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Diabetologia

, 54:2315 | Cite as

UCP2 −866G/A and Ala55Val, and UCP3 −55C/T polymorphisms in association with type 2 diabetes susceptibility: a meta-analysis study

  • K. Xu
  • M. Zhang
  • D. Cui
  • Y. Fu
  • L. Qian
  • R. Gu
  • M. Wang
  • C. Shen
  • R. Yu
  • T. Yang
Article

Abstract

Aims/hypothesis

A meta-analysis was performed to assess the association between the UCP2 −866G/A, UCP2 Ala55Val and UCP3 −55C/T polymorphisms and type 2 diabetes susceptibility.

Methods

A literature-based search was conducted to identify all relevant studies. The fixed or random effect pooled measure was calculated mainly at the allele level to determine heterogeneity bias among studies. Further analyses were performed that stratified for ethnicity.

Results

We examined 17 publications. Stratified analysis for ethnicity and sensitivity analysis revealed that there was no heterogeneity between studies for these variants. Using an additive model, no significant association of the UCP2 −866G/A polymorphism with type 2 diabetes risk was observed, either in participants of Asian (OR 1.05, 95% CI 0.96, 1.16) or of European (OR 1.03, 95% CI 0.99, 1.07) descent. Neither the UCP2 Ala55Val nor the UCP3 −55C/T polymorphism showed any significant association with type 2 diabetes risk in Europeans (OR 1.04, 95% CI 0.98, 1.09 for Ala55Val; OR 1.04, 95% CI 1.00, 1.09 for −55C/T). In contrast, a statistically significant association was observed for both polymorphisms in participants of Asian descent (OR 1.23, 95% CI 1.12, 1.36 for Ala55Val; OR 1.15, 95% CI 1.03, 1.28 for −55C/T).

Conclusions/interpretation

Our meta-analysis suggests that the UCP2 −866G/A polymorphism is unlikely to be associated with increased type 2 diabetes risk in the populations investigated. In contrast, our results indicate that the UCP2 Ala55Val and UCP3 −55C/T polymorphisms may indeed be risk factors for susceptibility to type 2 diabetes in individuals of Asian descent, but not in individuals of European descent. This conclusion warrants confirmation by further studies.

Keywords

Meta-analysis Polymorphism Type 2 diabetes UCP2 UCP3 

Abbreviations

DIAGRAM

Diabetes Genetics Replication and Meta-analysis

FEM

Fixed effect model

FPRP

False-positive report probability

GWAS

Genome-wide association study/studies

HWE

Hardy–Weinberg equilibrium

REM

Random effect model

SNP

Single nucleotide polymorphism

UCP

Uncoupling protein

Introduction

Uncoupling proteins (UCPs), a family of mitochondrial transporter proteins, uncouple the transport of protons across the inner mitochondrial membrane from electron transport and the synthesis of ATP from ADP [1]. Among the five UCP homologues (UCP1 to UCP5), UCP2 and UCP3 are located adjacent to one another on human chromosome 11q13 [2, 3] and are 73% identical to each other at the amino acid sequence level [4]. Studies indicate that UCP2, as a key component of the beta cell glucose-sensing mechanism that regulates glucose-stimulated insulin secretion [5, 6, 7], is a critical link between beta cell dysfunction and type 2 diabetes [8]. It has also been observed that lower UCP3 mRNA levels are present in the skeletal muscle of type 2 diabetes patients [9].

A number of studies have examined the association between genetic variability in the UCP2UCP3 gene cluster and the risk of type 2 diabetes, with most studies focusing on three common single nucleotide polymorphisms (SNPs) [10]. These are a SNP located in a multifunctional cis-regulatory site of the UCP2 promoter region (−866G/A, rs659366), a missense variant in exon 4 of UCP2 (Ala55Val, rs660339) and a SNP 6 bp upstream from the TATA box in the core promoter region of UCP3 (−55C/T, rs1800849). The −866G/A polymorphism, which acts as a binding site for the pancreatic transcription factors Paired box-containing 6 and Insulin promoter factor 1 [11, 12], has been associated with higher UCP2 mRNA levels, reduced insulin secretion and increased type 2 diabetes risk [13, 14, 15]. The Ala55Val polymorphism has been associated with a lower degree of uncoupling, lower energy expenditure [16] and a higher risk of obesity, as well as a higher incidence of diabetes [17, 18]. Similarly, the T allele of the UCP3 −55C/T polymorphism has been associated with a reduced risk of type 2 diabetes and higher plasma total cholesterol and LDL-cholesterol [19].

Despite strong functional evidence for the involvement of these three SNPs in the regulation of uncoupling, the results of the genetic association studies on association with type 2 diabetes remain inconclusive. To further examine the potential role of these three SNPs in influencing type 2 diabetes susceptibility, we performed a meta-analysis on eligible case–control studies. Our aim was to estimate the effect of these SNPs in populations of Asian and European descent. Our results suggest that the UCP2 Ala55Val and UCP3 −55C/T polymorphisms may have a selective effect on the development of type 2 diabetes in individuals of Asian descent.

Methods

Search strategy

PubMed and Embase were searched systematically to identify all available relevant articles. The most-studied SNPs (UCP2 −866G/A, UCP2 Ala55Val and UCP3 −55C/T) were investigated using combinations of the following search terms: ‘diabetes and UCP2’, ‘UCP3’, ‘uncoupling protein 2’, ‘uncoupling protein 3’, ‘variant’, and ‘polymorphism’. The search was limited to English language papers and completed on June 10, 2011. We also used the PubMed option ‘Related Articles’ for each research article to retrieve additional potentially relevant articles. All of the included articles were also hand-searched to identify any other relevant citations. No restriction was set on the source of control participants (general population, clinic or hospital).

Inclusion and exclusion criteria

To determine whether an individual study was eligible for inclusion in the meta-analysis, all of the studies identified were carefully reviewed by two investigators working independently, any discrepancies being resolved by discussion and, when necessary, adjudicated by a third reviewer. The inclusion and exclusion criteria were as follows: First, each case–control study had to have been published as an original study designed to evaluate the association. Second, numbers in case and control groups had to be reported for each allele or genotype. Third, case–control studies had to have sufficient published data to estimate an OR with 95% CI or to provide raw data that allowed us to calculate them. Fourth, if the data were duplicated and had been published more than once, the most recent and complete study was chosen. Fifth, studies were excluded if the genotype distribution of the controls deviated from Hardy–Weinberg equilibrium (HWE). Sixth, the following were excluded: animal studies, review articles, abstracts, editorials, reports with incomplete data, studies based on pedigree data, studies on other type of diabetes (type 1 diabetes, gestational diabetes, etc.) and prospective studies.

Data extraction

Data were independently extracted by two investigators who reached a consensus on all of the items. Information extracted from each study was considered as follows: name of first author, publication year, ethnic origin of the population studied, number of participants in case and control groups, genotype and allele frequency by case/control status, and OR (95% CI). Not all papers reported the necessary statistics directly, so in some instances we transformed and estimated an OR from the reported data [20]. We did not define a minimum number of patients for a study to be included in our meta-analysis.

Statistical analysis

HWE of the genotype distribution of controls was tested by a goodness-of-fit χ 2 analysis. The distribution was considered to have deviated from HWE at p < 0.05. Pooled ORs with 95% CI were used to assess the strength of association in the additive, dominant and recessive models, respectively. Pooled estimates of the OR were obtained by calculating a weighted average of ORs from each study, with the statistical significance of the pooled OR being determined by the Z test.

To examine the possibility of heterogeneity across the studies, a statistical test for heterogeneity was performed. This was based on the χ 2-based Q statistic and I 2 metric, and quantifies between-study heterogeneity irrespective of the number of studies. Heterogeneity was considered significant at p < 0.05 for the Q statistic and I 2 > 50% for the I 2 metric. In the presence of substantial heterogeneity, the DerSimonian and Laird random effect model (REM) was adopted as the pooling method; otherwise the fixed effect model (FEM) was used [21, 22]. Meta-regression and sensitivity analysis were conducted to evaluate the key studies with a substantial impact on between-study heterogeneity. Influence analysis was performed to assess the stability of the results, with a single study in the meta-analysis being deleted each time to reflect the influence of the individual data set on the pooled OR.

The statistical power for each of the three SNPs was calculated by power and sample size software [23], and the false-positive report probability (FPRP) test of Wacholder et al. [24] was applied to address the issue of false-positive SNP associations. All genetic variants were analysed using the Begg and Egger tests for potential publication bias [25]. The significance of the intercept was determined by the t test suggested by Egger, with p < 0.10 considered representative of statistically significant publication bias. All statistical analyses were conducted using STATA version 11.0 (Stata, College Station, TX, USA).

Results

Characteristics of study

The trial flow is summarised in Fig. 1 of the electronic supplementary material (ESM). A total of 17 published articles [11, 13, 14, 19, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38] with 28 outcomes met the inclusion and exclusion criteria. All were case–control studies and most were population-based. The allele and genotype distributions in the studies included are summarised in Tables 1, 2, and 3 for the UCP2 −866G/A, UCP2 Ala55Val and UCP3 −55C/T polymorphisms respectively. The association of the UCP2 −866G/A polymorphism, the UCP2 Ala55Val polymorphism and the UCP3 −55C/T polymorphism with type 2 diabetes risk was examined in 13, 7 and 8 studies respectively. Other characteristics (sex, age, etc.) are summarised in ESM Table 1.
Table 1

Characteristics of the UCP2 −866G/A polymorphism allelic and genotype distribution for type 2 diabetes risk in studies included in the meta-analysis

Study details

Cases (n) by total and genotype

Controls (n) by total and genotype

G allele frequency (%)

 

Reference

Year

Ethnicity

Total

GG

GA

AA

Total

GG

GA

AA

Cases

Controls

OR (95% CI)b

Krempler et al. [11]

2002

European

201

65

106

30

391

186

156

49

0.587

0.675

0.68 (0.53, 0.88)c

Wang et al. [26]

2003

European

131

ND

ND

ND

118

ND

ND

ND

0.67

0.58

1.45 (1.01, 2.09)c

D’Adamo et al. [13]

2004

European

483

222

197

64

563

247

266

50

0.664

0.673

0.95 (0.79, 1.14)c

Sasahara et al. [14]

2004

Asian

413

116

205

92

172

50

90

32

0.529

0.553

0.91 (0.71, 1.17)c

Ji et al. [27]

2004

Asian

184

53

94

37

134

37

69

28

0.543

0.534

1.04 (0.76, 1.43)c

Bulotta et al. [28]

2005

European

746

374

317

55

327

142

144

41

0.713

0.654

1.32 (1.08, 1.60)c

Pinelli et al. [29]

2006

European

342

167

145

30

305

147

124

34

0.700

0.685

1.07 (0.85, 1.36)c

Hsu et al. [30]

2008

European

968

ND

ND

ND

968

ND

ND

ND

ND

0.646

1.20 (0.80, 1.70)

Hsu et al. [30]

2008

African

366

ND

ND

ND

732

ND

ND

ND

ND

0.573

0.90 (0.60, 1.40)

Hsu et al. [30]

2008

Asian

98

ND

ND

ND

195

ND

ND

ND

ND

0.586

0.80 (0.20, 2.70)

Hsu et al. [30]

2008

European

152

ND

ND

ND

303

ND

ND

ND

ND

0.533

1.00 (0.50, 2.10)

Hsu et al. [30]

2008

Mixed

1584

ND

ND

ND

2,198

ND

ND

ND

ND

0.604

1.00 (0.80, 1.30)

Lee et al. [31]

2008

Asian

761

ND

ND

ND

632

ND

ND

ND

0.531

0.492

1.17 (1.01, 1.36)

Beitelshees et al. [32]

2010

European

107

37

56

14

341

132

151

58

0.607

0.608

1.00 (0.73, 1.36)c

Heidari et al. [33]

2010

Asian

75

27

41

7

75

29

38

8

0.633

0.64

0.97 (0.61, 1.56)c

Voight et al. [34]a

2010

European

8,130

ND

ND

ND

38,987

ND

ND

ND

ND

ND

1.02 (0.98, 1.06)

Vimaleswaran et al. [35]

2011

Asian

487

185

239

63

919

358

432

129

0.62

0.63

1.00 (0.85, 1.18)c

ND, no data (no genotype data available)

aThis was a DIAGRAM study, which included eight GWAS on type 2 diabetes

bData analysed under additive model; cCalculated from the reported genotypes

Table 2

Characteristics of the UCP2 Ala55Val polymorphism allelic and genotype distribution for type 2 diabetes risk in studies included in the meta-analysis

Study details

Cases (n) by total and genotype

Controls (n) by total and genotype

C allele frequency (%)

 

Reference

Year

Ethnicity

Total

CC

CT

TT

Total

CC

CT

TT

Cases

Controls

OR(95% CI)a

Kubota et al. [36]

1998

Asian

210

60

107

43

218

64

97

57

0.54

0.516

1.10 (0.84, 1.44)b

Wang et al. [26]

2003

European

131

ND

ND

ND

118

ND

ND

ND

0.37

0.45

0.71 (0.50, 1.03)b

Cho et al. [37]

2004

Asian

500

158

227

115

133

30

76

27

0.54

0.51

1.14 (0.87, 1.49)

Hsu et al. [30]

2008

European

968

ND

ND

ND

968

ND

ND

ND

ND

0.604

1.20 (0.80, 1.70)

Hsu et al. [30]

2008

African

366

ND

ND

ND

732

ND

ND

ND

ND

0.566

0.90 (0.60, 1.50)

Hsu et al. [30]

2008

Asian

98

ND

ND

ND

195

ND

ND

ND

ND

0.562

0.90 (0.30, 3.40)

Hsu et al. [30]

2008

European

152

ND

ND

ND

303

ND

ND

ND

ND

0.543

1.40 (0.70, 2.90)

Hsu et al. [30]

2008

Mixed

1,584

ND

ND

ND

2,198

ND

ND

ND

ND

0.581

1.10 (0.80, 1.40)

Lee et al. [31]

2008

Asian

761

ND

ND

ND

632

ND

ND

ND

0.536

0.498

1.16 (1.00, 1.35)

Voight et al. [34]

2010

European

8,130

ND

ND

ND

38,987

ND

ND

ND

ND

ND

1.04 (0.98, 1.09)

Vimaleswaran et al. [35]

2011

Asian

487

264

198

25

919

408

412

99

0.75

0.67

1.45 (1.22, 1.73)b

ND, no data (no genotype data available). aData were analysed under additive model; bCalculated from the reported genotypes

Table 3

Characteristics of the UCP3 −55C/T polymorphism allelic and genotype distribution for type 2 diabetes risk in studies included in the meta-analysis

Study details

Cases (n) by total and genotype

Controls (n) by total and genotype

C allele frequency (%)

OR(95% CI)a

Reference

Year

Ethnicity

Total

CC

CT

TT

Total

CC

CT

TT

Cases

Controls

Meirhaeghe et al. [19]

2000

European

49

36

13

0

894

542

312

40

0.867

0.78

1.84 (1.01, 3.33)b

Meirhaeghe et al. [19]

2000

European

171

116

49

6

124

70

46

8

0.822

0.75

1.54 (1.03, 2.29)b

Dalgaard et al. [38]

2001

European

455

253

169

33

521

280

192

49

0.742

0.722

1.11 (0.91, 1.35)b

Cho et al. [37]

2004

Asian

499

251

204

44

132

62

59

11

0.71

0.69

1.07 (0.80, 1.44)

Pinelli et al. [29]

2006

European

342

240

94

8

305

224

78

3

0.835

0.86

0.83 (0.61, 1.13)b

Hsu et al. [30]

2008

European

968

ND

ND

ND

968

ND

ND

ND

ND

0.774

1.20 (0.70, 2.00)

Hsu et al. [30]

2008

African

366

ND

ND

ND

732

ND

ND

ND

ND

0.863

0.70 (0.40, 1.30)

Hsu et al. [30]

2008

Asian

98

ND

ND

ND

195

ND

ND

ND

ND

0.85

0.70 (0.20, 2.50)

Hsu et al. [30]

2008

European

152

ND

ND

ND

303

ND

ND

ND

ND

0.724

1.10 (0.40, 2.70)

Hsu et al. [30]

2008

Mixed

1,584

ND

ND

ND

2,198

ND

ND

ND

ND

0.811

1.00 (0.70, 1.30)

Lee et al. [31]

2008

Asian

740

ND

ND

ND

647

ND

ND

ND

0.709

0.694

1.07 (0.91, 1.26)b

Voight et al. [34]

2010

European

8,130

ND

ND

ND

38,987

ND

ND

ND

ND

ND

1.03 (0.99, 1.08)

Vimaleswaran et al. [35]

2011

Asian

487

185

239

63

919

358

432

129

0.62

0.63

1.00 (0.85, 1.18)b

ND, no data (no genotype data available). aData were analysed under additive model; bCalculated from the reported genotypes

Quantitative synthesis

Results of pooled analyses are summarised in detail in Table 4. Our meta-analysis showed no significant association between the UCP2 −866G/A polymorphism and risk of type 2 diabetes, either by additive (REM OR 1.03, 95% CI 0.95, 1.11), dominant (FEM OR 1.03, 95% CI 0.90, 1.18) or recessive (REM OR 1.00, 95% CI 0.84, 1.18) models. Moreover, no significant association was observed when an additive model was used after stratification for ethnicity (Asian descent FEM OR 1.05, 95% CI 0.96, 1.16; European descent REM OR 1.04, 95% CI 0.92, 1.17) (Fig. 1).
Table 4

Pooled measures for the association between the UCP2 −866G/A, UCP2 Ala55Val and UCP3 −55C/T polymorphisms and susceptibility to type 2 diabetes

Inherited modela

Data

Before sensitivity analysis

After sensitivity analysis

n

I2 (%)

FEM OR (95% CI)

p value

REM OR (95% CI)

p value

n

I2 (%)

OR (95% CI)

p value

Excludedb

Stc

Cad

Coe

Stc

Cad

Coe

−866G/A

 Additive

Overall

13

13,644

45,162

51.8

1.03 (0.99, 1.06)

0.172

1.03 (0.95, 1.11)

0.465

12

13,443

44,771

24.9

1.03 (1.00, 1.07)

0.071

[11]

 Additive

Asian

6

2,018

2,127

0

1.05 (0.96, 1.16)

0.294

1.05 (0.96, 1.16)

0.294

 Additive

European

8

11,260

42,152

62.8

1.02 (0.98, 1.06)

0.255

1.04 (0.92, 1.17)

0.511

7

11,059

41,912

38.0

1.03 (0.99, 1.07)

0.106

[11]

 Dominant

Overall

10

3,799

3,859

43.7

1.03 (0.90, 1.18)

0.648

1.04 (0.86, 1.26)

0.688

 Recessive

Overall

10

3,799

3,859

61.5

1.03 (0.94, 1.14)

0.522

1.00 (0.84, 1.18)

0.996

9

3,598

3,468

5.9

1.09 (0.99, 1.21)

0.090

[11]

Ala55Val

 Additive

Overall

7

11,803

43,205

68.7

1.07 (1.03, 1.12)

0.002

1.11 (0.98, 1.26)

0.095

6

11,672

43,087

27.6

1.05 (1.00, 1.10)

0.043

[34]

 Additive

Asian

5

2,056

2,097

23.7

1.23 (1.12, 1.36)

5.4 × 10−5

1.22 (1.09, 1.38)

0.001

 Additive

European

3

9,281

40,376

43.6

1.04 (0.98, 1.09)

0.189

1.01 (0.82, 1.24)

0.924

 Dominant

Overall

3

1,197

1,270

76.0

1.42 (1.10, 1.84)

0.007

1.38 (0.80, 2.37)

0.243

 Recessive

Overall

3

1,197

1,270

44.9

1.39 (1.16, 1.66)

2.8 × 10−4

1.34 (1.02, 1.76)

0.034

−55C/T

 Additive

Overall

8

12,457

44,727

47.8

1.05 (1.01, 1.10)

0.012

1.11 (1.00, 1.21)

0.047

 Additive

Asian

4

1,824

1,893

2.7

1.15 (1.03, 1.28)

0.013

1.15 (1.02, 1.29)

0.016

 Additive

European

5

10,267

42,103

40.6

1.04 (1.00, 1.09)

0.073

1.10 (0.96, 1.26)

0.183

 Dominant

Overall

5

2,003

2,895

10.0

1.33 (1.02, 1.73)

0.04

1.30 (0.96, 1.76)

0.088

 Recessive

Overall

5

2,003

2,895

41.0

1.19 (1.04, 1.36)

0.009

1.20 (1.00, 1.44)

0.056

Sensitivity analysis was conducted to reduce heterogeneity by omitting studies if STATA gave I 2 ≥ 50%

aPer SNP; breference number; cSt, Studies; dCa, Cases; eCo, Controls

Fig. 1

Stratified analysis pooled ORs for the association between the UCP2 −866G/A polymorphism and susceptibility to type 2 diabetes by ethnicity. The area of the squares reflects the study-specific weight. The diamond shows the summary fixed-effects OR estimate from 12 studies

For the UCP2 Ala55Val polymorphism, the C allele was found to be significantly associated with an increased risk of type 2 diabetes when using a recessive model (FEM OR 1.39, 95% CI 1.16, 1.66), but not when using additive (REM OR 1.11, 95% CI 0.98, 1.26) or dominant (REM OR 1.38, 95% CI 0.80, 2.37) models. However, after stratification by ethnicity, a significant association was revealed by an additive model in populations of Asian descent (FEM OR 1.23, 95% CI 1.12, 1.36), but not in those of European descent (FEM OR 1.04, 95% CI 0.98, 1.09) (Table 4 and Fig. 2).
Fig. 2

Stratified analysis pooled ORs for the association between the UCP2 Ala55Val polymorphism and susceptibility to type 2 diabetes mellitus by ethnicity. The area of the squares reflects the study-specific weight. The diamond shows the summary fixed-effects OR estimate from seven studies

Our meta-analysis also showed a significant overall association between the UCP3 −55C/T polymorphism and increased risk of type 2 diabetes in all models (additive FEM OR 1.05, 95% CI 1.01, 1.10; dominant FEM OR 1.33, 95% CI 1.02, 1.73; recessive FEM OR 1.19, 95% CI 1.04, 1.36). Surprisingly, when stratified by ethnicity, the significant association between the UCP3 −55C/T polymorphism and risk of type 2 diabetes was most evident in individuals of Asian descent (FEM OR 1.15, 95% CI 1.03, 1.28), with only a marginal significance observed in persons of European descent (FEM OR 1.04, 95% CI 1.00, 1.09) (Table 4 and Fig. 3).
Fig. 3

Stratified analysis pooled ORs for the association between the UCP3 −55C/T polymorphism and susceptibility to type 2 diabetes mellitus by ethnicity. The area of the squares reflects the study-specific weight. The diamond shows the summary fixed-effects OR estimate from eight studies

Heterogeneity and sensitivity analyses

As shown in Table 4, significant heterogeneity was observed among studies of the UCP2 −866G/A and Ala55Val polymorphisms in the overall populations, but no heterogeneity was found in the inherited models for the UCP3 −55C/T polymorphism. To investigate this further, the following covariates were considered: publication year, sex (ratio of males in cases to that in controls), age (ratio of the mean age in cases to that in controls) and sample size. However, univariate meta-regression analysis showed that none of the tested covariates could by themselves explain the observed between-study heterogeneity. To identify the studies with the greatest impact on the overall between-study heterogeneity, sensitivity analyses were conducted in the overall population. The results indicated that two studies [11, 35] were mainly responsible for the observed heterogeneity. Moreover, when the data were stratified by ethnicity and an additive model used, the heterogeneity between the studies of the UCP2 Ala55Val polymorphism was significantly decreased or eliminated in populations of Asian and European descent (Table 4). Similarly, the heterogeneity was also effectively removed from the studies of the UCP2 −866G/A polymorphism in participants of Asian descent, but still existed in studies investigating individuals of European descent.

Influence analysis

To assess the degree to which each individual study affected the overall OR estimates, influence analysis was conducted by repeating the meta-analysis sequentially excluding one study at a time. As shown in Table 4, only one study [35] was found to have an excessive influence on the pooled effect. This was limited to analysis of the UCP2 Ala55Val polymorphism in the overall population using an additive model (FEM OR 1.05, 95% CI 1.00, 1.10). Otherwise no single study excessively influenced the analyses.

Publication bias

As expected, no significant publication bias was detected in the inherited models for any of the polymorphisms examined (ESM Table 2), confirming that our results are statistically robust.

Discussion

Results from several genome-wide association studies (GWAS) in a variety of populations have identified 37 replicating type 2 diabetes susceptibility loci [34, 39, 40, 41, 42, 43, 44]. However, the biological pictures revealed by GWAS remain incomplete. Thus, many of the associations identified by GWAS do not involve previously identified type 2 diabetes candidate genes, and many of the associated markers are in genomic locations containing genes whose function is currently unknown. Recently, several studies suggested an association between the UCP2 −866G/A, UCP2 Ala55Val and UCP3 −55C/T polymorphisms and type 2 diabetes risk. Despite strong functional evidence for the relevance of these three SNPs, the results for association with type 2 diabetes show significant between-study variation. To obtain a more definitive conclusion, we conducted a meta-analysis of 17 published articles with 28 outcomes from populations of different ethnic origins [11, 13, 14, 19, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]. We believe such a meta-analysis has a much greater possibility of reaching reasonably strong conclusions.

Heterogeneity is potentially a significant problem when interpreting the results of any meta-analysis of genetic association studies [45]. Our meta-analysis also showed significant between-study heterogeneity in most of the models that we used to examine the associations of the UCP2 −866G/A and Ala55Val polymorphisms. Many of the variables that varied between the various studies might be responsible for this observed heterogeneity, including the source of the controls, sex bias, ethnicity, etc. Initial inspection of the data did not immediately identify any likely candidate variable or study that was significantly impacting on our overall results. Thus, to explore this matter further, meta-regression and ‘leave one out’ sensitivity analyses were performed [46], revealing that ethnicity was the only covariate likely to have made an important contribution to the overall between-study heterogeneity. The reason for this is unclear, but it may be that populations of different ethnicity also have environmental differences that affect their sensitivity to particular genomic variants. Similarly, based on sensitivity analyses using I 2 > 50% as the cut-off criteria, two studies [11, 35] were identified as the principal outliers in our analyses.

The study by Voight et al. [34], which is a Diabetes Genetics Replication and Meta-analysis (DIAGRAM) study and includes eight GWAS on type 2 diabetes, also met our inclusion criteria. After confirming by sensitivity analysis that it would not contribute to overall heterogeneity, we combined this with the other studies included. This additional analysis indicated that the UCP2 Ala55Val and UCP3 −55C/T polymorphisms, but not the UCP2 −866G/A polymorphism were significantly associated with type 2 diabetes risk in the overall population. As heterogeneity still existed and the DIAGRAM study was from populations of European descent, we again stratified our analysis by ethnicity. The results indicated that no obvious heterogeneities among the stratified studies existed and that the UCP2 −866G/A, UCP2 Ala55Val and UCP3 −55C/T polymorphisms had no significant association with type 2 diabetes risk in populations of European descent, a finding consistent with the conclusions of the DIAGRAM study. Interestingly, the results from the studies examining populations of Asian descent conflicted with this conclusion and indicated that the association with type 2 diabetes was statistically significant for the UCP2 Ala55Val and UCP3 polymorphisms, but not for the UCP2 −866G/A polymorphism. Although our analysis of Asian populations had a relatively small sample size, we nevertheless had 80% power at a 0.05 significance level to detect an OR of 1.5 or greater (statistical power 0.996 and 0.793 for UCP2 Ala55Val and UCP3 −55C/T respectively). The FPRP value for the UCP3 −55C/T polymorphism suggested a <20% chance of the result being a false positive when assigned a relatively high prior probability range (i.e. 0.01–0.1) (data not shown). In contrast, the FPRP value for the UCP2 Ala55Val polymorphism remained below 0.2 even for a prior probability of 0.001, suggesting that the FPRP value is quite robust and that UCP2 may contain one or more genetic variants that increase type 2 diabetes risk in individuals of Asian descent.

The results of the present meta-analysis should also be interpreted within the context of its limitations. Thus previous studies have also indicated that the UCP2 −866G/A, UCP2 Ala55Val and UCP3 −55C/T polymorphisms are associated with obesity [10], and that the UCP2 −866G/A and Ala55Val polymorphisms are associated with proliferative diabetic retinopathy in type 2 diabetes patients or decreased risk of coronary artery disease in men with type 2 diabetes [47, 48]. However, the number of studies providing this clinical information was too low for us to take these covariates into account by meta-regression. Similarly, besides ethnicity, other potential environment × gene interactions may well be contributors to the observed disease-effect unconformity, but we had insufficient data to perform an evaluation of such interactions. Furthermore, one single study found that overweight white women with a potential high-risk haplotype (in high linkage disequilibrium with 866A- and 55T- alleles) had a 3.8-fold increased type 2 diabetes risk [30]. We again had insufficient data to confirm this association, but on the basis of our meta-analysis, we propose that this may be due to some other, as yet unidentified variants also contained within this diabetes-associated haplotype.

In conclusion, our results indicate that the UCP2 −866G/A polymorphism is not a candidate for susceptibility to type 2 diabetes in any ethnic population. However, our results do support the hypothesis that the UCP2 Ala55Val and UCP3 −55C/T polymorphisms are type 2 diabetes susceptibility loci in populations of Asian, but not European descent. We suggest that additional larger studies allowing stratification for other gene × environment interactions should be performed to further clarify the possible roles of the three UCP2 and UCP3 genetic variants in the aetiology of type 2 diabetes.

Notes

Acknowledgements

The study was supported by grants from the National Natural Science Foundation of China (numbers 30400219, 30671010 and 30971405). We would like to thank M. McCarthy and A. Morris from the Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), Oxford, UK. Thanks also go to: R. Venkatesan from the Madras Diabetes Research Foundation and Mohan’s Diabetes Specialities Centre, India; K. S. Park and Y. M. Cho from the Department of Internal Medicine, Seoul National University College of Medicine, Korea for their data about UCP2 and UCP3 variants; and J. Congqi from the Department of Epidemiology and Health Statistics, Shandong University, China and Z. Zhang from the Department of Molecular and Genetic Toxicology, School of Public Health, Nanjing Medical University, China for their helpful comments on our revised manuscript. We also thank H. Davidson at the Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO, USA for reviewing and editing a late version of the manuscript.

Contribution statement

K.X. was involved in conception and design, analysis and interpretation of data, drafting of the article and revising it critically for important intellectual content. M.Z., D.C., Y.F., L.Q. and R.G. worked on collection and interpretation of data, and critical revision of the manuscript for important intellectual content. M.W., C.S. and R.Y. were involved in analysis and interpretation of data and critical revision of the manuscript for important intellectual content. T.Y. was involved in conception and design, and critical revision of the manuscript for important intellectual content. All the co-authors gave final approval of the version to be published.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2011_2245_MOESM1_ESM.pdf (21 kb)
ESM Table 1 Summary of manuscripts included in the meta-analysis (PDF 20.8 kb)
125_2011_2245_MOESM2_ESM.pdf (10 kb)
ESM Table 2 Egger’s publication bias test for the UCP2 -866G/A, Ala55Val C/T, and UCP3 -55C/T polymorphisms in type 2 diabetes (PDF 9.71 kb)
125_2011_2245_MOESM3_ESM.pdf (18 kb)
ESM Fig. 1 Systematic review flow diagram. n, number of studies. (PDF 17.5 kb)

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • K. Xu
    • 1
  • M. Zhang
    • 1
  • D. Cui
    • 1
  • Y. Fu
    • 2
  • L. Qian
    • 1
  • R. Gu
    • 1
  • M. Wang
    • 3
  • C. Shen
    • 4
  • R. Yu
    • 4
  • T. Yang
    • 1
  1. 1.Department of EndocrinologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
  2. 2.Department of Nuclear MedicineThe First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
  3. 3.Department of Molecular and Genetic Toxicology, School of Public HealthNanjing Medical UniversityNanjingChina
  4. 4.Department of Epidemiology and Biostatistics, School of Public HealthNanjing Medical UniversityNanjingChina

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