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Endothelial nitric oxide synthase gene polymorphisms and risk of diabetic nephropathy: a systematic review and meta-analysis

  • Bruno Schmidt Dellamea1Email author,
  • Lana Catani Ferreira Pinto1,
  • Cristiane Bauermann Leitão1,
  • Katia Gonçalves Santos2 and
  • Luis Henrique Santos Canani1
BMC Medical Genetics201415:9

DOI: 10.1186/1471-2350-15-9

Received: 25 July 2012

Accepted: 6 January 2014

Published: 16 January 2014

Abstract

Background

Nitric oxide (NO) has numerous functions in the kidney, including control of renal and glomerular hemodynamics, by interfering at multiple pathological and physiologically critical steps of nephron function. Endothelial NOS (eNOS) gene has been considered a potential candidate gene to diabetic nephropathy (DN) susceptibility. Endothelial nitric oxide synthase gene (eNOS-3) polymorphisms have been associated with DN, however some studies do not confirm this association. The analyzed polymorphisms were 4b/4a, T-786C, and G986T.

Methods

The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement was used in this report. Case–control studies that had diabetic patients with DN as cases and diabetic patients without nephropathy as controls, as well as that evaluated at least one of the three polymorphisms of interest were considered eligible. All studies published up until December 31st, 2012 were identified by searching electronic databases. Hardy-Weinberg equilibrium assessment was performed. Gene-disease association was measured using odds ratio estimation based on the following genetic contrast/models: (1) allele contrast; (2) additive model; (3) recessive model; (4) dominant model and (4) co-dominant model.

Results

Twenty-two studies were eligible for meta-analysis (4b/a: 15 studies, T-786C: 5 studies, and G984T: 12 studies). Considering 4b/a polymorphism, an association with DN was observed for all genetic models: allele contrast (OR = 1.14, CI: 1.04-1.25); additive (OR = 1.77, CI: 1.37-2.28); recessive (OR = 1.77, CI: 1.38-2,27); dominant (OR = 1.12, CI: 1.01-1.24), with the exception for co-dominance model. As well, T-786C polymorphism showed association with all models, with exception for co-dominance model: allele contrast (OR = 1.22, CI: 1.07-1.39), additive (OR = 1.52, CI: 1.18-1.97), recessive (OR = 1.50, CI: 1.16-1.93), and dominant (OR = 1.11, CI: 1.01-1.23). For the G894T polymorphism, an association with DN was observed in allelic contrast (OR = 1.12, CI: 1.03-1.25) and co-dominance models (OR = 1.13, CI: 1.04-1.37).

Conclusions

In the present study, there was association of DN with eNOS 4b/a and T-786C polymorphism, which held in all genetic models tested, except for co-dominance model. G894T polymorphism was associated with DN only in allele contrast and in co-dominance model. This data suggested that the eNOS gene could play a role in the development of DN.

Background

Nitric oxide (NO) is a short-lived gaseous lipophilic molecule produced in almost all tissues and organs [1, 2]. It is a free radical that exerts a variety of biological actions under both physiological and pathological conditions [3]. NO is formed from its precursor L-arginine by a family of NO synthases (NOSs). NOS system consists of three distinct isoforms, encoded by three distinct genes, including neuronal (nNOS or NOS-1), inducible (iNOS or NOS-2), and endothelial (eNOS or NOS-3). The gene encoding eNOS is located on chromosome 7 (7q35-q36) and contains 26 exons, with an entire length of 21 kb [3, 4].

NO has numerous functions in the kidney, including control of renal and glomerular hemodynamics, by interfering at multiple pathological and physiologically critical steps of nephron function. NO dilates both the afferent and the efferent arteriole, augmenting the glomerular filtration rate (GFR) and influencing renal sodium handling [5]. NO also mediates pressure natriuresis, maintenance of medullary perfusion, decrease of tubuloglomerular reabsorption, and modulation of renal sympathetic nerve activity [6]. The net effect of NO in the kidney is to promote natriuresis and diuresis, along with renal adaptation to dietary salt intake [7, 8].

eNOS gene has been considered a potential candidate gene to diabetic nephropathy (DN) susceptibility. Since 1998, several polymorphisms of the eNOS gene have been identified, and their association with various diseases has been explored. Three polymorphisms have been the subject of research in relation to DN, however the results are highly variable. The polymorphisms potentially associated with DN are a 27-bp repeat in intron 4 (VNTR), the T-786C single nucleotide polymorphism (SNP) in the promoter region (rs2070744), and G894T missense mutation in exon 7 (rs1799983) [9]. Some of these polymorphisms are associated with reduction of either eNOS activity (-786C in the promoter area) or plasma concentrations of NO (four repeats in intron 4) [2].

However, the potential association of eNOS gene variants with the induction and progression of DN remains controversial. Some authors found a higher frequency of eNOS polymorphisms in patients with end-stage renal disease (ESRD) and DN [1017], but not all studies reported this association [1820].

The objective of the present study was to evaluate if eNOS gene polymorphisms are associated with DN through a systematic review of the literature and a meta-analysis.

Results and discussion

Three-hundred and nine studies were identified, and 281 were excluded based on review of titles and abstracts (70 animal experimental studies, 17 pharmacological studies, 86 without adequate cases or controls, 58 without the genes or polymorphisms of interest, 3 review articles, 5 meta-analysis, 35 studies with multiple publications of the same data presented with different titles, 7 no accesses to original data even after contacting authors). Twenty-eight articles were eligible and had the full text evaluated. Six studies were excluded due to lack of information regarding genotypic distribution. A total of 22 studies fulfilled the eligible criteria and were included for the meta-analysis (Figure 1).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2350-15-9/MediaObjects/12881_2012_Article_1163_Fig1_HTML.jpg
Figure 1

Search strategy.

Clinical characteristics of individual studies are described in Table 1. Regarding quality assessment, the phenotype definitions as cases or controls were appropriated, but none of the studies included information if genotyping was performed by personnel blinded to clinical status. Of the 22 studies included, 15 provided 4054/3405 cases/controls for 4b/a; 5 provided 1436/1286 cases/controls for T-786C; and 12 provided 3316/2765 cases/controls for G894T. The allelic frequency of 4b, T-786, and G894 in cases/controls was 6647/5702, 1863/1795, and 4691/4017 respectively (Table 2).
Table 1

Baseline studies characteristics

Author

Year

Polymorphism

Type of DM

Ethnicity

Cases/controls (n)

Criteria

Male/female (%)

Age

DM duration (years)

Ahluwalia et al. [21]

2008

G894T, 4a/b, T-786C

2

East Asians

Case (195)

Overt proteinuria

35/65

60.0 ± 6.15

16.5 ± 6.3

Control (255)

Normoalbuminuria

41/59

60.5 ± 5.7

15.6 ± 5.2

Bessa et al. [22]

2011

G894T

2

African

Case (40)

Albuminuria > 30 mg/24 h

21/19

58.8 ± 12.5

19.4 ± 4.2

Control (40)

Albuminuria < 30 mg/24 h

17/23

55.4 ± 8.8

15.3 ± 3.7

Cai et al. [23]

1998

G894T

2

Whites

Case (116)

Microalbuminuria

NA

NA

NA

Control (284)

Normoalbuminuria

NA

NA

NA

Degen et al. [24]

2001

4a/b

1and 2

Whites

Case (207)

AER >30 mg/24 h

NA

NA

>10 yrs

Control (418)

AER <30 mg/24 h

NA

NA

>10 yrs

Ezzidi et al. [10]

2008

G894T, 4a/b, T-786C

2

African

Case (515)

AER >30 mg/24 h

46/54

59.6 ± 10.8

13.5 ± 6.3

Control (402)

AER <30 mg/24 h

42/58

59.1 ± 11.2

11.5 ± 6.2

Fujita et al. [25]

2000

4a/b

2

East Asians

Case (102)

AER >200 mcg/min

60/40

61.0 ± 21.0

NA

Control (65)

AER <20 mcg/min

46/54

62.0 ± 10.0

NA

Ksiasek et al. [13]

2003

4a/b

2

Whites

Case (178)

With DN

48/52

57.9 ± 8.2

8.7 ± 3.1

Control (232)

Without DN

51/49

58.3 ± 6.8

8.0 ± 2.6

Lin et al. [25]

2002

4a/b

2

East Asians

Case (80)

With DN

NA

NA

NA

Control (48)

Normoalbuminuria

NA

NA

NA

Mollsten et al. [26]

2006

G894T, 4a/b

1

Whites

Case (955)

AER >20 mcg/min

58/42

40.3 ± 10.0

28 (5–65)

Control (555)

AER <20 mcg/min + DM duration >20 yrs

41/59

42.2 ± 10.2

28 (20–57)

Mollsten et al. [18]

2009

G894T

1

Whites

Case (458)

AER >300 mg/24 h

39/61

42.0 ± 10.4

27 (7–65)

Control (319)

AER <30 mg/24 h

55/45

43.7 ± 11.0

23 (15–63)

Neuguebauer et al. [14]

2000

4a/b

2

East Asians

Case 1 (104)

AER 20–200 mg/g Cr

53/47

59.0 ± 11.1

13.8 ± 5.1

Case 2 (39)

AER >200 mg/g Cr

74/26

59.0 ± 8.6

15.2 ± 4.5

Control (82)

AER <20 mg/g Cr

65/35

56.0 ± 8.6

13.3 ± 4.5

Rahimi et al. [27]

2012

G894T

2

West Asians

Case 1 (68)

Albumin to creatinin ratio >300 mg/g

33/35

57.1 ± 8.7

11.1 ± 6.4

Case 2 (72)

Albumin to creatinin ratio 30–299 mg/g

23/46

55.3 ± 8.6

8.6 ± 5.2

Control (72)

Albumin to creatinin ratio <30 mg/g

23/49

54.4 ± 7.9

7.7 ± 5.4

Rippin et al. [28]

2003

4a/b

1

Whites

Case (464)

Overt proteinuria

NA

NA

NA

Control (396)

Normoalbuminuria

NA

NA

NA

Santos et al. [29]

2009

G894T, 4a/b, T-786C

2

Whites

Case (376)

AER >20 mcg/min or >17 mg/dl

57/43

60.4 ± 9.7

15.0 ± 9.1

Control (268)

AER <20 mcg/min or <17 mg/dl

37/63

62.0 ± 9.4

16.7 ± 6.8

Shestakova et al. [16]

2006

4a/b

1

Whites

Case (63)

AER >300 mg/24 h

47/53

25.7 ± 6.4

12.6 ± 2.8

Control (66)

AER <30 mg/24 h

37/63

40.8 ± 10.2

26.8 ± 6.9

Shimizu et al. [30]

2002

4a/b

2

East Asian

Case 1 (107)

Overt proteinuria

70/30

63.1 ± 10.6

15.5 ± 11.0

Case 2 (124)

Overt proteinuria + Cr >1.5 mg/dl

75/25

65.1 ± 8.8

19.8 ± 7.8

Control (203)

Normoalbuminuria > DM >10 yrs

65/35

63.7 ± 8.8

18.6 ± 7.8

Shin Shin et al. [31]

2004

G894T

2

East Asians

Case 1 (35)

Microalbuminuria

46/54

62.9 ± 10.9

16 (12–20)

Case 2 (83)

Overt proteinuria

46/54

58.8 ± 9.7

16 (11–20)

Control (59)

Normoalbuminuric

25/75

61.6 ± 11.7

12 (10–16)

Shoukry et al. [32]

2012

G894T, 4a/b, T-786C

2

African

Case

Albumin to creatinin ratio >300 mg/g

108/92

55.3 ± 5.8

14.5 ± 4.3

Control

Albumin to creatinin ratio <30 mg/g

116/84

54.6 ± 5.2

13.8 ± 3.2

Tamemoto et al. [33]

2008

G894T

NA

East Asians

Case (124)

Microalbuminuria

NA

NA

NA

Control (211)

Normoalbuminuria

NA

NA

NA

Taniwaki et al. [20]

2001

4a/b

2

East Asians

Case 1 (44)

Microalbuminuria

59/41

60.5 ± 8.5

10.9 ± 7.4

Case 2 (22)

Overt proteinuria

68/32

59.0 ± 10.5

12.8 ± 6.5

Case 3 (20)

Overt proteinuria + Cr >1.5 mg/dl

50/50

64.2 ± 7.8

19.1 ± 9.7

Control (69)

Normoalbuminuria

59/41

60.1 ± 9.8

7.4 ± 4.5

Tiwari et al. [19]

2009

G894T

2

South Asians

Case 1 (90)

DM >2 yrs + Cr >2 mg/dl from N India

87/13

53.6 ± 11.0

9.6 ± 6.8

Case 2 (106)

DM >2 yrs + Cr >2 mg/dl from S India

76/24

55.9 ± 11.5

14.0 ± 6.4

Control 1 (75)

DM >10 yrs + Cr <2 mg/dl from N India

53/47

61.0 ± 8.9

15.4 ± 8.1

Control 2 (149)

DM >10 yrs + Cr <2 mg/dl from S India

68/32

60.5 ± 11.4

15.5 ± 6.91

Zanchi et al. [17]

2000

4a/b, T-786C

1

Whites

Case 1 (74)

AER >200 mcg/mg

42/58

35.5 ± 7.3

24.9 ± 9.0

Case 2 (78)

AER >200 mcg/mg + Cr >1.5 mg/dl

49/51

35.7 ± 6.5

24.5 ± 6.8

     

Control (195)

AER <20 mcg/mg + DM >15 yrs

52/48

36.5 ± 7.6

23.7 ± 6.3

Where: AER : albumin excretion rate; DM: diabetes mellitus; DN: diabetic nephropathy; NA: not available; Cr: creatinine.

Table 2

Polymorphisms distribution

Author

Distribution of the T-786C polymorphism

HWE

Cases

Controls

p value

 

TT

TC

CC

TT

TC

CC

 

Ahluwalia et al. [21]

121

62

12

165

87

3

0.020

Ezzidi et al. [10]

261

215

34

224

139

32

0.115

Santos et al. 2011 [29]

140

160

76

93

104

44

0.138

Shoukry et al. 2012 [32]

57

89

54

84

83

33

0.129

Zanchi et al. [17]

57

65

30

75

100

20

0.123

 

Distribution of the G894T polymorphism

HWE

 

Cases

Controls

p value

 

GG

GT

TT

GG

GT

TT

 

Ahluwalia et al. [21]

82

81

32

125

105

25

0.658

Bessa et al. 2011 [22]

10

18

12

17

19

4

1.000

Cai et al. [23]

65

44

7

148

109

27

0.310

Ezzidi et al. [10]

185

247

81

165

195

41

0.151

Mollsten et al. [34]

492

365

89

268

232

51

0.919

Mollsten et al. [18]

293

133

32

182

121

16

0.540

Rahimi et al. 2012 [27]

68

45

13

39

17

7

0.038

Santos et al. 2011 [29]

176

166

32

118

95

22

0.640

Shin Shin et al. [31]

95

23

0

52

7

0

1.000

Shoukry et al. 2012 [32]

66

94

40

99

77

24

0.140

Tamemoto et al. [33]

104

18

2

181

27

3

0.117

Tiwari et al. [19]

82

21

3

91

43

13

0.035

 

Distribution of the 4b/4a polymorphism

HWE

 

Cases

Controls

p value

 

bb

ba

aa

bb

ba

aa

 

Ahluwalia et al. [21]

146

28

21

189

61

5

1.000

Degen et al. [24]

229

94

4

297

105

9

1.000

Ezzidi et al. [10]

314

162

29

234

143

21

1.000

Fujita et al. [25]

81

21

0

55

10

0

1.000

Ksiasek et al. 2003 [13]

105

58

15

147

66

19

0.007

Lin et al. [26]

115

21

1

41

6

1

0.271

Mollsten et al. [34]

656

248

39

389

145

19

0.220

Neugebauer et al. [14]

101

26

6

71

10

1

0.351

Rippin et al. [28]

344

108

12

297

90

9

0.519

Santos et al. 2011 [29]

237

99

11

168

59

5

1.000

Shestakova et al. [16]

14

48

1

34

31

1

0.052

Shimizu et al. [30]

180

44

6

156

44

3

1.000

Shoukry et al. 2012 [32]

124

64

12

131

60

9

0.502

Taniwaki et al. [20]

63

21

2

50

19

0

0.340

Zanchi et al. [17]

80

27

37

144

47

4

1.000

HWE (Hardy-Weinberg equilibrium).

Hardy-Weinberg equilibrium (HWE) was assessed using exact test and P-value < 0.05 were considered significant. Only 4 studies (1 study for T-786C; 2 for G984T; and 1 for 4b/a) with controls were not in HWE (Table 2). These studies were subjected to a sensitive analysis, and their exclusion did not show significant difference on OR.

For the 4b/a polymorphism, an association with DN in all genetic models, except for co-dominance, was observed: allele contrast (OR = 1.15, CI (95%): 1.05-1.25, PQ <0.01, I2 = 66%); additive (OR = 1.52, CI (95%): 1.18-1.97, PQ <0.01, I2 = 62%); recessive (OR = 1.50, CI (95%): 1.16-1.93, PQ <0.01, I2 = 64%); and dominant (OR = 1.11, CI (95%): 1.01-1.23, PQ = 0.01, I2 = 49%). Similarly, for the T-786C polymorphism the association with DN was found with all models, with exception for co-dominance model: allele contrast (OR = 1.22, CI (95%): 1.07-1.39, PQ = 0.59, I2 = 0%), additive (OR = 1.52, CI (95%): 1.18-1.97, PQ < 0.01, I2 = 62%), recessive (OR = 1.50, CI (95%): 1.16-1.93, PQ <0.01, I2 = 64%) and dominant (OR = 1.11, CI (95%): 1.01-1.23, PQ <0.01, I2 = 49%). The G894T polymorphism showed association with DN in allelic contrast (OR = 1.12, CI (95%): 1.03-1.25, PQ <0.01, I2 = 75%) and co-dominance model (OR = 1.13, CI (95%): 1.04-1.37, PQ = 0.01, I2 = 60%) (Table 3 and Figure 2). A random model analysis was performed confirming the fixed model results.
Table 3

Meta-analysis in all genetic models with all patients and subgroup analysis, in fixed-model analysis, presenting heterogeneity (P Q and I 2 )

 

Population

Studies

OR

IC (95%)

P

PQ

I2(%)

4b/a

       

Allele contrast

All

15

1.15

1.05-1,25

<0.01

<0.01

66

African

2

0,98

0.81-1.18

0.88

0,25

22

East Asians

6

1.21

0.97-1.50.8

0.08

0.29

18

Whites

7

1.20

1.07-1.34

<0.01

<0.01

80

Type 1

5

1.17

1.02-1.34

0.02

0.07

54

Type 2

10

1.12

0.99-1.27

0.07

0.28

18

Additive

All

15

1.52

1.18-1.97

<0.01

<0.01

62

 

African

2

1,13

0,69-1,81

0.62

0.56

0

 

East Asians

6

3.25

1.58-6.68

<0.01

0.31

16

Whites

7

1.49

1.06-2.08

0.01

<0.01

74

Type 1

5

2.21

1.50-3.25

<0.01

<0.01

81

Type 2

11

1.36

0.98-1.88

0.06

0.08

41

Recessive

All

15

1.50

1.16-1.93

<0.01

<0.01

64

 

Africans

2

1.13

0.69-1.83

0,61

0.83

0

 

East Asians

6

3.44

1.68-7.05

<0.01

0.28

21

Whites

7

1.43

1.03-1.99

0.03

<0.01

75

Type 1

5

2.19

1.49-3.21

<0.01

<0.01

81

Type 2

11

1.49

1.07-2.07

0.02

0.08

42

Dominant

All

15

1.11

1.01-1.23

0.03

0,01

49

 

African

2

0.94

0.75-1.18

0.64

0.24

27

 

East Asians

6

1.04

0.81-1.34

0.71

0.44

0

Whites

7

1.20

1.05-1.36

<0.01

<0.01

67

Type 1

5

1.22

1.04-1.43

0.01

<0.01

78

Type 2

11

1.05

0.92-1.20

0.44

0.62

0

Codominant

All

15

0.98

0.88-1.09

0.81

0,02

46

 

African

2

1.09

0.87-1.38

0.42

0.29

7

 

East Asians

6

1.17

0.90-1.55

0.22

0.14

38

Whites

7

0.90

0.79-1.04

0.16

0.04

54

Type 1

5

0.94

0.80-1.11

0.46

0.01

68

Type 2

11

1.01

0.88-1.17

0.80

0.19

26

T-786C

       

Allele contrast

All

5

1.28

1.14-1.44

<0.01

0.25

24

African

2

1.44

1.21-1.71

<0.01

0.26

19

Whites

2

1.13

0.94-1.36

0.19

0.44

0

Type 2

4

1.29

1.13-1.46

<0.01

0.15

42

Additive

All

5

1.48

1.14-1.92

<0,01

0.01

67

 

African

2

1.43

0.98-2.09

0.05

0.01

84

 

Whites

2

1.36

0.93-1.98

0.10

0.18

42

Type 2

4

1.40

1.06-1.86

0.01

<0.01

73

Recessive

All

5

1.38

1,09-1.76

<0,01

0.01

68

 

African

2

1.24

0.88-1.76

0.21

0.01

81

 

Whites

2

1.39

0.98-1.95

0.06

0.09

0

Type 2

4

1.27

0.98-1.65

0.06

0.01

72

Dominant

All

5

1.21

1,04-1.42

0.01

0.29

18

 

African

2

1.39

1.11-1.73

<0.01

0.13

54

 

Whites

2

1.05

0.81-1.37

0.70

0.95

0

Type 2

4

1.24

1.05-1.47

<0.01

0.22

31

Codominant

All

5

0.95

0.81-1.11

0.53

0.12

45

 

African

2

0.78

0.62-0.98

0.03

0.48

0

 

Whites

2

1.15

0.89-1.49

0.28

0.24

25

Type 2

3

0.90

0.75-1.06

0.20

0.131

15

G986T

       

Allele contrast

All

12

1.12

1.03-1.21

<0.01

<0.01

75

African

3

1.63

1.39-1.91

<0.01

0.61

0

 

East Asian

3

1.33

1.05-1.70

0.01

0.74

0

Whites

4

0.93

0.84-1.04

0.20

0.67

0

Type 1

2

0.92

0.80-1.04

0.18

0.18

0

Type 2

9

1.27

1.15-1.42

<0.01

<0.01

72

Additive

All

12

1.19

0.99-1.43

0.05

<0.01

63

 

African

3

2.01

1.50-2.94

<0.01

0.27

22

 

East Asian

3

1.85

1,05-3.25

0.03

0.59

0

Whites

4

0.86

0.67-1.10

0.23

0.69

0

Type 1

2

0.87

0.65-1.16

0.34

0.44

0

Type 2

9

1.47

1.16-1.86

<0.01

<0.01

63

Recessive

All

12

1.16

0.97-1.38

0.09

0.02

52

 

Africa

3

1.80

1.31-2.46

<0.01

0.43

0

 

East Asian

3

1.73

1.01-2.96

0.04

0.63

0

 

Whites

4

0.88

0.69-1.11

0.29

0.62

0

Type 1

2

0.91

0.69-1.20

0.49

0.31

0

Type 2

9

1.36

1.08-1.70

<0.01

0.03

53

Dominant

All

12

0.99

0.89-1.11

0.92

0.07

45

 

African

3

1.46

1.17-1.82

<0.01

0.11

54

 

East Asian

3

1.32

0.98-1.79

0.06

0.73

0

 

Whites

4

0.93

0.80-1.07

0.31

0.59

0

Type 1

2

0.89

0.75-1.06

0.19

0.74

0

Type 2

9

1.19

0.92-1.26

0.35

0.04

57

Codominant

All

12

1.03

1.04-1.37

0.01

0.01

60

 

African

3

0.92

0.74-1.14

0.45

0.29

18

 

East Asian

3

0.89

0.65-1.21

0.48

0.52

0

 

Whites

4

1.02

0.89-1.18

0.69

0.44

0

Type 1

2

1.08

0.91-1.29

0.35

0.32

0

 

Type 2

9

0.94

0.82-1.08

0.41

0.34

11

https://static-content.springer.com/image/art%3A10.1186%2F1471-2350-15-9/MediaObjects/12881_2012_Article_1163_Fig2_HTML.jpg
Figure 2

Forest plot for contrast allele model for (A) 4b/a polymorphism; (B) T-786C polymorphism; and (C) G894T polymorphism.

Publication bias was observed for the majority of the polymorphisms evaluated and are presented as a funnel plot for 4b/a polymorphsism (Figure 3). In order to identify non published data, we performed manual search for abstracts in some of the major scientific meetings in the field in the last seven years. We estimated the effect of these potential publication biases using trim and fill method and no major differences were observed from the original results.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2350-15-9/MediaObjects/12881_2012_Article_1163_Fig3_HTML.jpg
Figure 3

Funnel plot for 4b/a polymorphism: (A) allele contrast; (B) additive; (C) recessive; (D) dominant; (E), codominant.

Since some studies included only subjects of specific ethnicities or with type 1 or type 2 DM, we performed a sensitive analysis stratifying the studies according to these characteristics. Considering 4b/a polymorphism, there was an association in White and East Asian populations in allele contrast, additive and recessive models; only for Whites in the dominant model; and none for the co-dominant model. For T-786C variant, no association was shown for Whites in allele contrast analysis or in any other genetic model, but in African populations the polymorphism was associated with DN in allele contrast, dominance and co-dominance models. Considering G894T polymorphism, in African populations the association was observed for all genetic models, with the exception of co-dominance model. There were insufficient studies to perform a meta-analysis for G894T in South Asians and West Asians.

According to the type of diabetes mellitus (DM), for 4b/a polymorphism an association was observed in additive and recessive models for both type 1 and type 2 diabetes, and only for type 1 in allele contrast and dominant models. There was no association with any type of DM in co-dominant model for 4b/a variant. For T-786C, no association in any genetic model was found in type 2 diabetes. There was insufficient data for this analysis in type 1 DM. Likewise, for G894T variant there was an association only in the allele contrast model with type 2 diabetes (Table 3).

We compared the ORs of our meta-analysis with the results from a previous meta-analysis that used non-diabetic patients as controls [30]. The results were similar and no statistical differences in the ORs of the two studies were observed in all genetic models analyzed (data not shown).

Conclusions

In the present study, the most robust association of DN was with eNOS 4b/a and T-786C polymorphism that held in all genetic models tested, except for co-dominance model. G894T polymorphism was associated with DN only in allele contrast and in co-dominance model. 4b/a polymorphism association with DN was confirmed in all ethnic groups evaluated and for all types of diabetes. The subgroup analysis of the T-786C variant should be viewed with caution, since it was limited due to the small number of studies.

Analyzing genetic model is important, considering the difference between them. Each individual genotype is formed by two alleles (for example G and T for G984T polymorphism), and the risk of every genotype depends on the number of variant allele copies carried, where one of which is thought to be associated with a disease (e.g., T), association studies will collect information on the numbers of diseased and disease-free subjects with each of the three genotypes (GG, GT, and TT). So we used the allele contrast, which compares the number of alleles G with the number of alleles G; the additive model, which contrasts extreme homozygotes, comparing the genotype GG with the genotype TT; in recessive model two copies of T allele are essential to modify the risk, combining the GG and GT genotypes and comparing with TT; the dominant model, which heterozygous GT and homozygous TT genotypes have the similar risk as a single copy of T is sufficient to alter the risk, then compares GG with combined GT and TT genotypes; and the codominance model, commonly used genetic model, where each genotype gives a diverse and non additive risk. which combines the GG and TT genotypes and compares with GT. So OR in each particular genetic model gives us different interpretations about the risk of the polymorphisms.

These results are original and help to understand the role of these polymorphisms in the development of DN. However, it was not possible to exclude a publication bias of negative studies. Therefore, the exact effect could be smaller. As discussed before, other explanations, besides classic risk factors, are needed for understanding the progression of a diabetic patient from normoalbuminuria to macroalbuminuria, and a polymorphism identification of a specific gene would propitiate the development of a new therapy aimed directly to it.

In contrast to a recent meta-analysis performed by Zintzaras et al. [30], which analyzed the same polymorphisms in the progression of DN, our analysis compared diabetic patients with DN (cases) with diabetic patients without DN (controls). In Zintzaras’ study, healthy subjects were used as controls, mixed with patients with DN. When the controls are defined as non-diabetic subjects, the observed association could reflect a genetic predisposition for individuals to develop “diabetic nephropathy”. The obtained results could reflect a mixture of a susceptibility to diabetes per se and to nephropathy, which cannot be discriminated. In this regard, to serve non-diabetic individuals as controls seem rationale to estimate a risk of diabetic nephropathy. However, from clinical points of view, most of medical staff would be interested in risks for nephropathy among individuals with diabetes, as in the case with hyperglycemia, rather than combined risks for developing diabetes and for nephropathy thereafter. That is why diabetic individuals showing no or little nephropathy despite a term of duration have been widely investigated as controls, in most of the previous studies. So, our work and Zintzaras are derived from different standing points: a clinical aspect and a bio-mathematic research.

In this sense, we considered that the optimal control group when studding a DM complication is a diabetic patient without the complication and with disease duration long enough the permit a genetic predisposition to become clinically detected in the presence of hyperglycemia. Moreover, the disease duration must be comparable between cases and controls. Most included studies fulfilled the two pre-requisitions. As can be seen in Table 1, the DM duration is similar between cases and controls in each study and the majority has more than 10 years of DM, reflecting that authors from original studies probably took this important issue in consideration.

Despite the different control used by Zintzaras, they found 92 articles, being 20 included for meta-analysis; that provided 1942/1461 cases/controls for G894T, 2663/2232; cases/controls for 4b/a, and 857/845 cases/controls for T-786C. That was similar to ours that had 22 studies included, but provided about one third more cases/controls. The OR observed in their analyzes showed significance in allelic contrast model for G894 polymorphism, recessive and additive model for 4b/a polymorphism, and allelic contrast model for T-786C, all observed in our study; but our analyze showed association in more genetic models than that, like codominant model for G894T; allele contrast and dominant model for 4b/a; recessive, dominant and additive model for T786C. Furthermore, we compared our ORs with those reported by Zintzaras et al. and no statistical differences were found. With that said, our study reinforce the findings from Zintzaras.

DN development predisposition has not been fully explained, since glycemic control and environmental factors, as well as traditional risk factors, do not accurately predict the occurrence of this diabetic complication in all patients. With this in mind, studies have been trying to resolve this question using genetic approaches. Many candidate genes have been explored in this context, and eNOS polymorphisms have been implicated in the susceptibility to glomerular disease, by mechanisms yet unknown [15]. However, there is no consensus on the role of these polymorphisms in modulation of risk for DN, since the available literature demonstrates mixed results and most of the studies have a small sample. In this scenario, the recommended approach to help investigators in understanding the effect of each polymorphism in DN development is a systematic review and meta-analysis. Our data suggest an association between eNOS polymorphisms and DN. Assuming a recessive model, the relative risk, attributable risk and population attributable risk for the 4a variant ranges are, respectively, 1.20; 0.11; and 0.09.

The present paper has some limitations. The inclusion of studies evaluating patients with DM in several stages of DN, ranging from microalbuminuria to chronic renal insufficiency in kidney replacement therapy, could bias the results due to clinical heterogeneity of cases. Some studies did not present the data separated by DN stages. Furthermore, inclusion criteria in the reviewed studies utilized different methods and cutoffs to define microalbuminuria or macroalbuminuria. Although all clinically validated [35], these aspects made impossible to evaluate the effect of each polymorphism in the stages of DN in this meta-analysis. Finally, the polymorphisms true effects could be overestimated in the present study, since there is some indication of publication bias.

In conclusion, this study shows an association between DN and polymorphisms in eNOS gene. This effect is very consistent for the 4b and T-786 polymorphism.

Methods

The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement was used in this report [36, 37].

Selection criteria and search strategy

Case–control studies that had diabetic patients with DN as cases and diabetic patients without nephropathy as controls, as well as that evaluated at least one of the three polymorphisms of interest (4b/4a, T-786C, G986T) were considered eligible. Only studies in humans and using validated genotyping methods were considered. No publication language, publication date, or publication status restrictions were imposed. All studies published up until December 31st, 2012 were identified by searching electronic databases: Medline (1966-Present), EMBASE (1980-Present), LILACS and Cochrane Library.

Abstracts presented at scientific events held by: The American Diabetes Association (ADA); The European Association for the Study of Diabetes (EASD); The National Kidney Association (NKA); and The American Society of Nephrology (ASN) were searched over the last seven years. The authors were contacted for more details in the case of abstracts with missing information.

The following index terms were used: (“Nitric Oxide Synthase Type III” OR “NOS3 protein, human”) AND (“Databases, Genetic” OR “Genetic Predisposition to Disease” OR “Genetic Phenomena” OR “Genetic Processes” OR “Genetic Markers” OR “Genetic Variation” OR “Polymorphism, Genetic” OR “Genetic Research” OR “Genetic Determinism” OR “Genes” OR “Genetics” OR “Mutation” OR “Genetics, Medical” OR “DNA”) AND (“Proteinuria” OR “Albuminuria” OR “Kidney Failure” OR “Kidney Failure, Chronic” OR “Kidney Diseases” OR “Diabetic Nephropathies”).

Study selection and data extraction

Eligibility assessment was made by title and abstracts review and in doubtful cases by full article review. This was performed independently in a standardized manner by two investigators (BSD and CBL). Disagreements between reviewers were resolved by consensus.

Two investigators extracted the data, one independent to another (BSD and LCFP). Disagreements were resolved by a third author (LHC). For articles with missing information, (n = 3) the authors were contacted for further information, but none responded. In the case of duplicate publications, the first manuscript published was included in the analysis. Information was extracted from each individual study based on: (1) characteristics of study participants (including age, gender, type of diabetes, diabetes duration, nephrologic status, and ethnicity) [38], (2) case and control definition; (3) genetic data (including allelic distribution and genotypic frequency).

Quality assessment

To ascertain the validity of each eligible case–control study, two investigators (BSD and LCFP) worked independently during the initial search and after worked together to determine the adequacy of studies selection. It was assessed if the same exclusion criteria for cases and controls were used; if cases were easily differentiated from controls; if analysis of studied polymorphisms were conducted in a standard, valid, and reliable way, if major biases were identified and considered in design and analysis; and how good the study was to minimize the risks of bias or confusion. Hardy-Weinberg equilibrium assessment among the control group within each polymorphism in all studies was checked by exact test using an online HWE calculator (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl).

Statistical analysis

Gene-disease association was measured using odds ratio estimation based on the following genetic contrast/models: (1) allele contrast; (2) additive model; (3) recessive model; (4) dominant model and (4) co-dominant model [39, 40]. Heterogeneity was tested by chi-squared test, Cochran’s Q, and inconsistency with I2. If PQ <0.10, then heterogeneity was considered statistically significant. Odds ratio was calculated using fixed-effect models (Mantel-Haenszel), and random models when heterogeneity was observed. Multiple comparisons were not made because meta-analysis of genetic association studies is considered an exploratory study, without a prespecified key hypothesis [41, 42].

The risk of publication bias was evaluated using funnel plot graphics [43].

Sensibility tests were made concerning to ethnia and type of diabetes.

Data were analyzed using Stata/SE 11.2 (http://www.stata.com).

We compared the ORs of our meta-analysis with the results from a previous one [44] that used non-diabetic patients as controls using the differences of OR and 95%CI (WinPepi version 11.3).

Abbreviations

ADA: 

The American Diabetes Association

ASN: 

The American Society of Nephrology

DM: 

Diabetes mellitus

DN: 

Diabetic nephropathy

EASD: 

The European Association for the Study of Diabetes

eNOS: 

Endothelial nitric oxide synthase

ESRD: 

End stage renal disease

GFR: 

Glomerular filtration rate

iNOS: 

Inducible nitric oxide synthase

NKA: 

The National Kidney Association

nNOS: 

Neuronal nitric oxide synthase

NO: 

Nitric oxide

NOS: 

Nitric oxide synthase

PRISMA: 

The preferred reporting items for systematic reviews and meta-analysis

SNP: 

Single nucleotide polymorphism.

Declarations

Funding

LHC and CBL received a scholarship from Conselho Nacional de Desenvolvimento Cinetífico e Tecnológico (CNPq).

Authors’ Affiliations

(1)
Endocrine Division, Hospital de Clínicas de Porto Alegre
(2)
Universidade Luterana do Brasil

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  45. Pre-publication history

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Copyright

© Dellamea et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.