INSULINA X GLP-1 AGONISTAS

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

1
Department of Endocrinology, the Second Affiliated Hospital, Chongqing Medical

University, 400010 Chongqing, China

2
The Key Laboratory of Laboratory Medical Diagnostics in the Ministry of Education

and Department of Clinical Biochemistry, Chongqing Medical University, 400016

Chongqing, China

3
Department of Pediatrics, University of Mississippi Medical Center, 2500 North

State Street, Jackson, Mississippi, MS 39216-4505, USA

4
The 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: gangyiyang@yahoo.com.cn

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

1

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

3

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

4

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

9

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].

10

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

13

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

14

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.

21

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.

22

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treatment of type 2 diabetes (DURATION-1 trial). Expert Opin Pharmacother

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27

35. Tzefos M, Olin JL. Glucagon-like Peptide-1 analogue and insulin combination

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1509-1515.

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: gangyiyang@yahoo.com.cn

28

Figure legends

Figure 1. Flowchart of search strategy and results

29

932 articles screened.
Medline (n=407)
Embase (n-508)
Cochrane (n=17) 825 excluded ased on abstract review:
392 duplicate publications from different
database
67 abstracts or letters only
123 reviews
243 unrelated themes

107 full-text manuscript
or conference abstract
retrieved for detailed
evaluation
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

19 potential eligible
reports.
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]

30

Figure 2. Forest plot illustrating the change in HbA1c levels following treatment with
GLP-1 RA or insulin. IV= inverse variance.

GLP-1 insulin Std. Mean Difference Std. Mean Difference
Study or Subgroup Mean SD Total Mean SD Total Weight IV, Random, 95% CI IV, Random, 95% CI
2.1.1 exenatide vs insulin
Barnett et al.2007 -1.36 1.03 136 -1.36 1.01 127 17.3% 0.00 [-0.24, 0.24]
Bergenstal et al.2009 -2.67 1.79 124 -1.75 1.57 124 16.8% -0.54 [-0.80, -0.29]
Bunck et al.2009 -0.8 0.6 36 -0.7 1.15 33 9.1% -0.11 [-0.58, 0.36]
Davies et al.2009 -1.25 0.89 98 -1.26 0.9 102 15.7% 0.01 [-0.27, 0.29]
Heine et al 2005 -1 1 275 -1.05 0.97 260 20.7% 0.05 [-0.12, 0.22]
Nauck et al.2007 -1.04 1.11 253 -0.89 0.94 248 20.4% -0.15 [-0.32, 0.03]
Subtotal (95% CI) 922 894 100.0% -0.12 [-0.30, 0.06]
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 glargine
Barnett et al.2007 -1.36 1.03 136 -1.36 1.01 127 14.9% 0.00 [-0.24, 0.24]
Bunck et al.2009 -0.8 0.6 36 -0.7 1.15 33 4.8% -0.11 [-0.58, 0.36]
Davies et al.2009 -1.25 0.89 98 -1.26 0.9 102 12.1% 0.01 [-0.27, 0.29]
Diamante et al.2010 -1.5 0.76 228 -1.3 0.92 220 21.7% -0.24 [-0.42, -0.05]
Heine et al 2005 -1 1 275 -1.05 0.97 260 24.3% 0.05 [-0.12, 0.22]
Russell-jones et al.2009 -1.33 1.37 232 -1.09 1.38 234 22.3% -0.17 [-0.36, 0.01]
Subtotal (95% CI) 1005 976 100.0% -0.08 [-0.19, 0.03]

2.1.3 GLP-1 vs non-TTT insulin
Bergenstal et al.2009 -2.67 1.79 124 -1.75 1.57 124 15.2% -0.54 [-0.80, -0.29]
Bunck et al.2009 -0.8 0.6 36 -0.7 1.15 33 7.5% -0.11 [-0.58, 0.36]
Diamante et al.2010 -1.5 0.76 228 -1.3 0.92 220 18.9% -0.24 [-0.42, -0.05]
Heine et al 2005 -1 1 275 -1.05 0.97 260 19.8% 0.05 [-0.12, 0.22]
Nauck et al.2007 -1.04 1.11 253 -0.89 0.94 248 19.5% -0.15 [-0.32, 0.03]
Subtotal (95% CI) 1148 1119 100.0% -0.19 [-0.34, -0.03]

2.1.4 total
Barnett et al.2007 -1.36 1.03 136 -1.36 1.01 127 12.1% 0.00 [-0.24, 0.24]
Bergenstal et al.2009 -2.67 1.79 124 -1.75 1.57 124 11.6% -0.54 [-0.80, -0.29]
Bunck et al.2009 -0.8 0.6 36 -0.7 1.15 33 5.4% -0.11 [-0.58, 0.36]
Davies et al.2009 -1.25 0.89 98 -1.26 0.9 102 10.6% 0.01 [-0.27, 0.29]
Diamante et al.2010 -1.5 0.76 228 -1.3 0.92 220 14.7% -0.24 [-0.42, -0.05]
Heine et al 2005 -1 1 275 -1.05 0.97 260 15.5% 0.05 [-0.12, 0.22]
Nauck et al.2007 -1.04 1.11 253 -0.89 0.94 248 15.2% -0.15 [-0.32, 0.03]
Subtotal (95% CI) 1382 1348 100.0% -0.14 [-0.27, -0.02]

-0.5 -0.25 0 0.25 0.5
Favours GLP-1 Favours insulin

31

Figure 3. Forest plot illustrating the change in fasting plasma glucose levels following
treatment with GLP-1 RA or insulin. IV= inverse variance.

GLP-1 insulin Mean Difference Mean Difference
Barnett et al.2007 -2.9 2.33 136 -4.1 2.25 127 14.4% 1.20 [0.65, 1.75]
Bergenstal et al.2009 -1.2 0.3 124 -3.5 0.3 124 15.6% 2.30 [2.23, 2.37]
Bunck et al.2009 -1.6 1.8 36 -2.9 2.3 33 12.3% 1.30 [0.32, 2.28]
Davies et al.2009 -2.12 2.54 103 -3.61 2.51 101 13.8% 1.49 [0.80, 2.18]
Diamante et al.2010 -2.1 2.9 214 -2.8 2.9 207 14.4% 0.70 [0.15, 1.25]
Heine et al 2005 -1.25 2.18 282 -2.56 2.16 267 15.1% 1.31 [0.95, 1.67]
Nauck et al.2007 -1.8 3.2 253 -1.7 3.15 248 14.4% -0.10 [-0.66, 0.46]

Total (95% CI) 1148 1107 100.0% 1.18 [0.43, 1.93]
Heterogeneity: Tau² = 0.93; Chi² = 142.75, df = 6 (P < 0.00001); I² = 96%
-2 -1 0 1 2

32

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.

GLP-1 insulin Mean Difference Mean Difference
4.1.1 exenatide vs insulin
Barnett et al.2007 -1.6 3.5 136 0.6 3.38 127 17.5% -2.20 [-3.03, -1.37]
Bergenstal et al.2009 -1.9 3.8 124 4.1 5.4 124 16.0% -6.00 [-7.16, -4.84]
Bunck et al.2009 -3.6 3.6 36 1 4.6 33 12.2% -4.60 [-6.56, -2.64]
Davies et al.2009 -2.73 3.1 100 2.98 3.16 104 17.4% -5.71 [-6.57, -4.85]
Heine et al 2005 -2.32 3.19 231 1.75 3.28 244 18.4% -4.07 [-4.65, -3.49]
Nauck et al.2007 -2.5 3.2 253 2.9 3.1 248 18.5% -5.40 [-5.95, -4.85]
Subtotal (95% CI) 880 880 100.0% -4.65 [-5.78, -3.52]
Test for overall effect: Z = 8.08 (P < 0.00001)

4.1.2 exenatide vs biphasic insulin
Bergenstal et al.2009 -1.9 3.8 124 4.1 5.4 124 18.4% -6.00 [-7.16, -4.84]
Nauck et al.2007 -2.5 3.2 253 2.9 3.1 248 81.6% -5.40 [-5.95, -4.85]
Subtotal (95% CI) 377 372 100.0% -5.51 [-6.01, -5.01]

4.1.3 GLP-1 vs glargine
Barnett et al.2007 -1.6 3.5 136 0.6 3.38 127 17.5% -2.20 [-3.03, -1.37]
Bunck et al.2009 -3.6 3.6 36 1 4.6 33 10.2% -4.60 [-6.56, -2.64]
Davies et al.2009 -2.73 3.1 100 2.98 3.16 104 17.3% -5.71 [-6.57, -4.85]
Diamante et al.2010 -2.6 3.1 233 1.4 3 223 19.1% -4.00 [-4.56, -3.44]
Heine et al 2005 -2.32 3.19 231 1.75 3.28 244 19.0% -4.07 [-4.65, -3.49]
Russell-jones et al.2009 -1.8 5 230 1.6 5.03 232 16.9% -3.40 [-4.31, -2.49]
Subtotal (95% CI) 966 963 100.0% -3.95 [-4.83, -3.07]

4.1.4 total
Barnett et al.2007 -1.6 3.5 136 0.6 3.38 127 13.0% -2.20 [-3.03, -1.37]
Bergenstal et al.2009 -1.9 3.8 124 4.1 5.4 124 11.6% -6.00 [-7.16, -4.84]
Bunck et al.2009 -3.6 3.6 36 1 4.6 33 8.2% -4.60 [-6.56, -2.64]
Davies et al.2009 -2.73 3.1 100 2.98 3.16 104 12.9% -5.71 [-6.57, -4.85]
Diamante et al.2010 -2.6 3.1 233 1.4 3 223 13.9% -4.00 [-4.56, -3.44]
Heine et al 2005 -2.32 3.19 231 1.75 3.28 244 13.9% -4.07 [-4.65, -3.49]
Nauck et al.2007 -2.5 3.2 253 2.9 3.1 248 14.0% -5.40 [-5.95, -4.85]
Russell-jones et al.2009 -1.8 5 230 1.6 5.03 232 12.6% -3.40 [-4.31, -2.49]
Subtotal (95% CI) 1343 1335 100.0% -4.40 [-5.23, -3.56]

-4 -2 0 2 4

33

Figure 5. Forest plot illustrating the overall incidence of hypoglycemia following
treatment with GLP-1 RA or insulin. M-H= Mantel-Haenszel.

GLP-1 insulin Odds Ratio Odds Ratio
Study or Subgroup Events Total Events Total Weight M-H, Random, 95% CI M-H, Random, 95% CI
Barnett et al.2007 20 136 32 127 17.1% 0.51 [0.28, 0.95]
Bergenstal et al.2009 36 124 76 124 18.3% 0.26 [0.15, 0.44]
Bunck et al.2009 3 36 8 33 8.3% 0.28 [0.07, 1.18]
Davies et al.2009 59 118 68 116 18.5% 0.71 [0.42, 1.18]
Diamante et al.2010 19 233 58 223 18.0% 0.25 [0.14, 0.44]
Russell-jones et al.2009 63 230 67 232 19.9% 0.93 [0.62, 1.39]

Total (95% CI) 877 855 100.0% 0.45 [0.27, 0.76]
Total events 200 309
0.1 0.2 0.5 1 2 5 10

Figure 6. Forest plot illustrating the overall incidence of adverse events following
treatment with GLP-1 RA or insulin. M-H= Mantel-Haenszel.

GLP-1 insulin Odds Ratio Odds Ratio
Study or Subgroup Events Total Events Total Weight M-H, Random, 95% CI M-H, Random, 95% CI
Davies et al.2009 83 118 25 116 20.0% 8.63 [4.77, 15.62]
Diamante et al.2010 72 233 22 223 20.3% 4.09 [2.43, 6.88]
Heine et al 2005 263 282 43 267 20.1% 72.11 [40.84, 127.32]
Nauck et al.2007 153 253 14 248 20.0% 25.57 [14.10, 46.38]
Russell-jones et al.2009 85 230 11 232 19.7% 11.78 [6.07, 22.83]

Total (95% CI) 1116 1086 100.0% 15.00 [5.44, 41.35]
Total events 656 115
0.01 0.1 1 10 100

34

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.

35

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