insulina x glp-1 agonistas
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Glucagon-like peptide-1 receptor agonists (GLP-1 RA) versus insulin
in inadequately controlled patients with type 2 diabetes mellitus: a
meta-analysis of clinical trials Running title: GLP-1 RA vs insulin: a meta-analysis of clinical trials
Yisu Wang1, Ling Li 2, Mengliu Yang1, Hua Liu3, Guenther Boden4, Gangyi Yang1
1Department of Endocrinology, the Second Affiliated Hospital, Chongqing Medical
University, 400010 Chongqing, China
2The Key Laboratory of Laboratory Medical Diagnostics in the Ministry of Education
and Department of Clinical Biochemistry, Chongqing Medical University, 400016
Chongqing, China
3Department of Pediatrics, University of Mississippi Medical Center, 2500 North
State Street, Jackson, Mississippi, MS 39216-4505, USA
4The Division of Endocrinology/Diabetes/Metabolism and the Clinical Research
Center, Temple University School of Medicine, Philadelphia, Pennsylvania, USA
Corresponding author: Gangyi Yang, Department of Endocrinology, the Second
Affiliated Hospital, Chongqing Medical University, 400010 Chongqing, China
Tel: +86-23-68485216 / Fax: +86-23-68486115
e-mail: [email protected]
This is an Accepted Article that has been peer-reviewed and approved for publication in the Diabetes, Obesity and Metabolism, but has yet to undergo copy-editing and proof correction. Please cite this article as an "Accepted Article"; doi: 10.1111/j.1463-1326.2011.01436.x
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Abstract
Aims To compare the effect and safety of GLP-1 receptor agonists (GLP-1 RA) with
insulin therapy on type 2 diabetes mellitus (T2DM) patients inadequately controlled
with metformin (MET) and/or sulfonylurea (SU).
Methods A systematic literature search on Medline, Embase and Cochrane for
randomized controlled trials (RCTs) was conducted using specific search terms
“GLP-1 insulin type2 diabetes clinical trials” and 8 eligible studies were retrieved.
Data on mean change in Haemoglobin A1c (HbA1C), weight loss, fasting plasma
glucose (FPG), incidence of hypoglycemia and gastrointestinal adverse events were
extracted from each study and pooled in meta-analysis. Data on postprandial plasma
glucose (PPG) and adverse events were also described or tabulated.
Results Data from 8 RCTs enrolling 2782 patients were pooled using a random-effects
model. The mean net change(95% confidence interval(CIs)) for HbA1c, weight loss
and FPG for patients treated with GLP-1 RA as compared with insulin was -0.14%
(-2mmol/mol)[(-0.27, -0.02)%; 95%CI; P=0.03]; -4.40kg [(-5.23,-3.56)kg; 95%CI;
P<0.01]; 1.18mmol/l[(0.43, 1.93) mmol/l; 95%CI; P<0.01], respectively, with
negative values favoring GLP-1 and positive values favoring insulin. The GLP-1
group was associated with a greater reduction in PPG than the insulin group. Overall,
hypoglycemia was reported less in the GLP-1 group (M-H OR 0.45[0.27, 0.76];
P<0.01) while there was no significant difference in occurrence of severe
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hypoglycemia (M-H OR 0.65 [0.29,1.45]; P=0.29). A significantly higher number of
gastrointestinal adverse events were reported with GLP-1 group (M-H OR 15.00
[5.44,41.35] P<0.01).
Conclusions GLP-1 RA are promising new agents compared with insulin. Further
prospective clinical trials are expected to fully evaluate the long-term effectiveness
and safety of these therapies within the T2DM treatment paradigm.
Keywords GLP-1, insulin, type 2 diabetes mellitus, clinical trials.
Abbreviations
T2DM type 2 diabetes mellitus
DPP-IV Dipeptidyl peptidase 4
GLP-1 RA Glucagon-like peptide-1 receptor agonist
FPG fasting plasma glucose
PPG postprandial plasma glucose
ITT intention-to-treat
RCT randomized controlled trials
M-H OR Mantel-Haenszel odds ratio
ADA American Diabetes Association
EASD The European Association for the Study of Diabetes
UKPDS United Kingdom Prospective Diabetes Study
MET Metformin
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SU Sulfonylurea drugs
TZD Thiazolidinedione
HbA1c Haemoglobin A1c
FDA Food and Drug Administration
Mesh Medical subject headings
CIs Confidence intervals
BIAsp Biphasic insulin aspart
TTT Treat-to-Target algorithm
Introduction
Type 2 diabetes mellitus (T2DM) is characterized by two core defects in most
cases, progressive beta-cell dysfunction against a background of obesity-related
insulin resistance [1], making it difficult for patients to maintain glycemic control.
Recently, it has been reported that impairments in the secretion levels and/or the
activity of incretin hormones may also play an important role in the development and
progression of hyperglycemia in T2DM [2].
According to 2009 American Diabetes Association (ADA)/ The European
Association for the Study of Diabetes (EASD) consensus [3], standard therapy is
initiated by lifestyle changes and glucose-lowering agents, often followed by insulin
treatment. Data from UK Prospective Diabetes Study (UKPDS) shows that long-term
effectiveness of these drugs can be unsatisfactory, leading to progressively worsening
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glycemic control, which is largely attributed to progressive dysfunction of beta-cells
that occurs irrespective of whether metformin (MET), sulfonylureas (SU), or insulin
are used[4]. On the other hand, except for a few oral agents like dipeptidyl
peptidase-IV(DPP-IV) inhibitors and acarbose, most of the current treatments are
accompanied by weight gain---a contributing factor to insulin resistance and the
elevated fasting plasma glucose (FPG) and postprandial plasma glucose (PPG)
concentrations[5], which still worsens glycemic control. Thus, a novel agent that
protects the beta-cells and causes no weight gain and hypoglycemia is needed..
Glucagon-like peptide-1 (GLP-1) is an incretin hormone secreted from endocrine
K and L cells in response to nutrient ingestion, and is responsible for up to 70% of the
insulin response following a meal [6]. However, native GLP-1 has a short plasma
half-life of only 1-2 min; as it is rapidly degraded by the enzyme DPP-IV, it is not
properly suitable to intermittent administration. Thus, research efforts directed at
potentiation of incretin action have focused on a glucagon-like peptide-1 receptor
agonist (GLP-1 RA), which is degradation-resistant. Exenatide and Liraglutide are
representatives of GLP-1 RA that offer potential benefits over traditional therapies.
Exenatide( Exendin-4), which shares 53% sequence homology with human GLP-1,
has been proven to produce glucose-dependent enhancement of insulin secretion,
suppression of inappropriately elevated postprandial glucagon secretion, slowing of
gastric emptying, and a reduction of food intake [7], and has been approved by Food
and Drug Administration (FDA) for use as monotherapy along with lifestyle changes
in T2DM patients since November, 2009. Liraglutide is a GLP-1 analogue based on
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the structure of native GLP-1 with minor amino acid substitution and attachment.
Data from large, controlled, clinical studies have confirmed its therapeutic profile
with robust reductions in glycosylated hemoglobin (HbA1c), low risk of
hypoglycemia and clinically relevant reductions in body weight and systolic blood
pressure [8]. Since January 2010, Liraglutide has been licensed for the treatment for
T2DM in conjunction with diet and exercise.
However, in the position statement developed by experts from the ADA and
EASD[3], GLP-1 RA are considered a possible albeit less well validated alternative to
insulin initiation after failure of lifestyle changes and MET, with or without a SU.
Both insulin and GLP-1 RA have shown to be effective for inadequately controlled
T2DM. Nevertheless, limited data is available from clinical trials with respect to
head-to-head comparisons of efficacy and safety between these two drugs.
Thus, the purpose of our study was to assess the efficacy and safety of these two
drugs by using all available data in eligible clinical trials, when added to treatment
regimens of patients whose glucose levels were inadequately controlled with oral
agents alone.
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Research design and methods
Data Sources and Searches
We searched the MEDLINE database for articles dated from October 1990 to October
2010, as well as the Cochrane Library Central Registry of Controlled Trials during the
same period of time. A further search was performed on EMBASE database for
articles dated from 1990 to 2011 for relevant publications, all using the following
medical subject headings (MeSH) “diabetes mellitus”, “Type 2”, “insulin”, “insulin
isophane”, “glucagon-like peptide 1”, “clinical trial”. The search was restricted to
publications in English and in humans. Completed but unpublished trials were
identified through a search of www.clinicaltrials.gov website. A manual search of
references cited in the published studies and relevant review articles was also
performed to identify additional studies suitable for our purpose. For unpublished and
published trials which were not exhaustively disclosed, an attempt was made (through
e-mail) to contact principal investigators in order to retrieve missing data. Finally,
known experts in the area were contacted to ensure that all relevant data were
captured.
Study Selection
The identification of relevant abstracts and the selection of studies based on the
criteria described below were performed independently by two of the authors (Wang
Y and Li L), and any discrepancy was resolved by a third investigator.
Clinical trials were included if they met the following criteria: (i) randomized
controlled trials (RCT) using either crossover or parallel designs, conducted in
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human adults and published in English; (ii) included were nonpregnant adults at least
18 years of age with T2DM for at least three months, suboptimally controlled with
oral agents(e.g. MET and/or SU) with HbA1c levels between 7-11%(53-97mmol/mol);
(iii) use of subcutaneous injection as the only administration route; (iv) the
intervention duration was at least 16 weeks; (v) comparisons of GLP-1 RA (exenatide
or liraglutide) with insulin, e.g. glargine or biphasic insulin aspart (BIAsp); (vi) use of
HbA1c as the primary outcome in a manner that allowed data analysis, and data on
weight changes, FPG, PPG, hypoglycemia, adverse effect from baseline to end of
trial.
We excluded the following trials: Those on type 1 diabetes or with ages less than
18 years, those that used a GLP-1 RA as monotherapy or, adjunctive therapy to
insulin, and those with integrated analysis, post hoc analysis or open-label extensions
of the original ones.
Data extraction and Quality Assessment
The following variables in each study were extracted: 1) Title, primary author’s name,
year and source of publication. 2) Patient demographics, study design (cross over,
parallel, factorial, or Latin square), treatment allocation procedures, blinding (open,
single or double-blind) and interventions. 3) Inclusion and exclusion criteria for each
individual study, diabetes history including duration of diabetes, mean HbA1c, FPG
and body weight. 4) Description of the study medication exposure, study completion
status, definition and assessment of diagnosis and outcome of diabetes. The primary
clinical outcome of interest was the effect of a GLP-1 RA on HbA1c at the end of the
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trial. Secondary outcomes included weight loss from baseline to end of the trial.
Furthermore, data on FPG, PPG, adverse events and incidence of hypoglycemia
(number of patients with at least one event) were extracted in the form of net change
values with confidential intervals (CIs), standard error or P-value.
Any case of life threatening adverse events were considered severe and were
tabulated specially, together with death for any cause. If data concerning the outcome
were missing from an article, an effort was made to contact the primary author in
order to obtain the missing data.
The methodological quality of the included randomized clinical trials was
assessed based on criteria suggested by Jadad et al [9]. It awards a maximum of 5
points to each study based on three main criteria: study randomization (1–2 pts),
double-blinding of the study (1–2 pts), and a description of withdrawals or dropouts
(1 pt). Any disagreement regarding study quality was resolved by discussion among
the authors.
Data Synthesis and Analysis
This meta-analysis was carried out according to the QUOROM guidelines for the
conducting and reporting of meta-analyses of RCTs [10].Statistical analyses were
performed using the Review Manager (RevMan) version 5.0 for Windows
software(The Nordic Cochrane Centre, Copenhagen).
Outcome variables (e.g. changes in HbA1c, FPG or body weight from baseline to
end of trial) were converted to standard units (percentage and mmol/mol, mmol/l or
kg). For continuous variables (HbA1c, FPG, weight), WMD with 95% CIs was
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calculated for change from baseline in the GLP-1 RA or insulin groups. For
dichotomous variables (eg. percentages with hypoglycemia), Mantel-Haenszel odds
ratio (M-H OR) with 95% CIs was calculated. Mean net change values were
calculated as the difference (GLP-1 RA minus insulin glargine) of the changes
(baseline minus follow-up) of the mean values. Each study was weighted of its
variance in order to pool the data for overall effect size. The variances were calculated
using CIs, P-values, t-statistics or individual variances for the two treatment groups.
For trials that reported no standardized difference for the changes of the two
intervention groups, it was presented as reductions from baseline separately. We used
the method of Follmann et al., in which a correlation coefficient of 0.5 between initial
and final values is assumed [11]. All tests were two-sided with statistical significance
when P<0.05, if not otherwise specified. If data from more than two trials were
available, we combined data within a class (insulin glargine or BIAsp ; exenatide or
liraglutide) by type of group, duration of intervention, and available formulation
within each class and explored heterogeneity. For PPG, we did not perform a
meta-analysis because of the diverse methods used to assess outcomes and/or because
of insufficiently reported data.
Definitions and units for hypoglycemia differed substantially from one trial to
another. The most consistently reported measure of hypoglycemia was the percentage
of participants experiencing an episode of a specific type (symptomatic,
asymptomatic, nocturnal and severe). Therefore, the first measure was meta-analyzed
with the description of the rest two trials presented as event/patient/year [20, 21].
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We used the I2 statistic to evaluate statistical heterogeneity in each meta-analysis.
The I2 statistic describes the percentage of the variability that is due to heterogeneity
rather than sampling error. The Cochrane handbook suggests that a value greater than
50% may be considered substantial heterogeneity. Possible sources of heterogeneity
were assessed through pre-stated subgroup analyses by intervention regimens. As
statistical heterogeneity were presented, possibly related to different demographic
characteristics and study intervention measurements, we utilized a
DerSimonian-Laird’s random effects methodology throughout to calculate the pooled
effect size [12].
We examined each study for potential selection, attrition, and detection bias [13].
In order to verify possible bias associated with inadequate allocation concealment or
randomization procedure, study quality characteristics were tabulated and evaluated.
A funnel plot of primary end point outcomes or important secondary outcomes was
examined to assess the potential publication bias by constructing it with variance
plotted against the corresponding effect sizes. In addition, the association between
variance and effect size was analyzed by the Begg adjusted rank correlation test,
based on the Kendall’s tau [14].
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Results
932 articles identified from Medline, Embase and Cochrane were screened, from
which 825 were excluded based on title and abstract. After detailed evaluation of
potential eligible reports, 8 reports [15-22] met all of the inclusion criteria and were
retrieved for meta-analysis. The trial flowchart is summarized in Figure 1.
Characteristics of the studies included in the meta-analysis are presented in
Table 1. In total, data from 2782 participants in 8 trials were included. Of those, the
mean values were: age 57.4 years, BMI 31.6 kg/m2, duration of diabetes 8.9 years,
HbA1c 8.6%(70mmol/mol) and FPG 10.4 mmol/l. The average length of studies was
30.2 weeks, with a range from 16 to 52 weeks, the average study size was 332
participants with a range from 69 to 549 participants. The average insulin dose in the
insulin group was 37.6 IU/d. All 8 trials included were parallel, open-label trials. Two
trials in the insulin group used BIAsp, while the rest used glargine. In the GLP-1 RA
group, one trial used liraglutide while the others used exenatide (including long-acting
exenatide). Basic treatment protocols were slightly different in all trials: some trials
[16,17,19-22] followed a fixed algorithm for titration of insulin [23] based on fasting
concentration of blood glucose while others [15,18] followed a treat-to-target
principle [24]. Both exenatide and liraglutide were used with a strict titration principle
except for the trial of Bunck et al. [17], where exenatide was titrated to a maximum
dose of 20 μg if necessary. All but 2 trials had only a 30-week or slightly longer
duration, thus, long-term efficacy and safety could not be evaluated.
Selected study design characteristics are shown in Table 2. Mean net changes and
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corresponding 95% CIs for outcomes from each individual trial and pooled data are
shown in the following figures respectively.
1. Changes in HbA1c
We included a total of 2782 patients for whom complete HbA1c measurements
were available with individual treatment arm size ranging from 36 to 282 patients.
Study duration was at least 16 weeks in all cases, allowing sufficient time for changes
in glycemic control to be reflected in changes in HbA1c.
When combining all available trials, data showed a comparative differences in
HbA1c decline from baseline favoring GLP-1 RA therapy[WMD-0.14(-0.27, -0.02)%,
(-1.5mmol/mol); 95%CI; P=0.03](Figure 2). Exclusion of trials [15,18,22] where
insulin was titrated following Treat-to-Target algorithm (TTT) [24] revealed slightly
significant decrease in HbA1c [WMD-0.19(-0.34, -0.03)% (-2mmol/mol); 95%CI;
P=0.02]. No significant differences were found between the exenatide BID and the
insulin groups [WMD-0.12(-0.30, 0.06) % (-1.3mmol/mol); 95%CI; P=0.19], nor
between the GLP-1 and glargine subgroups [WMD-0.08(-0.19, 0.03) %
(-0.9mmol/mol); 95%CI; P=0.14]. There appeared to be a trend towards greater
HbA1c reductions in populations with higher compared to lower baseline HbA1c (r =
-0.837, P<0.001).
2. Changes in FPG
FPG was reduced in both groups but more so in the insulin group (WMD 1.18(0.43,
1.93) mmol/l; 95%CI; P<0.01) (Figure 3). The trial of liraglutide versus insulin
glargine by Russell-Jones et al [22] was not included due to lack of standard
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deviations in FPG results. The mean reductions in FPG from baseline in the two
groups of that study was 1.55 and 1.79mmol/l, respectively.
3. Changes in bodyweight
In the trials that reported data on changes in bodyweight, there was a statistically
significant net weight loss observed in the GLP-1 RA compared with the insulin
groups (WMD -4.40(-5.23,-3.56) kg; 95%CI; P<0.01)(Figure 4). Net weight loss was
greater comparing exenatide and insulin groups when BIAsp was used (WMD
-5.51(-6.01,-5.01) kg; 95%CI; P<0.01) than when insulin glargine was used (WMD
-3.95(-4.83,-3.07) kg; 95%CI; P<0.01).
4. Changes in PPG
A statistical comparison with GLP-1 RA and insulin on effect of PPG was not
possible since in the seven out of eight trials where PPG was mentioned, specific data
of PPG changes were only given in three. The trial by Davies et al. [18] did not
mention PPG. However, even without a valid meta-analysis, we can describe the
results of several studies that came to similar conclusions. For instance, a cross over
study [15] reported that exenatide was associated with significant lower PPG
concentrations compared with insulin glargine (mean [SEM] -1.5 [0.3] ( -2.1 to-0.9)
mmol/l;95%CI, P<0.001), as 2-hour PPG excursions were lower with exenatide in the
morning (mean[SEM] -2.2[0.3]( -2.8 to-1.7) mmol/l; 95%CI, P<0.001), at
midday(mean [SEM] -0.5[0.2]( -0.9 to-0.1)mmol/l; P=0.016), and in the
evening(mean [SEM] -2.1[0.3]( -2.7 to-1.5)mmol/l; P<0.001). A study by Heine et al.
[20] comparing exenatide and insulin glargine suggested a greater PPG reduction
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after-breakfast (-0.91 [-1.39 to -0.43]mmol/l; P<0.002) and after-dinner (-1.41[-1.89
to 0.93]mmol/l; P<0001) in the exenatide group. A similar reduction in PPG from
baseline was reported by Russell-Jones et al[22]in the liraglutide group(1.81mmol/l)
and insulin glargine group(1.61mmol/l). Three trials [17,19,21] described a significant
reduction of blood glucose after breakfast and dinner in the exenatide group, but no
specific data were shown. Results from the trial by Bengenstal et al.[16] were not
consistent with the rest, suggesting the reductions in the blood glucose values was
significantly greater for BIAsp 30 BID than for exenatide at all time points of the
8-point SMBG profile.
5. Risk of hypoglycemic events
Hypoglycemia was reported in the form of patient-reported incidence of
symptomatic hypoglycemia in six studies, and frequency of hypoglycaemic episodes
(event/patient/year) in another two studies [20, 21]. Of the data retrieved from these
studies, hypoglycemic episodes were reported by 509 patients, 200 out of 877 in the
GLP-1 RA group and 309 out of 855 in the insulin group. Based on the random
effects pooling, there was a statistically significant decrease in risk of hypoglycemia
associated with use of GLP-1 RA (M-H OR 0.45(0.27, 0.76); 95%CI; P<0.01)
(Figure 5). The trial by Heine et al. [20] found no statistical difference in the overall
incidence of hypoglycemic episodes (events/patient/year) between exenatide and
insulin glargine (n=549; 1.1[-1.3 to 3.4]). The trial by Nauck et al. [21] also showed
similar rates at endpoint (Mean [SEM]: exenatide 4.7 [0.7], premixed insulin 5.6 [0.7];
WMD -0.90 (-1.02 to -0.78); P<0.01)
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Severe hypoglycemia (requiring third-party medical assistance) was rare with
GLP-1 RA, reported in only 10 out of 1130 patients treated with exenatide compared
with 15 out of 1103 patients treated with insulin. No statistically significant increase
in risk of severe hypoglycemia was demonstrated with GLP-1 RA (M-H OR 0.65(0.29,
1.45); 95%CI; P=0.29). When all data were combined, nocturnal hypoglycemia was
less commonly reported in the GLP-1 RA group than in the insulin group.
6. Adverse effects
Data of diverse adverse effects were available for quantitative evaluation in 5
clinical trials [18-22]. The most commonly reported adverse effects, potentially
related to GLP-1 RA, were gastrointestinal disorders of mild to moderate severity,
such as nausea, diarrhea or vomiting, with a significantly increased risk ratio when
compared with insulin (M-H OR 15.00(5.44,41.35); 95%CI; P<0.01) (Figure 6).
Life-threatening adverse events were rarely reported. Individual cases are presented in
Table 3.
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Discussion
The introduction of GLP-1 RA over the past few years as new therapeutic agents
has led to a novel choice of treatment strategy in T2DM inadequately controlled by
MET and/or SU. Recent studies have suggested that GLP-1 RA may better control
glycemia, inducing weight loss and causing less hypoglycemia compared to insulin
therapy. Our current meta-analysis that involved 2782 T2DM patients confirmed these
findings.
The slightly larger reduction in HbA1c by GLP-1 RA compared with insulin was
not surprising, as the physiologic role of GLP-1 RA is to augment glucose-stimulated
insulin secretion. However, insulin was still more effective in reducing FPG. The two
agents lowered HbA1c through different mechanisms: GLP-1 RA primarily affected
PPG excursions with a modest effect on fasting glucose, whereas insulin
predominantly reduced FPG without influencing PPG levels [17]. This can explain the
more significant reduction of FPG with insulin therapy and the lower PPG
concentrations with GLP-1 RA therapy. Within a certain range, the reduction of
HbA1c with GLP-1 RA therapy was dependent on the baseline HbA1c, so that greater
reductions were seen in groups with higher baseline HbA1c. The smaller reductions in
HbA1c in these trials may be also attributed, at least in part, to the relatively low
baseline HbA1c (8.6%, or 70mmol/mol) compared with earlier trials with other
therapies where baseline HbA1c was higher, often in the 9-10% (75-86mmol/mol)
range. However, GLP-1 RA may be more suitable in early disease during which there
is more residual beta-cell function. For patients whose baseline HbA1c were
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comparatively high, treatment with GLP-1 RA was not very effective [16]. In another
pilot study of patients with longer diabetes duration and taking high doses of insulin
substituting exenatide for insulin was not associated with a favorable outcome [25]. In
comparison, insulin therapy is independent of residual pancreatic activity and is
therefore effective at all stages of the disease. These results suggest that the choice of
therapy should depend on HbA1c levels, the stage of disease (percentage of remaining
beta cells), and the target glucose level for each individual.
In contrast to the weight gain commonly observed with insulin treatment, there was
a substantial and progressive decrease in body weight associated with the use of
GLP-1 RA. Weight loss was found in one study to be associated with long-term
improvements in CV risk factors, decreased lipids and BP levels, as well as a reduced
need for antihypertensive medication [26]. It has been shown that a 5-kg weight gain
in individual can increase coronary heart disease risk as much as 30% [27]. Thus, the
average 4.4kg weight loss could be important in T2DM patients. The difference in
weight change was especially large in patients treated with GLP-1 RA compared to
biphasic insulin aspart, partly because higher daily doses of insulin were used in the
biphasic insulin aspart groups (insulin dose reached 96.1U/d in one trial [16]),
compared to the other trials where insulin doses remained at about 30U/d. It was
reported that weight loss was continuous and continued when HbA1c was not further
reduced [20]. Moreover, data from open-label extension trials indicated that weight
loss with exenatide remained progressive for up to 2 years [28]. Weight loss appeared
to be independent of gastro-intestinal side effects, as similar weight reductions were
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observed in subjects who did not experience side effects [29].
A low number of hypoglycemia events seen in all studies confirmed the
glucose-dependent action of GLP-1 RA. The low risk of hypoglycemia offers
advantages over other therapies, as hypoglycemia is a major problem for medication
adherence [30]. However, hypoglycemia can still occur when GLP-1 RA therapy is
combined with an insulin secretagogue such as a sulfonylurea [15,19,21]. Therefore,
the dose of an insulin secretagogue should be adjusted when it is combined with
GLP-1 RA[31]. GLP-1 RA can also cause transient and rarely serious, gastrointestinal
side effects which are less pronounced with exenatide LAR and liraglutide[31].
Some limitations of this study need to be considered. First, the limited number of
randomized trials available does not allow us to draw any definitive conclusion about
the efficacy and safety of these two therapies in patients failing on oral agents. Second,
none of the trials that we identified in this study were double-blinded. Most of them
were open-label studies due to a requirement of dose adjustments to ensure optimal
therapeutic effects. Third, only two types of insulin (glargine/BIAsp) were included in
the analysis. In addition, patients with HbA1c>11% (97mmol/mol) were not included
and long-term data for the efficacy and safety of GLP-1 RA were not available. These
trials exhibited a wide variation in study duration (from 16 weeks to 52 weeks) and
were accompanied by differences in baseline data and diagnostic threshold for
detecting and reporting outcomes. Unfortunately, the limited data do not allow
adjustment for these confounders. Fourth, most studies included predominantly
Caucasian participants; therefore, differential effects of race or ethnicity on GLP-1
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RA therapy could not be fully assessed due to limited data. Finally, most studies did
not use a true intention-to-treat (ITT) analysis. This may have resulted in
overestimation of the glycemic efficacy, especially given the relatively high dropout
rate. Further trials should be performed to test whether the effects of GLP-1 RA are
durable and whether, in the long term, they could modify the natural course of T2DM.
These results can not apply to all incretin-based therapy. Only two trials using
GLP-1 RA were not exenatide-related (one using liraglutide [21] and another using
exenatide LAR [18]). Recent data have shown that once-daily liraglutide and once
weekly exenatide LAR had a greater impact on HbA1c than exenatide and produced a
substantial reduction of FPG and weight loss [32-33]. A new study [34] has shown
that GLP-1 RA treatment was well tolerated and reduced HbA1c and body weight
more than DDP-IV inhibitors in patients inadequately controlled with metformin.
However, further studies are needed to address the long-term effects of these drugs.
Preliminary results of combination therapy with GLP-1 RA and insulin are
promising [35]. A study presented by Richard Bergenstal at the 46th EASD annual
meeting demonstrated that exenatide plus insulin resulted in greater improvement in
HbA1c glucose profile (with no increase in hypoglycemia), and in a modest weight
loss compared to the control group treated with insulin alone. Another prospective
study [36] has also shown that exenatide plus insulin therapy in obese patients with
T2DM was associated with significant reductions in body weight and insulin doses. In
addition, a proof-of-concept study [37] has shown improvement in PPG with addition
of a GLP-1 receptor agonist to combination therapy with insulin glargine and MET.
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Combination therapy with GLP-1 RA and insulin is currently not approved by the
FDA. However, it may be a promising therapeutic strategy for those patients with
insulin resistance and poor glycaemic control due to obesity. Additional
well-designed clinical trials will be required to test this hypothesis.
In summary, GLP-1 RA, such as exenatide and liraglutide have modest but
beneficial effects on glycemic control compared to insulin (insulin glargine or BIAsp)
and are associated with significant weight loss. They are also relatively safe in regard
to the adverse events studied. However, GLP-1 RA is not a substitute for insulin.
Further prospective clinical trials are needed to fully evaluate their long-term
effectiveness and safety and their place in the treatment of T2DM.
22
Acknowledgments
This work was supported by research grants from the National Natural Science Foundation of
China (30871199, 81070640, 30971388, 30771037) and Doctoral Fund of Ministry of Education
of China(20105503110002).
Declaration of Interests
This article has no declaration of competing interests to report.
23
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Corresponding author:
Gangyi Yang, Department of Endocrinology, the Second Affiliated Hospital,
Chongqing Medical University, 400010 Chongqing, China
Tel: +86-23-68485216 / Fax: +86-23-68486115 e-mail: [email protected]
29
Figure legends Figure 1. Flowchart of search strategy and results
30
932 articles screened. Medline (n=407) Embase (n-508) Cochrane (n=17)
107 full-text manuscript or conference abstract retrieved for detailed evaluation
825 excluded ased on abstract review: 392 duplicate publications from different
database 67 abstracts or letters only 123 reviews 243 unrelated themes
19 potential eligible reports.
88 Excluded: 39 No T2DM patients 35 No comparable insulin interventions or
adjunctive to insulin 3 Means of administration(1 inhaled; 2
intravenous) 11 Study durations<16 weeks
11 excluded: 6 not reporting HbA1c as first outcome: 1 glycemic variability; 1 PPG excursions;
4 cost-effectiveness evaluations 2 secondary analysis based on original
trials: 1 patient-report outcomes; 1 pooled post-hoc study
2 not RCTs: 1 cohort study; 1 matched study
1 GLP-1 RA substitution of insulin
8 RCTs included[14-21]
31
Figure 2. Forest plot illustrating the change in HbA1c levels following treatment with GLP-1 RA or insulin. IV= inverse variance.
Study or Subgroup2.1.1 exenatide vs insulinBarnett et al.2007Bergenstal et al.2009Bunck et al.2009Davies et al.2009Heine et al 2005Nauck et al.2007Subtotal (95% CI)Heterogeneity: Tau² = 0.03; Chi² = 16.39, df = 5 (P = 0.006); I² = 69%Test for overall effect: Z = 1.31 (P = 0.19)
2.1.2 glp-1 vs glargineBarnett et al.2007Bunck et al.2009Davies et al.2009Diamante et al.2010Heine et al 2005Russell-jones et al.2009Subtotal (95% CI)Heterogeneity: Tau² = 0.00; Chi² = 6.90, df = 5 (P = 0.23); I² = 28%Test for overall effect: Z = 1.49 (P = 0.14)
2.1.3 GLP-1 vs non-TTT insulinBergenstal et al.2009Bunck et al.2009Diamante et al.2010Heine et al 2005Nauck et al.2007Russell-jones et al.2009Subtotal (95% CI)Heterogeneity: Tau² = 0.02; Chi² = 15.52, df = 5 (P = 0.008); I² = 68%Test for overall effect: Z = 2.40 (P = 0.02)
2.1.4 totalBarnett et al.2007Bergenstal et al.2009Bunck et al.2009Davies et al.2009Diamante et al.2010Heine et al 2005Nauck et al.2007Russell-jones et al.2009Subtotal (95% CI)Heterogeneity: Tau² = 0.02; Chi² = 18.26, df = 7 (P = 0.01); I² = 62%Test for overall effect: Z = 2.20 (P = 0.03)
Mean
-1.36-2.67
-0.8-1.25
-1-1.04
-1.36-0.8
-1.25-1.5
-1-1.33
-2.67-0.8-1.5
-1-1.04-1.33
-1.36-2.67
-0.8-1.25
-1.5-1
-1.04-1.33
SD
1.031.79
0.60.89
11.11
1.030.6
0.890.76
11.37
1.790.6
0.761
1.111.37
1.031.79
0.60.890.76
11.111.37
Total
1361243698
275253922
1363698
228275232
1005
12436
228275253232
1148
1361243698
228275253232
1382
Mean
-1.36-1.75
-0.7-1.26-1.05-0.89
-1.36-0.7
-1.26-1.3
-1.05-1.09
-1.75-0.7-1.3
-1.05-0.89-1.09
-1.36-1.75
-0.7-1.26
-1.3-1.05-0.89-1.09
SD
1.011.571.15
0.90.970.94
1.011.15
0.90.920.971.38
1.571.150.920.970.941.38
1.011.571.15
0.90.920.970.941.38
Total
12712433
102260248894
12733
102220260234976
12433
220260248234
1119
12712433
102220260248234
1348
Weight
17.3%16.8%
9.1%15.7%20.7%20.4%
100.0%
14.9%4.8%
12.1%21.7%24.3%22.3%
100.0%
15.2%7.5%
18.9%19.8%19.5%19.1%
100.0%
12.1%11.6%
5.4%10.6%14.7%15.5%15.2%14.9%
100.0%
IV, Random, 95% CI
0.00 [-0.24, 0.24]-0.54 [-0.80, -0.29]-0.11 [-0.58, 0.36]0.01 [-0.27, 0.29]0.05 [-0.12, 0.22]
-0.15 [-0.32, 0.03]-0.12 [-0.30, 0.06]
0.00 [-0.24, 0.24]-0.11 [-0.58, 0.36]0.01 [-0.27, 0.29]
-0.24 [-0.42, -0.05]0.05 [-0.12, 0.22]
-0.17 [-0.36, 0.01]-0.08 [-0.19, 0.03]
-0.54 [-0.80, -0.29]-0.11 [-0.58, 0.36]
-0.24 [-0.42, -0.05]0.05 [-0.12, 0.22]
-0.15 [-0.32, 0.03]-0.17 [-0.36, 0.01]
-0.19 [-0.34, -0.03]
0.00 [-0.24, 0.24]-0.54 [-0.80, -0.29]-0.11 [-0.58, 0.36]0.01 [-0.27, 0.29]
-0.24 [-0.42, -0.05]0.05 [-0.12, 0.22]
-0.15 [-0.32, 0.03]-0.17 [-0.36, 0.01]
-0.14 [-0.27, -0.02]
GLP-1 insulin Std. Mean Difference Std. Mean DifferenceIV, Random, 95% CI
-0.5 -0.25 0 0.25 0.5Favours GLP-1 Favours insulin
32
Figure 3. Forest plot illustrating the change in fasting plasma glucose levels following treatment with GLP-1 RA or insulin. IV= inverse variance.
Study or SubgroupBarnett et al.2007Bergenstal et al.2009Bunck et al.2009Davies et al.2009Diamante et al.2010Heine et al 2005Nauck et al.2007
Total (95% CI)Heterogeneity: Tau² = 0.93; Chi² = 142.75, df = 6 (P < 0.00001); I² = 96%Test for overall effect: Z = 3.10 (P = 0.002)
Mean-2.9-1.2-1.6
-2.12-2.1
-1.25-1.8
SD2.330.31.8
2.542.9
2.183.2
Total13612436
103214282253
1148
Mean-4.1-3.5-2.9
-3.61-2.8
-2.56-1.7
SD2.25
0.32.3
2.512.9
2.163.15
Total12712433
101207267248
1107
Weight14.4%15.6%12.3%13.8%14.4%15.1%14.4%
100.0%
IV, Random, 95% CI1.20 [0.65, 1.75]2.30 [2.23, 2.37]1.30 [0.32, 2.28]1.49 [0.80, 2.18]0.70 [0.15, 1.25]1.31 [0.95, 1.67]
-0.10 [-0.66, 0.46]
1.18 [0.43, 1.93]
GLP-1 insulin Mean Difference Mean DifferenceIV, Random, 95% CI
-2 -1 0 1 2Favours GLP-1 Favours insulin
33
Figure 4. Forest plot illustrating the change in body weight from baseline to endpoint following treatment with GLP-1 RA or insulin. IV=inverse variance.
Study or Subgroup4.1.1 exenatide vs insulinBarnett et al.2007Bergenstal et al.2009Bunck et al.2009Davies et al.2009Heine et al 2005Nauck et al.2007Subtotal (95% CI)Heterogeneity: Tau² = 1.71; Chi² = 55.24, df = 5 (P < 0.00001); I² = 91%Test for overall effect: Z = 8.08 (P < 0.00001)
4.1.2 exenatide vs biphasic insulinBergenstal et al.2009Nauck et al.2007Subtotal (95% CI)Heterogeneity: Tau² = 0.00; Chi² = 0.84, df = 1 (P = 0.36); I² = 0%Test for overall effect: Z = 21.67 (P < 0.00001)
4.1.3 GLP-1 vs glargineBarnett et al.2007Bunck et al.2009Davies et al.2009Diamante et al.2010Heine et al 2005Russell-jones et al.2009Subtotal (95% CI)Heterogeneity: Tau² = 0.97; Chi² = 35.13, df = 5 (P < 0.00001); I² = 86%Test for overall effect: Z = 8.81 (P < 0.00001)
4.1.4 totalBarnett et al.2007Bergenstal et al.2009Bunck et al.2009Davies et al.2009Diamante et al.2010Heine et al 2005Nauck et al.2007Russell-jones et al.2009Subtotal (95% CI)Heterogeneity: Tau² = 1.23; Chi² = 63.52, df = 7 (P < 0.00001); I² = 89%Test for overall effect: Z = 10.28 (P < 0.00001)
Mean
-1.6-1.9-3.6
-2.73-2.32-2.5
-1.9-2.5
-1.6-3.6
-2.73-2.6
-2.32-1.8
-1.6-1.9-3.6
-2.73-2.6
-2.32-2.5-1.8
SD
3.53.83.63.1
3.193.2
3.83.2
3.53.63.13.1
3.195
3.53.83.63.13.1
3.193.2
5
Total
136124
36100231253880
124253377
13636
100233231230966
136124
36100233231253230
1343
Mean
0.64.1
12.981.75
2.9
4.12.9
0.61
2.981.4
1.751.6
0.64.1
12.98
1.41.75
2.91.6
SD
3.385.44.6
3.163.28
3.1
5.43.1
3.384.6
3.163
3.285.03
3.385.44.6
3.163
3.283.1
5.03
Total
127124
33104244248880
124248372
12733
104223244232963
127124
33104223244248232
1335
Weight
17.5%16.0%12.2%17.4%18.4%18.5%
100.0%
18.4%81.6%
100.0%
17.5%10.2%17.3%19.1%19.0%16.9%
100.0%
13.0%11.6%
8.2%12.9%13.9%13.9%14.0%12.6%
100.0%
IV, Random, 95% CI
-2.20 [-3.03, -1.37]-6.00 [-7.16, -4.84]-4.60 [-6.56, -2.64]-5.71 [-6.57, -4.85]-4.07 [-4.65, -3.49]-5.40 [-5.95, -4.85]-4.65 [-5.78, -3.52]
-6.00 [-7.16, -4.84]-5.40 [-5.95, -4.85]-5.51 [-6.01, -5.01]
-2.20 [-3.03, -1.37]-4.60 [-6.56, -2.64]-5.71 [-6.57, -4.85]-4.00 [-4.56, -3.44]-4.07 [-4.65, -3.49]-3.40 [-4.31, -2.49]-3.95 [-4.83, -3.07]
-2.20 [-3.03, -1.37]-6.00 [-7.16, -4.84]-4.60 [-6.56, -2.64]-5.71 [-6.57, -4.85]-4.00 [-4.56, -3.44]-4.07 [-4.65, -3.49]-5.40 [-5.95, -4.85]-3.40 [-4.31, -2.49]-4.40 [-5.23, -3.56]
GLP-1 insulin Mean Difference Mean DifferenceIV, Random, 95% CI
-4 -2 0 2 4Favours GLP-1 Favours insulin
34
Figure 5. Forest plot illustrating the overall incidence of hypoglycemia following treatment with GLP-1 RA or insulin. M-H= Mantel-Haenszel.
Study or SubgroupBarnett et al.2007Bergenstal et al.2009Bunck et al.2009Davies et al.2009Diamante et al.2010Russell-jones et al.2009
Total (95% CI)Total eventsHeterogeneity: Tau² = 0.30; Chi² = 23.03, df = 5 (P = 0.0003); I² = 78%Test for overall effect: Z = 3.03 (P = 0.002)
Events2036
3591963
200
Total136124
36118233230
877
Events3276
8685867
309
Total12712433
116223232
855
Weight17.1%18.3%
8.3%18.5%18.0%19.9%
100.0%
M-H, Random, 95% CI0.51 [0.28, 0.95]0.26 [0.15, 0.44]0.28 [0.07, 1.18]0.71 [0.42, 1.18]0.25 [0.14, 0.44]0.93 [0.62, 1.39]
0.45 [0.27, 0.76]
GLP-1 insulin Odds Ratio Odds RatioM-H, Random, 95% CI
0.1 0.2 0.5 1 2 5 10Favours GLP-1 Favours insulin
Figure 6. Forest plot illustrating the overall incidence of adverse events following treatment with GLP-1 RA or insulin. M-H= Mantel-Haenszel.
Study or SubgroupDavies et al.2009Diamante et al.2010Heine et al 2005Nauck et al.2007Russell-jones et al.2009
Total (95% CI)Total eventsHeterogeneity: Tau² = 1.25; Chi² = 60.15, df = 4 (P < 0.00001); I² = 93%Test for overall effect: Z = 5.23 (P < 0.00001)
Events8372
263153
85
656
Total118233282253230
1116
Events2522431411
115
Total116223267248232
1086
Weight20.0%20.3%20.1%20.0%19.7%
100.0%
M-H, Random, 95% CI8.63 [4.77, 15.62]
4.09 [2.43, 6.88]72.11 [40.84, 127.32]
25.57 [14.10, 46.38]11.78 [6.07, 22.83]
15.00 [5.44, 41.35]
GLP-1 insulin Odds Ratio Odds RatioM-H, Random, 95% CI
0.01 0.1 1 10 100Favours GLP-1 Favours insulin
35
Conflict of interest details: Design: Ling Li, Gangyi Yang Conduct/data collection:Yisu Wang, Mengliu Yang Analysis: Yisu Wang,Ling Li Writing manuscript and revise: Yisu Wang, Hua Liu, Guenther Boden, Gangyi Yang Authorship details: This article has no declaration of competing interests to report. The authors also denied any affiliation to any organization or entity which is relevant to the work reported.