Diabetologia

, Volume 54, Issue 4, pp 803–811 | Cite as

Association of the C47T polymorphism in SOD2 with diabetes mellitus and diabetic microvascular complications: a meta-analysis

Article

Abstract

Aims/hypothesis

A meta-analysis was performed to assess the association of C47T (rs4880) (also called Val16Ala) polymorphism in SOD2 gene with reduced risk of diabetes mellitus, including type 1 and type 2 diabetes, and diabetic microvascular complications (DMI) including diabetic nephropathy, diabetic retinopathy and diabetic polyneuropathy.

Methods

A comprehensive search was conducted to identify all case–control or cohort design studies of the above-mentioned associations. The fixed or random effect pooled measure was selected on the basis of homogeneity test among studies. Heterogeneity among studies was evaluated using the I2. Meta-regression and the ‘leave one out’ sensitive analysis of Patsopoulos et al. were used to explore potential sources of between-study heterogeneity. Publication bias was estimated using modified Egger’s linear regression test as proposed by Harbord et al.

Results

Seventeen articles were included. After excluding articles that deviated from Hardy–Weinberg equilibrium in cases and/or in controls, and were also the key contributors to between-study heterogeneity, the meta-analysis showed a significant association of the C allele with reduced risk of DMI in dominant (OR 0.788, 95% CI 0.680–0.914), recessive (OR 0.808, 95% CI 0.685–0.953) and codominant (OR 0.828, 95% CI 0.751–0.913) models. It also showed a significant association with reduced risk of diabetic nephropathy in the dominant model (OR 0.801, 95% CI 0.664–0.967), and reduced risk of diabetic retinopathy in the dominant (OR 0.601, 95% CI 0.423–0.855), recessive (OR 0.548, 95% CI 0.369–0.814) and codominant (OR 0.651, 95% CI 0.517–0.820) models.

Conclusions/interpretation

The meta-analysis suggested that C allele of C47T polymorphism in SOD2 gene has protective effects on risk of DMI, diabetic nephropathy and diabetic retinopathy. This risk needs to be confirmed by further studies.

Keywords

Diabetes Meta-analysis Microangiopathy MnSOD Nephropathy Oxidative stress Polymorphism Polyneuropathy Retinopathy SOD2 

Abbreviations

DMI

Diabetic microvascular complications

DPN

Diabetic polyneuropathy

FEM

Fixed effect model

HWE

Hardy–Weinberg equilibrium

MnSOD

Manganese superoxide dismutase

REM

Random effect model

SOD

Superoxide dismutase

Introduction

Oxidative stress induced by superoxide anion plays a key role in the initiation as well as progression of diabetes mellitus [1] and diabetic microvascular complications (DMI) [2]. Superoxide dismutase (SOD) 2 (also known as manganese superoxide dismutase [MnSOD]) is an essential defender against mitochondrial superoxide radicals among antioxidative enzymes [3]. The T to C nucleotide polymorphism (rs4880) (previously called C47T) [4], which is located in exon 2 of SOD2 gene and results in the amino acid substitution of valine with alanine at position 16 of the protein (Val16Ala), is considered the most interesting of several polymorphisms in SOD2 gene, because C allele instead of T allele results in more efficient transport of SOD2 into the mitochondrial matrix [5], which can increase the ability to neutralise superoxide radicals. Since Chistyakov et al first showed the significant association between C47T polymorphism in SOD2 gene and risk of diabetic polyneuropathy (DPN), and no significant association with type 1 diabetes risk [6], the associations of this polymorphism with diabetes mellitus, including type 1 and type 2 diabetes, and DMI including diabetic nephropathy, diabetic retinopathy and DPN have been investigated. However, the published results are controversial. Hence we conducted a meta-analysis to: (1) assess the effect of C47T polymorphism in SOD2 gene on risk of diabetes mellitus and of DMI including diabetic nephropathy, diabetic retinopathy and DPN; (2) evaluate the potential heterogeneity among studies; and (3) explore any potential publication bias.

Methods

Search strategy

A search was conducted for relevant available articles published in English or Chinese from five databases: (1) PubMed (1990–2010); (2) China National Knowledge Infrastructure (CNKI) (1990–2010); (3) Database of Chinese Scientific and Technical Periodicals (VIP) (1990–2010); (4) China Biology Medical literature database (CBM) (1990–2010); and (5) Web of Science (ISI) (1990–2010). The search strategy used the following keywords: ‘diabetes’ and ‘C47T’, ‘Val16Ala’, ‘MnSOD’, ‘SOD2’, ‘manganese superoxide dismutase’, ‘superoxide dismutase’, ‘polymorphism’, ‘mut*’ and ‘varia*’. Additional studies not captured by our database searches were identified by reviewing the bibliographies of relevant articles as well as those of relevant studies.

Inclusion criteria

All identified studies were carefully reviewed independently by two investigators to determine whether an individual study was eligible for inclusion in this meta-analysis. The inclusion criteria were as follows: (1) case–control or cohort study published as an original study to evaluate the association between C47T (Val16Ala) polymorphism in SOD2 gene and risk of diabetes mellitus and of DMI including diabetic nephropathy, diabetic retinopathy and DPN; (2) numbers in case and control groups or exposed and unexposed groups reported for each genotype, or data provided from which numbers could be calculated; (3) case and control groups in case–control study or exposed and unexposed groups in cohort study unrelated and drawn from the same temporally and geographically defined underlying population. If the two investigators disagreed about the eligibility of an article, it was resolved by consensus with a third reviewer.

Data extraction

Data were independently extracted by two investigators who reached a consensus on all of the items. The most recent and complete article was chosen, if a study had been published more than once. Information extracted from each study was as follows: publication year, name of first author, country, ethnic origin of the studied population, number in case (exposed) and control (unexposed) groups, genotype and allele distributions, mean age, male sex percentage in case (exposed) and control (unexposed) groups.

Statistical analysis

We used χ2 analysis with exact probability to test departure from Hardy–Weinberg equilibrium (HWE) for the C47T genotype distribution of SOD2 gene in case and control groups. Pooled measure was calculated as the inverse variance-weighted mean of the logarithm of OR with 95% CI to assess the strength of association between C47T polymorphism in SOD2 gene and risk of diabetes mellitus and of DMI including diabetic nephropathy, diabetic retinopathy and DPN for dominant (CC + TC vs TT), recessive (CC vs TC + TT) and codominant (C vs T) models, respectively. The I2 of Higgins and Thompson was used to assess heterogeneity among studies [7]. I2 describes the proportion of total variation attributable to between-study heterogeneity as opposed to random error or chance. In the presence of substantial heterogeneity (I2 > 50%) [8], the DerSimonian and Laird random effect model (REM) was adopted as the pooling method; otherwise, the fixed effect model (FEM) was used as the pooling method. Meta-regression with restricted maximum likelihood estimation was performed to assess the potentially important covariates exerting substantial impact on between-study heterogeneity. The ‘leave one out’ sensitive analysis [9] was carried out using I2 > 50% as the criteria to evaluate the key studies with substantial impact on between-study heterogeneity. Publication bias was estimated using modified Egger's linear regression test, as proposed by Harbord et al [10]. An analysis of influence was conducted [11], which describes how robust the pooled estimator is to removal of individual studies. An individual study is suspected of excessive influence, if the point estimate of its omitted analysis lies outside the 95% CI of the combined analysis. All statistical analyses were performed with STATA version 9.2 (Stata Corporation, College Station, TX, USA). All reported probabilities (p values) were two-sided, with p < 0.05 considered statistically significant.

Results

Characteristics of studies

We found 17 published articles [6, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27] with 31 outcomes eligible for this meta-analysis on the relation of C47T polymorphism in SOD2 gene to diabetes mellitus and DMI including diabetic nephropathy, diabetic retinopathy and DPN. All 17 eligible studies were case–control designs. General characteristics, the C47T allele and genotype distributions in the published articles included in this meta-analysis are showed in Tables 1 and 2.
Table 1

Characteristics of SOD2 gene C47T polymorphism genotype distributions for diabetes mellitus risk in studies included in the meta-analysis

Authors [ref.]

Year

Population

Case

Control

Genotypes (TT / TC / CC)

C allele frequency (%)

% of male (case/control)

Mean age (case/control)

Case

Control

Number

ph valuea

Number

ph valuea

Case

Control

p value

ORb

Chistyakov et al. [6]

2001

Russian

DM1

NC

15/104/47

0.001

5/47/36

0.053

59.6

67.6

0.084

0.708

58/67

25.0/28.3

el-Masry et al. [12]

2005

Egyptian

DM1

NC

13/88/49

0.003

3/21/16

0.478

62.0

66.3

0.517

0.831

58/63

16.6/19.0

Flekac et al. [13]d

2008

Czech

DM1

NC

79/36/5

0.767

52/90/38

1.000

19.2

46.1

0.000

0.277

48/52

40.0/39.0

Flekac et al. [13]d

2008

Czech

DM2

NC

220/80/6

0.825

52/90/38

1.000

15.0

46.1

0.000

0.207

51/52

57.0/39.0

Nomiyama et al. [14]c

2003

Japanese

DM2

NC

364/111/3

0.089

190/67/4

0.620

12.2

14.4

0.257

0.831

66/60

59.8/48.1

Lee et al. [15]c

2006

Korean

DM2

NC

243/57/4

0.761

152/37/3

0.712

10.7

11.2

0.835

0.949

59/58

53.4/52.1

Yang et al. [16]c

2007

Chinese

DM2

NC

114/67/6

0.384

66/27/4

0.503

21.1

18.0

0.440

1.217

52/55

55.0/42.0

Ye et al. [17]c

2008

Chinese

DM2

NC

192/18/54

0.000

133/15/50

0.000

23.9

29.0

0.082

0.766

49/54

55.0/52.0

Liu et al. [18]c

2009

Chinese

DM2

NC

214/39/4

0.244

134/35/3

0.712

9.1

11.9

0.207

0.744

54/52

65.2/59.6

Kangas-Kontio et al. [19]

2009

Finnish

DM1 and DM2

NC

59/105/58

0.423

132/246/115

1.000

49.8

48.3

0.607

1.062

56/49

58.1/51.4

aExact p value for HWE test

bOR ratio calculated by C vs T allele

cGenotype counts were calculated by the genotype frequency data in the publication

dOne study with different types of diseases

DM1, type 1 diabetes mellitus; DM2, type 2 diabetes mellitus; NC, normal controls; ref., reference number

Table 2

Characteristics of SOD2 gene C47T polymorphism genotype distributions for DMI risk in studies included in the meta-analysis

Authors [ref.]

Year

Population

Case

Control

Genotypes (TT/TC/CC)

 

% of male (case/control)

Mean age (case/control)

Case

Control

C allele frequency (%)

Number

ph valuea

Number

ph valuea

Case

Control

p value

ORb

el-Masry et al. [12]d

2005

Egyptian

DN+/DM1

DN– and DPN–/DM1

4/24/17

0.347

2/37/26

0.019

64.4

68.5

0.562

0.835

62/51

16.0/18.0

Mollsten et al. [20]d

2007

Finnish

DN+/DM1

DN–/DM1

212/392/197

0.572

91/183/84

0.751

49.1

49.0

1.000

1.002

59/39

38.7/41.3

Mollsten et al. [20]d

2007

Swedish

DN+/DM1

DN–/DM1

43/65/42

0.104

39/101/56

0.667

49.7

54.3

0.249

0.829

51/45

48.7/43.8

Mollsten et al. [21]c

2009

Danish

DN+/DM1

DN–/DM1

115/220/106

1.000

69/151/94

0.571

49.0

54.0

0.060

0.818

62/55

42.2/43.7

Hovnik et al. [22]d

2009

Slovene

DN+/DM1

DN– and DR–/DM1

7/23/7

0.198

20/42/25

0.830

50.0

52.9

0.781

0.891

56/56

27.4/26.8

Nomiyama et al. [14]c

2003

Japanese

DN+/DM2

DN–/DM2

158/28/1

1.000

206/83/2

0.038

8.0

14.9

0.002

0.496

66/66

60.6/59.3

Lee et al. [23]c

2006

Korean

DN+/DM2

DN–/DM2

111/13/3

0.020

203/24/17

0.000

7.5

11.9

0.075

0.599

68/56

53.2/53.9

Yang et al. [16]c

2007

Chinese

DN+/DM2

DN–/DM2

91/34/12

0.004

23/25/2

0.183

21.2

29.0

0.129

0.657

47/50

Na/Na

Tiwari et al. [24]d

2009

South Indians

DN+/DM2

DN–/DM2

13/42/51

0.374

34/68/47

0.325

67.9

54.4

0.002

1.778

76/69

56.0/60.5

Tiwari et al. [24]d

2009

North Indians

DN+/DM2

DN–/DM2

21/34/29

0.122

19/28/28

0.037

54.8

56.0

0.910

0.951

87/53

53.6/61.0

Liu et al. [18]c

2009

Chinese

DN+/DM2

DN–/DM2

136/17/1

0.446

78/22/3

0.390

6.2

13.6

0.005

0.418

58/49

64.3/66.6

Hovnik et al. [22]d

2009

Slovene

DR+/DM1

DN– and DR–/DM1

11/15/6

1.000

16/50/26

0.403

42.2

55.4

0.082

0.587

56/56

27.4/26.8

Lee et al. [15]c

2006

Korean

DR+/DM2

DR–/DM2

105/23/2

0.626

138/34/2

1.000

10.4

10.9

0.895

0.945

59/59

53.1/53.7

Petrovic et al. [25]

2008

Slovene

DR+/DM2

DR–/DM2

80/140/63

0.905

23/69/51

1.000

47.0

59.8

0.001

0.596

47/40

66.0/66.9

Ye et al. [17]c

2008

Chinese

DR+/DM2

DR–/DM2

99/8/18

0.000

93/10/36

0.000

17.6

29.5

0.002

0.511

Na/Na

Na/Na

Kangas-Kontio et al. [19]

2009

Finnish

DR+/DM1 and DM2

DR–/DM1 and DM2

30/56/40

0.281

29/49/18

0.837

54.0

44.3

0.045

1.476

60/51

59.6/56.1

Chistyakov et al. [6]

2001

Russian

DPN+/DM1

DPN–/DM1

13/55/14

0.004

2/49/33

0.002

50.6

68.5

0.001

0.472

59/58

23.5/32.1

Strokov et al. [26]

2003

Russian

DPN+/DM1

DPN–/DM1

11/35/8

0.054

3/34/17

0.018

47.2

63.0

0.028

0.526

Na/Na

23.0/31.0

Zotova et al. [27]

2003

Russian

DPN+/DM1

DPN–/DM1

16/53/17

0.051

5/57/32

0.003

50.6

64.4

0.010

0.567

60/36

25.5/27.5

el-Masry et al. [12]d

2005

Egyptian

DPN+/DM1

DN– and DPN–/DM1

7/27/6

0.055

2/37/26

0.019

48.8

68.5

0.006

0.438

65/51

15.0/18.0

Flekac et al. [13]c

2008

Czech

DR+ and DPN+/DM1 and DM2

DR– and DPN–/DM1and DM2

84/91/24

1.000

84/109/34

1.000

34.9

39.0

0.228

0.840

Na/Na

Na/Na

aExact p value for HWE test

bOR ratio calculated by C vs T allele

cGenotype counts were calculated by the genotype frequency data in the publication

dOne study with different types of diseases or populations

DMI, diabetic microvascular complications; DM1, type 1 diabetes mellitus; DM2, type 2 diabetes mellitus; DN, diabetic nephropathy; DR, diabetic retinopathy; DPN: diabetic polyneuropathy; DN+/DM1: DM1 patients with DN; DN–/DM1, DM1 patients without DN; DN+/DM2, DM2 patients with DN; DN–/DM2, DM2 patients without DN; DR+/DM1, DM1 patients with DR; DR–/DM1, DM1 patients without DR; DR+/DM2, DM2 patients with DR; DR–/DM2, DM2 patients without DR; DPN+/DM1, DM1 patients with DPN; DPN−/DM1, DM1 patients without DPN; DPN+/DM2, DM2 patients with DPN; DPN–/DM2, DM2 patients without DPN

Na, not available; ref., reference number

Quantitative synthesis

Results of pooled analysis are summarised in detail in Table 3. This meta-analysis showed a significant protective effect of C allele on risk of diabetes mellitus, including type 1 and type 2 diabetes, in the recessive (REM OR 0.509, 95% CI 0.300–0.861) and codominant (REM OR 0.672, 95% CI 0.459–0.984) models, but this was only marginally significant in the dominant model (REM OR 0.626, 95% CI 0.391–1.002). However, no significant association was found between C allele and reduced risk of diabetes mellitus after excluding articles that deviated from HWE in cases and/or in controls for dominant (REM OR 0.594, 95% CI 0.325–1.086), recessive (REM OR 0.430, 95% CI 0.162–1.141) and codominant (REM OR 0.637, 95% CI 0.371–1.093) models.
Table 3

Pooled measures on the relation of SOD2 gene C47T polymorphism with DM, DMI, DN, DR and DPN

Disease

Data

Inherited model

Before HETRED analysis

After HETRED analysis

Numbers of cases/controls

FEM Pooled OR (95% CI)

REM Pooled OR (95% CI)

I2 (%)

Numbers of cases/controls

Pooled OR (95% CI)

I2 (%)

Articles Excluded [ref.]

DM

All relevant articles

Dominant

2,454/1,901

0.632 (0.546–0.731)**

0.626 (0.391–1.002)

89.4

2,028/1,541

0.894 (0.758–1.054)

0.0

[13]

Recessive

2,454/1,901

0.675 (0.546–0.834)**

0.509 (0.300–0.861)*

78.6

2,028/1,541

0.839 (0.671–1.050)

0.0

[13]

Codominant

2,454/1,901

0.678 (0.608–0.755)**

0.672 (0.459–0.984)*

91.6

2,028/1,541

0.895 (0.793–1.010)

8.3

[13]

Excluded for DHWE

Dominant

1,874/1,575

0.611 (0.521–0.716)**

0.594 (0.325–1.086)

92.8

1,448/1,215

0.935 (0.777–1.126)

0.0

[13]

Recessive

1,874/1,575

0.664 (0.496–0.888)**

0.430 (0.162–1.141)

85.5

1,448/1,215

1.049 (0.757–1.452)

0.0

[13]

Codominant

1,874/1,575

0.652 (0.575–0.739)**

0.637 (0.371–1.093)

94.3

1,448/1,215

0.968 (0.836–1.121)

0.0

[13]

DMI

All relevant articles

Dominant

3,426/3,100

0.714 (0.631–0.808)**

0.649 (0.520–0.809)**

61.1

2,519/2,593

0.638 (0.554–0.734)**

49.6

[20, 24]

Recessive

3,426/3,100

0.805 (0.705–0.919)**

0.733 (0.573–0.940)*

62.4

3,194/2,855

0.718 (0.624–0.827)**

41.0

[19, 24]

Codominant

3,426/3,100

0.801 (0.740–0.866)**

0.739 (0.631–0.866)**

71.6

2,393/2,497

0.689 (0.628–0.756)**

34.7

[19, 20, 24]

Excluded for DHWE

Dominant

2,459/1,939

0.822 (0.712–0.950)**

0.812 (0.638–1.033)

57.8

2,353/1,790

0.788 (0.680–0.914)**

44.6

[24]

Recessive

2,459/1,939

0.924 (0.793–1.076)

0.922 (0.688–1.236)

63.9

2,227/1,694

0.808 (0.685–0.953)*

24.6

[19, 24]

Codominant

2,459/1,939

0.898 (0.819–0.984)*

0.883 (0.725–1.075)

74.1

2,227/1,694

0.828 (0.751–0.913)**

49.9

[19, 24]

DN

All relevant articles

Dominant

2,269/1,932

0.760 (0.651–0.887)**

0.734 (0.559–0.964)*

60.2

2,163/1,783

0.721 (0.615–0.844)**

45.0

[24]

Recessive

2,269/1,932

0.963 (0.815–1.138)

0.948 (0.725–1.239)

44.1

Codominant

2,269/1,932

0.879 (0.796–0.971)*

0.820 (0.670–1.003)

69.1

2,163/1,783

0.831 (0.750–0.922)**

47.4

[24]

Excluded for DHWE

Dominant

1,689/1,207

0.856 (0.714–1.026)

0.860 (0.610–1.212)

64.4

1,583/1,058

0.801 (0.664–0.967)*

43.4

[24]

Recessive

1,689/1,207

0.984 (0.821–1.179)

0.982 (0.694–1.389)

63.1

1,583/1,058

0.890 (0.734–1.079)

20.8

[24]

Codominant

1,689/1,207

0.937 (0.838–1.047)

0.917 (0.707–1.190)

76.5

1,429/955

0.903 (0.802–1.018)

0.0

[18, 24]

DR

All relevant articles

Dominant

696/644

0.690 (0.528–0.901)**

0.685 (0.444–1.057)

60.5

570/548

0.580 (0.431–0.782)**

16.7

[19]

Recessive

696/644

0.706 (0.525–0.950)*

0.773 (0.408–1.463)

72.2

570/548

0.528 (0.378–0.738)**

0.0

[19]

Codominant

696/644

0.747 (0.625–0.892)**

0.759 (0.503–1.147)

79.5

570/548

0.615 (0.502–0.752)**

13.0

[19]

Excluded for DHWE

Dominant

571/505

0.745 (0.550–1.010)

0.731 (0.425–1.258)

66.7

445/409

0.601 (0.423–0.855)**

42.3

[19]

Recessive

571/505

0.789 (0.564–1.104)

0.903 (0.393–2.075)

76.0

445/409

0.548 (0.369–0.814)**

0.0

[19]

Codominant

571/505

0.814 (0.668–0.991)*

0.841 (0.520–1.359)

80.8

445/409

0.651 (0.517–0.820)**

18.2

[19]

DPNa

All relevant articles

Dominant

262/297

0.197 (0.101–0.383)**

0.197 (0.101–0.383)**

0.0

Recessive

262/297

0.367 (0.246–0.549)**

0.367 (0.246–0.549)**

0.0

Codominant

262/297

0.506 (0.397–0.644)**

0.506 (0.397–0.644)**

0.0

Dominant model: CC + TC vs TT; recessive model: CC vs TC + TT; codominant model: C vs T

DHWE: deviated from HWE in cases and/or in controls

HETRED: sensitivity analysis for reducing heterogeneity by omitting study using the STATA module of HETRED when I2 ≥ 50%

aAll articles for DPN were DHWE in cases and/or in controls

*p < 0.05, **p < 0.01

DM, diabetes mellitus including type 1 and type 2 diabetes; DMI, diabetic microvascular complications; DN, diabetic nephropathy; DR, diabetic retinopathy; DPN, diabetic polyneuropathy; FEM, fixed effect model; ref., reference number; REM, random effect model

The C allele was found to be significantly associated with a reduced risk of diabetic nephropathy in the dominant (REM OR 0.734, 95% CI 0.559–0.964) model, with marginal significance in codominant (REM OR 0.820, 95% CI 0.670–1.003) models, and no significant association in the recessive model (REM OR 0.948, 95% CI 0.725–1.239). After exclusion of articles deviating from HWE in cases and/or in controls, the associations in the above-mentioned inherited models were not significant.

With regard to diabetic retinopathy, no significant association with the C allele was found in dominant (REM OR 0.685, 95% CI 0.444–1.057), recessive (REM OR 0.773, 95% CI 0.408–1.463) and codominant (REM OR 0.759, 95% CI 0.503–1.147) models. After exclusion of articles deviating from HWE in cases and/or in controls, the associations in the above-mentioned inherited models remained non-significant.

The meta-analysis also showed a significant association between the C allele and reduced risk of DPN in dominant (FEM OR 0.197, 95% CI 0.101–0.383), recessive (FEM OR 0.367, 95% CI 0.246–0.549) and codominant (FEM OR 0.506, 95% CI 0.397–0.644) models. However, all relevant articles regarding the association between C47T polymorphism and risk of DPN deviated from HWE in cases and/or in controls.

In addition, when we combined diabetic nephropathy, diabetic retinopathy and DPN into overall DMI, we found a significant effect of C allele on reduced risk of DMI in dominant (REM OR 0.649, 95% CI 0.520–0.809), recessive (REM OR 0.733, 95% CI 0.573–0.940) and codominant (REM OR 0.739, 95% CI 0.631–0.866) models. However, no significant association was found between C allele and reduced risk of DMI after excluding articles that deviated from HWE in cases and/or in controls with regard to the above-mentioned inherited models.

Sources of heterogeneity and sensitive analysis

As seen in Table 3, before the ‘leave one out’ sensitive analysis [9], strong evidence of heterogeneity among studies was demonstrated in the above-mentioned inherited models for risk of diabetes mellitus, DMI, diabetic nephropathy and diabetic retinopathy, with the exception in the recessive model for diabetic nephropathy risk. No significant heterogeneity among studies was found in the above-mentioned inherited models for DPN risk.

After excluding the articles deviating from HWE in cases and/or in controls, univariate meta-regression analysis, with the covariates publication year, ethnicity (categorised as Europeans, East Asians, South Asians and North Africans), sex (ratio of males in per cent in case group to that in control group), age (ratio of mean age in case group to that in control group) and sample size, showed that no covariates had a significant impact on between-study heterogeneity.

The key contributors of the articles to between-study heterogeneity assessed by the ‘leave one out’ sensitive analysis [9] are presented as ‘articles excluded’ in Table 3.

After exclusion of articles that deviated from HWE in cases and/or in controls, and were the key contributors to between-study heterogeneity, the meta-analysis showed a significant association of the C allele with reduced risk of DMI in dominant (OR 0.788, 95% CI 0.680–0.914) (Fig. 1), recessive (OR 0.808, 95% CI 0.685–0.953) and codominant (OR 0.828, 95% CI 0.751–0.913) models. It also showed a significant association of the C allele with reduced risk of diabetic nephropathy in the dominant model (OR 0.801, 95% CI 0.664–0.967) (Fig. 2), as well as with reduced risk of diabetic retinopathy in dominant (OR 0.601, 95% CI 0.423–0.855), recessive (OR 0.548, 95% CI 0.369–0.814) and codominant (OR 0.651, 95% CI 0.517–0.820) models.
Fig. 1

Forest plot of ORs for DMI in dominant model (CC + TC vs TT) of SOD2 gene C47T polymorphism. White diamond denotes the pooled OR. Black squares indicate the OR in each study, with square sizes inversely proportional to the standard error of the OR. Horizontal lines represent 95% CI. aOne study with different types of diseases or populations. Ref., reference number

Fig. 2

Forest plot of ORs for diabetic nephropathy in dominant model (CC + TC vs TT) of SOD2 gene C47T polymorphism. White diamond denotes the pooled OR. Black squares indicate the OR in each study, with square sizes inversely proportional to the standard error of the OR. Horizontal lines represent 95% CI. aOne study with different types of populations. Ref., reference number

Influence analysis

After excluding articles that deviated from HWE in cases and/or in controls, and were the key contributors to between-study heterogeneity, no individual study was found to have excessive influence on the pooled effect in the above-mentioned inherited models with regard to diabetes mellitus, DMI, diabetic nephropathy and diabetic retinopathy, with the exception of one study [19] on reduced risk of diabetes mellitus in the recessive model (OR of this study was 0.701, with 95% CI for pooled effect of 0.757–1.452).

Publication bias evaluation

After excluding articles that deviated from HWE in cases and/or in controls, and were the key contributors to between-study heterogeneity, no significant publication bias was detected in the above-mentioned inherited models for diabetes mellitus, DMI, diabetic nephropathy and diabetic retinopathy (data not shown).

Discussion

Clinical and experimental studies indicate that oxidative stress is associated with diabetes mellitus [1], diabetic nephropathy [28], diabetic retinopathy [29] and DPN [30]. The SOD2 encoded by the T allele, which disrupts the alpha-helix structure of the targeting sequence, is retained in the mitochondrial inner membrane and induces 30 to 40% lower activity and oxidative stress level. Chistyakov et al [6] first showed the significant protective effect of C47T polymorphism in SOD2 gene on DPN risk in 82 DPN cases and 84 non-DPN controls in type 1 diabetes mellitus patients, but no association with risk of type 1 diabetes mellitus was found in 166 type 1 diabetes mellitus cases and 88 normal controls in a Russian population. Nevertheless, results of subsequent studies about the effect of this polymorphism on risk of diabetes mellitus, including type 1 and type 2 diabetes, and of DMI including diabetic nephropathy, diabetic retinopathy and DPN were conflicting.

Because of the above-mentioned inconsistent results from relatively small studies underpowered to detect the effect, a meta-analysis is the appropriate approach to obtain a more definitive conclusion regarding the role of SOD2 gene C47T polymorphism on risk of diabetes mellitus and DMI including diabetic nephropathy, diabetic retinopathy and DPN. Our meta-analysis, of 17 published articles [6, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27] with 31 outcomes from different ethnic origins, allowed a much greater possibility of reaching reasonably strong conclusions.

As mentioned in one paper [31], between-study heterogeneity is common in meta-analysis for genetic association studies. Our meta-analysis also showed significant between-study heterogeneity in most of the inherited models regarding diabetes mellitus, DMI, diabetic nephropathy and diabetic retinopathy. An indeterminate number of characteristics that vary among studies could be the cause of between-study heterogeneity, e.g. design quality, population stratification, characteristics of the sample, non-comparable measure of genotyping, variation of the covariate, deviation from HWE in some studies, etc. Thus we used meta-regression and ‘leave one out’ sensitive analysis [9], which aims to reduce between-study heterogeneity and explore the potential important causes of between-study heterogeneity for both covariates and studies. Our meta-analysis of the studies that were in HWE in cases and controls did not identify any of the afore-mentioned covariates as being an important contributor to between-study heterogeneity. When, after the ‘leave one out’ sensitive analysis using I2 > 50% as the criteria, we performed data analysis on the studies that were in HWE in cases and controls, our results showed that the C allele of C47T polymorphism in SOD2 gene had a significant effect on reduced risk of DMI, diabetic nephropathy and diabetic retinopathy, but not of diabetes mellitus.

According to another article [32] and the SNP database at the NIH (www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=4880, accessed 24 September 2010), the C allele frequency in the Asian population is significantly lower than in most European populations. Such heterogeneous genetic backgrounds might be, in part, responsible for the heterogeneity of effect on the disease across the ethnicity, even though we could not detect this in our meta-analysis. Diabetes mellitus and DMI including diabetic nephropathy, diabetic retinopathy and DPN, have a complex aetiology and pathophysiology generated by the combined effects of genes and environment factors. Although ethnicity, sex and age were not found to be important sources of disease–effect heterogeneity across the studies in this meta-analysis, other genetic and environment variables, as well as their possible interaction, may well be potential contributors to this disease–effect unconformity. In this respect, the lack of relevant study-level covariates in the reported articles precluded a more robust assessment of sources of this heterogeneity. Other possibilities related to disease–effect diversity, such as variations in design quality, variations in genotyping, etc. could not be ruled out.

In this meta-analysis, we could not find any significant publication bias in the above-mentioned inherited models for diabetes mellitus, including type 1 and type 2 diabetes, DMI, diabetic nephropathy and diabetic retinopathy, this may be due to the small number of studies in those meta-analyses, especially for diabetic retinopathy risk evaluation.

In conclusion, this meta-analysis suggested that the C allele of C47T polymorphism in SOD2 gene had significant protective effects on risk of DMI, diabetic nephropathy and diabetic retinopathy. Since potential biases and confounders could not be ruled out completely in this meta-analysis, further studies are needed to confirm these results.

Notes

Acknowledgements

This study was sponsored by a grant from Shandong Provincial Natural Science Foundation, China (Y2007C005).

Duality of interest

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

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Department of Epidemiology and Health StatisticsShandong UniversityJinanPeople’s Republic of China

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