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Diabetes, Obesity and Metabolism 13: 594 – 603, 2011. © 2011 Blackwell Publishing Ltdoriginal article
Dipeptidyl peptidase-4 inhibitors and HbA1c target of <7% in type 2 diabetes: meta-analysis of randomized controlled trials K. Esposito1, D. Cozzolino2, G. Bellastella1, M. I. Maiorino1, P. Chiodini3, A. Ceriello4 & D. Giugliano1 1Department of Geriatrics and Metabolic Diseases, Second University of Naples, Naples, Italy 2 Department of Clinical and Experimental Medicine, Second University of Naples, Naples, Italy 3 Department of Medicine and Public Health, Second University of Naples, Naples, Italy 4 Institut d’Investigacions Biomèdique August Pi i Sunyer (IDIBAPS), Barcelona, Spain
Aim: We assessed the efficacy of dipeptidyl peptidase-4 (DPP-4) inhibitors vildagliptin, sitagliptin, saxagliptin and alogliptin to reach the haemoglobin HbA1c target of <7% in people with type 2 diabetes. Methods: We conducted an electronic search for randomized controlled trials (RCTs) involving DPP-4 inhibitors through September 2010. RCTs were included if they lasted at least 12 weeks, included 30 patients or more and reported the proportion of patients reaching the HbA1c target of <7%. Results: A total of 43 RCTs reporting 52 comparisons met the selection criteria, which included 19 101 study participants evaluated for the primary endpoint, 10 467 treated with a DPP-4 inhibitor and 8634 treated with placebo or a comparator drug. DPP-4 inhibitors showed a statistically significant reduction in HbA1c compared to placebo and approximately 40% of participants achieved the HbA1c goal of <7%: this was associated with weight neutrality and no greater hypoglycaemia. The reduction of the HbA1c level and the rate of HbA1c goal attainment was not different from comparator drugs, with similar hypoglycaemia, and different effect on weight owing to the nature of comparator (metformin, sulfonylurea or glitazones). Baseline HbA1c was the best predictor for achievement of A1C target (overall weighted r2 value = 0.410, p < 0.001). Conclusions: A greater proportion of type 2 diabetic patients can achieve the HbA1c goal <7% with DPP-4 inhibitors compared to placebo, with no weight gain, and no hypoglycaemic risk when used alone; DPP-4 inhibitors were not different from comparator drugs. Keywords: DPP-IV inhibitor, HbA1c <7%, type 2 diabetes, RCTs, meta-analysis
Date submitted 11 January 2011; date of first decision 1 February 2011; date of final acceptance 4 February 2011
Introduction Fewer than half of the US adults with type 2 diabetes reach HbA1c level of less than 7% recommended by the American Diabetes Association (ADA) for most patients to minimize the risk of vascular complications [1]. According to the consensus algorithm by ADA/EASD (European Association for the Study of Diabetes) [2], adjustment of therapy should be based on the HbA1c level and a change in therapy is recommended when HbA1c is above 7%. However, efficacy of available therapies, even when used appropriately, diminishes as the disease pro- gresses because of a steady, relentless decline in pancreatic beta cell function [3]. Therefore, therapies targeting the decline in pancreatic beta cell function without causing weight gain and with minimal hypoglycaemia are desirable.
The incretin pathway is now the direct target of new classes of hypoglycaemic agents. The incretin effect is the augmentation
Correspondence to: Dario Giugliano, Department of Geriatrics and Metabolic Diseases, Second University of Naples, Piazza L. Miraglia, 2, Naples 80138, Italy. E-mail: dario.giugliano@unina2.it
of glucose-stimulated insulin secretion by intestinally derived peptides, mainly glucagon-like peptide 1 (GLP-1), which are released in the presence of glucose or nutrients in the gut [4]. Incretins are rapidly inactivated by the enzyme dipeptidyl pep- tidase 4 (DPP-4), resulting in a very short half-life (minutes). The oral DPP-4 inhibitors, which increase circulating levels of GLP-1, have recently been approved for use in type 2 dia- betes [5]. DPP-4 inhibitors may offer an alternative option to currently available hypoglycaemic agents for non-pregnant adults with type 2 diabetes [6 – 8]. The role of these new drugs in the treatment of type 2 diabetes is debated. The consensus algorithm of the ADA and the EASD [2] suggests to limit the use of DPP-4 inhibitors only to some specific cases, without considering these agents in the mainstream of the algorithm, but other views exist [9]. We were unable to identify any reviews that systematically evaluated the proportion of type 2 diabetic patients achieving the HbA1c target <7% with the DPP-4 inhibitors. In this article, we performed this meta-analysis of randomized controlled trials (RCTs) that assessed the effective- ness of different DPP-4 inhibitors to reach the current HbA1c target <7% in people with type 2 diabetes.
DIABETES, OBESITY AND METABOLISM original article Methods We completed a systematic review of RCTs that met prede- termined methodological criteria. We followed the PRISMA (Preferred Reporting Items for Systematic reviews and Meta- Analyses) checklist for reporting systematic reviews and meta- analyses [10].
Data Sources and Searches
We searched MEDLINE (1966 through September 2010), EMBASE (1980 through September 2010), the Cochrane Central Register of Controlled Trials and CINAHL from inception through September 2010. We used the following search terms: type 2 diabetes, glycohaemoglobin, haemoglobin A1c, HbA1c, A1C, incretin, GLP-1, DPP-4, vildagliptin, sitagliptin, saxagliptin, alogliptin, human and clinical trial. We searched for additional trials in the prescribing infor- mation documents of approved medications, at relevant websites (http://www.clinicalstudyresults.org and http://www. clinicaltrials.gov), and in personal reference lists of recovered articles.
Study Selection
To be included, RCTs had to be published in a peer-reviewed journal and meet the following criteria: (i) report data on non- pregnant participants aged 18 and older with type 2 diabetes; (ii) report the effect of the addition of DPP-4 inhibitors on the HbA1c level in subjects who were either drug naı̈ve or on background therapy with metformin or other oral agents; (iii) include at least 30 subjects in every arm of the study and (iv) report the effect of therapy on the HbA1c levels after a minimum of 12 weeks. RCTs were excluded if (i) they reported data on subjects who did not have type 2 diabetes; (ii) the intervention included the initiation of two agents at the same time and (iii) the doses were different from the maximum dose currently recommended in the clinical practice.
Data Abstraction
Two investigators (D. G. and K. E.), through use of a standard- ized tool, independently abstracted all data with disagreements resolved by consensus. The following information was sought from each trial: (i) author identification; (ii) year of publi- cation; (iii) study design and method quality; (iv) sample size; (v) duration of follow-up; (vi) drug, dose and schedule used and (vii) baseline characteristics (age, sex, anthropomet- rics, HbA1c and duration of diabetes). End points collected included proportion of patients achieving HbA1c of less than 7%, mean change in HbA1c, change in weight and incidence of hypoglycaemia. For hypoglycaemia, we present data on the percent of patients per treatment group who reported at least one episode of hypoglycaemia.
Methodological quality was scored using criteria set out by Jadad et al. [11]. This five-point quality scale assesses inherent controllers of bias by assessing randomization, double-blinding and patient withdrawals. These individual components were assessed and an aggregate score was calculated for each individual trial (0 = weakest; 5 = strongest). Trials scoring less than 3 were deemed to have lower methodological quality.
Statistical Analysis
The proportion of patients reaching the HbA1c value of less than 7% at the end of treatment was the primary outcome. Secondary outcomes were the absolute change of HbA1c from baseline, hypoglycaemic events and weight change. As binomial proportion lacks consensus over the calculation of the confi- dence intervals (CIs), we transformed the proportions into a quantity suitable for the usual fixed and random effects sum- maries (the Freeman – Tukey variant of the arcsine square root transformed proportion). The pooled proportion is calculated as back-transformation of the weighted mean of the trans- formed proportions, using inverse arcsine variance weights for the fixed effects model and DerSimonian – Laird weights [12] for the random effects model. Meta-regression models to check for prognostic factors of response were fitted using the SAS proc Mixed procedure. The effect of baseline HbA1c on the proportion of patients with HbA1c <7% after therapy with DPP-4 inhibitors was analysed by a weighted least-squared regression model, in which the weight was computed as the inverse of variance.
Denominators used for calculating the response rate on each treatment group were those reported within original papers as eligible patients coming from randomization. The association between exposure (treatments) and outcome (proportions of patients with HbA1c <7%) was measured by odds ratio (OR). When the OR and 95% CIs were available, we transformed them into log OR and calculated the corresponding variance and standard error using the formula proposed by Greenland [13]. When the OR was not directly available from paper, we calculated it from tabular data, using the Woolf formula to evaluate the standard error of the log OR [14]. If tabular data were not given and only response rate was available, the number of responses was calculated by multiplying the response rate by the number of randomized/eligible patients. Pooled OR (POR) with 95% CI was estimated pooling the study-specific estimates by random effect models fitted using SAS (proc Mixed) with maximum-likelihood estimate. These models provided estimates adjusted for the heterogeneity between studies and the correlation within studies given by the randomized studies with more than two groups. That is a conservative approach as it raises the variability because of the correlation within the study, providing, as a result, wider and more reliable CIs.
Changes in HbA1c and body weight were analysed as continuous variables. Separate analyses were conducted for each DPP-4 inhibitor. Weighted mean differences (WMDs) and associated 95% CIs were calculated using a DerSimonian and Laird random effects model [9]. Net changes in each study variable were calculated as the difference between treatment groups in the changes (baseline – follow up) in the mean values. Overall hypoglycaemic events were meta-analysed as dichotomous endpoints, with weighted averages reported as relative risks (RR) and associated 95% CI.
Heterogeneity of the effect across studies was assessed by the I2 statistics, which is distributed as a chi-squared statistics. A value greater than 50% represented substantial variability. The method of Macaskill et al [15] was used for assessing publication bias. It consists of a funnel-plot regression of log(RR) or log(OR) on the sample size, weighted by the inverse
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original article DIABETES, OBESITY AND METABOLISM of the pooled variance. To assess the potential confounding effect of heterogeneity, subgroup and sensitivity analyses were performed, by which trials were stratified by specific drugs or trial characteristics and data from specific trials reanalysed. Trials of shorter duration (12 – 24 weeks inclusive) and those of longer duration (>24 weeks) were analysed separately in the subgroup analyses. In addition, a sensitivity analysis was performed whereby the meta-analysis was reanalysed, excluding studies with a Jadad score of less than 3.
Results We identified 1438 citations, of which we reviewed 61 and selected 43 studies, with 52 comparisons and 19 101 patients (figure 1). All studies were RCTs [16 – 58] (Table 1). Most trials were multinational and sponsored by the industry. The trials were published between 2005 and 2010, with more than 80% of the studies published on or after 2007, and 7 (16%) in 2010. All studies were of parallel group; most studies were of double-
Figure 1. Systematic review flow diagram; n, number of trial reports. *Refs 16 – 58.
or triple-blind design and two were of open label design. The duration of the studies ranged from 12 to 52 weeks, most arms were of 24 – 26 weeks. Ten studies enrolled drug naı̈ve patients; background diabetes treatment included one or more oral antidiabetic drugs (OADs) in 25 studies and in 9 studies the subjects discontinued OADs prior to randomization. The characteristics of trial participants were as follows: mean age range: 50.2 – 58.4 years; trial duration range: 12 – 52 weeks and mean baseline A1C range: 7.3 – 9.6%.
Vildagliptin
There were 14 RCTs with vildagliptin and 14 comparisons, 7 against placebo and 7 against comparator drugs. The currently recommended dose of 100 mg/day was used in all the selected RCTs. The direct random effect meta-analysis of RCTs (figure 2A; Table 2) showed a greater chance to reach the HbA1c goal of <7% with vildagliptin compared with placebo (POR = 3.29, 95% CI: 2.5 – 4.01), but not with comparator drugs (0.82, 0.52 – 1.15). Vildagliptin was also associated with a greater decline in HbA1c from baseline compared with placebo (WMD = −0.69%, 95% CI: −0.92 to −0.45), but not with comparator drugs (0.17%, −0.19 to 0.5). There was no evidence of publication bias in these comparisons (p = 0.13 – 0.20).
Data on hypoglycaemia were retrieved from all studies (figure 2B; Table 2). Hypoglycaemia was not frequent (range 0 – 7.9%). Vildagliptin was associated with a similar risk of hypoglycaemia compared with placebo (RR: 2.2, 95% CI: 0.81 – 5.41) or comparator drugs (RR: 0.85, 0.60 – 1.30). Absolute change in weight was small and not significantly different from baseline in vildagliptin trials (0.43 kg); however, the difference with placebo was significant (1.08 kg, 95% CI: 0.61 – 1.50). Overall change in weight with vildagliptin was not different from that of comparator drugs; however, owing to the different nature of the comparator, weight decreased with metformin and increased with glitazones (Table 2).
Sitagliptin
There were 18 RCTs with sitagliptin and 22 comparisons, 12 against placebo and 10 against comparator drugs. The currently recommended dose of 100 mg/day was used in all the selected RCTs. The direct random effect meta-analysis of RCTs (figure 2A; Table 2) showed a greater chance to reach the HbA1c goal of <7% with sitagliptin compared with placebo (POR = 3.15, 95% CI: 2.47 – 3.72), but not with comparator drugs (0.70, 0.35 – 1.12); sitagliptin was also associated with a greater decline in HbA1c from baseline compared with placebo (WMD = −0.78%, 95% CI: −0.93 to −0.63), but not with comparator drugs (0.19%, −0.13 to 0.52). There was no evidence of publication bias in these comparisons (p = 0.13 – 0.20).
Data on hypoglycaemia were retrieved from all studies (figure 2B; Table 2). Sitagliptin was associated with a similar risk of hypoglycaemia compared with placebo (RR = 1.8, 95% CI: 0.61 – 2.5) or comparator drugs (RR = 0.87, 0.30 – 2.80). Absolute change in weight was small and not significantly different from baseline in sitagliptin trials (0.08 kg); however,
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DIABETES, OBESITY AND METABOLISM original article Table 1. Characteristics of the studies included in the meta-analyses.
Studies (references) N Group Weeks Design OADs HbA1c basal HbA1c �
HbA1c <7% Weight �
Overall hypoglycaemia (%)
Vildagliptin 14 RCTs Ristic [16] 63 Vildagliptin 12 R, DB, P None 7.64 (0.75) −0.53 (0.77) 46% −0.07 (2.3) 7.9
58 Placebo 7.76 (0.8) −0.13 (0.74) 23% −0.73 (2.4) 5.4 Pi-Sunyer [17] 91 Vildagliptin 24 R, DB, P Naive 8.3 (0.8) −0.8 (0.95) 39.1% −0.4 (2.8) 0
92 Placebo 8.5 (0.8) 0 (0.7) 14% −1.4 (3.7) 0 Schweizer [18] 526 Vildagliptin 52 R, DB, P Naive 8.7 (1.1) −1.1 (2.2) 35% 0.3 (4.5) 0.6
254 Metformin 8.7 (1.1) −1.4 (1.6) 45% −1.9 (4.8) 0.4 Dejager [19] 92 Vildagliptin 24 R, DB, P Naive 8.4 (0.8) −0.9 (0.9) 27.5% −0.8 (3.8) 0.6
94 Placebo 8.4 (0.8) −0.3 (0.9) 16% −1.4 (3.8) 0 Rosenstock [20] 154 Vildagliptin 24 R, DB, P Naive 8.6 (1) −1.1 (1.2) 42.5% 0.2 (3.7) 0
161 Pioglitazone 8.7 (1) −1.4 (1.2) 43% 1.5 (3.8) 0.6 Garber [21] 136 Vildagliptin 24 R, DB, P Pio 8.7 (1.29) −1.2 (1.16) 36% 2.7 (3.4) 1.5
138 Placebo 8.7 (1.2) −0.3 (1.17) 15% 1.4 (4.7) 1.9 Rosenstock [22] 459 Vildagliptin 24 R, DB, P Naive 8.7 (1.1) −1.1 (2.1) 30% −0.3 (4.2) 0.2
238 Rosiglitazone 8.7 (1.1) −1.3 (1.5) 38% 1.6 (4.6) 0 Bosi [23] 143 Vildagliptin 24 R, DB, P Met 8.4 (1) −0.9 (1.19) 38% 0.2 (3.5) 0.7
143 Placebo 8.3 (0.9) 0.2 (1.19) 16% −0.1 (3.5) 0.7 Garber [24] 132 Vildagliptin 24 R, DB, P Su 8.6 (1) −0.63 (1.03) 25% 1.3 (3.4) 3.6
144 Placebo 8.5 (1) 0.07 (1.08) 12% −0.4 (3.6) 0.6 Bolli [25] 295 Vildagliptin 24 R, DB, P Met 8.4 (1) −0.88 (0.86) 27% 0.3 (3.2) 0.34
281 Pioglitazone 8.4 (0.9) −0.98 (1) 36% 1.9 (3.3) 0 Pan [26] 441 Vildagliptin 24 R, DB, P None 8.6 (0.9) −1.4 (2.1) 46% −0.4 (2.1) 0
220 Acarbose 8.6 (1) −1.3 (1.5) 47% −1.7 (2.9) 0 Ferrannini [27] 1118 Vildagliptin 52 R, DB, P Met 7.3 (0.64) −0.44 (0.66) 54% −0.23 (3.6) 3.5
1072 Glimepiride 7.3 (0.65) −0.53 (0.65) 55% 1.56 (3.9) 16 Goodman [28] 119 Vildagliptin 24 R, DB, P Met 8.5 (1) −0.66 (1.2) 27% 0.06∗ 0.8
117 Placebo 8.7 (1.1) 0.17 (1.2) 6% −0.69∗ 0 Bosi [29] 300 Vildagliptin 24 R, DB, P None 8.7 (1.02) −1.1 (1.04) 40% −0.59 (3.7) 0.7
294 Metformin 8.6 (0.9) −1.4 (1) 43% −1.62 (3.7) 0.6 Sitagliptin 18 RCTs
Charbonnel [30] 453 Sitagliptin 24 R, DB, P Met 7.9 (0.81) −0.67 (1) 47% −0.6∗ 11.9 224 Placebo 8.03 (0.82) −0.02 (1) 18% −0.6∗ 10.5
Raz [31] 193 Sitagliptin 18 R, DB, P None 8.04 (0.8) −0.48 (0.9) 36% −0.6 (2.8) 1.5 103 Placebo 8.05 (0.9) 0.12 (0.88) 15.5% −0.7 (3.6) 0
Rosenstock [32] 163 Sitagliptin 24 R, DB, P Pio 8.1 (0.8) −0.85 (0.8) 45.4% 1.8 (4.6) 1.1 174 Placebo 8 (0.8) −0.15 (0.87) 23% 1.5 (4.7) 0
Aschner [33] 229 Sitagliptin 24 R, DB, P None 8.01 (0.88) −0.61 (0.99) 41% −0.2 (3) 1.3 244 Placebo 8.03 (0.82) 0.18 (0.95) 17% −1.1 (3.1) 0.8
Hermansen [34] 222 Sitagliptin 24 R, SB, P Met/ Met+Su
8.34 (0.76) −0.45 (0.97) 17% 0.8 (2.9) 12.1
219 Placebo 8.34 (0.74) 0.28 (0.9) 5% −0.4 (3) 1.8 Goldstein [35] 175 Sitagliptin 24 R, DB, P None 8.87 (0.99) −0.66 (1.05) 20% 0∗ 0.6
165 Placebo 8.68 (1) 0.17 (1.1) 6% −0.6∗ 0.6 177 Metformin 8.68 (0.9) −1.13 (1) 38% −1.0∗ 1.1
Hanefeld [36] 107 Sitagliptin 12 R, DB, P None 7.6 (0.9) −0.44 (0.73) 46% −0.6∗ 3 106 Placebo 7.6 (0.9) 0.12 (0.7) 29% −0.5∗ 0
Nauck [37] 382 Sitagliptin 52 R, DB, P Met 7.48 (0.76) −0.67 (0.72) 63% −1.5 (5.8) 5 411 Glipizide 7.52 (0.85) −0.67 (0.72) 59% 1.1 (5) 32
Scott [38] 121 Sitagliptin 12 R, DB, P None 7.8 (1) −0.76 (0.8) 41% 0.8 (2.2) 1.6 121 Placebo 7.9 (0.94) 0.23 (0.73) 15.5% −0.4 (2) 2.4 119 Glipizide 7.8 (0.95) −0.76 (0.8) 50% 0.9 (2.24) 12
Scott [39] 91 Sitagliptin 18 R, DB, P Met 7.75 (0.99) −0.73 (0.66) 55% −0.4 (1.9) 1.1 88 Placebo 7.68 (0.88) −0.22 (0.65) 38% −0.8 (2.2) 2 87 Rosiglitazone 7.73 (0.81) −0.79 (0.65) 63% 1.5 (2.3) 1
Raz [40] 95 Sitagliptin 30 R, DB, P Met 9.3 (0.9) −1 (1.46) 22% −0.5∗ 1 92 Placebo 9.1 (0.8) 0 (1.22) 3% −0.5∗ 0
Nonaka [41] 75 Sitagliptin 12 R, DB, P None 7.5 (0.9) −0.65 (0.65) 58% −0.1 (1.5) 0 75 Placebo 7.7 (0.9) 0.41 (0.66) 14.5% −0.7 (1.3) 0
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original article DIABETES, OBESITY AND METABOLISM Table 1. Continued.
Studies (references) N Group Weeks Design OADs HbA1c basal HbA1c �
HbA1c <7% Weight �
Overall hypoglycaemia (%)
Mohan [42] 339 Sitagliptin 18 R, DB, P None 8.7 (1) −0.7 (0.9) 21% 0.6 (1.8) 0 169 Placebo 8.7 (1) 0.3 (1.3) 5% 0 (0.6) 0
Bergenstal [43] 166 Sitagliptin 26 R, DB, P Met 8.5 (1.2) −0.9 (1.2) 30% −0.8 (3.6) 3 160 Exenatide 8.6 (1.2) −1.5 (0.9) 57% −2.3 (3.8) 1 165 Pioglitazone 8.5 (1.1) −1.2 (0.9) 43% 2.8 (3.6) 1
Pratley [44] 219 Sitagliptin 26 R, O, P Met 8.5 (0.7) −0.9 (0.89) 24% −0.96 (3.6) 5 218 Liraglutide 8.4 (0.7) −1.5 (0.9) 54% −3.38 (3.5) 5
Derosa [45] 75 Sitagliptin 52 R, DB, P Pio 8.5 (0.9) −1.4 (0.6) 50%∗ −1.6 (2.5) NR 76 Metformin 8.4 (0.8) −1.4 (0.6) 50% −2.4 (2.5) NR
Rigby [46] 56 Sitagliptin 16 R, O, P Met 8.17 (0.91) −0.4 (0.95) 27% −1.15∗ NR 56 Rosiglitazone 8.06 (0.75) −0.6 (0.9) 35% 0.26∗ NR
Scheen [47] 343 Sitagliptin 18 R, DB, P Met 7.7 (0.9) −0.62 (0.75) 39% −0.4∗ 2.8 334 Saxagliptin 7.68 (0.95) −0.52 (0.7) 33% −0.4∗ 3.2
Saxagliptin 8 RCTs Rosenstock [48] 47 Saxagliptin 12 R, DB, P Naive 7.9 (1.09) −0.9 (0.9) 47% −0.23 (2.28) 6.3
41 Placebo 7.5 (1.05) −0.27 (0.8) 20% −1.03 (2.79) 1.5 DeFronzo [49] 186 Saxagliptin 24 R, DB, P Met 8.1 (0.8) −0.69 (0.95) 43.5% −0.87∗ 0.5
175 Placebo 8.0 (0.8) 0.13 (0.94) 17% −0.92 0.6 Jadzinsky [50] 317 Saxagliptin 24 R, DB, P Naive 9.6 (1.3) −1.7 (1.7) 32% −1.1∗ 1.5
313 Metformin 9.4 (1.3) −2 (1.9) 41% −1.6∗ 4 Hollander [51] 183 Saxagliptin 24 R, DB, P Tzd 8.4 (1.1) −0.94 (1.3) 41% 1.4∗ 2.7
180 Placebo 8.2 (1.1) −0.3 (1.2) 26% 0.9∗ 3.8 Rosenstock [52] 103 Saxagliptin 24 R, DB, P Naive 8.0 (1.1) −0.46 (1) 38% −0.1∗ 5.2
92 Placebo 7.9 (0.9) 0.19 (1) 24% −1.4∗ 6.3 Chacra [53] 250 Saxagliptin 24 R, DB, P Su 8.5 (0.9) −0.64 (0.7) 23% 0.8∗ 0.8
264 Placebo 8.4 (0.9) 0.08 (0.7) 9% 0.3∗ 0.7 Scheen [47] 334 Saxagliptin 18 R, DB, P Met 7.7 (0.95) −0.52 (0.7) 33% −0.4∗ 3.2
343 Sitagliptin 7.7 (0.9) −0.62 (0.75) 39% −0.4∗ 2.8 Göke [54] 324 Saxagliptin 52 R, DB, P Met 7.46 (0.9) −0.74 (0.65) 43% −1.1 (4.1) 3
320 Glipizide 7.5 (0.9) −0.8 (0.65) 48% 1.1 (4.1) 36 Alogliptin 5 RCTs
DeFronzo [55] 133 Alogliptin 12.5 26 R, DB, P Naive 7.9 (0.9) −0.56 (0.6) 47% −0.09 (2.9) 1.5 – 3 131 Alogliptin 25 7.9 (0.9) −0.59 (0.6) 44% −0.22 (2.9) 1.5 – 3
64 Placebo 7.9 (0.9) −0.02 (0.6) 23% 0.18 (2.9) 1.5 – 3 Nauck [56] 213 Alogliptin 12.5 26 R, DB, P Met 7.9 (0.7) −0.6 (1.45) 52% 0 (5.2) 1
210 Alogliptin 25 7.9 (0.8) −0.6 (1.45) 44% −0.3 (5.1) 0 104 Placebo 8 (0.9) −0.1 (1) 18% 0 (6.9) 3
Pratley [57] 203 Alogliptin 12.5 26 R, DB, P Su 8.1 (0.8) −0.39 (0.8) 30% 0.6 (2.7) 15.8 198 Alogliptin 25 8.1 (0.9) −0.53 (0.75) 35% 0.68 (2.6) 9.6
99 Placebo 8.1 (0.9) 0.01 (0.8) 18% −0.2 (2.8) 11 Pratley [58] 197 Alogliptin 12.5 26 R, DB, P Pio/
+Met/ +Su
8.1 (0.9) −0.66 (0.9) 44% −0.58∗ 5.1
199 Alogliptin 25 8 (0.8) −0.80 (0.8) 49% −0.95∗ 7 97 Placebo 8 (0.8) −0.19 (0.9) 34% −1∗ 5.2
Rosenstock [64]† 164 Alogliptin 25 26 R, DB, P Naive 8.8 (1) −0.96 (1) 24.5% −0.29 (3.8) <3 163 Pioglitazone 8.8 (1) −1.15 (1) 34% 2.2 (3.8) <3
All data are mean (SD). OADs, oral antidiabetic drugs; HbA1c, glycated haemoglobin A1c; R, randomized; DB, double-blind; TB, triple-blind; O, open; P, parallel; CO, cross-over; Met, metformin; SU, sulfonylurea; Tzd, thiazolidinedione; Pio, pioglitazone; Rosi, rosiglitazone. ∗Reported without measures of variance. †Not used in meta-analysis (single study of comparison).
the difference with placebo was significant (0.48 kg, 95% CI: 0.19 – 0.77). Overall change in weight with sitagliptin was not different from that of comparator drugs; however, owing to the different nature of the comparator, weight decreased with metformin and increased with glipizide or glitazones (Table 2).
Saxagliptin
There were eight RCTs with saxagliptin and eight compar- isons, five against placebo and three against comparator drugs. The currently recommended dose of 5 mg/day was used in all the selected RCTs. The direct random effect meta-analysis of RCTs (figure 3A; Table 2) showed a greater chance to reach the
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Figure 2. Trials with vildagliptin and sitagliptin. (A) Proportion of patients (%) with HbA1c <7% (pooled estimate and 95% CI∗, white bars) and decrement of HbA1c (mean and 95% CI, black bars). (B) Incidence of hypoglycaemia (mean and SD, white bars) and change in weight (mean and SD, black bars). The absolute weight change was positive for vildagliptin, (first column, 0.4 kg), comparator drugs of vildagliptin (fourth column, 0.2 kg), and sitagliptin (fifth column, 0.08 kg), all not significant from baseline. ∗Mixed effect model: estimates adjusted for the correlation within studies and heterogeneity between studies.
HbA1c goal of <7% with sitagliptin compared with placebo (POR = 2.81, 95% CI: 2.31 – 3.72), but not with compara- tor drugs (0.95, 0.8 – 1.11); saxagliptin was also associated with a greater decline in HbA1c from baseline compared with placebo (WMD = −0.69%, 95% CI: −0.1 to −0.37), but not with comparator drugs (0.15%, −0.14 to 1.7). There was no evidence of publication bias in these comparisons (p = 0.13 – 0.20).
Data on hypoglycaemia were retrieved from all studies (figure 3B, Table 2). Saxagliptin was associated with similar risk of hypoglycaemia compared with placebo (RR = 1.1, 95% CI: 0.81 – 1.42) or comparator drugs (RR = 0.55, 0.4 – 1.9). Saxagliptin was associated with small and not significant
absolute weight change from baseline, but with a significant difference with placebo (0.63 kg, 95% CI: 0.03 – 1.17), and with similar weight change compared to other antidiabetic drugs (WMD = −0.56 kg, −2.8 to 1.7).
Alogliptin
There were four RCTs with alogliptin and eight comparisons, all against placebo with two different doses of the drug (12.5 and 25 mg/day). The direct random effect meta-analysis of RCTs (figure 3A; Table 2) showed a greater chance to reach the HbA1c goal of <7% with both doses compared with placebo (POR = 3.8 and 3.76, respectively); alogliptin was also associated with a greater decline in HbA1c from baseline compared with placebo [WMD = −0.48% (12.5 mg) and −0.55% (25 mg)]. There was no evidence of publication bias in these comparisons (p = 0.13 – 0.20). Data on hypoglycaemia were retrieved from all studies (figure 3B; Table 2). Both doses of alogliptin were associated with similar risk of hypoglycaemia compared with placebo (RR = 0.89 and 1.11, respectively) and with similar weight change (WMD = 0.23 and 0.13 kg, respectively).
Gender, mean age, concomitant oral drug use, and trial duration (12 vs. 16 – 52 weeks) did not significantly influence the response in the meta-regression analysis, or subgroup analysis for the primary outcome. In patients with baseline HbA1c of 8% or more, there was an association with greater decreases in HbA1c. The overall weighted r2 values for the analysis assessing the association between baseline HbA1c and proportion of patients at target were 0.410 (p < 0.001): 0.298 for vildagliptin, 0.516 for sitagliptin, 0.332 for saxagliptin and 0.596 for alogliptin. There was no significant change from results reported above when studies with a Jadad score of less than 3 were excluded from the analysis.
Discussion Several DPP-4 inhibitors are available for the treatment of type 2 diabetes, and additional agents are awaiting Food and Drug Administration (FDA) approval or are in late. The ADA position statement on standards of medical care in diabetes [59] insists on the optimal A1C target of <7% for most non- pregnant adults with type 2 diabetes: attainment of the HbA1c goal should drive the starting, adjustment and modification of drug therapy in type 2 diabetic patients at any stage of their disease [2]. In terms of clinical efficacy, as measured by the proportion of patients achieving the HbA1c goal at the end of treatment, patients treated with all DPP-4 inhibitors had an increased OR of achieving the A1c goal of <7% compared with placebo; however, none was associated with increased chance of attainment of the HbA1c goal compared with other antidiabetic drugs. In absolute terms, all DPP-4 inhibitors were associated with an attainment rate of HbA1c goal around 40%. We also found a robust association between baseline HbA1c and attainment of the HbA1c target: the lower the basal HbA1c level, the higher the chance to be at the HbA1c target. This suggest that the baseline HbA1c level must be taken into consideration in the interpretation of the results and hopefully in the design of future trials.
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original article DIABETES, OBESITY AND METABOLISM Table 2. Results of meta-analyses comparing DDP-4 inhibitors with placebo or comparator drugs on change in HbA1c, HbA1c goal achieved, overall hypoglycaemia and change in body weight.
Groups vs. No. of comparisons
No. of subjects∗
% change in HbA1c WMD (95% CI)
HbA1c goal (<7%) Pooled OR
Overall hypoglycaemia RR (95% CI)
Change in weight (kg) WMD (95% CI)
Placebo Vildagliptin 7 995/791 −0.69 (−0.92; −0.45)‡ 3.29 (2.5; 4.01)‡ 2.2 (0.81; 5.41)§ 1.08 (0.61; 1.50)§ Refs [16,17,19,21,23,24,28] Sitagliptin 12 2405/1802 −0.78 (−0.93; −0.63)‡ 3.15 (2.47; 3.72)‡ 1.8 (0.61; 2.5)§ 0.48 (0.19; 0.77)§ Refs [30 – 36,38 – 42] Saxagliptin 5 769/752 −0.69 (−1.0; −0.37)‡ 2.81 (2.31; 3.22)‡ 1.1 (0.81; 1.42)§ 0.63 (0.03; 1.17)§ Refs [48,49,51 – 53] Alogliptin (12.5 mg) 4 746/364 −0.48 (−0.6; −0.3)‡ 3.8 (2.9; 4.8)‡ 0.89 (0.61; 1.18)§ 0.23 (−0.52; 1.0)§ Alogliptin (25 mg) 4 738/364 −0.55 (−0.73; −0.37)‡ 3.76 (2.85; 4.7)‡ 1.11 (0.75; 1.4)§ 0.13 (−0.72; 1.01)§ Refs [55 – 58] Comparators† Vildagliptin 7 3293/2520 0.17 (−0.19; 0.5)‡ 0.82 (0.52; 1.15)‡ 0.85 (0.60; 1.70)‡ −0.29 (−1.8; 1.2)‡ Refs [18,20,22,25 – 27,29] vs metformin 2 826/548 0.31 (0.12; 0.48)¶ 0.79 (0.57; 1.03)‡ 1.21 (0.91; 1.5)‡ 1.6 (−0.1; 3.0)§ Refs [18,19] vs glitazones 3 908/680 0.16 (0.01; 0.32)‡ 0.83 (0.55; 1.04)‡ 0.69 (0.40; 1.1)‡ −1.6 (−2.1; −1.1)¶ Refs [20,22,25] Sitagliptin 10 1797/1806 0.19 (−0.13; 0.52)‡ 0.70 (0.35; 1.12)§ 0.87 (0.3; 2.8)‡ −0.25 (−1.48; 1.33)‡ Refs [35,37 – 39,43 – 47] vs metformin 2 250/253 0.21 (−0.05; 0.27)§ 0.76 (0.42; 1.10)‡ — 1.0 (−0.6; 2.7)‡ Refs [35,45] vs glipizide 2 506/534 0.10 (−0.09; 0.30)‡ 0.92 (0.70; 1.14)§ 0.35 (0.14; 0.95)‡ 1.7 (−2.4; −0.9)‡ Refs [37,38] vs glitazones 3 313/308 0.18 (−0.10; 0.46)‡ 0.70 (0.40; 1.05)‡ 1.2 (0.8; 1.6)‡ −2.2 (−3.1; 1.3)¶ Refs [39,43,46] Saxagliptin 3 975/976 0.15 (−1.4; 1.7)‡ 0.95 (0.8; 1.11)‡ 0.55 (0.4; 1.9)§ −0.56 (−2.8; 1.7)§ Refs [50,47,54]
∗DDP-4 inhibitor/placebo or comparator. †Comparators for vildagliptin were metformin (n = 2), pioglitazone (n = 2), rosiglitazone, acarbose and glimepiride; comparators for sitagliptin were metformin (n = 2), glipizide (n = 2), rosiglitazone (n = 2), exenatide, liraglutide, pioglitazone and saxagliptin; comparators for saxagliptin were metformin, sitagliptin and glipizide. ‡I2, 50 – 75%. §I2 > 75%. ¶I2 < 50%.
The ADA guidelines emphasize the prevention of hypo- glycaemia as critical to the treatment strategy in type 2 diabetes [59]. One should note the importance of achiev- ing glycaemic control while reducing adverse events such as hypoglycaemia or weight gain. Fear of hypoglycaemia still rep- resents a major barrier to implementation of intensive therapy as has affected adherence to the prescribed medication regimen. Severe hypoglycaemia (requiring assistance) was very rare with DPP-4 inhibitors and the low hypoglycaemia event rate seen in all studies confirms their glucose-dependent action.
A modest weight gain was seen for all DPP-4 inhibitors compared to placebo. On the other hand, the general percep- tion of weight neutrality of these drugs may not be disturbed by our findings as the amount of weight increase was neg- ligible (ranging from 0.13 to 1 kg compared with placebo); more importantly, there was a modest weight loss compared with other antidiabetic drugs, including metformin, glitazones, sulfonylureas, and acarbose, suggesting their potential in most obese and overweight type 2 diabetic patients, many of which having other comorbidities that can be affected by body weight.
Although newer medications offer more options for gly- caemic control in type 2 diabetes, they come at increased costs. In a cost-effectiveness analysis for the US population aged 25 – 64 years [60], the glycaemic control strategy based on sitagliptin as a second-line treatment is associated with an additional cost of $20 213 per person over a lifetime compared with glyburide as second-line therapy. However, the analysis was based on the assumption that the therapeutic strategies confer the same benefits in terms of reductions in major health outcomes. Moreover, another large retrospective cohort study including 786 656 type 2 diabetic patients showed increased incidence of acute pancreatitis in diabetic vs. non-diabetic patients, but did not find an association between the use of sitagliptin and acute pancreatitis [61]. Similarly, recent pooled analyses [62,63] did not find safety concerns associated with the use of vildagliptin or alogliptin.
This study has limitations. Most trials lasted less than 30 weeks, limiting the assessment of long-term efficacy. Long- term data are particularly important for any newest class of antidiabetic drugs. There is some statistical heterogeneity in
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Figure 3. Trials with saxagliptin and alogliptin. (A) Proportion of patients (%) with HbA1c <7% (pooled estimate and 95% CI∗, white bars) and decrement of HbA1c (mean and 95% CI, black bars). (B) Incidence of hypoglycaemia (mean and SD, white bars) and change in weight (mean and SD, black bars). Two different daily doses (12.5 and 25 mg) of alogliptin were compared with placebo. The absolute weight change was positive for saxagliptin (first column, 0.2 kg, not significant from baseline). ∗Mixed effect model: estimates adjusted for the correlation within studies and heterogeneity between studies.
the results of the included studies, regardless of class or drug. While this heterogeneity may have been because of study dif- ferences (design, patient characteristics, disease duration, and previous drug therapy) or confounding, it was managed by using a random-effects model. Moreover, even adjusting for many covariates, including age, study duration, duration of diabetes, gender and treatment as monotherapy or in combi- nation, there were only very few instances where the covariates had a statistically significant impact on the outcome (such as baseline HbA1c). Finally, the possibility exists that this analy- sis was influenced by publication bias given that studies with positive results are generally more likely to get published. This study also has strengths as it was restricted to RCTs that met
predetermined methodological criteria: it entailed a compre- hensive search for all DPP-4 inhibitors at the doses currently used in the clinical practice, including those of next launch (alogliptin); it focused on the effect of individual drugs and may be relevant to new drugs from the same class; lastly, it is the first to give a comprehensive analysis of the proportion of patients at target for HbA1c with each drug.
In this meta-analysis of 43 studies, including a total of 19 101 participants with type 2 diabetes, DPP-4 inhibitors showed a statistically significant reduction if HbA1c compared to placebo and approximately 40% of participants reached the HbA1c target <7%. These efficacy parameters were similar to efficacy of comparator drugs. Achievement of the HbA1c target is highly correlated with the baseline HbA1c, which suggests that the design of future trials should take into consideration this strong relation. Overall, incidence of hypoglycaemia was low with DPP-4, with no weight gain. Long-term follow-up is needed to confirm the promise of these new agents in the treatment of type 2 diabetes.
Conflict of Interest The authors declare that there is no duality of interest associated with this manuscript.
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