:

Participants

CGM profile data were available for 448 subjects at baseline (54.9% women, 94.4%

white race), following exclusion of three subjects due to missing HbA1c data at

baseline. Mean age was 25.1 years (SD 15.8) and mean diabetes duration 13.6 years

(SD 11.7). 231 subjects were randomised to the CGM group and 217 subjects to the

control group. At 26 weeks, CGM profile data were available for all the 231 subjects

in the CGM group and 214 subjects in the control group (blinded CGM). At 52

weeks, data were available for 207 subjects in the control group (unblinded CGM).

Baseline characteristics were similar between the two groups (table 1). There was no

statistically significant difference in HbA1c and the evaluated GV measures between

the two groups at baseline.

Measures of glycemic variability and glycemic control at baseline:

Reference means and 95 percent confidence intervals for several measures of GV

have been previously described by analysing CGM profiles of 70 subjects without

diabetes (6). As shown in table 2, the mean values of GV indices at baseline in the

448 subjects with type 1 diabetes were all appreciably higher than the reference

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means, with complete non-overlap of 95 percent confidence intervals throughout

(P<0.001).

Intercorrelation between measures of GV and glycemic control at baseline and

correlation with HbA1c:

Statistically significant inter-correlation between measures of GV and HbA1c was

common. The GV index, ADRR showed the strongest inter-correlation with other

measures with r>0.7 in 9 out of 13 inter-correlations. By contrast, CV, LBGI and

%GRADEhypoglycaemia showed the weakest inter-correlation. Similarly, all these

measures, apart from CV, LBGI and %GRADEhypoglycaemia, correlated significantly,

but moderately (r 0.35 – 0.66), with HbA1c as shown in table 3. The relationship

between percent of time spent in hypoglycaemia (glucose level<3.9 mmol/L, <3.3

mmol/L and <2.8 mmol/L), CV, LBGI, %GRADEhypoglycaemia, and HbA1c was

analysed using Spearman correlation. CV, LBGI and %GRADEhypoglycaemia correlated

strongly with time spent in hypoglycaemia (<3.9 mmol/L, 3.3 mmol/L and 2.8

mmol/L) (r>0.88, P<0.001). No relationship between HbA1c and time spent in

hypoglycaemia was seen (table 4).

Effect of CGM on GV measures in the intervention group:

Analysis of between group differences demonstrated a significant difference between

the intervention and control group in all measures of GV, with the exception of CV,

LI and MAG (table 5). At 26 weeks, there was a significant reduction in all measures

of GV, with the exception of LI and MAG, from baseline in the intervention group.

There was a significant reduction in HbA1c in the intervention group from

58mmol/mol (7.4%) to 55mmol/mol (7.2%) (P<0.001). Correspondingly, the

achieved relative reductions in M-value, LBGI, and GRADE were 25.7%, 24.9%, and

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16.5%, respectively (P<0.001) (table 5). In contrast, there was no statistically

significant reduction in any of the measures of GV and glycemic control in the control

group at 26 weeks compared to baseline.

Effect of unmasking of CGM in the control group:

The effect of unmasking CGM in the control group at 26 weeks was evaluated by

comparing GV measures at 52 weeks to those at 26 weeks (immediately prior to

unmasking CGM). Despite the non-significant change in HbA1c from 26 to 52 weeks,

there was a significant reduction in all measures of GV, with the exception of LI and

MAG, from baseline (Supplementary table 1).

Factors predictive of response in the CGM group:

Univariate analysis showed that baseline GV was a significant predictor of change in

all measures of GV at 26 weeks in the intervention group, with higher baseline GV

associated with greater reduction in GV at 26 weeks. Similarly, baseline HbA1c was

also a significant predictor of change in GV in the majority of the evaluated GV

measures (all except LBGI, CV, LI, and MAG), with higher baseline HbA1c

associated with greater reduction in GV measures at 26 weeks. However,

multivariable analysis showed that higher HbA1c at baseline was associated with less

of a reduction of GV measures at 26 weeks (Supplementary table 2). In the

multivariable analysis, treatment with insulin pump predicted a reduction in LBGI

and M-value, while frequent use of SMBG predicted a reduction in LI. Other

variables, including education level of caregiver, did not predict change in any of the

evaluated GV measures.

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Discussion:

The increased availability of CGM provides a wealth of data and enables assessment

of GV. Several measures of GV have been described (18-23). These can be broadly

subdivided into measures based on glucose distribution (e.g. SD, CV, MAGE,

CONGA, MODD, LI, and MAG), and measures based on risk and quality of glycemic

control that are also sensitive to GV (e.g. GRADE, M-value, ADRR, J-Index, LBGI,

and HBGI) (24). There are several challenges related to GV measurements and

interpretation (24), including correlation with mean glycaemia, making it difficult to

evaluate if the change in GV measures following an intervention is related to change

in mean glycaemia, GV or both. These challenges are further complicated by the

plethora of GV measures proposed in the literature with the lack of a “gold standard”

measure (18-23).

The data reported here are from the largest available CGM dataset, with evaluation of

measures of GV, the effect of real-time CGM on these measures and predictors of

changes to variability. The analysis demonstrates the magnitude of variability in

people with type 1 diabetes and the impact that real-time CGM has on GV measures.

Measures of dispersion (SD and CV), glucose risk indices (LGBI, HGBI, ADRR,

GRADE), measures of unknown significance (CONGA, MAGE, J-index, M-

value), and day to day change (MODD) all fell significantly in the intervention group

over 26 weeks, and (with the exception of CV) fell significantly more than in the

control group. Moreover, over a further 26 weeks, in the control group, unblinding

resulted in significant falls in these measures of GV. However, two measures of

variability over time (LI and MAG) were entirely unchanged. This may suggest that

the use of real-time glucose trends and alarms addresses glucose dispersion and risk

while underlying temporal glucose variability remains unchanged. It could also reflect

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the impact of interventions based on CGM use such as more frequent insulin

correction boluses or carbohydrate ingestion, resulting in more high frequency (rapid)

glucose fluctuations that LI and MAG are more sensitive to. The impact of CGM on

GV measures is consistent with previous studies (25-27). However, these studies have

been for a shorter duration (25, 26) or in limited numbers of participants (27).

Similarly, the correlation between GV measures reported in this analysis confirms

previous findings. A correlation analysis of GV measures in 48 subjects with type 1

diabetes showed similar relationships to our study between MAGE and other

measures of GV, with a significant correlation of 0.74 between MAGE and SD and a

correlation of 0.58 between SD and CONGA1 (28). Rodbard also reported a strong

correlation between SD and MAGE (r=0.89), SD and CONGA1 (r= 0.71), SD and

MODD (r=0.81), and between MAGE and MODD (r=0.74) (29). Compared to our

analysis, other studies also showed similar relationships between HbA1c and HBGI

(r=0.63) (30) and between HbA1c and MAGE (r=0.49) (31) . In another retrospective

analysis of 72-hour CGM data in 815 outpatients (48 with type 1 diabetes and 767

with type 2 diabetes), correlation between MAG and SD was stronger (r= 0.88

compared to 0.66 in our analysis), as was the relationship between MAG and other

GV measures (32).

Common inter-correlation between evaluated GV measures are demonstrated

alongside a moderate correlation between most of the evaluated GV measures and

HbA1c (table 3). This suggests that these measures convey similar information,

reflecting mean glycaemia, as well as information on glucose variability.

LBGI and %GRADEhypoglycaemia, which are sensitive to hypoglycaemia alone,

correlated poorly with other GV measures and not at all with HbA1c, supporting the

hypothesis that HbA1c has a limited role in reflecting or predicting the risk of

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hypoglycaemia (20) and suggesting that these two metrics may offer additional

information when assessing CGM data. The relationship between time spent in

hypoglycaemia, LBGI and %GRADEhypoglycaemia may suggest that direct assessment of

time spent in hypoglycaemia, routinely reported from CGM data, is sufficient.

However, these two metrics provide a continuous scale for hypoglycaemia with a

different risk score assigned to glucose values rather than the categorical information

provided by time spent below a glucose threshold which may not consider severity,

with all values assigned an equal weight (33). Similarly, CV correlated with time

spent in hypoglycaemia (table 4) and not with HbA1c or mean glucose (table 3). CV

has been previously proposed as the best parameter to characterise GV since it is

corrected for the mean and avoids the dependency of SD and other measures of GV

on mean glucose or HbA1c (34). The weaker impact of CGM on CV demonstrated in

the intervention group supports the role of CV in providing distinct information from

the other GV measures, and from HbA1c.

There was a complex of associations between HbA1c and GV at baseline and

subsequent change in GV in the CGM group. In univariate analyses higher baseline

HbA1c was associated with greater GV (except for CV and LGBI), and higher

baseline HbA1c and GV were associated with greater reductions in GV over 26

weeks. However, multivariable analysis showed that the association between higher

baseline HbA1c and greater reduction in GV was secondary to the association

between baseline GV and reduction in GV. In fact, when variation in baseline GV

was taken into account, the association between baseline HbA1c and subsequent

reduction in GV changed direction: higher baseline HbA1c was associated with an

increase in the majority of GV measures over 26 weeks, indicating that among those

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with high baseline HbA1c, the subsequent reduction in GV was not as great as would

have been expected from their baseline GV.

Limitations of this analysis include the reliability of some of the evaluated GV

measures. ADRR requires data collected typically over one month with a minimum of

14 days and with typical frequency of 3-5 glucose measurements per day, whereas

CGM data used to compute ADRR in control group at 26 weeks in this analysis were

collected for only one week. Another limitation is the possible different outcomes

when calculating GV measures using different automated GV calculators. Several

methods and automated calculators have been described for calculating MAGE (35-

38). EasyGV uses a modified method (MAGE-CGM) for MAGE calculation. The

MAGE-CGM formula selects a peak or trough based on direction of change (rising or

falling) of the preceding and succeeding data points. It also contains a 15-min lag

window for the direction of change based on the lag between interstitial fluid glucose

measurement and plasma glucose concentrations. It also contains an algorithm that

eliminates short-term fluctuations related to sensor inaccuracies (6). The correlation

between MAGE calculated using EasyGV and MAGE calculated based on Fritzsche

or Baghurst's methods was reported to be of 0.87 (39). Comparison between EasyGV

(v8.8.2.R2) and a validated automated GV calculator showed a correlation of 0.76 in

calculating MODD but poor correlation in calculating CONGA1(40). EasyGV has

now been updated to address an issue with the MODD calculation but the reason for

discrepancies in CONGA1 between calculators remain unlcear and is the subject of

ongoing investigation. It is also important to note that the normative range of GV

measures referred to in our manuscript was based on the analysis of 55,000 data

points points from only 72 hours of CGM obtained from 70 subjects with fasting

plasma glucose of <120mg/dL. Although this excludes subjects with diabetes, it is

Page 13 of 27

possible that some subjects had impaired glucose tolerance. This could explain why

SD in this cohort, reported to be 1.5, is larger compared to SD reported in other

studies in subjects with normal glucose tolerance (ranged 0.5 – 0.9). However, other

GV measures were similar to those reported from analysis of other datasets (28, 31,

41-43). The accuracy of the CGM systems used in the study, particularly in the

hypoglycemic range, is a further limitation and may affect GV measurements. Since

the CGM study was conducted, the accuracy of available systems has improved

significantly and the large volume of data analysed here mitigates much of this

problem.

Conclusion:

In sumary, we report the largest study to date of the long-term impact of continuous

glucose monitoring on glycemic variability in people with type 1 diabetes. There was

a significant reduction in the evaluated measures of GV with CGM use, with the

exception of LI and MAG, suggesting that CGM reduces mean glycaemia, glucose

dispersion and risk but not glucose fluctuation. The strong correlation between time

spent in hypoglycaemia, CV, LBGI and %GRADEhypoglycaemia, but not with HbA1c,

highlights the value of these metrics in assessing hypoglycaemia and as a useful

adjunct to HbA1c in overall assessment of glycemic status in people with type 1

diabetes. A large-scale longitudinal intervention study is required to evaluate the

HbA1c-independent role of GV in development of diabetes-related vascular

complications and this will additionally require the definition of an agreed “gold-

standard” GV measure.

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Author contributions:

A.EL. analysed the data and wrote the manuscript; I.G. supervised data analysis and

reviewed/edited the manuscript; D.J. contributed to the discussion and

reviewed/edited the manuscript; N.O. processed original glucose profile data and

reviewed/edited the manuscript.

Author Disclosure Statement:

N.O. has received honoraria for consultancy from Abbott Diabetes Care and Roche.

References:

1. UKPDS. Intensive blood-glucose control with sulphonylureas or insulin

compared with conventional treatment and risk of complications in patients with

type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group.

Lancet. 1998 Sep 12;352(9131):837-53. PubMed PMID: 9742976.

2. DCCT. The effect of intensive treatment of diabetes on the development

and progression of long-term complications in insulin-dependent diabetes

mellitus. The Diabetes Control and Complications Trial Research Group. N Engl J

Med. 1993 Sep 30;329(14):977-86. PubMed PMID: 8366922.

3. Coban E, Ozdogan M, Timuragaoglu A. Effect of iron deficiency anemia on

the levels of hemoglobin A1c in nondiabetic patients. Acta haematologica.

2004;112(3):126-8. PubMed PMID: 15345893.

4. Miller KM, Foster NC, Beck RW, Bergenstal RM, DuBose SN, DiMeglio LA,

et al. Current State of Type 1 Diabetes Treatment in the U.S.: Updated Data From

the T1D Exchange Clinic Registry. Diabetes Care. 2015 Jun;38(6):971-8. PubMed

PMID: 25998289.

5. Kilpatrick ES, Rigby AS, Goode K, Atkin SL. Relating mean blood glucose

and glucose variability to the risk of multiple episodes of hypoglycaemia in type

1 diabetes. Diabetologia. 2007 Dec;50(12):2553-61. PubMed PMID: 17882397.

6. Hill NR, Oliver NS, Choudhary P, Levy JC, Hindmarsh P, Matthews DR.

Normal reference range for mean tissue glucose and glycemic variability derived

from continuous glucose monitoring for subjects without diabetes in different

ethnic groups. Diabetes Technol Ther. 2011 Sep;13(9):921-8. PubMed PMID:

21714681. Pubmed Central PMCID: 3160264.

7. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, et al. Activation of

oxidative stress by acute glucose fluctuations compared with sustained chronic

hyperglycemia in patients with type 2 diabetes. JAMA. 2006 Apr

12;295(14):1681-7. PubMed PMID: 16609090.

8. Ceriello A, Esposito K, Piconi L, Ihnat MA, Thorpe JE, Testa R, et al.

Oscillating glucose is more deleterious to endothelial function and oxidative

Page 15 of 27

stress than mean glucose in normal and type 2 diabetic patients. Diabetes. 2008

May;57(5):1349-54. PubMed PMID: 18299315.

9. Krinsley JS. Glycemic variability: a strong independent predictor of

mortality in critically ill patients. Critical care medicine. 2008 Nov;36(11):3008-

13. PubMed PMID: 18824908.

10. Siegelaar SE, Barwari T, Kulik W, Hoekstra JB, DeVries JH. No relevant

relationship between glucose variability and oxidative stress in well-regulated

type 2 diabetes patients. J Diabetes Sci Technol. 2011 Jan;5(1):86-92. PubMed

PMID: 21303629. Pubmed Central PMCID: 3045241.

11. Kilpatrick ES, Rigby AS, Atkin SL. The effect of glucose variability on the

risk of microvascular complications in type 1 diabetes. Diabetes Care. 2006

Jul;29(7):1486-90. PubMed PMID: 16801566.

12. Smith-Palmer J, Brandle M, Trevisan R, Orsini Federici M, Liabat S,

Valentine W. Assessment of the association between glycemic variability and

diabetes-related complications in type 1 and type 2 diabetes. Diabetes Res Clin

Pract. 2014 Sep;105(3):273-84. PubMed PMID: 25023992.

13. Group JCS. JDRF randomized clinical trial to assess the efficacy of real-

time continuous glucose monitoring in the management of type 1 diabetes:

research design and methods. Diabetes Technol Ther. 2008 Aug;10(4):310-21.

PubMed PMID: 18828243. Epub 2008/10/03. eng.

14. Tamborlane WV, Beck RW, Bode BW, Buckingham B, Chase HP, Clemons

R, et al. Continuous glucose monitoring and intensive treatment of type 1

diabetes. N Engl J Med. 2008 Oct 2;359(14):1464-76. PubMed PMID: 18779236.

Epub 2008/09/10. eng.

15. Beck RW, Hirsch IB, Laffel L, Tamborlane WV, Bode BW, Buckingham B, et

al. The effect of continuous glucose monitoring in well-controlled type 1

diabetes. Diabetes Care. 2009 Aug;32(8):1378-83. PubMed PMID: 19429875.

Pubmed Central PMCID: 2713649.

16. EasyGV software

(http://www.phc.ox.ac.uk/research/diabetes/software/easygv/) - last accessed

22/08/2016.

17. Service FJ. Glucose variability. Diabetes. 2013 May;62(5):1398-404.

PubMed PMID: 23613565. Pubmed Central PMCID: 3636651.

18. Hill NR, Thompson B, Bruce J, Matthews DR, Hindmarsh P. Glycaemic risk

assessment in children and young people with Type 1 diabetes mellitus. Diabet

Med. 2009 Jul;26(7):740-3. PubMed PMID: 19573125.

19. Service FJ, Molnar GD, Rosevear JW, Ackerman E, Gatewood LC, Taylor

WF. Mean amplitude of glycemic excursions, a measure of diabetic instability.

Diabetes. 1970 Sep;19(9):644-55. PubMed PMID: 5469118.

20. Kovatchev BP, Otto E, Cox D, Gonder-Frederick L, Clarke W. Evaluation of

a new measure of blood glucose variability in diabetes. Diabetes Care. 2006

Nov;29(11):2433-8. PubMed PMID: 17065680.

21. Ryan EA, Shandro T, Green K, Paty BW, Senior PA, Bigam D, et al.

Assessment of the severity of hypoglycemia and glycemic lability in type 1

diabetic subjects undergoing islet transplantation. Diabetes. 2004

Apr;53(4):955-62. PubMed PMID: 15047610.

22. Molnar GD, Taylor WF, Ho MM. Day-to-day variation of continuously

monitored glycaemia: a further measure of diabetic instability. Diabetologia.

1972 Nov;8(5):342-8. PubMed PMID: 4641791.

Page 16 of 27

23. Hermanides J, Vriesendorp TM, Bosman RJ, Zandstra DF, Hoekstra JB,

Devries JH. Glucose variability is associated with intensive care unit mortality.

Critical care medicine. 2010 Mar;38(3):838-42. PubMed PMID: 20035218.

24. Rodbard D. The challenges of measuring glycemic variability. J Diabetes

Sci Technol. 2012 May;6(3):712-5. PubMed PMID: 22768904. Pubmed Central

PMCID: 3440043.

25. Danne T, de Valk HW, Kracht T, Walte K, Geldmacher R, Solter L, et al.

Reducing glycaemic variability in type 1 diabetes self-management with a

continuous glucose monitoring system based on wired enzyme technology.

Diabetologia. 2009 Aug;52(8):1496-503. PubMed PMID: 19526212.

26. Rodbard D, Bailey T, Jovanovic L, Zisser H, Kaplan R, Garg SK. Improved

quality of glycemic control and reduced glycemic variability with use of

continuous glucose monitoring. Diabetes Technol Ther. 2009 Nov;11(11):717-

23. PubMed PMID: 19905888.

27. Tumminia A, Crimi S, Sciacca L, Buscema M, Frittitta L, Squatrito S, et al.

Efficacy of real-time continuous glucose monitoring on glycaemic control and

glucose variability in type 1 diabetic patients treated with either insulin pumps

or multiple insulin injection therapy: a randomized controlled crossover trial.

Diabetes/metabolism research and reviews. 2015 Jan;31(1):61-8. PubMed PMID:

24816997.

28. Cameron FJ, Donath SM, Baghurst PA. Measuring glycaemic variation.

Current diabetes reviews. 2010 Jan;6(1):17-26. PubMed PMID: 20214595.

29. Rodbard D. New and improved methods to characterize glycemic

variability using continuous glucose monitoring. Diabetes Technol Ther. 2009

Sep;11(9):551-65. PubMed PMID: 19764834.

30. Kovatchev BP, Cox DJ, Gonder-Frederick LA, Clarke W. Symmetrization of

the blood glucose measurement scale and its applications. Diabetes Care. 1997

Nov;20(11):1655-8. PubMed PMID: 9353603.

31. Borg R, Kuenen JC, Carstensen B, Zheng H, Nathan DM, Heine RJ, et al.

Associations between features of glucose exposure and A1C: the A1C-Derived

Average Glucose (ADAG) study. Diabetes. 2010 Jul;59(7):1585-90. PubMed

PMID: 20424232. Pubmed Central PMCID: 2889756.

32. Kohnert KD, Heinke P, Fritzsche G, Vogt L, Augstein P, Salzsieder E.

Evaluation of the mean absolute glucose change as a measure of glycemic

variability using continuous glucose monitoring data. Diabetes Technol Ther.

2013 Jun;15(6):448-54. PubMed PMID: 23550553.

33. Rodbard D. Glycemic variability: measurement and utility in clinical

medicine and research--one viewpoint. Diabetes Technol Ther. 2011

Nov;13(11):1077-80. PubMed PMID: 21815751.

34. Rodbard D. Hypo- and hyperglycemia in relation to the mean, standard

deviation, coefficient of variation, and nature of the glucose distribution.

Diabetes Technol Ther. 2012 Oct;14(10):868-76. PubMed PMID: 22953755.

35. Zaccardi F, Stefano PD, Busetto E, Federici MO, Manto A, Infusino F, et al.

Group of signs: a new method to evaluate glycemic variability. J Diabetes Sci

Technol. 2008 Nov;2(6):1061-5. PubMed PMID: 19885294. Pubmed Central

PMCID: 2769822.

36. Baghurst PA. Calculating the mean amplitude of glycemic excursion from

continuous glucose monitoring data: an automated algorithm. Diabetes Technol

Ther. 2011 Mar;13(3):296-302. PubMed PMID: 21291334.

Page 17 of 27

37. Fritzsche G, Kohnert KD, Heinke P, Vogt L, Salzsieder E. The use of a

computer program to calculate the mean amplitude of glycemic excursions.

Diabetes Technol Ther. 2011 Mar;13(3):319-25. PubMed PMID: 21291337.

38. Czerwoniuk D, Fendler W, Walenciak L, Mlynarski W. GlyCulator: a

glycemic variability calculation tool for continuous glucose monitoring data. J

Diabetes Sci Technol. 2011 Mar;5(2):447-51. PubMed PMID: 21527118. Pubmed

Central PMCID: 3125941.

39. Sechterberger MK, Luijf YM, Devries JH. Poor agreement of computerized

calculators for mean amplitude of glycemic excursions. Diabetes Technol Ther.

2014 Feb;16(2):72-5. PubMed PMID: 24191760.

40. El-Laboudi A, Oliver N. Agreement Between Glycaemic Variability

Calculators: Another Limitation In Measuring Glycaemic Variability. Diabetes

Technology & Therapeutics. 2016 2016/02/01;18(S1):A-90.

41. Wang YM, Zhao LH, Su JB, Qiao HF, Wang XH, Xu F, et al. Glycemic

variability in normal glucose tolerance women with the previous gestational

diabetes mellitus. Diabetology & metabolic syndrome. 2015;7:82. PubMed PMID:

26405461. Pubmed Central PMCID: 4581077.

42. Hanefeld M, Sulk S, Helbig M, Thomas A, Kohler C. Differences in Glycemic

Variability Between Normoglycemic and Prediabetic Subjects. J Diabetes Sci

Technol. 2014 Mar 2;8(2):286-90. PubMed PMID: 24876580. Pubmed Central

PMCID: 4455406.

43. Zhou J, Li H, Ran X, Yang W, Li Q, Peng Y, et al. Establishment of normal

reference ranges for glycemic variability in Chinese subjects using continuous

glucose monitoring. Medical science monitor : international medical journal of

experimental and clinical research. 2011 Jan;17(1):CR9-13. PubMed PMID:

21169911. Pubmed Central PMCID: 3524682.

Page 18 of 27

Tables:

Control CGM

Number 217 231

Female sex – no.(%) 124 (57.1%) 122 (52.8%)

Age – no. (%)

8 – 14 years

15 – 24 years

≥ 25 years

68 (31.35%)

70 (32.25%)

79 (36.4%)

74 (32%)

71 (30.7%)

86 (37.2%)

White race – no. (%) 205 (94.4%) 218 (94.3%)

Duration of diabetes – years 9.2 (5.3, 17.5) 9.6 (5.6, 18.2)

Insulin administration – no. (%)

Pump

MDI

175 (80.6%)

42 (19.4%)

190 (82.2%)

41 (17.8%)

HbA1c - mmol/mol (%)

< 53 (7.0) - no. (%)

53 – 64 (7.0-8.0) - no. (%)

65-74 (8.1 – 8.9) - no. (%)

≥ 75 (9.0) - no. (%)

61 (28.1%)

109 (50.2%)

39 (18%)

8 (3.7%)

67 (29%)

109 (47.2%)

44 (19%)

11 (4.8%)

≥ one episode of severe hypoglycaemia in last 6

months – no. (%)

17 (7.8%) 21 (9.1%)

Self-reported daily SMBG – no./day 6.26 (2.73) 6.11 (2.42)

College graduate (patient or primary care giver) –

no. (%)

194 (89.4%) 197 (85.3%)

Table 1: Baseline characteristics of participants in the control and intervention groups. [Data are presented as

number (%), mean (SD) or median (IQR)].

Page 19 of 27

GV measure Reference

(mean, 95%CI)

Type 1 DM

(mean, 95%CI)

Mean 5.10 (4.98, 5.22) 9.27 (9.10, 9.43)

SD 1.50 (1.34, 1.66) 3.73 (3.64, 3.82)

CV Calculated as 29.4% 40.43% (39.66, 41.19)

CONGA1 4.60 (4.48, 4.72) 8.29 (8.13, 8.44)

LI 0.40 (-0.12, 0.92) 6.17 (5.84, 6.51)

J index 14.30 (13.20, 15.40) 52.72 (50.83, 54.67)

LBGI 3.10 (2.65, 3.55) 4.10 (3.79, 4.43)

HBGI 0.20 (-0.69, 1.09) 12.03 (11.53, 12.53)

GRADE 0.40 (-0.09, 0.89) 8.37 (7.98, 8.76)

MODD 0.80 (0.47, 1.13) 4.06 (3.95, 4.16)

MAGE-CGM 1.40 (1.24, 1.56) 6.76 (6.54, 6.98)

ADRR 0.40 (-0.56, 1.36) 33.35 (32.21, 34.48)

M-value 4.70 (3.81, 5.59) 14.70 (13.83, 15.62)

MAG 1.30 (1.21, 1.39) 2.70 (2.61, 2.79)

Table 2: Descriptive statistics (mean, 95% CI) for GV indices in 70 subjects without diabetes representing normal

reference range (6) and in the 448 subjects with type 1 diabetes at baseline. Difference between measures of GV

and glycemic control in the two groups is statistically significant (P<0.001). Measures are expressed in mmol/L.

Page 20 of 27

Mean SD CV GRADE GRADE

%Hypo

GRADE

%Eugly

GRADE

%Hyper CONGA LI JINDEX LBGI HBGI MODD MAGE ADDR M-value MAG

HbA1c .679** .514** 0.06 .655** -.266** -.535** .424** .664** .402** .676** -0.07 .659** .483** .446** .613** .548** .345**

Mean 1.00 .648** -0.04 .922** -.566** -.769** .781** .976** .515** .956** -.192** .890** .620** .547** .843** .725** .431**

SD

1.00 .701** .674** .286** -.603** -0.06 .677** .768** .833** .378** .888** .936** .710** .844** .892** .658**

CV

1.00 0.06 .746** -.114* -.646** 0.01 .536** .224** .695** .356** .649** .448** .336** .520** .472**

GRADE

1.00 -.290** -.926** .546** .897** .577** .911** 0.04 .892** .654** .608** .826** .820** .492**

GRADE

%Hypo 1.00 0.09 -.909** -.515** .283** -.297** .940** -.133* .317** .207** -.145** .141** .317**

GRADE

%Eugly 1.00 -.385** -.765** -.420** -.831** -.136* -.879** -.554** -.495** -.724** -.861** -.318**

GRADE

%Hyper 1.00 .728** -.121* .549** -.776** .410** -0.09 -0.02 .358** .129* -.177**

CONGA

1.00 .423** .954** -.173** .900** .636** .511** .803** .741** .325**

LI

1.00 .646** .358** .680** .741** .728** .806** .710** .926**

JINDEX

1.00 0.00 .975** .788** .654** .921** .860** .546**

LBGI

1.00 .120* .389** .310** 0.09 .362** .360**

HBGI

1.00 .841** .691** .913** .917** .570**

MODD

1.00 .683** .798** .836** .640**

MAGE

1.00 .721** .701** .623**

ADDR

1.00 .844** .727**

M-value

1.00 .615**

MAG 1.00

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Table 3: Correlation matrix examining the relationship between various measures of glycemic variability and quality of glycemic control in 448 subjects with type 1 diabetes at baseline

(data on %GRADEhypoglycaemia, %GRADEeuglycaemia and %GRADEhyperglycaemia were only available on 340 subjects at baseline).

Page 21 of 27

HbA1c CV LBGI

GRADE

%Hypo

≤2.8

mmol/L

≤3.3

mmol/L

≤3.9

mmol/L

HbA1c 1.00 0.06 -0.07 -.266** -.101* -.213** -.285**

CV

1.00 .695** .746** .639** .681** .693**

LBGI

1.00 .940** .894** .923** .889**

GRADE %Hypo

1.00 .923** .980** .977**

≤2.8 mmol/L

1.00 .888** .822**

≤3.3 mmol/L

1.00 .965**

≤3.9 mmol/L

1.00

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Table 4: Correlation matrix examining the relationship between time spent in various glucose ranges, HbA1c,

SD, CV LBGI and %GRADEhypoglycaemia in 448 subjects with type 1 diabetes at baseline (data on

%GRADEhypoglycaemia were only available on 340 subjects at baseline).

Page 22 of 27

*Between-group difference for change in measures of GV and quality of glycemic control at end of 26 weeks from baseline.

Table 5: showing mean (SD) values of measures of GV at 26 weeks compared to baseline in the intervention group, control group and between-group differences in change

of these measures at 26 weeks from baseline. The table also shows the absolute and relative change in the mean values in the intervention group. Measures are expressed in

mmol/L.

CGM Control

Difference*

n Baseline 26 weeks Change %

Change t

P vs.

baseline n Baseline 26 weeks

P vs.

baseline

Mean P

HbA1c

(mmol/mol)

229 58 (10) 55 (10) -3 -3.28 -5.4 <0.001 212 58 (9.2) 58 (8.2) 0.75 -3 <0.001

HbA1c (%) 229 7.4 (0.9) 7.2 (0.9) -0.24 -3.28 -5.4 <0.001 212 7.46 (0.84) 7.44 (0.75) 0.75 - 0.23 <0.001

Mean

glucose

231 9.24 (1.85) 8.89 (1.34) -0.34 -3.69 -3.9 <0.001 214 9.29 (1.7) 9.25 (1.5) 0.6 -0.29 0.018

SD 231 3.65 (0.97) 3.42 (0.79) -0.24 -6.46 -5.3 <0.001 214 3.82 (0.98) 3.83 (0.85) 0.969 -0.24 <0.001

CV 231 39.66 (8.03) 38.23 (5.76) -1.43 -3.61 -3.9 <0.001 214 41.34 (8.4) 41.55 (7.4) 0.683 -1.65 0.011

CONGA1

231 8.26 (1.76) 7.83 (1.29) -0.43 -5.18 -4.9 <0.001 214 8.32 (1.62) 8.25 (1.44) 0.454 -0.36 0.003

LI 231 6.94 (4.27) 6.77 (3.6) -0.18 -2.53 -0.9 0.35 214 7.56 (4.1) 7.66 (4.3) 0.701 -0.27 0.383

J-index 231 56.01 (22.16) 50.43 (16.78) -5.58 -9.96 -5.1 <0.001 214 57.61 (21.2) 56.89 (19.3) 0.5 -4.85 0.002

LBGI 231 5.38 (4.36) 4.06 (2.23) -1.32 -24.50 -6.0 <0.001 214 5.63 (4.37) 5.43 (4.14) 0.521 -1.12 0.003

HBGI 231 11.79 (5.5) 10.03 (4.21) -1.76 -14.91 -6.7 <0.001 214 12.29 (5.3) 11.91 (4.7) 0.153 -1.37 <0.001

GRADE 231 8.34 (4.31) 6.97 (3.1) -1.38 -16.49 -6.4 <0.001 214 8.40 (4.1) 8.08 (3.65) 0.167 -1.06 0.001

MODD 231 3.98 (1.12) 3.66 (0.89) -0.33 -8.16 -6.0 <0.001 214 4.14 (1.16) 4.52 (0.73) <0.001 -0.7 <0.001

MAGE 222 6.75 (2.1) 6.29 (1.35) -0.46 -6.81 -4.0 <0.001 205 7.17 (2.1) 7.37 (1.9) 0.140 -0.66 <0.001

ADRR 231 32.38 (12.03) 30.35 (9.99) -2.03 -6.27 -3.6 <0.001 214 34.43 (12.5) 41.09 (9.6) <0.001 -8.68 <0.001

M-value 231 17.51 (11.12) 12.99 (7.61) -4.52 -25.81 -8.0 <0.001 214 18.33 (10.7) 17.43 (10.23) 0.151 -3.62 <0.001

MAG 231 2.84 (1.22) 2.89 (1.19) 0.05 1.76 0.8 0.40 214 2.96 (1.1) 3.08 (1.3) 0.108 -0.66 0.474

Page 23 of 27

Page 24 of 27

26 weeks

Mean (SD)

52 weeks

Mean (SD)

Change % Change P value

HbA1c (mmol/mol) 58 (9) 58 (10) 0 -0.27 0.63

HbA1c (%) 7.44 (0.81) 7.42 (0.95) -0.02 -0.27 0.63

Mean glucose 9.27 (1.5) 9.16 (1.3) -0.11 -1.22 0.07

SD 3.85 (0.84) 3.56 (0.79) -0.28 -7.36 <0.001

CV 41.64 (7.0) 38.81 (5.85) -2.82 -6.78 <0.001

CONGA1 8.29 (1.4) 8.05 (1.23) -0.24 -2.90 <0.001

LI 7.62 (4.27) 7.55 (4.44) -0.07 -0.94 0.72

J-index 57.23 (19.2) 53.66 (16.74) -3.56 -6.23 <0.001

LBGI 5.34 (3.5) 4.17 (2.4) -1.16 -21.82 <0.001

HBGI 11.99 (4.66) 10.85 (4.2) -1.14 -9.51 <0.001

GRADE 8.04 (3.57) 7.55 (3.09) -0.48 -6.03 <0.001

MODD 4.52 (0.72) 3.80 (0.89) -0.72 -15.95 <0.001

MAGE 7.41 (1.87) 6.52 (1.58) -0.89 -12.04 <0.001

ADRR 41.14 (9.54) 32.42 (9.92) -8.72 -21.20 <0.001

M-value 17.30 (9.51) 14.25 (7.99) -3.04 -17.59 <0.001

MAG 3.08 (1.3) 3.05 (1.24) -0.04 -1.19 0.58

Supplementary table 1: showing mean (SD) values of measures of GV at 52 weeks compared to baseline (26 weeks)

in the control group following unmasking of CGM. The table also shows the absolute and relative change in the mean

values. Measures are expressed in mmol/L.

Page 25 of 27

SD CV CONGA1 LI J-index LBGI HBGI GRADE MODD MAGE ADRR M-value MAG

Age

Gender (female)

Diabetes duration

Frequency of SMBG use -0.14

Baseline HbA1c 0.27 0.41 0.38 0.28 0.33 0.27 0.27 0.31 0.29

Baseline GV -0.75 -0.7 -0.94 -0.54 -0.86 -0.93 -0.82 -0.88 -0.74 -0.88 -0.69 -0.91 -0.37

Education level of care-giver

(College graduate)

Insulin modality (pump)

-0.32

-0.37

White race

Severe hypoglycaemia in the

preceding 6 months

Supplementary table 2: showing coefficients of significant predictors of change in measures of GV at 6 months in the RT-CGM group. *Predictor variables included in

the multiple linear regression model included: age, gender, race, education level of care giver (college vs non-college graduates), Insulin modality (pump vs MDI),

frequency of daily self-reported blood glucose monitoring, frequency of severe hypoglycemia in last 6 months, diabetes duration, baseline HbA1c and baseline GV.

Standardised variables for the change in each measure of GV at 26 weeks and for continuous predictor variables at baseline were entered into the analysis.

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