The Use of Continuous Glucose Monitoring System in Combination with Individualized Lifestyle and Therapeutic Recommandations on Glycemic Control of Type 2 Diabetes Patients

Abstract

Background and aims. The aim of our research was to evaluate the impact of short-time continuous glucose monitoring (CGM) on glycemic control evaluated by HbA1c and within-day glucose variability. We also assessed if the initiation of insulin therapy in conjunction with lifestyle recommendations may prevent the weight gain. Materials and
method. We included 28 patients with type 2 diabetes with 2 consecutive CGMS recordings available (baseline and follow-up) and for which were collected data on weight, body mass index (BMI), percentage (%) of body fat, visceral fat area, HbA1c and glycemic variability. Results. The HbA1c decreased significantly from 8.8% at baseline to 7.3% at follow-up (p

Full text


 24 Louis Pasteur Street, Cluj-Napoca, Cluj County, Romania; Phone:+40749243686; Fax:+40364405704
corresponding author e-mail: anca.craciun@umfcluj.ro
THE USE OF CONTINUOUS GLUCOSE MONITORING SYSTEM
IN COMBINATION WITH INDIVIDUALIZED LIFESTYLE AND
THERAPEUTIC RECOMMANDATIONS ON GLYCEMIC
CONTROL OF TYPE 2 DIABETES PATIENTS
Anca-Elena Crăciun 1,2, , Cornelia Bala 1,2, Cristian Crăciun 1, Gabriela Roman 1,
Carmen Georgescu 1, Nicolae Hâncu 1,2
1 University of Medicine and Pharmacy „Iuliu Hațieganu” Cluj-Napoca, Romania
2 „Regina Maria” Clinic, Cluj-Napoca, Romania
received:
November 10, 2014
accepted:
November 21, 2014
available online:
December 15, 2014
Abstract
Background and aims. The aim of our research was to evaluate the impact of short-time
continuous glucose monitoring (CGM) on glycemic control evaluated by HbA1c and
within-day glucose variability. We also assessed if the initiation of insulin therapy in
conjunction with lifestyle recommendations may prevent the weight gain. Materials and
method. We included 28 patients with type 2 diabetes with 2 consecutive CGMS
recordings available (baseline and follow-up) and for which were collected data on
weight, body mass index (BMI), percentage (%) of body fat, visceral fat area, HbA1c and
glycemic variability. Results. The HbA1c decreased significantly from 8.8% at baseline to
7.3% at follow-up (p <0.0001) in the whole group, and from 10.5% to 7.5% in the
subgroup for which the insulin therapy was initiated at baseline (p=0.011). The BMI, %
body fat and visceral fat area decreased significantly from 29.2 kg/m2 to 28.4 kg/m2; from
32.3% to 30.4%; and from 141.6 to 129.3 (cm2), respectively. No increase of these
parameters was observed in the subgroup for which the insulin therapy was initiated at
baseline. Conclusion. The use of CGMS in combination with individualized lifestyle and
therapeutic recommendations may have a beneficial effect on glycemic control and may
prevent the weight gain associated with insulin initiation.
key words: type 2 diabetes mellitus, glycemic control, CGMS, weight gain, insulin
therapy
Introduction
Landmark diabetes trials have shown that
tight blood-glucose control can delay the
progression of diabetic microvascular
complication, but has limited effect on
macrovascular complications. Additionally, it is
widely accepted that tight glucose control and
especially insulin therapy is associated with
weight gain [1]. A meta-analysis published by
Pontiroli et al including 46 clinical trials and
14,250 participants has shown that the initiation
of insulin therapy is followed by an increase in
body weight during the first year, ranging from
3.1 kg for basal regimens to 6.4 kg for prandial
insulin regimens [2].
© 2014 ILEX PUBLISHING HOUSE, Bucharest, Roumania
http://www.jrdiabet.ro
Rom J Diabetes Nutr Metab Dis. 21(4):291-299
doi: 10.2478/rjdnmd-2014-0036
- 10.2478/rjdnmd-2014-0036
Downloaded from PubFactory at 08/24/2016 05:07:05PM
via free access

292 Romanian Journal of Diabetes Nutrition & Metabolic Diseases / Vol. 21 / no. 4 / 2014
Hyperglycemia, and its surrogate marker,
glycated hemoglobin (HbA1c), are used to
estimate the risk of developing diabetic
complications, to define the targets and measure
the efficacy of diabetes treatments. The current
guidelines have established targets for fasting
and post-prandial glucose values and for HbA1c
[3]. But fasting glucose and/or HbA1c levels
cannot entirely explain the risk of complications
and cardiovascular death associated with
diabetes. The role of glucose variability in the
development of microvascular complications
was initially suggested by the analysis of the
Diabetes Control and Complications Trial
(DCCT) data [4]. Subsequently, it has been
postulated that blood glucose instability, through
oxidative stress and free radical production,
activate vascular damage and may contribute,
perhaps even more than HbA1c, to the
development of diabetes complications [5-8]. A
systematic review of clinical studies has shown
that, in patients with type 2 diabetes, glucose
variability, regardless of HbA1c values, may
represent a predictor for diabetic retinopathy,
cardiovascular events and mortality [9].
The concept of glycemic variability is
heterogeneous [10]. A number of concepts have
been proposed for glycemic variability: between-
day fasting glycemic variability, postprandial
glycemia peaks, HbA1c variability over time,
hypoglycemic episodes or, the most common,
within-day glucose variability, evaluated by self-
monitoring or continuous glucose monitoring
using a continuous glucose monitoring system
(CGMS). No golden standard has been
established for glycemic variability evaluation.
Several tens of different indices for glycemic
variability quantification have been proposed,
but many of them provide similar information.
Recently, Fabris et al showed that a subset of up
to 10 different glucose variability indices may be
sufficient to describe more than 60% of the
variance originally explained by 25 indices
selected for evaluation [11].
During the past years new technologies for
glucose monitoring have emerged. Glucose
levels from interstitial fluid can accurately be
measured at every 5 minutes using a disposable
glucose sensor which is approved for 3–5 days
of use. CGMS provides information about the
direction, magnitude, duration, frequency and
causes of fluctuations in blood glucose levels
[12]. The professional CGMS has some
advantages: there is no feedback to the user so
that no immediate regimen changes can be made
and there are no alarms to warn of
hyperglycemia or hypoglycemia (because for the
majority of the patients these were very
annoying).
The aim of the present analysis was to
evaluate the impact of short-time professional
CGMS on glycemic control as evaluated by
HbA1c and within-day glucose variability. As a
second objective, we aimed to investigate if
insulin treatment initiated in conjunction with
lifestyle recommendations has any impact on
body weight and body fat and to assess the
relationship between the changes in these later
parameters and the therapeutic effect on
glycemic control and glycemic variability.
Material and methods
Study design and study patients
In this retrospective observational study
performed in an outpatient clinic form Cluj-
Napoca, Romania, we enrolled patients with type
2 diabetes who had 2 consecutive CGMS
recordings available. As this was not an
interventional study, the time period between the
2 CGMS testing was not pre-set and the time
period was decided by the doctor together with
the patient, ranging from 3 to 6 months. The
CGMS recordings were downloaded from our
clinic´s database stored on iPRO (Medtronic)
- 10.2478/rjdnmd-2014-0036
Downloaded from PubFactory at 08/24/2016 05:07:05PM
via free access

Romanian Journal of Diabetes Nutrition & Metabolic Diseases / Vol. 21 / no. 4 / 2014 293
Carelink site and the patients´ data were
collected from their medical charts. All patients
received at baseline lifestyle recommendations
according to the anthropometric evaluation and
medical status of the patient.
Evaluated parameters
The following data, recorded on the day of
each sensor insertion, were collected from the
charts: age, sex, weight, height, body mass index
(BMI), diabetes treatment, percentage (%) of
body fat, visceral fat area, and HbA1c. As per
institutions` laboratory procedures, HbA1c
levels were measured by high-performance
liquid chromatography at the first and the second
CGMS recording, and one year after first
recording. Additionally, during the first and the
second CGMS recording, patients were asked to
fill-in a qualitative food diary.
The CGMS recordings were performed
using the iPROTM device (Medtronic,
Northridge, CA) over a 3-5 day interval, in a
blinded manner. The iPRO was placed on and
removed from the patient by a trained member of
the medical staff, in abdominal area, left or right
part, depending on patient preferences, in
recumbent position, at distance from the sites
used for insulin injection (although recent data
support the idea that insulin infusion near sensor
insertion do not influence glycemic values [13]).
The CGMS recordings were downloaded and
were delivered to the treating physician within
the day of removal of the device. Between the 2
CGMS recordings, patients were managed by
their doctors according to the individual
preferences, which typically involved office
follow-ups at 1-3 months intervals.
The parameters of glycemic variability were
calculated with Glyculator, using glycemic
values recorded by the iPRO device during the
first 24 hours of full recording (288 glucose
values – between 00:00 and 23:59 of the day
following the day of the device insertion) [14].
We did not use the values recorded immediately
after the insertion because current sensors are
generally less accurate during this time period
due to local tissue inflammation following tissue
trauma associated with sensor insertion [15].
The glycemic variability indices assessed on
CGM readings were [14]:
Mean level of 24h interstitial glucose value
(MG) and standard deviation (SD) - an index of
the dispersion of data around mean blood
glucose.
Percentage coefficient of variation (%CV) is
the ratio of SD of the glucose values to mean of
the glucose values. This parameter describes the
magnitude sample values and the variation
within them.
M100-weighted average of glucose values;
provides a measure of stability of glycemia in
comparison with an arbitrary assigned glucose
value, initially set to 100 mg/dl.
Mean amplitude of glycemic excursion
(MAGE) - calculated based on mean of
differences between consecutive glucose values
picks and nadirs, only for differences greater
than SD. The small variations are excluded.
MAGE provides a measure of intra-day, high
amplitude, glucose variability.
Fractal Dimension (FD) - an experimental
method based on the works of Higuchi and
adapted by the authors of Glyculator [14] that
describes glucose variability of high frequency
and small amplitude.
Continuous overall net glycemic action
(CONGA) at 1, 2, 4 and 6 hours (CONGA -1, -2,
-4, -6) - shows glycemic variability within a
predetermined time window. It is an indicator of
within-day glucose variability.
Percent of body fat and visceral fat area
were measured by bioelectric impedance, using
InBody (720) (Biospace, Korea). This is a
multifrequency impedance plethysmograph body
- 10.2478/rjdnmd-2014-0036
Downloaded from PubFactory at 08/24/2016 05:07:05PM
via free access

294 Romanian Journal of Diabetes Nutrition & Metabolic Diseases / Vol. 21 / no. 4 / 2014
composition analyzer, which takes readings from
the body using an eight-point tactile electrode
method, measuring resistance at five specific
frequencies (1 kHz, 50 kHz, 250 kHz, 500 kHz,
and 1 MHz) and reactance at three specific
frequencies (5 kHz, 50 kHz, and 250 kHz) which
were pre-set by the manufacturer to assess
extracellular fluid and total body water and
introduced into the body in ascending order of
frequency. VFA are automatically determined
when the patient stands on the electrodes
embedded within the scale platform of each
octapolar analyzer. The % body fat is computed
through the proprietary algorithms, displayed on
the analyzer’s control panel, and recorded.
Statistical analysis
Statistical analysis was carried out using
SPSS-PC 15.0 software (SPSS Inc., Chicago, IL,
USA). Distribution of variables was tested with
Kolmogorov-Smirnov test. Statistical data is
presented as mean ± standard deviation (SD) for
normally-distributed variables, median (1st
quartile; 3rd quartile) for variables with
abnormal distribution and percentage for
categorical variables. Student t-test was used to
compare variables with normal distribution, and
Mann-Whitney U test for variables with
abnormal distribution. The correlation between
HbA1c, parameters evaluating glycemic
variability, % body fat and visceral fat area was
assessed by Spearman correlation coefficient.
The level of significance was set at 0.05.
Results
Baseline characteristics of the patients
included in the analysis
We included in our analysis 28 patients with
type 2 diabetes who had 2 consecutive CGMS
recordings available. Patients’ characteristics at
baseline (first evaluation) are displayed in
Table 1. At the initial CGMS evaluation 11
(39.3%) patients were treated with insulin, 8
(28.6%) with metformin as monotherapy or in
combination with other oral anti-diabetic drugs,
1 (3.6%) with a GLP-1 analogue, and 8 (28.6%)
were on diet alone. After the evaluation, the
insulin therapy was stopped in 2 patients and
was initiated in 6 patients receiving other types
of hypoglycemiant treatment. At the date of the
second CGMS recording 15 (53.6%) patients
were treated with insulin, 9 (32.2%) with
metformin as monotherapy or in combination
with other oral anti-diabetic drugs, 3 (10.8%)
with a GLP-1 analogue, and 1 (3.6%) was on
diet alone.
Table 1. The baseline characteristics of patients
included in the analysis.
Parameter
CGMS group
N=28
Age, years
55.7±5.8
Women, n (%)
12 (42.9)
BMI, kg/m2
29.2±5.8
Diabetes therapy
Diet, n (%)
Oral hypoglycemiants, n (%)
GLP-1 agonists, n (%)
Insulin, n (%)
8 (28.6)
8 (28.6)
1 (3.6)
11 (39.3)
BMI, body mass index; GLP-1, glucagon like peptide-1
analogues; N/n,number; %, percentage
Glycemic control and glycemic variability
In the CGMS group included in the analysis,
HbA1c had a statistically significant decrease
from 9.8% before the initial CGMS to 7.3% after
the second CGMS (p<0.0001).
At both timepoints and for each patient we
analyzed 288 glycemic values recorded during
the CGMS. The mean glucose values and the SD
decreased significantly from 183.9±36.3 mg/dl
at baseline to 132.1±24.5 mg/dl at the time of the
second CGMS evaluation (follow-up). A similar
significant decrease was observed for MAGE,
M100, CONGA-1, CONGA-2, CONGA-4 and
CONGA-6 (Table 2). The parameters evaluating
the small amplitude glycemic excursions (%CV
and FD) were not significantly changed during
the observation period (p >0.05) for both.
- 10.2478/rjdnmd-2014-0036
Downloaded from PubFactory at 08/24/2016 05:07:05PM
via free access

Romanian Journal of Diabetes Nutrition & Metabolic Diseases / Vol. 21 / no. 4 / 2014 295
Table 2. Glycemic control and glycemic variability parameters.
Parameter
Total
Baseline CGMS
N=2
Follow-up
(2nd CGMS)
N=28
p-value
HbA1c (%)
9.8±2.1
7.3±1.1
<0.0001
Mean glucose values (mg/dl)
183.9 (136.4; 238.9)
132.1 (115.5; 175.3)
0.007
SD (mg/dl)
36.3 (25.6; 52.2)
24.5 (20.1; 35.8)
0.001
%CV
21.8 (15.5; 28.8)
18.4 (14.9; 21.1)
0.104
M100
25.2 (7.1; 62.6)
3.7 (1.9; 21.1)
0.005
FD
1.1 (1.0; 1.1)
1.1 (1.0; 1.1)
0.417
MAGE
107.7 (78.4; 148.7)
79.9 (59.0; 103.3)
0.002
CONGA-1
27.7 (19.3; 36.8)
23.7 (20.0; 27.1)
0.014
CONGA-2
41.3 (27.6; 57.8)
32.6 (27.7; 39.1)
0.007
CONGA-4
43.0 (33.3; 69.9)
39.1 (31.9; 48.6)
0.010
CONGA-6
44.4 (33.2; 72.3)
35.9 (27.4; 48.7)
0.016
CGMS - continuous glucose monitoring system; HbA1c - glycated hemoglobin; SD - standard deviation; %CV -
percentage coefficient of variation; M100 - weighted average of glucose values; FD - fractal dimension; MAGE -mean
amplitude of glucose excursions; CONGA-1, -2, -4,-6 - continuous overall net glycemic action at 1, 2, 4 and 6 hours.
Table 3. Correlations between HbA1c and mean glucose values recorded during the CGMS.
Baseline CGMS
Follow-up (2nd CGMS)
ρ
p
ρ
p
Mean glucose values (mg/dl)
0.894
<0.001
0.242
0.223
SD (mg/dl)
0.540
0.003
0.557
0.003
%CV
-0.143
0.467
0.417
0.030
M100
0.891
<0.001
0.521
0.005
FD
-0.149
0.451
-0.277
0.162
MAGE
0.506
0.006
0.554
0.003
CONGA1h
0.437
0.020
0.330
0.093
CONGA2h
0.464
0.013
0.343
0.006
CONGA4h
0.461
0.013
0.484
0.011
CONGA6h
0.447
0.017
0.636
<0.001
CGMS - continuous glucose monitoring system; HbA1c - glycated hemoglobin; SD - standard deviation; %CV -
percentage coefficient of variation; M100 - weighted average of glucose values; FD - fractal dimension; MAGE - mean
amplitude of glucose excursions; CONGA-1, -2, -4,-6 - continuous overall net glycemic action at 1, 2, 4 and 6 hours; ρ –
Spearman’s coefficient of correlation
The most impressive decrease in HbA1c was
observed in patients from CGMS group for
whom insulin therapy was initiated after the
baseline evaluation: from 10.4% at baseline to
7.7% at follow-up (p=0.009). In this subgroup,
the following parameters related to glycemic
variability decreased significantly at follow-up
compared to baseline: mean level of 24 h
interstitial glucose value 126.2 mg/dl vs. 268.5
mg/dl (p = 0.021); and M100 5.4 vs. 86.1 (p =
0.026). The values of the other parameters were
not statistically different at follow-up compared
with baseline: SD 27.2 mg/dl vs. 46.3 mg/dl;
MAGE 82.9 mg/dl vs. 127.8 mg/dl; CONGA-1
23.1 vs. 28.3; CONGA-2 31.6 vs. 41. 6; COGA-
4 41.5 vs. 50.3; CONGA-6 41.6 vs. 54.9; %CV
19.2 vs. 16.9; FD 1.05 vs. 1.04 (p >0.05 for all).
Correlations between HbA1c and mean
glucose values recorded during the CGMS are
depicted in Table 3. The HbA1c values at
baseline were significantly correlated with the
mean glucose values, SD, M100, MAGE and
CONGA calculated for the first CGMS. HbA1C
recorded at the second time point did not
correlate with mean glucose values, but
correlated with SD, %CV, M100, MAGE and
CONGA calculated for the second CGMS.
- 10.2478/rjdnmd-2014-0036
Downloaded from PubFactory at 08/24/2016 05:07:05PM
via free access

296 Romanian Journal of Diabetes Nutrition & Metabolic Diseases / Vol. 21 / no. 4 / 2014
Body weight, percent body fat
and visceral fat area changes
The body weight, BMI, % body fat and
visceral fat area decreased significantly during
the follow-up (p<0.05 for all parameters) as
shown in Table 4. Because the initiation of
insulin treatment is usually associated with
weight gain, we analyzed the parameters related
to body composition separately for the subgroup
of 6 patients with insulin therapy initiated at
baseline. For this subgroup no significant
changes were observed in the BMI between the 2
timepoints. The % body fat and visceral fat area
decreased also non-significantly from 31.3 to
29.6% and from 111.4 to 104.1 cm2,
respectively.
Table 4. Body weight, percent body fat and visceral fat area at baseline and follow-up.
Parameter
Baseline
Follow-up
p-value
Whole CGMS group
Weight (kg)
85.1±20.5
82.6±19.5
0.008
BMI (kg/m2)
29.2±5.8
28.4±5.7
0.005
Percentage of body fat (%)
32.3±9.1
30.4±9.1
0.024
Visceral fat area (cm2)
141.6±50.1
129.3±44.5
0.006
Subgroup with insulin treatment initiated at baseline
Weight (kg)
73.3±9.8
72.8±11.1
0.003
BMI (kg/m2)
26.4±1.1
26.2±3.2
0.739
Percentage of body fat (%)
31.3±9.6
29.6±7.1
0.316
Visceral fat area (cm2)
111.4±27.3
104.1±21.2
0.361
CGMS - continuous glucose monitoring system; BMI - body mass index; % percentage
We did not observe any correlations between
the parameters describing the glycemic control at
follow-up and the% body fat, visceral fat area or
the changes of these 2 parameters in the whole
group or in the sub-group with insulin therapy
initiated at baseline (p >0.005 for all
correlations; data not shown).
Discussions
The role of using professional CGMS in the
clinical setting of an office practice in order to
influence the value of HbA1c is controversial. In
our analysis we have shown that the CGMS
monitoring used in conjunction with lifestyle
recommendations was associated with improved
glycemic control (as evaluated by HbA1c and
mean glucose values) and glycemic variability.
Except for the parameters evaluating the small
amplitude glycemic excursions (%CV and FD),
all other parameters were improved after the first
CGMS insertion, probably due to the life style
optimization and the adequate pharmacological
recommendation.
The role of CGMS in improving the
glycemic control of patients with type 1 and type
2 diabetes is controversial. A meta-analysis
including 5 trials and 131 type 1 diabetic patients
showed that CGMS use did not reduce
significantly HbA1c levels as compared with
self-monitoring of blood-glucose and increased
the number of changes of insulin doses changes
per patient [16]. Another meta-analysis of 7
randomized controlled trials comparing CGMS
and SBGM in patients with type 1 diabetes
showed that when compared with self-blood
finger-stick glucose monitoring, CGMS was
associated with a non-significant reduction in
HBA1c (0.22%; 95% CI: -0.439%; 0.004%,
p=0.055) [17]. In a study enrolling 102
consecutive patients with type 1 or type 2
diabetes showed no improvement of HbA1c at 7
months after the CGMS procedure (7.7 ± 1.0%
vs 7.8±1.1%) [18]. However, more recent
randomized controlled trials demonstrated that
real time-CGM use lowered HbA1c levels and
time spent with blood sugar levels in the
- 10.2478/rjdnmd-2014-0036
Downloaded from PubFactory at 08/24/2016 05:07:05PM
via free access

Romanian Journal of Diabetes Nutrition & Metabolic Diseases / Vol. 21 / no. 4 / 2014 297
hypoglycemia range in adults and children with
good baseline blood sugar control and was
associated with reduced rates of hypoglycemia in
both adults and children [18-20]. In a case-
control study with 52 participants with type 2
diabetes (non-insulin requiring, sedentary
lifestyle), the use of CGMS to clearly depict
glucose reductions in response to physical
activity was accompanied by a significant
increase in physical activity and a significant
decrease of HbA1c and BMI after 8 months [19].
We think that the reduction of HbA1c observed
in our study was due to the fact that the CGMS
findings helped the physician to choose the most
appropriate pharmacological antidiabetic agent
for either postprandial picks or “a plateau“
hyperglycemia. Additionally, the use of a food
diary during the CGMS recording enabled both
physicians and patients to identify foods and
drinks which increased glycemic values and to
adapt the treatment and lifestyle
recommendations.
Previously it has been shown that HbA1c
values correlate with mean glucose values
recorded during the CGMS. Thus, in a 12-weeks
longitudinal study by Nathan et al. enrolling 22
patients with type 1, type 2 diabetes and 3 non-
diabetic participants, mean HbA1c levels at
weeks 8 and 12 correlated strongly with the
CGMS results [21]. A multicenter Chinese study
enrolling 742 participants (with no diabetes,
prediabetes or newly diagnosed type 2 diabetes)
showed a strong correlation between the level of
HbA1c and the mean blood glucose values
registered by CGMS [22]. In our study, we
observed a correlation between the HbA1c
values and the mean glycemic values recorded
by CGMS only at baseline. A possible
explanation for these results may a “Big
Brother” phenomenon that occurred during the
second CGMS recording: the patient already
knew that all glycemic values were recorded and
his adherence to lifestyle recommendations were
increased during the follow-up CGMS.
The individualized lifestyle recom-
mendations probably can explain the significant
decrease in weight, BMI, % body fat and
visceral fat area observed in our study. The lack
of association between the HbA1c, the
parameters describing the glucose variability and
the changes of the body weight and body
composition support the hypothesis that the
improvement in glycemic control in our study
was independent of the changes in the body
weight and body composition, and was mainly
linked to the changes in the diabetes treatment
and lifestyle recommendations. The most
important finding in our study was the decrease
in the body weight in patients for which the
insulin was initiated after the baseline CGMS. It
is widely accepted that the initiation of insulin
therapy is associated with weight gain and this
weight gain in already obese patients may
represent a barrier for insulin initiation and may
be associated with an adverse effect on the
cardiovascular risk profile [23]. In the UKPDS,
weight gain was significantly higher in patients
assigned to insulin therapy than in those
assigned to other therapies (4.0 kg vs. 1.7-2.6)
[24]. Several mechanisms have been proposed to
explain this change in the body weight: the
anabolic effects of insulin, decreased glycosuria
linked to improved glycemic control, decreased
metabolic rate, aggressive treatment of
hypoglycemia and eating to prevent
hypoglycemia [25-27]. Our results showed that
with the appropriate lifestyle recommendations
the weight gain after the insulin initiation can be
avoided in patients well motivated. It should be
noted that our patients were followed for a
maximum of 12 months. Previous studies have
shown that the weight gain occurs during the
first 3 years following the insulin initiation.
Therefore, due to the limited follow-up of our
- 10.2478/rjdnmd-2014-0036
Downloaded from PubFactory at 08/24/2016 05:07:05PM
via free access

298 Romanian Journal of Diabetes Nutrition & Metabolic Diseases / Vol. 21 / no. 4 / 2014
patients, we cannot conclude on the possibility to
avoid the weight gain on a long term basis.
Our study has some limitations resulting
mainly from its retrospective design. The main
limitation is the lack of control group evaluated
by intermittent self-blood finger-stick glucose
monitoring. The presence of this group would
have allowed the evaluation of the CGMS effect
on the reduction of HbA1c.
Conclusion
In conclusion, the use of short-term CGMS
in clinical practice and the individualized
lifestyle and therapeutic recommendations based
on these recordings have a beneficial effect on
glycemic control and body weight of diabetic
patients. Furthermore, they may prevent the
weight gain associated with insulin initiation.
Acknowledgements: “This paper was
published under the frame of European Social
Found, Human Resources Development
Operational Programme 2007-2013, project no.
POSDRU/159/1.5/S/138776”.
REFERENCES
1. UK Prospective Diabetes Study (UKPDS)
Group. Intensive blood-glucose control with
sulphonylureas or insulin compared with conventional
treatment and risk of complications in patients with type 2
diabetes (UKPDS 33). Lancet 352: 837–853, 1998.
2. Pontiroli AE, Miele L and Morabito A. Increase
of body weight during the first year of intensive insulin
treatment in type 2 diabetes: systematic review and meta-
analysis. Diabetes Obes Metab 13: 1008–19, 2011.
3. American Diabetes Association. Standards of
medical care in diabetes--2014. Diabetes Care 37 Suppl 1:
S14–80, 2014.
4. Cefalu WT, Ratner RE. The diabetes control and
complications trial/epidemiology of diabetes interventions
and complications study at 30 years: the “gift” that keeps
on giving! Diabetes Care 37: 5–7, 2014.
5. Siegelaar SE, Holleman F, Hoekstra JBL,
DeVries JH. Glucose variability; does it matter? Endocr
Rev 31: 171–822, 2010.
6. Wentholt IM, Kulik W, Michels RP, Hoekstra
JB, DeVries JH. Glucose fluctuations and activation of
oxidative stress in patients with type 1 diabetes.
Diabetologia 51: 183–90, 2008.
7. Benhamou PY, Somers F, Lablanche S et al.
Impact of flexible insulin therapy on blood glucose
variability, oxidative stress and inflammation in type 1
diabetic patients: the VARIAFIT study. Diabetes Metab
40: 278–83, 2014.
8. Monnier L, Colette C and Owens DR. Glycemic
variability: the third component of the dysglycemia in
diabetes. Is it important? How to measure it? J Diabetes
Sci Technol 2: 1094–1100, 2008.
9. Nalysnyk L, Hernandez-Medina M,
Krishnarajah G. Glycaemic variability and complications
in patients with diabetes mellitus: evidence from a
systematic review of the literature. Diabetes. Obes. Metab.
12: 288–98, 2010.
10. Frontoni S, Di Bartolo P, Avogaro A, Bosi E,
Paolisso G, Ceriello A. Glucose variability: An emerging
target for the treatment of diabetes mellitus. Diabetes Res
Clin Pract 102: 86–95, 2013.
11. Fabris C, Facchinetti A, Sparacino G et al.
Glucose variability indices in type 1 diabetes:
parsimonious set of indices revealed by sparse principal
component analysis. Diabetes Technol. Ther. 16: 644-652,
2014.
12. Klonoff DC. Continuous glucose monitoring:
roadmap for 21st century diabetes therapy. Diabetes Care
28: 1231-1239, 2005.
13. Ward WK, Castle JR, Jacobs PG, Cargill RS.
Can glucose be monitored accurately at the site of
subcutaneous insulin delivery? J Diabetes Sci Technol 8:
568–574, 2014.
14. Czerwoniuk D, Fendler W, Walenciak L,
Mlynarski W. GlyCulator: a glycemic variability
calculation tool for continuous glucose monitoring data. J
Diabetes Sci Technol 5: 447–51, 2011.
- 10.2478/rjdnmd-2014-0036
Downloaded from PubFactory at 08/24/2016 05:07:05PM
via free access

Romanian Journal of Diabetes Nutrition & Metabolic Diseases / Vol. 21 / no. 4 / 2014 299
15. Khadilkar KS, Bandgar T, Shivane V, Lila A,
Shah N. Current concepts in blood glucose monitoring.
Indian J Endocrinol Metab 17(Suppl 3): S643–S649,
2013.
16. Golicki DT, Golicka D, Groele L, Pankowska
E. Continuous Glucose Monitoring System in children
with type 1 diabetes mellitus: a systematic review and
meta-analysis. Diabetologia 51: 233–40, 2008.
17. Chetty VT, Almulla A, Odueyungbo A,
Thabane L. The effect of continuous subcutaneous
glucose monitoring (CGMS) versus intermittent whole
blood finger-stick glucose monitoring (SBGM) on
hemoglobin A1c (HBA1c) levels in Type I diabetic
patients: a systematic review. Diabetes Res Clin Pract 81:
79–87, 2008.
18. Juvenile Diabetes Research Foundation
Continuous Glucose Monitoring Study Group. The
effect of continuous glucose monitoring in well-controlled
type 1 diabetes. Diabetes Care 32: 1378-1383, 2009.
19. Battelino T, Phillip M, Bratina N, Nimri R,
Oskarsson P, Bolinder J. Effect of continuous glucose
monitoring on hypoglycemia in type 1 diabetes. Diabetes
Care 34: 795–800, 2011.
20. Slover R H, Welsh JB, Criego A et al.
Effectiveness of sensor-augmented pump therapy in
children and adolescents with type 1 diabetes in the STAR
3 study. Pediatr Diabetes 13: 6–11, 2012.
21. Nathan DM, Turgeon H, Regan S. Relationship
between glycated haemoglobin levels and mean glucose
levels over time. Diabetologia 50: 2239–44, 2007.
22. Zhou J, Mo Y, Li H et al. Relationship between
HbA1c and continuous glucose monitoring in Chinese
population: a multicenter study. PLoS One 8: e83827,
2013.
23. Russell-Jones D, Khan R. Insulin-associated
weight gain in diabetes--causes, effects and coping
strategies. Diabetes Obes Metab 9: 799–812, 2007.
24. UK Prospective Diabetes Study Group. Effect
of intensive blood-glucose control with metformin on
complications in overweight patients with type 2 diabetes.
UKPDS 34. Lancet 352: 854-865, 1998.
25. Carlson MG, Campbell PJ. Intensive Insulin
Therapy and Weight Gain in IDDM. Diabetes 42: 1700–
1707, 1993.
26. Hermansen K, Davies M. Does insulin detemir
have a role in reducing risk of insulin-associated weight
gain? Diabetes Obes Metab 9: 209–17, 2007.
27. Tuomilehto J, Lindstrom J, Eriksson JG et al.
Prevention of type 2 diabetes mellitus by changes in
lifestyle among subjects with impaired glucose tolerance.
N Engl J Med 344: 1343-1350, 2001.
- 10.2478/rjdnmd-2014-0036
Downloaded from PubFactory at 08/24/2016 05:07:05PM
via free access