Open Access

Meta-analyses between 18 candidate genetic markers and overweight/obesity

Diagnostic Pathology20149:56

DOI: 10.1186/1746-1596-9-56

Received: 2 January 2014

Accepted: 4 March 2014

Published: 12 March 2014

Abstract

Aims

The goal of our study is to investigate the associations between 18 candidate genetic markers and overweight/obesity.

Methods

A total of 72 eligible articles were retrieved from literature databases including PubMed, Embase, SpingerLink, Web of Science, Chinese National Knowledge Infrastructure (CNKI), and Wanfang. Meta-analyses of 18 genetic markers among 56,738 controls and 48,148 overweight/obese persons were done by Review Manager 5.0.

Results

Our results showed that SH2B1 rs7498665 polymorphism was significantly associated with the risk of overweight/obesity (overall odds ratio (OR) = 1.21, 95% confidence interval (CI) = 1.09-1.34, P = 0.0004). Increased risk of overweight/obesity was also observed in FAIM2 rs7138803 polymorphism (overall OR = 1.11, 95% CI = 1.01-1.22, P = 0.04).

Conclusion

Our meta-analyses have shown the important role of 2 polymorphisms (SH2B1 rs7498665 and FAIM2 rs7138803) in the development of overweight/obesity. This study highlighted the importance of above two candidate genes (SH2B1 and FAIM2) in the risk of overweight/obesity.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2785487401176182.

Keywords

SH2B1 FAIM2 Polymorphism Overweight Obesity Meta-analysis

Introduction

Overweight/obesity as a metabolic disorder is closely associated with diabetes mellitus and cardiovascular disease, which are chronic diseases influencing the average life expectancy [1, 2]. In 2008, world health organization (WHO) has reported that a large portion of adults (>20 yr) were overweight (35%) and obese (12%) [3]. The overweight/obesity will become an epidemic [4] and cause a huge economic burden of society [4] in the near future.

The occurrence and the development of obesity are influenced by both environmental and genetic factors [5, 6]. Environmental factors, such as poor nutritional state and a lack of physical exercise, have an impact on the development of overweight/obesity [7, 8] through the epigenetic modifications such as gene methylation [9]. Genetic polymorphisms can confer the susceptibility of overweight/obesity and obesity-related morbidities [10]. Recent genome-wide association studies (GWAS) have identified a handful of candidate genetic markers to the risk of overweight/obesity [11].

In the present study, we performed a systematic search for eligible studies in the meta-analyses. Our results identified 18 polymorphisms among 16 genes that were all the candidate genes of obesity. Among these genes, GNB3 encodes β3-subunit protein which is involved in the process of hypertension and obesity [12]. MTHFR gene encodes methylenetetrahydrofolate reductase that is shown to be associated with increased fasting homocysteine [13]. MTHFR polymorphism is shown to be associated with lipid metabolism in the elderly women [14]. CNR1 is shown to regulate the endocannabinoid system that might stimulate the metabolism of lipogenesis through central and peripheral mechanisms [15, 16]. CNR1 is associated with low HDL dyslipidemia and a common haplotype of CNR1 could be a protective factor of obesity-related dyslipidemia [17]. BDNF is shown to play an important role in the development of several neuronal systems [18]. As an effector on energy homeostasis through MC4R signaling pathway, BDNF has an effect on the glucose and lipid metabolism in obese diabetic animals [19, 20]. FAAH gene encodes fatty acid amide hydrolase [21] and plays an important role in the development of obesity [22]. ADRB1 is shown to mediate in lipolysis and thus is important for obesity [23]. Rat study identifies that ADRB1 mediates the sympathetic nervous system (SNS) stimulation of thermogenesis in brown adipose tissue [24]. SH2B1 is able to bind leptin to its receptor, and thus increases the JAK2 activation which is involved in the insulin and leptin signaling [25, 26]. PCSK1 encodes prohormone convertase 1/3 that is a vital enzyme in the regulation of a majority of neuroendocrine body weight control [27]. A novel homozygous missense mutation in PCSK1 leads to early-onset obesity [28]. NPY2R is a presynaptic receptor [29] playing an inhibitory role in the control of appetite regulation [30], and thus influences the development of obesity [31]. FAIM2 (Fas apoptotic inhibitory molecule 2) is an anti-apoptotic gene [32]. Mutations of FAIM2 which interferes with Fas-mediated cell death confer risk for obesity [33]. SERPINE1 encodes a member of serine proteinase inhibitor which influences plasma PAI-1 activity with relation to obesity [34]. Serum paraoxonase-1 (PON1) encoded by PON1 as an enzyme associated with HDL-C could be a protector against oxidative damage in obesity [35]. CETP protein product transfers cholesterylesters from HDL to pro-atherogenic apoB-lipoproteins and thus has an impact on the lipid and HDL metabolism [36, 37]. UCP1 encodes uncoupling protein 1 that is mediated by long-chain fatty acids (LCFAs) from brown adipose tissue [38]. UCP1 expression in adipose tissue has an impact on regulating the thermogenesis and lipolysis [39, 40]. Mitochondrial uncoupling by UCP1 has demonstrated to be a target in antiobesity therapies [41]. ABCA1 gene product mediates the transport of cholesterol, phospholipids, and other metabolites [42]. Exercise has an impact on ABCA1 expression along with increased HDL levels in obese boys [43]. APOE plays a fundamental role with ligand-receptor in uptaking lipoproteins, and thus participates in the lipid metabolism [44]. In addition, APOE correlates with inflammation in adipose tissue in high-fat diet-induced obesity [45].

Meta-analysis is a systematic evaluation by combining the results from collected studies [46, 47]. The major advantages of meta-analysis are to improve the precision and accuracy by pooling up the data from multiple sources, and to analyze and quantify the inconsistency of results and the publish bias [48]. In the present study, we conducted comprehensive meta-analyses to identify the contribution of 18 polymorphisms to overweight/obesity.

Materials and methods

Literature search and data extraction

We performed the literature research using related databases such as PubMed, Embase, SpingerLink, Web of Science, Chinese National Knowledge Infrastructure (CNKI), and Wanfang. The combination of keywords in the literature search was obesity or overweight together with polymorphism or mutation or variant or single nucleotide polymorphism (SNP). The studies excluded in the meta-analysis met the following criteria: (1) the study had been included in the previous meta-analysis; (2) the study was not involved with genetic testing; (3) the study was not a case–control study. The criteria for overweight or obesity in adolescents and children were defined by WHO [49, 50]. Finally, we harvested 18 polymorphisms of 16 genes in the current meta-analysis. These included GNB3 rs5443, MTHFR rs1801133, CNR1 rs806381, BDNF rs6265, FAAH rs324420, ADRB1 rs1801253, SH2B1 rs7498665, PCSK1 rs6232 and rs6235, NPY2R rs1047214, FAIM2 rs7138803, SERPINE1 rs1799768, PON1 rs854560 and rs662, CETP TaqIB, UCP1 rs1800592, ABCA1 rs2230806 and APOE ϵ2/ϵ3/ϵ4.

Statistical analysis

Meta-analysis was performed by using Statistical software Review Manager 5.0 [51]. Forest plots included the ORs with the corresponding 95% CIs, cochran’s Q and the inconsistency index (I2). If there were no significant heterogeneity (I2 < 50%, P > 0.05) of the studies in the meta-analysis, we used a fixed-effect model for the analysis. Otherwise, a random-effect model was used for the meta-analysis with large heterogeneity (I2 > 50%, P < 0.05). The weight of each involved study was calculated whatever in fixed-effect or random-effect model in forest plots by Review Manager 5.0. Two tailed P value < 0.05 was treated as significant. Power analyses were calculated by Power and Sample Size Calculation software (v3.0.43) [52].

Results

An initial search returned a total of 7,750 literatures from databases including PubMed, Embase, SpingerLink, Web of Science, Chinese National Knowledge Infrastructure (CNKI), and Wanfang. After a systematic filtration, 72 eligible articles, including 64 English, 6 Chinese, 1 German and 1 Spanish articles, were left for the meta-analyses (Additional file 1: Table S1). The detailed information for the retrieved studies was shown in Tables 1 and 2.
Table 1

Characteristics of 17 single nucleotide polymorphisms

Gene

SNP

Year

Author

Race

Cases/Controls (n)

Allele 1

Allele 2

Model selected

Heterogeneity

P value

Odds ratio (95% confidence interval)

(Case/Controls, n)

(Case/Controls, n)

(I2)%

GNB3

rs5443

1999

Siffert W

Caucasian

92/207

108/392

76/122

Fixed

42

0.47

1.04 (0.93-1.16)

 

(C/T)

1999

Siffert W

Asian Chinese

186/832

166/886

206/778

  

1999

Siffert W

African

127/607

34/219

220/995

  

2000

Siffert W

Caucasian

207/92

292/108

122/76

  

2001

Hinney A

Caucasian

491/330

695/442

287/218

  

2001

Benjafield AV

Caucasian

92/188

133/284

51/92

  

2001

Ohshiro Y

Asian Japanese

208/150

215/148

201/152

  

2004

Suwazono Y

Asian Japanese

505/2120

517/2177

493/2063

  

2008

Wang X

Asian Chinese

129/270

442/285

376/255

  

2013

Hsiao TJ

Asian Chinese

467/505

402/441

532/569

MTHFR

rs1801133

2007

Terruzzi I

Caucasian

84/52

90/61

78/43

Fixed

0

0.59

1.05 (0.87-1.27)

 

(C/T)

2010

Tavakkoly Bazzaz J

Asian Iranian

74/207

109/306

39/108

  

2012

Yin RX

Asian Chinese

751/978

1049/1383

453/573

CNR1

rs806381

2008

Benzinou M

Caucasian

839/1726

1163/2362

515/1090

Fixed

0

0.5

1.04 (0.93-1.17)

 

(A/G)

2008

Jaeger JP

Caucasian

430/317

613/464

247/170

  

2012

Zhuang M

Asian Chinese

1662/1070

2345/1550

979/590

BDNF

rs6265

2005

Friedel S

Caucasian

183/283

342/448

81/118

Fixed

46

0.8

1.01 (0.92-1.11)

 

(G/A)

2009

Hotta K

Asian Japanese

1127/1733

1367/2013

887/1453

  

2009

Marti A

Caucasian

155/147

242/226

68/68

  

2011

Xi B

Asian Chinese

1229/1619

1095/1554

1363/1684

  

2011

Rouskas K

Caucasian

510/469

826/732

194/206

  

2012

Skledar M

Caucasian

74/226

111/374

37/78

FAAH

rs324420

2005

Sipe JC

Caucasian

1094/1594

1777/984

411/204

Random

79

0.54

0.94 (0.76-1.16)

 

(C/A)

2005

Sipe JC

African

507/107

687/161

327/53

  

2005

Sipe JC

Asian

271/94

471/148

71/40

  

2007

Jensen DP

Caucasian

4190/2507

6817/3991

1563/1023

  

2008

Durand E

Caucasian

1517/1320

2473/2104

561/536

  

2008

Papazoglou D

Caucasian

158/121

265/209

51/33

  

2008

Moneletone P

Caucasian

378/110

614/194

142/26

  

2010

Muller TD

Caucasian

2818/2818

3027/4607

689/1029

ADRB1

rs1801253

2001

Rydén M

Caucasian

141/157

206/214

76/100

Fixed

0

0.5

1.03 (0.94-1.14)

 

(C/G)

2004

Tafel J

Caucasian

296/134

403/180

189/88

  

2007

Gjesing AP

Caucasian

4575/3073

6781/4609

2369/1537

  

2008

Ohshiro Y

Asian Japanese

180/132

284/215

76/49

SH2B1

rs7498665

2009

Hotta K

Asian Japanese

1129/1735

1943/3003

315/467

Fixed

0

0.0004

1.21 (1.09-1.34)

 

(A/G)

2010

Shi J

Asian Chinese

829/1859

1427/3317

231/401

  

2011

Beckers S

Caucasian

1045/317

1223/401

867/223

  

2011

Rouskas K

Caucasian

510/469

673/675

347/263

  

2012

Volckmar AL

Caucasian

3139/424

3728/557

2550/311

PCSK1

rs6232

2009

Happé F

Caucasian

3570/7933

6735/15028

405/838

Fixed

34

0.08

1.14 (0.97-1.12)

 

(A/G)

2011

Rouskas K

Caucasian

510/469

969/882

51/56

  

2012

Villalobos-Comparán M

South American Mexican

1018/1364

2005/2709

31/19

  

2013

Choquet H

European American

263/547

485/1041

41/53

  

2013

Dušátková L

Asian Czech

668/770

1255/1469

81/71

PCSK1

rs6235

2009

Happé F

Caucasian

3559/7793

5164/11432

1954/4154

Fixed

0

0.26

1.04 (0.97-1.12)

 

(G/C)

2012

Villalobos-Comparán M

South America Mexican

994/1336

1575/2156

413/516

  

2013

Choquet H

European - American

263/547

368/793

158/301

  

2013

Choquet H

African - American

453/251

740/432

166/70

  

2013

Dušátková L

Asian Czech

670/772

996/1130

344/414

  

2014

Hsiao TJ

Asian Chinese

290/175

406/229

174/121

NPY2R

rs1047214

2006

Torekov SS

Caucasian

939/4767

1026/5295

852/4239

Fixed

0

0.54

0.97 (0.88-1.07)

 

(T/C)

2007

Siddiq A

Caucasian

953/1042

1048/1132

858/952

  

2007

Wang HJ

Caucasian

184/183

189/169

179/197

  

2009

Zhang J

Asian Chinese

705/1325

1171/2133

239/517

FAIM2

rs7138803

2009

Hotta K

Asian Japanese

1125/1726

1408/2251

842/1201

Fixed

0

0.04

1.11 (1.01-1.22)

 

(G/A)

2011

Xi B

Asian Chinese

1229/1619

1711/2332

747/906

  

2011

Rouskas K

Caucasian

510/469

643/610

377/328

  

2013

Li C

Asian Chinese

242/469

331/663

153/275

  

2013

Zhao XY

Asian Chinese

371/393

534/565

208/221

SERPINE1

rs1799768

2001

Sartori MT

Caucasian

93/79

95/84

91/74

Fixed

39

0.07

0.83 (0.67-1.02)

 

(4G/5G)

2002

Hoffstedt J

Caucasian

317/188

305/141

329/235

  

2006

Berberoğlu M

Asian Turk

126/133

151/133

101/133

  

2008

Solá E

Caucasian

67/67

70/65

64/69

  

2008

Kinik ST

Asian Turk

39/38

52/36

26/40

  

2011

Espino A

South American Chilean

50/71

32/51

44/52

  

2012

Wingeyer SD

South American Argentine

110/111

92/109

128/113

PON1

rs854560

2011

Veiga L

Caucasian

81/74

101/90

61/58

Fixed

31

0.4

0.87 (0.62-1.21)

 

(A/T)

2011

Martínez-Salazar MF

South American Mexican

63/64

114/101

12/27

  

2013

Rupérez AI

Caucasian

177/81

210/219

137/143

PON1

rs662

2011

Veiga L

Caucasian

81/74

68/44

94/104

Fixed

18

0.6

1.09 (0.79-1.51)

 

(G/A)

2011

Martínez-Salazar MF

South American Mexican

63/64

66/65

60/63

  

2013

Rupérez AI

Caucasian

177/81

252/249

102/111

CETP

TaqIB

2006

Huang ZY

Asian Chinese

199/141

243/162

155/120

Fixed

0

0.23

0.91 (0.79-1.06)

 

(B1/B2)

2008

Srivastava N

Asian Indian

159/278

153/263

165/293

  

2010

Ruan X

Asian Chinese

934/924

1104/1028

764/820

  

2011

Huang Y

Asian Chinese

206/132

250/155

162/109

UCP1

rs1800592

1998

Gagnon J

Caucasian

674/311

1013/473

335/149

Random

60

0.23

1.19 (0.90-1.57)

 

(A/G)

2000

Proenza AM

Asian Turk

136/94

189/131

83/57

  

2002

Kieć-Wilk B

Caucasian

12/106

18/146

6/66

  

2002

Nieters A

Caucasian

154/153

232/231

76/75

  

2003

Forga Ll

Caucasian

159/154

258/244

60/64

  

2004

Ramis JM

Caucasian

82/170

259/433

49/81

  

2008

Mottagui-Tabar S

Caucasian

91/479

433/736

149/222

  

2009

Shen ZN

Asian Chinese

127/257

129/240

125/274

ABCA1

rs2230806

2006

Porchay I

Caucasian

2097/2947

2992/4238

1202/1656

Fixed

0

0.87

1.01 (0.90-1.13)

 

(G/A)

2007

Kitjaroentham A

Asian Thai

112/117

143/143

81/91

  

2011

Huang Y

Asian Chinese

206/132

233/141

179/123

Table 2

Characteristics of APOE ϵ2/ϵ3/ϵ4 polymorphism

Year

Author

Race

Case/Controls (n)

Genotypes (case/controls, n)

Alleles (case/controls, n)

    

ϵ2/ϵ2

ϵ2/ϵ3

ϵ2/ϵ4

ϵ3/ϵ3

ϵ3/ϵ4

ϵ4/ϵ4

ϵ2

ϵ3

ϵ4

2003

Guerra A

Caucasian

31/81

0/0

6/4

0/0

63/20

13/7

0/0

6/4

145/51

13/7

2008

Srivastava N

Asian Indian

159/278

0/1

17/18

2/6

90/198

41/55

9/0

19/30

238/469

61/61

2010

Ergun MA

Asian Chinese

38/42

0/2

2/0

12/4

8/9

16/26

0/1

14/8

34/44

28/32

2012

Zhang J

Asian Chinese

282/172

1/3

46/16

7/2

186/123

40/27

2/1

55/24

458/289

51/31

2012

Zarkesh M

Asian Iran

463/370

1/1

48/38

6/7

348/268

63/53

3/3

56/47

807/627

75/66

Module

Case/Controls (n)

Model selected

Heterogeneity (I2)%

P value

OR (95% CI)

       

ϵ2/ϵ2/ϵ3/ϵ3

954/813

Fixed

0

0.12

0.35 (0.09-1.32)

       

ϵ2/ϵ3ϵ3/ϵ3

814/694

Fixed

48

0.07

1.33 (0.98-1.82)

       

ϵ2/ϵ4/ϵ3/ϵ3

695/618

Fixed

0

0.92

0.96 (0.45-2.05)

       

ϵ3/ϵ4/ϵ3/ϵ3

868/786

Fixed

28

0.7

1.05 (0.82-1.35)

       

ϵ4/ϵ4/ϵ3/ϵ3

695/618

Random

63

0.54

1.89 (0.25-14.46)

       

ϵ2/ϵ3

1832/1593

Fixed

23

0.26

1.16 (0.90-1.51)

       

ϵ4/ϵ3

1910/1681

Random

65

0.54

1.13 (0.77-1.66

       
Heterogeneity is an important indicator to identify if there is difference in the collected studies. According to the extent of heterogeneity, we categorized the meta-analyses into three groups that have minimal (I2 = 0), moderate (I2 < 50%), and significant heterogeneity (I2 ≥ 50%), respectively. As shown in Figure 1, minimal heterogeneity (I2 = 0) was found for the meta-analyses of 10 polymorphisms that included MTHFR rs1801133, CNR1 rs806381, ADRB1 rs1801253, SH2B1 rs7498665, PCSK1 rs6235, NPY2R rs1047214, FAIM2 rs7138803, CETP TaqIB and ABCA1 rs2230806. Moderate heterogeneity was found for 5 polymorphisms, including BDNF rs6265 (I2 = 46%), PCSK1 rs6232 (I2 = 34%), GNB3 rs5443 (I2 = 42%), PON1 rs854560 (I2 = 31%), PON1 rs662 (I2 = 18%), and SERPINE1 rs1799768 (I2 = 39%). Significant heterogeneity was found for UCP1 rs1800592 (I2 = 60%) and FAAH rs324420 (I2 = 79%). Moreover, As shown in Figure 2, various heterogeneities were shown in the meta-analyses of APOE ϵ2/ϵ3/ϵ4 polymorphism under the seven genetic models (ϵ2/ϵ3 versus ϵ3/ϵ3: I2 = 48%; ϵ2/ϵ4 versus ϵ3/ϵ3: I2 = 0%; ϵ3/ϵ4 versus ϵ3/ϵ3: I2 = 28%; ϵ4/ϵ4 versus ϵ3/ϵ3: I2 = 63%; ϵ2/ϵ3 versus ϵ3/ϵ3: I2 = 0%; ϵ2 versus ϵ3: I2 = 23%; ϵ4 versus ϵ3: I2 = 65%). No obvious publication bias was observed based on their funnel plots (Figures 3 and 4).
https://static-content.springer.com/image/art%3A10.1186%2F1746-1596-9-56/MediaObjects/13000_2014_Article_1106_Fig1_HTML.jpg
Figure 1

Forest plots of the association studies between 17 SNPs and overweight/obesity.

https://static-content.springer.com/image/art%3A10.1186%2F1746-1596-9-56/MediaObjects/13000_2014_Article_1106_Fig2_HTML.jpg
Figure 2

Forest plots of the association studies between APOE ϵ2/ϵ3/ϵ4 polymorphism and overweight/obesity.

https://static-content.springer.com/image/art%3A10.1186%2F1746-1596-9-56/MediaObjects/13000_2014_Article_1106_Fig3_HTML.jpg
Figure 3

Funnel plots of the studies of 17 SNPs involved in meta-analysis.

https://static-content.springer.com/image/art%3A10.1186%2F1746-1596-9-56/MediaObjects/13000_2014_Article_1106_Fig4_HTML.jpg
Figure 4

Funnel plots of the studies of APOE ϵ2/ϵ3/ϵ4 involved in meta-analysis.

Our results showed that SH2B1 rs7498665 was significantly associated with the risk of overweight/obesity among 6,142 cases and 4,345 controls from four studies (overall OR = 1.21, 95% CI = 1.09-1.34, P = 0.0004, Figure 1). Increased risk of overweight/obesity was also observed in rs7138803 of FAIM2 among 3,477 cases and 4,676 controls from five studies (overall OR = 1.11, 95% CI = 1.01-1.22, P = 0.04, Figure 1). No evidence of association was observed for the meta-analyses of the rest 16 variants (Figures 1 and 3). For the meta-analyses with large heterogeneity, we further performed subgroup meta-analyses by ethnicity. No significant association of UCP1 rs1800592 with overweight/obesity was observed in Caucasian (P = 0.13, I2 = 62%), and Asian (P = 0.59, I2 = 0%, Additional file 2: Figure S1). And the subgroup meta-analysis of APOE ϵ2/ϵ3/ϵ4 polymorphism by excluding the study of Srivastava et al. [53] didn’t produce any significant association of APOE ϵ2/ϵ3/ϵ4 with overweight/obesity (Additional file 3: Figure S2). There was no visual publication bias in all the above meta-analyses (Additional file 4: Figure S3).

Discussion

Current meta-analyses were performed among 48,148 cases and 56,738 controls from 72 studies, covering a total of 6 populations, including Caucasian, Asian, Japanese-American, European-American, African-American, South American, and African. Among the tested 18 polymorphisms, there were two (SH2B1 rs7498665 and FAIM2 rs7138803) with significant association results (P < 0.05). Power analysis also showed large power existed in our meta-analyses of two significant polymorphisms including SH2B1 rs7498665 (100%) and FAIM2 rs7138803 (100%).

SH2B1 encodes an adaptor protein associated with leptin and insulin signaling in the lipid metabolism [54]. SH2B1 is an enhancer that may influence the phenotype of obesity through JAK-STAT pathway [55], which is important in the development and function of adipocytes [56]. SH2B1 acts as a mediator through PI3-kinase pathway which is correlated with the biological actions of leptin [26]. Many animal studies have shown that SH2B1 is involved in the development of obesity. SH2B1 through its participation in the regulation of leptin sensitivity, energy metabolism and body weight [57]. SH2B1 has been identified to be related to obesity through genome-wide association studies (GWAS) [55]. Our meta-analysis of SH2B1 rs7498665 was performed among 6,652 cases and 4,814 controls with four studies. Among the tested populations, no heterogeneity was observed (I2 = 0). Our results confirmed the relationship between SH2B1 and the risk of overweight/obesity (overall OR = 1.21, 95% CI = 1.09-1.34, P = 0.0004, Figure 1).

FAIM2 is an anti-apoptotic gene that provides protection from Fas-mediated cell death [32] that is associated with extreme overweight by GWAS [58]. FAIM2 rs7138803 polymorphism is associated with increased risk of obesity in Japanese [59]. But there is no relationship between FAIM2 rs7138803 and obesity in Chinese [60]. Minor allele frequency of rs7138803 in Chinese populations ranges from 0.28 to 0.29, while FAIM2 rs7138803 is monomorphic in Japanese and Caucasian populations. Our meta-analysis among 3477 cases and 4676 controls demonstrated that FAIM2 rs7138803 was associated with the risk of overweight/obesity (overall OR = 1.11, 95% CI = 1.01-1.22, P = 0.04, Figure 1).

Although meta-analysis is an important method to improve the precision and accuracy, to analyze and quantify the published results [6163], some disadvantages exist in the meta-analysis. For the current meta-analyses, several limitations need to be taken with cautions. Firstly, obesity is always accompanied by other complications such as coronary artery diseases and hypertension. These confounding factors needed to be adjusted in the original case–control studies. We were unable to obtain the related information. Therefore we can’t exclude a chance of the positive findings confounded by these obesity-related factors. Secondly, the significant result of FAIM2 rs7138803 needs to be validated in the future. However, after Bonferroni’s correction by the number of testing, the association of FAIM2 rs7138803 was unable to retain significant. Thirdly, power analysis suggested moderate power in the meta-analyses of MTHFR rs1801133 (power = 78.2%) and SERPINE1 rs1799768 (power = 69.4%) The negative results of them might be caused by a lack of power in our meta-analyses. Future studies with larger samples may help clarify the contribution of these biomarkers to the risk of overweight/obesity.

Our results identified significant associations between 2 polymorphisms (SH2B1 rs7498665 and FAIM2 rs7138803) and overweight/obesity. Moreover, overweight/obesity is a complicated disease influenced by both genetic and environmental factors. The potential mechanism of interaction between gene and environment could be taken into consideration in the future study. Well-designed studies with large samples could help elucidate the contribution of above polymorphisms to overweight/obesity.

Authors’ information

Linlin Tang and Huadan Ye: co-first authors of this work.

Declarations

Acknowledgments

The research was supported by the grants from: National Natural Science Foundation of China (31100919 and 81371469), Natural Science Foundation of Zhejiang Province (LR13H020003), K. C. Wong Magna Fund in Ningbo University, and Ningbo Social Development Research Projects (2012C50032).

Authors’ Affiliations

(1)
Zhejiang Provincial Key Laboratory of Pathophysiology, School of Medicine, Ningbo University
(2)
The Affiliated Hospital, Ningbo University
(3)
Diabetes Center, School of Medicine, Ningbo University
(4)
Institute of Hypertension and Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

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