Open Access

VDR Gene variation and insulin resistance related diseases

  • Fei-fei Han1,
  • Ya-li Lv1,
  • Li-li Gong1,
  • He Liu1,
  • Zi-rui Wan1 and
  • Li-hong Liu1Email author
Lipids in Health and Disease201716:157

https://doi.org/10.1186/s12944-017-0477-7

Received: 13 March 2017

Accepted: 1 May 2017

Published: 19 August 2017

Abstract

Background

Vitamin D status may influence the risk of Insulin resistance related diseases such as Type 2 diabetes (T2DM), metabolic syndrome (MetS), and polycystic ovarian syndrome (PCOS). Several studies have assessed vitamin D receptor (VDR) gene polymorphism in relationship with these diseases; however, results remain inconsistent. Our study was conducted to elucidate whether VDR Gene polymorphisms could predict insulin resistance on a large scale.

Methods

A meta-analysis using MEDLINE and EMBASE, was performed up to December 16th, 2016. Studies reporting association of vitamin D gene polymorphism with incident T2DM, MetS and PCOS outcomes were included and sub-group analysis by pigment of skin and latitude were performed.

Results

A total of 28 articles based on four gene variation, and comprising 9232 participants with 5193 Insulin resistance related diseases patients were included. No significant associations of the VDR ApaI, BsmI, FokI and TaqI variant with Insulin resistance related diseases were found. However, sub-group analysis analysis showed that PCOS in TaqI (OR = 1.47, 95% CI = 1.03–2.09, P = 0.03) for T allele and MetS for G allele (OR = 1.41, 95% CI = 1.07–1.85, P = 0.01) in BsmI was significant association with VDR gene polymorphism. Simultaneously, sub-group analysis showed VDR ApaI rs7975232(G > T)variant was associated with insulin resistance related diseases in Asians (GG/GT + TT) (OR, 1.62; 95% CI, 1.03–2.53; P = 0.04) and population who lived in middle latitude district (30–60°) (GG/GT + TT) (OR, 1.22; 95% CI, 1.04–1.43; P = 0.02), VDR BsmI rs1544410 (A > G)and VDR Taq1rs731236 (T/C) variant were associated with insulin resistance related diseases in Caucasian (dark-pigmented).

Conclusion

The results suggested that the association between insulin resistance related diseases and VDR ApaI, BsmI, FokI variant was more obvious in dark-pigmented Caucasians and Asians but not in Caucasian with white skin.

Keywords

VDR Gene polymorphisms Type 2 diabetes (T2DM) Metabolic syndrome (MetS) Polycystic ovarian syndrome (PCOS)

Background

Vitamin D deficiency as a common health problem is a global problem, thought to be related to lack of sunlight exposure, and usually accompanied by reduced dietary intake [1]. The Vitamin-D receptor (VDR) was studied as a genetic factor of spine pathologies and plays a part in normal bone mineralization and remodeling. It is an endocrine member belongs to the nuclear receptor superfamily for steroid hormones. Its gene polymorphisms are thought to contribute to osteoarthritis, osteoporosis and degenerative disc disease. Also researchers found that VDR regulates vitamin D levels and calcium metabolism in the body and these are known to be associated with endocrine dysfunctions, insulin resistance [2, 3]. Vitamin D has been reported to influence glucose regulation via effects on insulin secretion and action [4]. Evidence is accumulating to suggest that altered vitamin D and Ca homoeostasis may play a role in the development of metabolic disturbances in insulin resistance related diseases [57]. More and more studies found that the vitamin D was useful for insulin resistance diseases [810].

T2DM, MetS, and IFG are common metabolic disorders which are observed with increasing prevalence, and which are caused by a complex interplay between genetic and environmental factors, and these metabolic disorders are all characterized by insulin resistance [1113]. PCOS is by far the most common cause of anovulatory infertility and has been reported to be associated with insulin resistance (IR), hyperinsulinemia, dyslipidemia, and central obesity, which are all risk factors for the MetS, T2DM, and cardiovascular disease. Several studies have assessed vitamin D receptor gene polymorphism in relationship with these diseases; however, results remain inconsistent.

Vitamin D condition depends mainly on the sunlight and skin. It is both an environmental and biological determinant of health. Skin pigmentation may predispose subpopulations to vitamin D deficiency [14]. Some studies demonstrate that vitamin D deficiency is much higher in dark-pigmented population and Asians due to a reduced ability to produce vitamin D in their skin [15, 16]. Wondering whether there was any correlation or diverseness among these different population and their living latitude, in this research we also performed sub-group studies by skin pigmentation and latitude. Our study was conducted to elucidate whether VDR Gene polymorphisms could predict insulin resistance on a large scale.

Methods

Search strategy and selection criteria

Two investigators (Fei-fei Han, Ya-li Lv) independently searched PubMed and Embase (from 1980 until December 16th, 2016) database using the terms ((Gene polymorphism or gene variation)) AND (((((((diabetes mellitus) OR Diabetes) OR insulin resistance) OR metabolic syndrome) OR polycystic ovarian syndrome)) AND (vitamin D receptor OR VDR)).

Furthermore, we reviewed citations in the retrieved articles to search for additional relevant studies. Articles included in meta-analysis were in English or Chinese, with human subjects, published in primary literature and with no obvious overlap of subjects with other studies. The retrieved literatures were then read in their entirety to assess their appropriateness for the inclusion in this meta-analysis. Conference abstracts, case reports, editorials, review articles, and letters were excluded. We defined strict criteria for inclusion of studies. Studies were included if the exposure of interest was the VDR genotype.

Data extraction

Two independent authors extracted data and reached a consensus on the author, year of publication, ethnicity, number of patients and controls and disease types.

Statistical analysis

All statistical analyses were performed using Review Manager (Review Manager 5.0 software) and Stata/MP 11.0. Cochran’s w2 test and the inconsistency index (I2) were used to evaluate heterogeneity across the included studies. Random-effects model was applied in all the analysis. OR and their corresponding 95% confidence intervals (CI) were estimated. Z-test was performed to determine the statistical significance of pooled OR, and was considered significant when P < 0.05. We assessed potential publication bias by using a funnel plot and Egger’s test. Sensitivity analysis was performed by sequential removal (statistics of study remove) of individual studies (we did not show these results) [17].

Results

Eligible studies for meta-analysis

This study is focusing on VDR ApaI rs7975232 (G > T) variant, BsmI rs1544410 (A > G) variant, Taq1rs731236 (T > C) variant and FokIrs2228570 (C > T) variant and Insulin resistance related diseases susceptibility including (T2DM, MetS and PCOS). Characteristics of studies investigating the association of the variants with Insulin resistance related diseases susceptibility are presented in Table 1. The research of the VDR variant identified 54 articles. However, 26 studies were excluded for no case–control or no data. Finally, 28 studies were included in the current meta-analysis (Fig. 1).
Table 1

Characteristics of studies on VDR ApaI rs7975232 (G > T) variant and Insulin resistance related diseases susceptibility

Author

Year

Country

Ethic

City latitude

Disease

Case

Control

TT

TG

GG

TT

TG

GG

Al-Daghri NM [18]

2012

Saudi

Caucasian (dark)

Riyadh 24°38′N

T2DM

148

172

48

101

106

52

Boullu-Sanchis, S [19]

1999

France (migrant Indian population)

Caucasian (Dark)

Guadeloupe 16°15′N

T2DM

22

42

25

22

47

31

Dasgupta S [48]

2015

India

Caucasian (Dark)

Hyderabad 17°23′N

PCOS

117

120

13

120

117

13

Dilmec F [21]

2008

India

Caucasian (Dark)

Sanliurfa 37°17′N

T2DM

27

38

7

61

82

26

El-Shal AS [20]

2013

Egypt

Caucasian (Dark)

Zagazig 30°35′N

PCOS

63

65

22

68

64

18

Oh, J° Y° [22]

2001

USA

Caucasian

Southern California 32°42′N

T2DM

84

92

66

452

552

265

Jedrzejuk D [23]

2015

Poland

Caucasian

Wroclaw 51°1′N

PCOS

19

52

19

32

49

17

Mahmoudi T [24]

2009

Iran

Caucasian (Dark)

Tehran 35°40′N

PCOS

58

68

36

49

90

23

Malecki MT [25]

2003

Poland

Caucasian

Krakow 50°08′N

T2DM

71

153

84

60

124

56

Rivera-Leon EA [49]

2015

Mexico

Mix

Western of Mexico (Guadalajara 20°67′N)

T2DM

47

64

14

31

78

16

Wehr E [27]

2011

Austria

Caucasian

Graz 47°4′N

PCOS

142

274

127

48

60

37

Ye WZ [28]

2001

France

Caucasian

Paris 48°52′N

T2DM

98

142

65

35

78

30

Zhong X [30]

2015

China

Asian

Anhui Province 31°52′N

T2DM

29

114

61

28

59

29

Zhang H [29]

2012

China

Asian

Changsha 28°12′N

T2DM

30

154

120

12

53

35

Fig. 1

Flow diagram for study selection in meta-analysis

Of these, 14 case–control studies examined the association of the ApaI rs7975232 (G > T) variant [3, 1830] (Table 1), 22 studies in 20 case–control papers examined the association of the BsmI rs1544410 (A > G) variant [18, 22, 23, 2739] (Table 2), 19 studies in 18 case–control studies examined the association of the Taq1rs731236 (T > C) variant[3, 1828, 32, 33, 35, 3840] (Table 3) and 18 studies in 16 case–control studies in15 papers examined the association of FokIrs2228570 (C > T)variant [3, 18, 2325, 27, 3032, 36, 4145] (Table 4) with Insulin resistance related diseases susceptibility.
Table 2

Characteristics of studies on VDR BsmI rs1544410 (A > G) variant and Insulin resistance related diseases susceptibility

Author

Year

Country

Ethic

City latitude

Disease

Case

Control

GG

AG

AA

GG

AG

AA

Al-Daghri NM [18]

2012

Saudi

Caucasian (dark)

Riyadh 24°38′N

T2DM

105

201

62

114

95

50

Bagheri M [31]

2012

Iran

Caucasian (dark)

Urmia 37°33′N

PCOS

15

27

4

20

24

2

Bid HK [32]

2009

India

Caucasian (dark)

North Indian

About 22–37°N

T2DM

30

52

18

60

77

23

Jedrzejuk D [23]

2015

Poland

Caucasian

Wroclaw 51°1′N

PCOS

31

45

14

43

42

13

Oh, J° Y° [22]

2001

USA

Caucasian

Southern California 32°42′N

T2DM

86

107

49

460

590

253

Mahmoudi T [24]

2009

Iran

Caucasian (dark)

Tehran 35°40′N

PCOS

53

85

24

53

91

18

Malecki MT [25]

2003

Poland

Caucasian

Krakow 50°08′N

T2DM

131

142

35

92

116

32

Mukhopadhyaya PN [33]

2010

India

Caucasian (dark)

Pune 18°52′N

T2DM

17

14

9

26

4

10

Mackawy A M [50]

2014

Eygpt

Caucasian (dark)

Zagazig 30°35′N

T2DM

17

33

80

9

16

38

Mets

8

17

42

9

16

38

Speer G [34]

2001

Hungary

Caucasian

Budapest 47°30′N

MetS

40

46

14

33

48

19

Schuch NJ [42]

2013

Brazil

Mix

São Paulo 23°33′N

Mets

20

43

37

9

41

50

Vural HC [35]

2012

Turkey

Caucasian

Konya 37°86′N

T2DM

37

43

20

50

41

9

Wehr E [27]

2011

Austria

Caucasian

Graz 47°4′ N

PCOS

216

244

77

49

66

22

Xia Z [36]

2014

China

Asian

Beijing 39°26′-41°03′N

T2DM

209

27

2

82

8

1

Xu, J° R°[39]

2014

China

Asian

Ningxia province 35–39′N

T2DM

176

24

1

172

47

0

Chinese hui population

T2DM

122

30

3

87

28

0

Xu JR [37]

2007

China

Asian

Ningxia province

35–39°N

T2DM

41

46

19

68

28

6

Ye WZ [28]

2001

France

Caucasian

Paris 48°52′N

T2DM

119

135

52

54

65

24

Zhang H [29]

2012

China

Asian

Changsha 28°12’N

T2DM

218

83

3

85

14

1

Zhong X [30]

2015

China

Asian

Anhui Province 31°52′N

T2DM

11

54

139

2

18

96

Yi Zhao [45]

2014

China

Asian

Yinchuan, Ningxia 38°2′N

MetS

347

42

1

328

69

3

Table 3

Characteristics of studies on VDR Taq1rs731236 (T/C) variant and Insulin resistance related diseases susceptibility

Author

Year

Ethic

Ethic

City latitude

Disease

Case

Control

CC

CT

TT

CC

CT

TT

Al-Daghri NM [18]

2012

Saudi

Caucasian (dark)

Riyadh 24°38′N

T2DM

65

195

108

50

114

95

Bagheri M [40]

2013

Iran

Caucasian (dark)

Urmia 37°33′N

PCOS

8

14

16

2

19

17

Bid HK [32]

2009

Indian

Caucasian (dark)

North Indian About 22–37°N

T2DM

15

49

36

28

65

67

Boullu-Sanchis, S [19]

1999

France

Caucasian (dark)

Guadeloupe 16°15′N

T2DM

48

33

8

44

39

17

Dasgupta S [48]

2015

India

Caucasian (dark)

Hyderabad 17°23′N

PCOS

47

92

113

37

105

110

Dilmec F [21]

2008

Turkey

Caucasian

Sanliurfa 37°17′N

T2DM

14

25

33

19

81

69

El-Shal AS [20]

2013

Egypt

Caucasian (dark)

Zagazig 30°35′N

PCOS

36

74

40

20

61

69

Oh, J° Y° [22]

2002

USA

Caucasian

Southern California 32°42′N

T2DM

41

108

93

219

581

503

Jedrzejuk D [23]

2015

Poland

Caucasian

Wroclaw 51°1′N

PCOS

8

45

37

12

37

49

Mahmoudi T [24]

2009

Iran

Caucasian (dark)

Tehran35°40′N

PCOS

20

71

71

14

76

72

Malecki MT [25]

2003

Poland

Caucasian

Krakow 50°08′N

T2DM

71

153

84

60

124

56

Mukhopadhyaya PN [33]

2010

Indian

Caucasian (dark)

Pune 18°52′N

T2DM

5

12

23

8

25

7

Rivera-Leon EA [49]

2015

Mexico

Mix

western of Mexico (Guadalajara 20°67′N)

T2DM

25

62

38

19

72

34

Vural HC [35]

2012

Turkey

Caucasian

Konya 37°86′N

T2DM

3

46

51

16

49

35

Wehr E [27]

2011

Austria

Caucasian

Graz 47°4′N

PCOS

72

238

226

23

65

49

Xu, J. R. [39]

2014

Chinese Han

Asian

Ningxia province 35–39°N

T2DM

176

24

1

172

47

0

Chinese Hui

T2DM

134

17

3

99

16

0

Xu J.R. [38]

2012

China

Asian

Ningxia province 35–39°N

T2DM

182

19

0

188

25

1

Ye WZ [28]

2001

France

Caucasian

Paris 48°52′N

T2DM

49

136

120

23

66

54

Table 4

Characteristics of studies on VDR FokIrs2228570 (C > T) variant and Insulin resistance related diseases susceptibility

Author

Year

Country

Ethic

City latitude

Disease

Case

Control

TT

TC

CC

TT

TC

CC

Al-Daghri NM [18]

2012

Saudi

Caucasian (dark)

Riyadh 24°38′N

T2DM

213

133

22

129

111

19

Bagheri M [31]

2012

Iran

Caucasian (dark)

Urmia 37°33′N

PCOS

22

20

4

29

15

2

Bid HK [32]

2009

India

Caucasian (dark)

North Indian

About 22–37°N

T2DM

2

60

38

1

79

80

Dasgupta S [48]

2015

India

Caucasian (dark)

Hyderabad 17°23′N

PCOS

8

87

155

9

88

152

Jia J [51]

2015

China

Asian

Nanjing 31°14′N

T2DM

120

336

212

408

973

579

IFG

233

515

336

408

973

579

Jedrzejuk D [23]

2015

Poland

Caucasian

Wroclaw 51°1′N

PCOS

11

51

28

25

50

23

Mahmoudi T [24]

2009

Iran

Caucasian (dark)

Tehran 35°40′N

PCOS

12

67

83

7

59

96

Malecki MT [25]

2003

Poland

Caucasian

Krakow 50°08′N

T2DM

64

159

85

52

110

77

Mackawy A M [50]

2014

Eygpt

Caucasian (dark)

Zagazig 30°35′N

T2DM

34

40

66

5

11

44

Mets

11

13

39

5

11

44

Shah DB [43]

2015

India

Caucasian (dark)

Telangana 17°49′N

T2DM

15

9

10

11

10

2

Schuch NJ [42]

2013

Brazil

Mix

São Paulo 23°33′N

Mets

40

47

13

35

57

8

Vedralová M [44]

2012

Czech Republic

Caucasian

Prague 50°05′N

T2DM

11

58

63

12

76

25

Wehr E [27]

2011

Austria

Caucasian

Graz 47°4′N

PCOS

82

241

215

22

60

53

Xia Z [36]

2014

China

Asian

Beijing 39°26′-41°03′N

T2DM

19

94

124

9

47

35

Yi Zhao [45]

2014

China

Asian

Yinchuan, Ningxia 38°2′N

MetS

75

184

132

80

207

112

Zhong X [30]

2015

China

Asian

Anhui Province 31°52′N

T2DM

44

114

46

18

58

40

Association between VDR ApaI rs7975232 (G > T) variant and insulin resistance related diseases susceptibility

Fourteen studies (3212 cases and 3360 controls) examining the association between the VDR ApaI rs7975232 (G > T) variant and Insulin resistance related diseases susceptibility were included. Sub-group analysis (nine studies about T2DM and five studies about PCOS) was performed. All the original data were combined by means of the Random effect model. We found no association of the VDR ApaI rs7975232 (G > T) variant with Insulin resistance related diseases (OR, 1.08; 95% CI, 0.91–1.28; P = 0.37) in the recessive genetic model (G/G vs.G/T or T/T), dominant genetic model in the (G/G or G/T vs.T/T) (OR, 1.04; 95% CI, 0.89–1.21; P = 0.62) and G allele vs. T allele analysis (OR, 1.04; 95% CI, 0.95–1.1; P = 0.36). sub-group analysis indicated that there was no association between VDR ApaI rs7975232 (G > T)variant and T2DM, PCOS patients (Table 5). sub-group analysis by skin pigmentation and living latitude showed that ApaI rs7975232 (G > T) variant was associated with insulin resistance related diseases in Asians (GG/GT + TT) (OR, 1.62; 95% CI, 1.03–2.53; P = 0.04) and population who lived in middle latitude district (30–60°) (GG/GT + TT) (OR, 1.22; 95% CI, 1.04–1.43; P = 0.02). No publication bias was detected by either the funnel plot or Egger’s tests (P > 0.05, each comparison).
Table 5

Summary of meta-analysis

Comparison of outcome

No. of trials

No. of Case

No. of Control

Effect size (95% confidence intervals)

P

Test for heterogeneity

I 2 (%)

P

ApaI

 GG/GT + TT

14

3212

3360

1.08 [0.91, 1.28]

0.37

30

0.14

 T2DM

9

2017

2555

1.00 [0.78, 1.28]

1

51

0.05

 PCOS

5

1195

805

1.15 [0.88, 1.50]

0.31

0

0.47

 GG + GT/TT

14

3212

3360

1.04 [0.89, 1.21]

0.62

38

0.08

 T2DM

9

2017

2555

0.93 [0.79, 1.11]

0.44

17

0.29

 PCOS

5

1195

805

1.15 [0.90, 1.45]

0.27

30

0.22

 G allele

14

3212

3360

1.04 [0.95, 1.14]

0.36

26

0.18

 T2DM

9

2017

2555

0.97 [0.85, 1.11]

0.7

42

0.1

 PCOS

5

1195

805

1.11 [0.96, 1.27]

0.15

0

0.84

 T allele

14

3212

3360

1.02 [0.91, 1.15]

0.7

56

0.0005

 T2DM

9

2017

2555

1.03 [0.90, 1.18]

0.68

43

0.09

 PCOS

5

1195

805

1.07 [0.83, 1.37]

0.62

70

0.01

Ethic

 GG/GT + TT

13

3087

3235

1.09 [0.91, 1.30]

0.34

34

0.11

 Caucasian

5

1488

1929

1.20 [0.99, 1.45]

0.06

0

0.41

 Caucasian (dark)

6

1091

1090

0.94 [0.64, 1.36]

0.73

52

0.07

 Asian

2

508

216

1.24 [0.88, 1.76]

0.22

0

0.88

 GG + GT/TT

13

3087

3235

1.08 [0.94, 1.24]

0.29

21

0.23

 Caucasian

5

1488

1929

1.13 [0.87, 1.46]

0.36

49

0.1

 Caucasian (dark)

6

1091

1090

0.97 [0.81, 1.15]

0.7

0

0.89

 Asian

2

508

216

1.62 [1.03, 2.53]

0.04

0

0.35

 G allele

13

3087

3235

1.06 [0.98, 1.16]

0.16

13

0.31

 Caucasian

5

1488

1929

1.11 [0.98, 1.27]

0.06

0

0.51

 Caucasian (dark)

6

1091

1090

0.96 [0.85, 1.09]

0.51

0

0.66

 Asian

2

508

216

1.25 [0.99, 1.57]

0.1

17

0.3

 T allele

13

3087

3235

1.01 [0.89, 1.14]

0.93

56

0.008

 Caucasian

5

1488

1929

0.94 [0.80, 1.09]

0.4

42

0.14

 Caucasian (dark)

6

1091

1090

1.16 [0.97, 1.38]

0.1

47

0.009

 Asian

2

508

216

0.80 [0.64, 1.01]

0.06

0

0.51

Latitude

 GG/GT + TT

14

3212

3360

1.08 [0.91, 1.28]

0.37

30

0.14

 Low (<30)

5

1136

834

0.86 [0.65, 1.14]

0.3

19

0.29

 Middle (30–60)

9

2076

2526

1.22 [1.04, 1.43]

0.02

0

0.43

 GG + GT/TT

14

3212

3360

1.04 [0.89, 1.21]

0.62

38

0.08

 Low (<30)

5

1136

834

0.91 [0.73, 1.15]

0.44

17

0.31

 Middle (30–60)

9

2076

2526

1.12 [0.92, 1.36]

0.27

42

0.08

 G allele

14

3212

3360

1.04 [0.95, 1.14]

0.36

26

0.18

 Low (<30)

5

1136

834

0.92 [0.80, 1.07]

0.27

10

0.35

 Middle (30–60)

9

2076

2526

1.12 [1.01, 1.23]

0.02

0

0.44

 T allele

14

3212

3360

1.02 [0.91, 1.15]

0.7

56

0.005

 Low (<30)

5

1136

834

1.09 [0.94, 1.25]

0.26

10

0.35

 Middle (30–60)

9

2076

2526

0.99 [0.84, 1.18]

0.95

66

0.003

BsmI

 AA/GA + GG

22

4294

4157

0.95 [0.78, 1.16]

0.64

41

0.02

 T2DM

14

2802

3051

0.99 [0.75, 1.31]

0.93

55

0.007

 PCOS

4

835

443

1.11 [0.77, 1.58]

0.58

0

0.61

 MetS

4

657

663

0.72 [0.50, 1.05]

0.09

0

0.5

 AA + GA/GG

22

4294

4157

1.06 [0.86, 1.31]

0.59

69

<0.00001

 T2DM

14

2802

3051

1.19 [0.90, 1.57]

0.21

71

<0.001

 PCOS

4

835

443

1.06 [0.79, 1.42]

0.7

19

0.29

 MetS

4

657

663

0.62 [0.45, 0.86]

0.005

11

0.34

 A allele

22

4294

4157

0.97 [0.83, 1.13]

0.67

72

<0.00001

 T2DM

14

2802

3051

1.05 [0.85, 1.28]

0.67

76

<0.00001

 PCOS

4

835

443

0.96 [0.79, 1.16]

0.65

12

0.33

 MetS

4

657

663

0.71 [0.54, 0.93]

0.01

37

0.19

 G allele

22

4294

4157

1.08 [0.89, 1.32]

0.42

83

<0.00001

 T2DM

14

2802

3051

0.96 [0.78, 1.17]

0.67

76

<0.00001

 PCOS

4

835

443

1.27 [0.67, 2.40]

0.73

91

0.00001

 MetS

4

657

663

1.41 [1.07, 1.85]

0.01

37

0.19

Ethic

 AA/GA + GG

21

4194

4057

0.98 [0.80, 1.21]

0.87

40

0.03

 Caucasian

7

1683

2121

1.01 [0.81, 1.26]

0.92

9

0.36

 Caucasian (dark)

7

913

793

1.05 [0.82, 1.35]

0.69

0

0.82

 Asian

7

1598

1143

0.90 [0.39, 2.08]

0.81

67

0.006

 AA + GA/GG

21

4194

4057

1.10 [0.89, 1.36]

0.38

68

<0.00001

 Caucasian

7

1683

2121

0.98 [0.82, 1.18]

0.84

25

0.24

 Caucasian (dark)

7

913

793

1.50 [1.16, 1.93]

0.002

19

0.29

 Asian

7

1598

1143

0.89 [0.49, 1.61]

0.69

80

<0.00001

 A allele

21

4194

4057

1.02 [0.87, 1.19]

0.84

72

<0.00001

 Caucasian

7

1683

2121

1.03 [0.86, 1.23]

0.75

59

0.02

 Caucasian (dark)

7

913

793

1.23 [1.07, 1.42]

0.004

0

0.91

 Asian

7

1598

1143

0.81 [0.49, 1.34]

0.42

86

<0.00001

 G allele

21

4194

4057

1.06 [0.87, 1.29]

0.57

83

<0.00001

 Caucasian

7

1683

2121

1.19 [0.85, 1.65]

0.32

89

<0.00001

 Caucasian (dark)

7

913

793

0.81 [0.70, 0.94]

0.004

0

0.91

 Asian

7

1598

1143

1.23 [0.74, 2.04]

0.42

86

<0.00001

Latitude

 AA/GA + GG

22

4294

4157

0.95 [0.78, 1.16]

0.64

41

0.02

 Low (<30)

5

912

659

0.74 [0.52, 1.05]

0.09

39

0.16

 Middle (30–60)

17

3382

3498

1.05 [0.83, 1.33]

0.68

37

0.06

 AA + GA/GG

22

4294

4157

1.06 [0.86, 1.31]

0.59

69

<0.00001

 Low (<30)

5

912

659

1.32 [0.73, 2.38]

0.35

70

0.009

 Middle (30–60)

17

3382

3498

1.00 [0.81, 1.23]

0.97

61

0.0005

 A allele

22

4294

4157

0.97 [0.83, 1.13]

0.67

72

<0.00001

 Low (<30)

5

912

659

0.96 [0.64, 1.43]

0.83

80

0.0005

 Middle (30–60)

17

3382

3498

0.97 [0.82, 1.15]

0.7

70

<0.00001

 Gallele

22

4294

4157

1.08 [0.89, 1.32]

0.42

83

<0.00001

 Low (<30)

5

912

659

1.04 [0.70, 1.56]

0.83

80

0.0005

 Middle (30–60)

17

3382

3498

1.09 [0.87, 1.37]

0.44

84

<0.00001

TaqI

 TT/TC + CC

19

3533

4024

1.00 [0.82, 1.21]

0.96

60

0.004

 T2DM

13

2305

3187

1.09 [0.84, 1.42]

0.51

60

0.003

 PCOS

6

1228

837

0.86 [0.62, 1.20]

0.37

65

0.01

 TT + TC/CC

19

3533

4024

0.88 [0.73, 1.06]

0.17

43

0.02

 T2DM

13

2305

3187

0.92 [0.74, 1.14]

0.43

41

0.06

 PCOS

6

1228

837

0.77 [0.51, 1.16]

0.22

52

0.06

 T allele

19

3533

4024

0.89 [0.75, 1.06]

0.18

79

<0.0001

 T2DM

13

2305

3187

1.01 [0.86, 1.18]

0.95

60

0.003

 PCOS

6

1228

837

0.68 [0.48, 0.96]

0.03

84

<0.0001

 C allele

19

3533

4024

1.13 [0.95, 1.34]

0.18

79

<0.0001

 T2DM

13

2305

3187

0.99 [0.85, 1.17]

0.95

60

0.03

 PCOS

6

1228

837

1.47 [1.03, 2.09]

0.03

84

0.00001

Ethic

 TT/TC + CC

17

3368

3859

0.93 [0.78, 1.12]

0.45

49

0.01

 Caucasian

7

1653

2190

1.10 [0.90, 1.36]

0.35

38

0.14

 Caucasian (dark)

7

1159

1121

0.75 [0.58, 0.97]

0.03

46

0.08

 Asian

3

556

548

1.94 [0.32, 11.77]

0.47

0

0.44

 TT + TC/CC

17

3368

3859

0.88 [0.72, 1.07]

0.2

48

0.01

 Caucasian

7

1653

2190

1.12 [0.82, 1.53]

0.47

50

0.06

 Caucasian (dark)

7

1159

1121

0.76 [0.57, 1.02]

0.07

39

0.13

 Asian

3

556

548

0.67 [0.47, 0.96]

0.03

0

0.4

 T allele

17

3368

3859

0.84 [0.71, 1.01]

0.06

78

<0.00001

 Caucasian

7

1653

2190

0.94 [0.66, 1.33]

0.73

90

<0.00001

 Caucasian (dark)

7

1159

1121

0.80 [0.68, 0.95]

0.01

41

0.12

 Asian

3

556

548

0.73 [0.51, 1.04]

0.08

10

0.33

 C allele

17

3368

3859

1.18 [0.99, 1.41]

0.06

78

<0.00001

 Caucasian

7

1653

2190

1.06 [0.75, 1.51]

0.73

90

<0.00001

 Caucasian (dark)

7

1159

1121

1.24 [1.05, 1.47]

0.01

42

0.11

 Asian

3

556

548

1.37 [0.96, 1.94]

0.08

10

0.33

Latitude

 TT/TC + CC

18

3493

3984

0.95 [0.80, 1.12]

0.52

47

0.02

 Low (<30)

5

934

896

0.86 [0.67, 1.09]

0.2

24

0.26

 Middle (30–60)

13

2559

3088

1.00 [0.79, 1.25]

0.97

52

0.01

 TT + TC/CC

18

3493

3984

0.87 [0.72, 1.05]

0.15

45

0.02

 Low (<30)

5

934

896

0.88 [0.70, 1.12]

0.3

0

0.44

Middle (30–60)

13

2559

3088

0.87 [0.67, 1.13]

0.29

56

0.007

 T allele

18

3493

3984

0.85 [0.72, 1.01]

0.06

77

<0.00001

 Low (<30)

5

934

896

0.90 [0.78, 1.02]

0.11

0

0.69

 Middle (30–60)

13

2559

3088

0.84 [0.66, 1.07]

0.15

83

<0.00001

 C allele

18

3493

3984

1.17 [0.99, 1.39]

0.06

77

<0.00001

 Low (<30)

5

934

896

1.11 [0.97, 1.27]

0.12

0

0.68

 Middle (30–60)

13

2559

3088

1.19 [0.94, 1.51]

0.15

83

<0.00001

FokI

 CC/CT + TT

18

4992

6230

1.03 [0.82, 1.30]

0.79

80

<0.00001

 T2DM

9

1086

690

1.10 [0.75, 1.60]

0.63

81

<0.00001

 PCOS

5

631

559

1.20 [0.97, 1.48]

0.1

0

0.49

 MetS

3

1084

1960

0.60 [0.16, 2.33]

0.46

93

<0.00001

 CC + CT/TT

18

4992

6230

0.92 [0.72, 1.17]

0.49

74

<0.00001

 T2DM

9

1086

690

1.02 [0.76, 1.37]

0.88

58

0.01

 PCOS

5

631

559

1.29 [0.82, 2.03]

0.27

41

0.15

 MetS

3

1084

1960

0.35 [0.10, 1.19]

0.09

93

<0.00001

 C allele

18

4992

6230

0.99 [0.87, 1.12]

0.84

73

<0.00001

 T2DM

9

1086

690

1.00 [0.79, 1.26]

0.99

81

<0.00001

 PCOS

5

631

559

1.09 [0.85, 1.39]

0.49

54

0.07

 MetS

3

1084

1960

0.75 [0.49, 1.14]

0.18

72

0.03

 T allele

18

4992

6230

1.01 [0.89, 1.15]

0.85

73

<0.00001

 T2DM

9

1086

690

1.00 [0.79, 1.26]

0.99

81

<0.00001

 PCOS

5

631

559

0.92 [0.72, 1.17]

0.49

54

0.07

 MetS

3

1084

1960

1.33 [0.87, 2.02]

0.19

73

0.03

Ethic

 CC/CT + TT

17

4892

6130

1.01 [0.80, 1.28]

0.92

80

<0.00001

 Caucasian

4

1068

585

1.36 [0.77, 2.41]

0.29

83

0.0006

 Caucasian (dark)

8

1240

1019

0.75 [0.41, 1.36]

0.35

86

<0.00001

 Asian

5

2584

4526

1.13 [0.98, 1.30]

0.1

24

0.26

 CC + CT/TT

17

4892

6130

0.91 [0.69, 1.20]

0.49

76

<0.00001

 Caucasian

4

1068

585

1.25 [0.90, 1.74]

0.19

21

0.28

 Caucasian (dark)

8

1240

1019

0.54 [0.26, 1.11]

0.09

82

<0.00001

 Asian

5

2584

4526

1.13 [0.87, 1.47]

0.36

56

0.06

 C allele

17

4892

6130

0.99 [0.86, 1.13]

0.83

74

<0.00001

 Caucasian

4

1068

585

1.24 [0.92, 1.69]

0.16

74

0.01

 Caucasian (dark)

8

1240

1019

0.77 [0.57, 1.04]

0.09

74

0.0003

 Asian

5

2584

4526

1.06 [0.94, 1.18]

0.35

49

0.1

 T allele

17

4892

6130

1.01 [0.89, 1.16]

0.84

74

<0.00001

 Caucasian

4

1068

585

0.80 [0.59, 1.09]

0.16

74

0.01

 Caucasian (dark)

8

1240

1019

1.29 [0.96, 1.74]

0.09

74

0.0003

 Asian

5

2584

4526

0.95 [0.85, 1.06]

0.33

46

0.12

Latitude

 CC/CT + TT

18

4992

6230

1.03 [0.82, 1.30]

0.79

80

<0.00001

 Low (<30)

5

852

791

1.00 [0.65, 1.52]

0.99

52

0.08

 Middle (30–60)

13

4140

5439

1.03 [0.79, 1.36]

0.82

84

<0.00001

 CC + CT/TT

18

4992

6230

0.92 [0.72, 1.17]

0.49

74

<0.00001

 Low (<30)

5

852

791

0.78 [0.60, 1.01]

0.06

0

0.75

 Middle (30–60)

13

4140

5439

0.94 [0.69, 1.26]

0.66

80

<0.00001

 C allele

18

4992

6230

0.99 [0.87, 1.12]

0.84

73

<0.00001

 Low (<30)

5

852

791

0.91 [0.74, 1.11]

0.36

33

0.2

 Middle (30–60)

13

4140

5439

1.01 [0.86, 1.18]

0.93

78

<0.00001

 T allele

18

4992

6230

1.01 [0.89, 1.15]

0.85

73

<0.00001

 Low (<30)

5

852

791

1.10 [0.90, 1.35]

0.36

33

0.2

 Middle (30–60)

13

4140

5439

0.99 [0.85, 1.16]

0.92

78

<0.00001

Association between VDR BsmI rs1544410 (A > G) variant and insulin resistance related diseases susceptibility

Twenty-two studies (4294 cases and 4157 controls) in 17 papers examining the association between the VDR BsmI rs1544410 (A > G) variant and Insulin resistance related diseases susceptibility were included. Sub-group analysis (14 studies about T2DM, four studies about PCOS and four studies about Mets) was performed. All the original data were combined by means of the Random effect model. We found no association of the VDR BsmI rs1544410 (A > G)variant with Insulin resistance related diseases (OR, 0.95; 95% CI, 0.78–1.16; P = 0.64) in the recessive genetic model (A/A vs.A/G or G/G), dominant genetic model in th e (A/A or A/G vs. G/G) (OR, 1.06; 95% CI, 0.86–1.31; P = 0.59) and A allele vs. G allele analysis (OR, 0.97; 95% CI, 0.83–1.13; P = 0.67). sub-group analysis indicated that there was no association between BsmI rs1544410 (A > G) variant and T2DM, PCOS patients. However, significant association was found in MetS sub-group analysis G allele vs. A allele analysis (OR, 1.41; 95% CI, 1.07–1.85; P = 0.01) (Table 5). sub-group analysis by skin pigmentation and living latitude showed that VDR BsmI rs1544410 (A > G) variant was associated with insulin resistance related diseases in Caucasian (dark-pigmented) (AA + GA/GG) (OR, 1.50; 95% CI, 1.16–1.93; P = 0.002), (A allele) (OR, 1.23; 95% CI, 1.07–1.42; P = 0.004). No publication bias was detected by either the funnel plot or Egger’s tests (P > 0.05, each comparison).

Association between VDR TaqI rs731236 (T/C) variant and insulin resistance related diseases susceptibility

Nineteen studies (3533 cases and 4024 controls) examining the association between the VDR Taq1rs731236 (T/C) variant and Insulin resistance related diseases susceptibility were included. Sub-group analysis (13 studies about T2DM, six studies about PCOS) was performed. All the original data were combined by means of the Random effect model. We found no association of the VDR TaqI rs731236 (T/C) variant with Insulin resistance related diseases (OR, 1.00; 95% CI, 0.82–1.21; P = 0.96) in the recessive genetic model (T/T vs.T/C or C/C), dominant genetic model in the (T/T or T/C vs. C/C) (OR, 0.88; 95% CI, 0.73–1.06; P = 0.17), T allele (OR, 0.89; 95% CI, 0.75–1.06; P = 0.18). Sub-group analysis indicated significant association between VDR Taq1rs731236 C allele and PCOS in C allele analysis (OR1.47; CI 1.03–2.09; P = 0.03) (Table 5). sub-group analysis by skin pigmentation and living latitude showed that VDR TaqI rs731236 (T/C) variant was associated with insulin resistance related diseases in Caucasian (dark-pigmented) (C allele) (OR, 1.24; 95% CI, 1.05–1.47; P = 0.01). No publication bias was detected by either the funnel plot or Egger’s tests (P > 0.05, each comparison).

Association between VDR FokI rs2228570 (C > T) variant and insulin resistance related diseases susceptibility

Eighteen studies (4851 cases and 6174 controls) from 17 papers examining the association between the VDR FokIrs2228570 (C > T) variant and Insulin resistance related diseases susceptibility were included. Sub-group analysis (nine studies about T2DM, five studies about PCOS, three studies about MetS and one study about IFG) was performed. All the original data were combined by means of the Random effect model. We found no association of the VDR FokIrs2228570 (C > T)variant with Insulin resistance related diseases (OR, 1.00; 95% CI, 0.68–1.47; P = 0.99) in the recessive genetic model (C/C vs.C/T or T/T), dominant genetic model in the ((C/C or C/T vs. T/T) (OR, 0.86; 95% CI, 0.67–1.09; P = 0.21) and C allele vs. T allele analysis (OR, 0.96; 95% CI, 0.84–1.10; P = 0.53). sub-group analysis indicated that there was no association between FokIrs2228570 (C > T) variant and T2DM, PCOS and MetS patients (Table 5). sub-group analysis by skin pigmentation and living latitude showed that there were no association between VDR TaqI rs731236 (T/C) variant and insulin resistance related diseases in ethics with different skin pigment and in different latitudes. No publication bias was detected by either the funnel plot or Egger’s tests (P > 0.05, each comparison).

Discussion

VDR, which is considered as a pleiotropic gene, is a transcription factor that mediates the action of vitamin D3 by controlling the expression of hormone sensitive genes such as Calmodulin-Dependent Kinase (CaMKs), and CaMKs stimulates VDR-Mediated transcription by phosphorylation levels of VDR [46]. Recent research found that deletion of macrophage VDR promotes insulin resistance and monocyte cholesterol transport to accelerate atherosclerosis in mice [47] which suggested that VDR dysfunction might result in insulin resistance. The association between VDR polymorphisms and insulin resistance related diseases including T2DM, PCOS and Mets has been extensively researched, but the results obtained so far are conflictive, and the role of VDR polymorphisms remains unclear. The reasons for this disparity may be small sample sizes, low statistical power, differences in ethnicities, extensive geographic variations, and interactions with other genetic or environmental factors. Therefore, in order to overcome the limitations of individual studies, we performed a meta-analysis. Meta-analysis increases statistical power and resolution by pooling the results of independent analyses. In this meta-analysis, we combined data from published case–control studies to evaluate the genetic associations of TaqI, BsmI, ApaI and FokI polymorphisms with these insulin resistance diseases.

To the best of our knowledge, this is the first meta-analysis which takes into account the interaction of individual VDR polymorphisms with in insulin resistance diseases. This meta-analysis, which included a total of 28 articles, examined the associations among four studied polymorphisms in the VDR ApaI variant, VDR BsmI variant, VDR Taq1 variant and VDR FokI variant and insulin resistance related diseases. The results indicated that VDR ApaI variant, VDR BsmI variant and VDR FokI variant were not conspicuous risk factors for insulin resistance related diseases. The result provided no evidence of the association between VDR variant and Insulin resistance related diseases. Yet the results were different when the researches were grouping by skin pigment and living latitude. Sub-group analysis suggested that the association between insulin resistance related diseases and VDR ApaI, BsmI, FokI variant was obvious in dark-pigmented Caucasian population and Asians.

However, to make conclusive estimates, many factors should be considered. In complex diseases such as T2DM, complex interactions between genetic and environmental factors have differential effects on disease susceptibility. Further characterization of VDR, in addition to traditional and related risk factors may facilitate early identification of patients at high risk for T2DM, and then elucidate new approaches for prevention and treatment. However, several limitations of the meta-analysis should be addressed. First, lack of the original data of the reviewed studies limited our further evaluation of potential interactions, because the interactions between and even different polymorphic loci of the same gene may influence the risk. Second, our results were based on unadjusted published estimates, and hence, we were unable to adjust them by possible confounders, for example Vitamin D level, and diet did not take into consider. Third, the number of articles and cases taking in this research is relatively small. In order to provide a more precise estimation on the basis of adjustment for confounders, more well-designed studies should be taking into account. Additionally, current evidence from prospective studies on the association between vitamin D gene polymorphism and risk of insulin resistance related diseases was limited by the use of vitamin D gene polymorphism or a single measurement of 25(OH)D concentrations. A single baseline measure of dietary vitamin D may not be able to take into account the within-individual variations of vitamin D levels across seasons or geographical location, as evident in sub-group analysis. Studies are, therefore, needed with geographical location and dietary vitamin D levels to adjust for its variability while quantifying the associations.

Conclusion

In summary, this meta-analysis provided evidence of the association between VDR BsmI variant and MetS and supporting that VDR BsmI variant G allele might be a susceptibility marker of MetS. TaqI variant was associated with PCOS for C allele and supporting that VDR TaqI variant C allele might be a susceptibility marker of PCOS. No significant association was found in the rest gene polymorphisms and these diseases related with insulin resistance diseases. The relationship of VDR gene polymorphism was more important with PCOS and MetS than T2DM. However, sub-group analysis showed VDR ApaI variant was associated with insulin resistance related diseases in Asians, VDR BsmI and VDR TaqI variant was associated with insulin resistance related diseases in Caucasian (dark-pigmented).The results suggested that the association between insulin resistance related diseases and VDR ApaI, BsmI, FokI variant was more obvious in dark-pigmented Caucasians and Asians but not in Caucasian with white skin.

Abbreviations

MetS: 

Metabolic syndrome

PCOS: 

Polycystic ovarian syndrome

T2DM: 

Type 2 diabetes

VDR: 

Vitamin D receptor

Declarations

Acknowledgements

Not applicable.

Funding

This work is supported Foundation of National Natural Science Foundation 81,500,495.

Availability of data and materials

Please contact author for data requests.

Authors’ contributions

FH designed the study and revised the manuscript, FH and YL extracted the data, LG, ZW, LL, HL verified the data. FH researched the data and wrote the manuscript. FH contributed to interpreting the results, draft reviewing, and finalizing the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

All the authors have agreed to publish this article.

Ethics approval and consent to participate

Not applicable.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Beijing Chao-Yang Hospital, Capital Medical University

References

  1. Ning Z, Song S, Miao L, Zhang P, Wang X, Liu J, Hu Y, Xu Y, Zhao T, Liang Y, et al. High prevalence of vitamin D deficiency in urban health checkup population. Clin Nutr. 2015;35:859–63.View ArticlePubMedGoogle Scholar
  2. Peterson CA, Tosh AK, Belenchia AM. Vitamin D insufficiency and insulin resistance in obese adolescents. Ther Adv Endocrinol Metab. 2014;5:166–89.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Dasgupta S, Dutta J, Annamaneni S, Kudugunti N, Battini MR. Association of vitamin D receptor gene polymorphisms with polycystic ovary syndrome among Indian women. Indian J Med Res. 2015;142:276–85.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Lee S, Clark SA, Gill RK, Christakos S. 1,25-Dihydroxyvitamin D3 and pancreatic beta-cell function: vitamin D receptors, gene expression, and insulin secretion. Endocrinology. 1994;134:1602–10.View ArticlePubMedGoogle Scholar
  5. Pittas AG, Sun Q, Manson JE, Dawson-Hughes B, Hu FB. Plasma 25-hydroxyvitamin D concentration and risk of incident type 2 diabetes in women. Diabetes Care. 2010;33:2021–3.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Scragg R, Sowers M, Bell C. Serum 25-hydroxyvitamin D, diabetes, and ethnicity in the third National Health and nutrition examination survey. Diabetes Care. 2004;27:2813–8.View ArticlePubMedGoogle Scholar
  7. Liu E, Meigs JB, Pittas AG, Economos CD, McKeown NM, Booth SL, Jacques PF. Predicted 25-hydroxyvitamin D score and incident type 2 diabetes in the Framingham offspring study. Am J Clin Nutr. 2010;91:1627–33.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Zhang Q, Cheng Y, He M, Li T, Ma Z, Cheng H. Effect of various doses of vitamin D supplementation on pregnant women with gestational diabetes mellitus: a randomized controlled trial. Exp Ther Med. 2016;12:1889–95.PubMedPubMed CentralGoogle Scholar
  9. Zhang J, Ye J, Guo G, Lan Z, Li X, Pan Z, Rao X, Zheng Z. Vitamin D Status Is Negatively Correlated with Insulin Resistance in Chinese Type 2 Diabetes. Int J Endocrinol. 2016;2016:1794894.PubMedPubMed CentralGoogle Scholar
  10. Sung KC, Chang Y, Ryu S, Chung HK. High levels of serum vitamin D are associated with a decreased risk of metabolic diseases in both men and women, but an increased risk for coronary artery calcification in Korean men. Cardiovasc Diabetol. 2016;15:112.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Hojlund K. Metabolism and insulin signaling in common metabolic disorders and inherited insulin resistance. Dan Med J. 2014;61:B4890.PubMedGoogle Scholar
  12. Grundy SM. Metabolic syndrome update. Trends Cardiovasc Med. 2016;26;364-73.View ArticlePubMedGoogle Scholar
  13. Krul-Poel YH, Snackey C, Louwers Y, Lips P, Lambalk CB, Laven JS, Simsek S. The role of vitamin D in metabolic disturbances in polycystic ovary syndrome: a systematic review. Eur J Endocrinol. 2013;169:853–65.View ArticlePubMedGoogle Scholar
  14. Chale A, Chale C. Color by numbers: when population skin pigmentation is not political but a polytypical evaluation exercise to measure vitamin D, diseases, and skin pigmentation. J Acad Nutr Diet. 2016;116:1251–6.View ArticlePubMedGoogle Scholar
  15. Calvo MS, Whiting SJ, Barton CN. Vitamin D intake: a global perspective of current status. J Nutr. 2005;135:310–6.PubMedGoogle Scholar
  16. Moore CE, Murphy MM, Holick MF. Vitamin D intakes by children and adults in the United States differ among ethnic groups. J Nutr. 2005;135:2478–85.PubMedGoogle Scholar
  17. Yuan W, Xu L, Feng Y, Yang Y, Chen W, Wang J, Pang D, Li D. The hOGG1 Ser326Cys polymorphism and breast cancer risk: a meta-analysis. Breast Cancer Res Treat. 2010;122:835–42.View ArticlePubMedGoogle Scholar
  18. Al-Daghri NM, Al-Attas O, Alokail MS, Alkharfy KM, Draz HM, Agliardi C, Mohammed AK, Guerini FR, Clerici M. Vitamin D receptor gene polymorphisms and HLA DRB1*04 cosegregation in Saudi type 2 diabetes patients. J Immunol. 2012;188:1325–32.View ArticlePubMedGoogle Scholar
  19. Boullu-Sanchis S, Lepretre F, Hedelin G, Donnet JP, Schaffer P, Froguel P, Pinget M. Type 2 diabetes mellitus: association study of five candidate genes in an Indian population of Guadeloupe, genetic contribution of FABP2 polymorphism. Diabetes Metab. 1999;25:150–6.PubMedGoogle Scholar
  20. El-Shal AS, Shalaby SM, Aly NM, Rashad NM, Abdelaziz AM. Genetic variation in the vitamin D receptor gene and vitamin D serum levels in Egyptian women with polycystic ovary syndrome. Mol Biol Rep. 2013;40:6063–73.View ArticlePubMedGoogle Scholar
  21. Dilmec F, Uzer E, Akkafa F, Kose E, van Kuilenburg AB. Detection of VDR gene ApaI and TaqI polymorphisms in patients with type 2 diabetes mellitus using PCR-RFLP method in a Turkish population. J Diabetes Complicat. 2010;24:186–91.View ArticlePubMedGoogle Scholar
  22. Oh JY, Barrett-Connor E. Association between vitamin D receptor polymorphism and type 2 diabetes or metabolic syndrome in community-dwelling older adults: the rancho Bernardo study. Metabolism. 2002;51:356–9.View ArticlePubMedGoogle Scholar
  23. Jedrzejuk D, Laczmanski L, Milewicz A, Kuliczkowska-Plaksej J, Lenarcik-Kabza A, Hirnle L, Zaleska-Dorobisz U, Lwow F. Classic PCOS phenotype is not associated with deficiency of endogenous vitamin D and VDR gene polymorphisms rs731236 (TaqI), rs7975232 (ApaI), rs1544410 (BsmI), rs10735810 (FokI): a case–control study of lower Silesian women. Gynecol Endocrinol. 2015;31:976–9.View ArticlePubMedGoogle Scholar
  24. Mahmoudi T. Genetic variation in the vitamin D receptor and polycystic ovary syndrome risk. Fertil Steril. 2009;92:1381–3.View ArticlePubMedGoogle Scholar
  25. Malecki MT, Frey J, Moczulski D, Klupa T, Kozek E, Sieradzki J. Vitamin D receptor gene polymorphisms and association with type 2 diabetes mellitus in a polish population. Exp Clin Endocrinol Diabetes. 2003;111:505–9.View ArticlePubMedGoogle Scholar
  26. Rivera-Leon EA, Palmeros-Sanchez B, Llamas-Covarrubias IM, Fernandez S, Armendariz-Borunda J, Gonzalez-Hita M, Bastidas-Ramirez BE, Zepeda-Moreno A, Sanchez-Enriquez S. Vitamin-D receptor gene polymorphisms (TaqI and ApaI) and circulating osteocalcin in patients with type 2 diabetes and healthy subjects. Endokrynologia Polska. 2015;66:329–33.View ArticlePubMedGoogle Scholar
  27. Wehr E, Trummer O, Giuliani A, Gruber HJ, Pieber TR, Obermayer-Pietsch B. Vitamin D-associated polymorphisms are related to insulin resistance and vitamin D deficiency in polycystic ovary syndrome. Eur J Endocrinol. 2011;164:741–9.View ArticlePubMedGoogle Scholar
  28. Ye WZ, Reis AF, Dubois-Laforgue D, Bellanne-Chantelot C, Timsit J, Velho G. Vitamin D receptor gene polymorphisms are associated with obesity in type 2 diabetic subjects with early age of onset. Eur J Endocrinol. 2001;145:181–6.View ArticlePubMedGoogle Scholar
  29. Zhang H, Wang J, Yi B, Zhao Y, Liu Y, Zhang K, Cai X, Sun J, Huang L, Liao Q. BsmI polymorphisms in vitamin D receptor gene are associated with diabetic nephropathy in type 2 diabetes in the Han Chinese population. Gene. 2012;495:183–8.View ArticlePubMedGoogle Scholar
  30. Zhong X, Du Y, Lei Y, Liu N, Guo Y, Pan T. Effects of vitamin D receptor gene polymorphism and clinical characteristics on risk of diabetic retinopathy in Han Chinese type 2 diabetes patients. Gene. 2015;566:212–6.View ArticlePubMedGoogle Scholar
  31. Bagheri M, Rad IA, Jazani NH, Nanbakhsh F. Lack of Association of Vitamin D Receptor FokI (rs10735810) (C/T) and BsmI (rs1544410) (a/G) genetic variations with polycystic ovary syndrome risk: a case–control study from Iranian Azeri Turkish women. Maedica (Buchar). 2012;7:303–8.Google Scholar
  32. Bid HK, Konwar R, Aggarwal CG, Gautam S, Saxena M, Nayak VL, Banerjee M. Vitamin D receptor (FokI, BsmI and TaqI) gene polymorphisms and type 2 diabetes mellitus: a North Indian study. Indian J Med Sci. 2009;63:187–94.View ArticlePubMedGoogle Scholar
  33. Mukhopadhyaya PN, Acharya A, Chavan Y, Purohit SS, Mutha A. Metagenomic study of single-nucleotide polymorphism within candidate genes associated with type 2 diabetes in an Indian population. Genet Mol Res. 2010;9:2060–8.View ArticlePubMedGoogle Scholar
  34. Speer G, Cseh K, Winkler G, Vargha P, Braun E, Takacs I, Lakatos P. Vitamin D and estrogen receptor gene polymorphisms in type 2 diabetes mellitus and in android type obesity. Eur J Endocrinol. 2001;144:385–9.View ArticlePubMedGoogle Scholar
  35. Vural HC, Maltas E. RT-qPCR assay on the vitamin D receptor gene in type 2 diabetes and hypertension patients in Turkey. Genet Mol Res. 2012;11:582–90.View ArticlePubMedGoogle Scholar
  36. Xia Z, Hu Y, Zhang H, Han Z, Bai J, Fu S, Deng X, He Y. Association of vitamin D receptor Fok I and Bsm I polymorphisms with dyslipidemias in elderly male patients with type 2 diabetes. Nan Fang Yi Ke Da Xue Xue Bao. 2014;34:1562–8.PubMedGoogle Scholar
  37. Xu JR, Lu YB, Geng HF, Wu J, Maio H. Association between the polymorphism of human vitamin D receptor gene and type2 diabetes. J Clin Rehab Tissue Eng Res. 2007;11:5881–3.Google Scholar
  38. Xu JR, Na XF, Yang Y. Relevance analysis on polymorphisms of four SNPs of VDR gene and type 2 diabetes mellitus in Ningxia Han population. J Jilin Univ Med Ed. 2012;38:985–9.Google Scholar
  39. Xu JR, Yang Y, Liu XM, Wang YJ. Association of VDR polymorphisms with type 2 diabetes mellitus in Chinese Han and Hui populations. Genet Mol Res. 2014;13:9588–98.View ArticlePubMedGoogle Scholar
  40. Bagheri M, Abdi Rad I, Hosseini Jazani N, Nanbakhsh F. Vitamin D receptor taqi gene variant in exon 9 and polycystic ovary syndrome risk. Int J Fert Ster. 2013;7:116–21.Google Scholar
  41. Jia J, Ding H, Yang K, Mao L, Zhao H, Zhan Y, Shen C. Vitamin D receptor genetic polymorphism is significantly associated with risk of type 2 diabetes mellitus in Chinese Han population. Arch Med Res. 2015;46:572–9.View ArticlePubMedGoogle Scholar
  42. Schuch NJ, Garcia VC, Vivolo SR, Martini LA. Relationship between vitamin D receptor gene polymorphisms and the components of metabolic syndrome. Nutr J. 2013;12:96.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Shah DB, Doshi DD, Singh KM, Patel RK. Investigation of the VDR gene polymorphism in unrelated gujarati group with and without diabetic mellitus type-2. Res J Pharm, Biol Chem Sci. 2015;6:465–8.Google Scholar
  44. Vedralová M, Kotrbova-Kozak A, Železníková V, Zoubková H, Rychlík I, Černá M. Polymorphisms in the vitamin D receptor gene and parathyroid hormone gene in the development and progression of diabetes mellitus and its chronic complications, diabetic nephropathy and non-diabetic renal disease. Kidney Blood Press Res. 2012;36:1–9.View ArticlePubMedGoogle Scholar
  45. Zhao Y, Liao S, He J, Jin Y, Fu H, Chen X, Fan X, Xu H, Liu X, Jin J, Zhang Y. Association of vitamin D receptor gene polymorphisms with metabolic syndrome: a case–control design of population-based cross-sectional study in North China. Lipids Health Dis. 2014;13:129.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Ellison TI, Dowd DR, MacDonald PN. Calmodulin-dependent kinase IV stimulates vitamin D receptor-mediated transcription. Mol Endocrinol. 2005;19:2309–19.View ArticlePubMedGoogle Scholar
  47. Oh J, Riek AE, Darwech I, Funai K, Shao J, Chin K, Sierra OL, Carmeliet G, Ostlund RE Jr, Bernal-Mizrachi C. Deletion of macrophage vitamin D receptor promotes insulin resistance and monocyte cholesterol transport to accelerate atherosclerosis in mice. Cell Rep. 2015;10:1872–86.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Shilpi Dasgupta, Joyita Dutta, Sandhya Annamaneni, Neelaveni Kudugunti, MohanReddy Battini. Association of vitamin D receptor gene polymorphisms with polycystic ovary syndrome among Indian women. Indian J Med Res. 2015;142(3):276.Google Scholar
  49. Rivera-Leon EA, Palmeros-Sanchez B, Llamas-Covarrubias IM, Fernandez S, Armendariz-Borunda J, Gonzalez-Hita M, Bastidas-Ramirez BE, Zepeda-Moreno A, Sanchez-Enriquez S. Vitamin-D receptor gene polymorphisms (TaqI and ApaI) and circulating osteocalcin in type 2 diabetic patients and healthy subjects. Endokrynol Pol. 2015;66:329–33.Google Scholar
  50. Mackawy AMH, Badawi, MEH. Association of vitamin D and vitamin D receptor gene polymorphisms with chronic inflammation, insulin resistance and metabolic syndrome components in type 2 diabetic Egyptian patients. Meta Gene. 2014;2:540–56.Google Scholar

Copyright

© The Author(s). 2017

Advertisement