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. 2016 Mar 18;9:18–25. doi: 10.1016/j.mgene.2016.03.002

Association between GNB3 c.825C > T polymorphism and the risk of overweight and obesity: A meta-analysis

Hui-Lan Li a,b, Yan-Jiao Zhang c,d, Xiao-Ping Chen c,d, Jian-Quan Luo c,d, Si-Yun Liu e, Zan-Lin Zhang a,⁎
PMCID: PMC4833052  PMID: 27114919

Abstract

Background

The association between G protein β-polypeptide 3 gene (GNB3) c.825C > T polymorphism (rs5443) and the risk of overweight/obesity has been investigated in many published studies, but the results were conflicting and inconclusive. A meta-analysis was performed to make a more accurate assessment of the relationship.

Methods

The PubMed, ProQuest Health & Medical Complete, Web of Science, Chinese Biomedical Medical databases (CBM), Chinese National Knowledge Infrastructure (CNKI), and Wan Fang databases were searched to identify eligible literatures. Pooled odds ratios (ORs) with the corresponding 95% confidence intervals (CIs) were used to assess the strength of association between GNB3 c.825C > T polymorphism and overweight/obesity.

Results

Eleven articles including 15 case–control studies with a total of 10,396 subjects (3171 cases of overweight/obesity and 7225 controls) were enrolled in the meta-analysis. The GNB3 c.825C > T was significantly associated with overweight/obesity under a recessive model (OR = 1.22, 95% CI: 1.04–1.44, P = 0.015). Moreover, the GNB3 825T allele was obviously associated with overweight alone in all inheritable models (P < 0.05) except in a recessive model (P = 0.084). In the stratification analysis by potential confounding variables, a significant association was observed between GNB3 c.825C > T polymorphism and overweight/obesity risk in males under an allelic model (P = 0.008), a homozygous model (P = 0.014), a recessive model (P = 0.005), and a dominant model (P = 0.049). And the results also showed that GNB3 c.825C > T polymorphism was significantly associated with overweight/obesity in subgroups of mean age less than 30 years, consistent with HWE, and high-quality studies (P = 0.027, P = 0.043, P = 0.040, respectively) under a recessive model, but not in other subgroups. Meta-regression also revealed that P value of HWE, publication year, and the quality scores of studies were the sources of heterogeneity in a recessive model and an allelic model. “Leave one out” sensitivity analyses indicated that the association was more significant after excluding some studies. The funnel plot and Egger's linear regression test and Begg's test revealed no apparent publication bias.

Conclusion

This meta-analysis suggests that the presence of TT homozygote might be one of the genetic factors susceptible to overweight/obesity and that males or aged under 30 years increase the genetic susceptibility.

Abbreviations: GNB3, G protein β-polypeptide 3 gene; CBM, Chinese Biomedical Medical databases; CNKI, Chinese National Knowledge Infrastructure; ORs, pooled odds ratios; CIs, confidence intervals; WHO, World Health Organization; NOS, the Newcastle–Ottawa Quality Assessment Scale; HWE, Hardy–Weinberg equilibrium; MOOSE, guidelines from meta-analysis of observational studies in epidemiology; BMI, body mass index; PCR–RFLP, polymerase chain reaction–restriction fragment length polymorphism; PB, population based; HB, hospital based

Keywords: G protein β-polypeptide 3, Polymorphism, Overweight, Obesity, Meta-analysis

1. Introduction

The prevalence of overweight and obesity has been rapidly increasing in the world. According to World Health Organization (WHO), more than 1.9 billion adults aged above 18 years and 42 million children under the age of 5 were overweight or obese globally in 2014. Obesity is characterized by abnormal or excessive body fat accumulation, which is a risk factor for type 2 diabetes, hypertension, cardiovascular diseases, cancers, and cognitive dysfunction (Mitchell et al., 2011). Therefore, obesity has become a major public health challenge. It is generally accepted that obesity is attributed to a shift in dietary and physical activity habits (El-Sayed Moustafa and Froguel, 2013). In addition to the unhealthy lifestyle, genetic factors are assumed to play an important role in obesity susceptibility, which hereditability has been estimated about 61%–80% (Nan et al., 2012). Numerous genes related to obesity have been found by candidate gene and genome-wide association approaches (Rao et al., 2014).

One commonly studied candidate gene for obesity is G protein β-polypeptide 3 gene (GNB3). Heterotrimeric G proteins are composed of an alpha, a beta, and a gamma subunit encoded by families of related genes, which are critical to translate signals from the cell surface into a cellular response in all cells of the body (Downes and Gautam, 1999). A study by Wang HY et al. showed a critical role in adipogenesis played by G proteins (Wang and Malbon, 1996). Elevation expression of Gi alpha 2 subunit or expression of constitutively active Gi alpha 2 promotes lipid accumulation and adipogenic conversion of cells (Su et al., 1993). In addition, transgenic mice lacking the Gi alpha 2 subunit are lean and deficient in fat mass (Moxham et al., 1993). G protein beta subunits are important regulators of alpha subunits and of certain signal transduction. A recent study by Goldlust IS et al. reported that the duplication of the GNB3 gene has been linked to an obesity phenotype not only in humans but also in the transgenic mouse model (Goldlust et al., 2013). A single-nucleotide polymorphism (c.825C > T, rs5443) in exon 10 of GNB3 has been reported that associated with the occurrence of a splice variant Gβ3s (Siffert et al., 1998). Despite a deletion of 41 amino acids and one WD repeat domain of the G beta subunit, splice variant Gβ3s is proved to be a biologically active protein and can ultimately enhance signal transduction via pertussis toxin-sensitive G proteins (Siffert et al., 1998, Rosskopf et al., 2000). GNB3 c.825C > T polymorphism influences G protein receptor mediated signal transduction, including lipolysis. Functional studies have established that GNB3 825TT homozygote has lowered Gβ3 protein and impaired the function of adrenoceptors, thus reducing lipolysis in human fat cells (Rydén et al., 2002, Hauner et al., 2002).

The first study on the relationship between overweight/obesity and GNB3 c.825C > T polymorphism was conducted in 1999 by Siffert W et al. They found a significant association between the 825T allele and overweight and obesity with odds ratios between 2 and 3 in Germans, Chinese, and black South Africans (Siffert et al., 1999). Since then, much research has focused on the association between GNB3 c.825C > T polymorphism and overweight/obesity. However, the results were inconsistent and conflicting. Therefore, we performed a meta-analysis of previous studies to comprehensively evaluate the relationship between GNB3 c.825C > T polymorphism and overweight/obesity susceptibility.

2. Methods

2.1. Search strategy

We searched the PubMed, ProQuest Health & Medical Complete, Web of Science, Chinese Biomedical Medical databases (CBM), Chinese National Knowledge Infrastructure (CNKI), and Wan Fang (Chinese) databases for all publications on the association between GNB3 c.825C > T polymorphism and the risk of overweight and obesity, using the following search terms: (‘G protein beta 3 polypeptide’ or ‘GNB3’) and (‘obesity’ or ‘obese’ or ‘overweight’ or ‘body mass index’ or ‘BMI’) and (‘polymorphism’ or ‘variant’). All studies were published from 1999, when the first study of the topic was reported, to 20 May, 2015. There were no language limitations on the search. “Related articles” option in PubMed was examined. Reference lists from the retrieved articles were also screened.

2.2. Inclusion and exclusion criteria

The inclusion criteria were as follows: 1) the study had a case control design for the association between the GNB3 c.825C > T and the risk of overweight and obesity. 2) Complete information of allelic frequencies and genotypic frequencies was available in cases and controls for calculating the odds ratio (OR) with 95% CI directly and indirectly. 3) The criteria for diagnosis was established using body mass index (BMI) cut-off points for obesity. We have excluded studies for 1) review articles, case reports, editorials, or animal research, and 2) overlapping and insufficient data.

2.3. Quality assessment and data extraction

The quality of the studies was critically assessed using the Newcastle–Ottawa Quality Assessment Scale (NOS) by two authors (Li HL and Zhang YJ). The following aspects of each study were appraised: selection of cases and controls, comparability, and outcome or exposure. Quality scores ranged from 0 to 9. In addition, we assessed the quality of included studies by P value of Hardy–Weinberg equilibrium (HWE) for the control genotype. Studies only consistent with HWE were scored in part of “selection of control” (Table 2). The study that scored seven or more stars was considered as a high-quality study, otherwise, the low-quality study. Guidelines from meta-analysis of observational studies in epidemiology (MOOSE) group were followed to extract the following data: first author, publication year, country and ethnicity, source of control, sample size and mean age of cases and controls, genotyping method, P value for HWE, BMI cut-off points, number of gender (males/females), and the genotypic and allelic frequencies in cases and controls. The studies were reviewed by two authors respectively. The results were compared and disagreements were solved with consensus.

Table 2.

Assessment study quality based on the Newcastle–Ottawa scale.

Author (year) Is the case definition adequate? Representativeness of the cases Selection of controls Definition of controls Comparability of cases and controls on the basis of the design or analysis Ascertainment of exposure Same method of ascertainment for cases and controls Non-response rate Total score
Lee et al. (2015) ★ ★ ☆c ★ ★☆ ★ ★ ☆ 6
Hsiao et al. (2013) ★ ★ ★ ★ ★☆ ★ ★ ☆ 7
Wang et al. (2008) ★ ★ ★ ★ ★☆ ★ ★ ☆ 7
Chen et al. (2006) ★ ★ ☆ ★ ☆☆ ★ ★ ☆ 5
Zhou et al. (2005) ★ ★ ★ ☆ ☆☆ ★ ★ ☆ 5
Lee et al. (2005) ★ ☆ ★ ★ ★☆ ★ ★ ☆ 6
Suwazono et al. (2004)a ★ ☆ ★ ☆ ★☆ ★ ★ ☆ 5
Suwazono et al. (2004)b ★ ☆ ☆c ☆ ★☆ ★ ★ ☆ 4
Benjafield et al. (2001) ★ ★ ★ ★ ★★ ★ ★ ☆ 8
Hinney et al. (2001) ★ ★ ★ ★ ★☆ ★ ★ ★ 8
Ohshiro et al. (2001) ★ ☆ ★ ☆ ★★ ★ ★ ☆ 6
Siffert et al. (1999) ★ ★ ★ ★ ★★ ★ ★ ☆ 8
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a

Indicates inconsistency with Hardy–Weinberg equilibrium.

b

indicates that the subjects were female.

c

Indicates that the subjects were male.

2.4. Statistical analysis

The STATA 12.0 software (StataCorp, College Station, TX, USA) was used for all statistical analyses. The association between GNB3 c.825C > T polymorphism and overweight/obesity was assessed using crude odds ratio (OR) with 95% confidence interval (CI). The pooled ORs were determined for allelic model (T versus C), homozygous model (TT versus CC), heterozygous model (CT versus CC), recessive model (TT versus CT/CC), and dominant model (CT/TT versus CC). The pooled OR was calculated using the Z test with the significance set at P < 0.05. HWE was assessed using the Chi-square test. The heterogeneity between studies was evaluated using the I2 test and Q statistic test. The value of I2 lies between 0% and 100%, and the larger value indicates increasing heterogeneity (Higgins et al., 2003). If heterogeneity was observed between studies (I2 > 50% or P < 0.05), the DerSimonian and Laird method for random-effects model was used to calculate the pooled OR and 95% CI. Otherwise, the Mantel–Haenszel method for fixed-effects model was adopted for the meta-analysis. Stratification analysis according to the P value of HWE, ethnicity, mean age, gender, and quality scores of studies were also performed to evaluate the association. Meta-regression was applied to explore the sources of between-study heterogeneity. The study publication year, ethnicity, BMI cut-off points, number of cases and controls, quality scores of studies and the P value of HWE were regarded as the potential confounding factors. Sensitivity analysis was performed to evaluate the effect of individual study on pooled results and assess the stability of results. The potential publication bias was graphically represented by funnel plots, and the funnel plot asymmetry was evaluated with Egger's linear regression test and Begg's test (Egger et al., 1997, Begg and Mazumdar, 1994).

3. Results

3.1. Study characteristics

A total of 780 studies were identified by searching databases. After carefully screening titles and abstracts, 26 articles were chosen after removing duplicates, animal research, and not significantly relevant papers. Finally, 11 articles including 15 case–control studies with a total of 3171 cases of overweight/obesity and 7225 controls were included after detailed evaluation in the present meta-analysis (Fig. 1). And the main characteristics of included studies are presented in Table 1. Two studies used polymerase chain reaction–restriction fragment length polymorphism (PCR–RFLP) as genotyping methods (Lee et al., 2015, Hsiao et al., 2013), whereas thirteen studies used Taqman assay (Siffert et al., 1999, Wang et al., 2008, Chen et al., 2006, Zhou et al., 2005, Lee et al., 2005, Suwazono et al., 2004, Benjafield et al., 2001, Hinney et al., 2001, Ohshiro et al., 2001). All studies showed that source of controls was population-based except one (Hsiao et al., 2013). The quality of the studies was assessed by NOS scale and quality scores of the included studies were detailed in Table 2. Some included studies were separately analyzed to investigate the relationship between GNB3 c.825C > T polymorphism and overweight or extremely obese alone. We also conducted a meta-analysis to estimate the association between GNB3 c.825C > T polymorphism and overweight or extremely obese respectively. The characteristics of studies of overweight or extremely obese and GNB3 c.825C > T polymorphism are showed in Table 3.

Fig. 1.

Fig. 1

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Flow diagram of study selection.

Table 1.

Characteristics of studies of overweight/obesity and GNB3 c.825C > T polymorphism.

Author Year Country Ethnicity Source of control Genotyping method BMI cut-off points Group Number Allele and genotypes
 Mean age (y) Gender (M/F) PHWE
C T CC CT TT
Lee et al. 2015 Korea Asian PB TaqMan 85th Case 394 379 409 85 209 100 8–9 231/163 0.023
Control 1769 1746 1792 407 932 430 8–9 875/894
Hsiao et al. 2013 China Asian HB TaqMan 24 Case 467 402 532 87 228 152 About 40 333/134 0.188
Control 505 441 569 89 263 153 40 ± 12 257/247
Wang et al. 2008 China Asian PB PCR–RFLP 25 Case 129 137 121 38 61 30 55 ± 10 85/44 0.495
Control 270 285 255 78 129 63 54 ± 11 140/130
Chen et al. 2006 China Asian PB PCR–RFLP 23 Case 161 149 173 38 73 50 NA 58/103 0.001
Control 313 358 268 88 182 43 NA 151/162
Zhou et al. 2005 China Asian PB PCR–RFLP 25 Case 142 154 130 43 68 31 > 30 NA 0.584
Control 85 83 87 19 45 21 > 30 NA
Lee et al. 2005 Korea Asian PB PCR–RFLP 25 Case 130 132 128 32 68 30 25 ± 3 130/0 0.082
Control 152 166 138 40 86 26 24 ± 3 152/0
Suwazono et al. 2004 Japan Asian PB PCR–RFLP 25 Case 183 195 171 52 91 40 42 ± 9 0/183 < 0.001
Control 989 1033 945 242 549 198 38 ± 9 0/989
Suwazono et al. 2004 Japan Asian PB PCR–RFLP 25 Case 322 322 322 80 162 80 40 ± 9 322/0 0.179
Control 1131 1144 1118 278 588 265 38 ± 10 1131/0
Benjafield et al. 2001 Britain Caucasian PB PCR–RFLP 25 Case 84 122 46 42 38 4 48 ± 9 54/30 0.054
Control 105 162 48 59 44 2 48 ± 10 55/50
Hinney et al. 2001 Germany Caucasian PB PCR–RFLP 90th Case 491 695 287 251 193 47 About 20 225/296 0.957
Control 110 144 76 47 50 13 25 ± 3 55/55
Ohshiro et al. 2001 Japan Asian PB PCR–RFLP 30 Case 263 272 254 71 130 62 About 50 146/117 0.872
Control 150 148 152 37 74 39 49 ± 1 80/70
Siffert et al. 1999 Germany Caucasian PB PCR–RFLP 25 Case 92 108 76 32 44 16 About 20 92/0 0.753
Control 207 292 122 102 88 17 About 20 207/0
Siffert et al. 1999 China Asian PB PCR–RFLP 25 Case 186 166 206 33 100 53 About 20 186/0 0.903
Control 832 868 778 235 416 181 About 20 832/0
Siffert et al. 1999 South Africa Black African PB PCR–RFLP 25 Case 75 17 133 0 17 58 About 20 75/0 0.706
Control 201 68 334 5 58 138 About 20 201/0
Siffert et al. 1999 Zimbabwe Black African PB PCR–RFLP 25 Case 52 17 87 1 15 36 About 20 52/0 0.753
Control 406 151 661 15 121 270 About 20 406/0
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Abbreviations: BMI, body mass index; PB, population based; HB, hospital based; HWE, Hardy–Weinberg equilibrium.

PHWE indicates the P value of Hardy–Weinberg equilibrium.

Table 3.

Characteristics of studies of overweight or extremely obese and GNB3 c.825C > T polymorphism.

Author Year Country Ethnicity Source of control Genotyping method BMI cut-off points Group Number Allele and genotypes
 Mean age (y) Gender (M/F) PHWE
C T CC CT TT
Overweight
Hsiao et al. 2013 China Asian HB TaqMan 24–27 Case 291 249 333 47 155 89 41 ± 1 204/87 0.188
Control 505 505 441 569 89 263 40 ± 12 257/247
Siffert et al. 1999 Germany Caucasian HB PCR–RFLP 25–27 Case 70 85 55 26 33 11 24 ± 3 70/0 0.753
Control 207 292 122 102 88 17 207/0
Siffert et al. 1999 China Asian HB PCR–RFLP 25–27 Case 128 118 138 24 70 34 25 ± 4 128/0 0.903
Control 832 868 778 235 416 181 832/0
Siffert et al. 1999 South Africa Black African HB PCR–RFLP 25–27 Case 53 13 93 0 13 40 22 ± 4 53/0 0.706
Control 201 68 334 5 58 138 201/0
Chen et al. 2006 China Asian PB PCR–RFLP 23–25 Case 57 58 56 12 34 11 NA 58/103 0.001
Control 313 358 268 88 182 43



Extremely obese
Hinney et al. 2001 Germany Caucasian PB PCR–RFLP 99th Case 440 622 258 226 170 44 14 ± 3 199/241 0.957
Control 110 144 76 47 50 13 25 ± 3 55/55
Ohshiro et al. 2001 Japan Asian PB PCR–RFLP 35 Case 55 57 53 13 31 11 50 ± 1 28/27 0.872
Control 150 148 152 37 74 39 49 ± 1 80/70
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Abbreviations: BMI, body mass index; PB, population based; HB, hospital based; HWE, Hardy–Weinberg equilibrium.

PHWE indicates the P value of Hardy–Weinberg equilibrium.

3.2. Meta-analysis

In the meta-analysis of all involved studies, there was a significant association between GNB3 c.825C > T polymorphism and overweight/obesity under an allelic model (OR = 1.08, 95% CI: 1.02–1.16, P = 0.017), a homozygous genetic model (OR = 1.19, 95% CI: 1.04–1.36, P = 0.014), as well as a recessive genetic model (OR = 1.19, 95% CI: 1.06–1.32, P = 0.002) (Table 4). But no significant association was observed under a heterozygous genetic model (OR = 1.00, 95% CI: 0.89–1.12, P = 0.994), and a dominant genetic model (OR = 1.05, 95% CI: 0.94–1.17, P = 0.350) (Table 4). Furthermore, the I2 test and Q statistic test suggested an existence of heterogeneity in included studies under an allelic model, a homozygous model, a recessive model, and a dominant model. Therefore, the random effects model was used to calculate the pooled effects in those models. The results showed a significant association in a recessive model (OR = 1.22, 95% CI: 1.04–1.44, P = 0.015) between GNB3 c.825C > T polymorphism and overweight/obesity, but not in an allelic model (OR = 1.10, 95% CI: 0.99–1.22, P = 0.076) and in a homozygous genetic model (OR = 1.23, 95% CI: 0.98–1.24, P = 0.078) (Table 4).

Table 4.

Pooled ORs and 95% CIs of the association between GNB3 c.825C > T polymorphism and overweight/obesity in both fixed and random effect model.

Inherited model Fixed effect model
 Random effect model
 I2 (%) PH Egger P Begg P
OR 95% CI P OR 95% CI P
T vs. C 1.08 1.02–1.16 0.017 1.10 0.99–1.22 0.076 53.3 0.008 0.412 0.520
TT vs. CC 1.19 1.04–1.36 0.014 1.23 0.98–1.54 0.078 54.0 0.007 0.296 0.656
CT vs. CC 1.00 0.89–1.12 0.994 1.00 0.87–1.14 0.945 16.9 0.264 0.454 0.299
TT vs. (CT + CC) 1.19 1.06–1.32 0.002 1.22 1.04–1.44 0.015 46.3 0.025 0.294 0.458
(TT + CT) vs. CC 1.05 0.94–1.17 0.350 1.06 0.91–1.22 0.484 38.6 0.063 0.381 0.458
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PH indicates the P value of heterogeneity.

Subgroup analysis was conducted in four models in which heterogeneity existed. In the stratified analyses using the P value of HWE, the results indicated only significant association in a recessive model consistent with HWE (OR = 1.15, 95% CI: 1.00–1.32, P = 0.043) (Table 5, and Fig. 2). Stratified analyses by ethnicity (categorized as Asian, Caucasian, and Black African) suggested only slightly significant association in the recessive model (OR = 1.19, 95% CI: 1.00–1.43, P = 0.055) in the Asian subgroup (Table 5). In the subgroup analysis by mean age (categorized as mean age less than 30 years and mean age more than 30 years), we found only significant association in the recessive model (OR = 1.26, 95% CI: 1.04–1.55, P = 0.029) in the mean age less than 30 years subgroup (Table 5, and Fig. 3). Stratified analyses by gender (categorized as all, male and female) showed a significant association between GNB3 c.825C > T polymorphism and overweight/obesity in the male subgroup under an allelic model (OR = 1.27, 95% CI: 1.06–1.51, P = 0.008), a homozygous model (OR = 1.68, 95% CI: 1.11–2.54, P = 0.014), a recessive model (OR = 1.30, 95% CI: 1.08–1.56, P = 0.005) and a dominant model (OR = 1.38, 95% CI: 1.00–1.88, P = 0.049) (Table 5, and Fig. 4). In addition, we stratified the studies by quality scores, and found that an obvious association was found in a high-quality study under the recessive model (OR = 1.30, 95% CI: 1.01–1.66, P = 0.040) (Table 5).

Table 5.

Pooled ORs and 95% CIs of subgroup analysis.

 T vs. C
 TT vs. CC
 TT vs. (CT + CC)
 (TT + CT) vs. CC
Subgroups OR (95% CI) P I2 (%) OR (95% CI) P I2 (%) OR (95% CI) P I2 (%) OR (95% CI) P I2 (%)
HWE
 Yes (n = 12) 1.09 (0.96–1.23) 0.184 49.7 1.18 (0.91–1.54) 0.213 47.3 1.15 (1.00–1.32) 0.043 4.5 1.07 (0.89–1.30) 0.504 45.3
 No (n = 3) 1.14 (0.89–1.46) 0.289 75.0 1.37 (0.79–2.35) 0.259 78.5 1.46 (0.84–2.52) 0.178 85.6 1.03 (0.82–1.29) 0.815 25.3
Ethnicity
 Asian (n = 10) 1.08 (0.97–1.19) 0.174 48.9 1.18 (0.94–1.48) 0.165 56.1 1.19 (1.00–1.43) 0.055 54.2 1.03 (0.89–1.20) 0.689 31.9
 Caucasian (n = 3) 1.18 (0.72–1.92) 0.517 80.6 1.64 (0.52–5.15) 0.399 76.4 1.50 (0.64–3.55) 0.355 0 1.16 (0.65–2.09) 0.605 75.6
 Black African (n = 2) 1.36 (0.92–2.01) 0.129 0 2.65 (0.49–14.18) 0.256 0 1.33 (0.86–2.06) 0.202 62.6 2.52 (0.47–13.45) 0.278 0
Mean age
 Less than 30 (n = 7) 1.19 (0.99–1.44) 0.067 60.9 1.48 (0.99–2.21) 0.057 54.7 1.26 (1.04–1.55) 0.029 22.9 1.25 (0.91–1.70) 0.171 58.0
 More than 30 (n = 7) 0.99 (0.90–1.09) 0.852 0 0.97 (0.80–1.17) 0.759 0 1.06 (0.91–1.23) 0.478 0 0.92 (0.80–1.08) 0.311 0
 NA (n = 1) 1.55 (1.18–2.03) 0.001 – 2.69 (1.54–4.70) 0.000 – 2.83 (1.78–4.50) 0.000 – 1.27 (0.82–1.96) 0.293 –
Gender
 All (n = 8) 1.03 (0.89–1.18) 0.702 53.6 1.08 (0.80–1.46) 0.631 56.2 1.15 (0.87–1.51) 0.332 64.1 0.98 (0.85–1.12) 0.742 0
 Male (n = 6) 1.27 (1.06–1.51) 0.008 44.8 1.68 (1.11–2.54) 0.014 46.9 1.31 (1.08–1.57) 0.005 0.9 1.38 (1.00–1.88) 0.049 45.3
 Female (n = 1) 0.96 (0.77–1.20) 0.711 – 0.94 (0.60–1.48) 0.790 – 1.12 (0.76–1.64) 0.570 – 0.82 (0.57–1.16) 0.259 –
Study quality
 High (n = 8) 1.20 (0.97–1.50) 0.093 59.7 1.56 (0.94–2.60) 0.087 54.4 1.30 (1.01–1.66) 0.040 17.5 1.27 (0.89–1.82) 0.188 58.4
 Low (n = 7) 1.05 (0.94–1.17) 0.388 41.8 1.11 (0.88–1.41) 0.372 49.7 1.19 (0.96–1.47) 0.118 61.0 0.98 (0.86–1.11) 0.730 0
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n is the number of studies.

Fig. 2.

Fig. 2

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Forest plot of overweight/obesity associated with GNB3 c.825C > T polymorphism in the recessive model (TT versus CT/CC) stratified by the P value of Hardy–Weinberg equilibrium.

Fig. 3.

Fig. 3

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Forest plot of overweight/obesity associated with GNB3 c.825C > T polymorphism in the recessive model (TT versus CT/CC) stratified by mean age.

Fig. 4.

Fig. 4

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Forest plot of overweight/obesity associated with GNB3 c.825C > T polymorphism in the recessive model (TT versus CT/CC) stratified by gender.

3.3. Heterogeneity analysis

The subgroup analysis revealed that heterogeneity was removed in the analysis of mean age above 30 years, and black African subgroup. Moreover, the heterogeneity consistent with the HWE subgroup was obviously removed with an I2 value of 4.5% in the recessive model. In the allelic model, meta-regression showed that the P value of HWE, publication year, and study quality scores were the sources of heterogeneity between studies (P = 0.043, P = 0.055, P = 0.047, respectively), as well as in the homozygous model (P = 0.013, P = 0.013, P = 0.013, respectively). Furthermore, in the recessive model, meta-regression also revealed that the P value of HWE, publication year, and study quality scores were the sources of heterogeneity (P = 0.008, P = 0.009, P = 0.021, respectively), those three covariates were able to explain 86.47% of between-study variance, which was consistent with the subgroup analysis. In addition, meta-regression revealed that the study quality score was the source of heterogeneity in the dominant model (P = 0.080), which also was in agreement with the subgroup analysis.

3.4. Sensitivity analysis and publication bias

Sensitivity analysis was performed to evaluate the stability of results by excluding individual study each time. The analysis results demonstrated that the association was more significant after excluding the study by Hinney et al. under an allelic model (OR = 1.12, 95% CI: 1.01–1.24, P = 0.027), a homozygous model (OR = 1.28, 95% CI: 1.01–1.61, P = 0.040), and a recessive model (OR = 1.25, 95% CI: 1.06–1.47, P = 0.009), and the study by Zhou et al. under an allelic model (OR = 1.12, 95% CI: 1.00–1.24, P = 0.041), a homozygous model (OR = 1.27, 95% CI: 1.00–1.60 P = 0.042), and a recessive model (OR = 1.24, 95% CI: 1.05–1.47, P = 0.010). Moreover, the inter-study heterogeneity was completely removed after excluding the study by Chen et al. in recessive model. Deleting other individual study make little difference in corresponding pooled effects of all models (data not shown), which suggesting that our results are statistically robust. The funnel in all models revealed no obvious publication bias. Fig. 5 showed the funnel plots of the recessive model. The results were confirmed by Egger's linear regression test and Begg's test (Table 4).

Fig. 5.

Fig. 5

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Funnel plots of overweight/obesity associated with GNB3 c.825C > T polymorphism in the recessive model (TT versus CT/CC).

3.5. Association between GNB3 c.825C > T polymorphism with overweight or extremely obese

Of 11 articles, five studies were separately analyzed the relationship between overweight and GNB3 c.825C > T polymorphism. We evaluated the relationship between overweight and GNB3 c.825C > T polymorphism alone. The results revealed a significant association between overweight and GNB3 c.825C > T polymorphism in all inherited models (P < 0.05) except a recessive model (P = 0.084) (Table 6). As noted, two studies were individually analyzed the association between extremely obese and GNB3 c.825C > T polymorphism. In contrast, no significant association was found in all inherited models (P > 0.05) between extremely obese and GNB3 c.825C > T polymorphism (Table 6).

Table 6.

Pooled ORs and 95% CIs of the association between GNB3 c.825C > T polymorphism and overweight or extremely obese.

Inherited model Overweight
 Extremely obese
OR 95% CI P I2 (%) OR 95% CI P I2 (%)
T vs. C 1.21 1.05–1.38 0.008 8.5 0.82 0.64–1.06 0.140 0
TT vs. CC 1.51 1.12–2.04 0.007 7.1 0.74 0.42–1.29 0.288 0
CT vs. CC 1.36 1.05–1.75 0.018 0 0.81 0.55–1.19 0.280 26.3
TT vs. (CT + CC) 1.21 0.98–1.51 0.084 0 0.77 0.47–1.27 0.307 0
(TT + CT) vs. CC 1.42 1.11–1.81 0.005 0 0.78 0.54–1.12 0.184 0
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4. Discussion

In the present meta-analysis with 11 eligible articles of total 10,396 subjects (3171 cases of overweight/obesity and 7225 controls), it is demonstrated that GNB3 825TT and the risk of overweight/obesity were significantly associated in recessive model, as well as overweight only in all models, but not in extremely obese. Further stratified analysis revealed that GNB3 825TT homozygote was a high risk factor of overweight/obesity in males or under age of 30 populations. To our knowledge, this is the first study to examine the subgroup analysis of mean age and potential sex differences in overweight/obesity with the association of GNB3 c.825C > T polymorphism.

Generally, it is considered that genes exert their effects across different ethnic groups. We performed a subgroup analysis by ethnicity, but no association was found under any inherited models. It is worth noting that the number of studies from Asian population was much more than for other races. And it is likely that the relationship between GNB3 c.825C > T polymorphism and overweight/obesity is more prevalent in ethnicities from Asia. Notoriously, overweight/obesity could be affected by age and sex. According to obesity epidemic in the United States, the overweight/obesity combined prevalence increase with age and more men than women were overweight/obesity, but there was no such trend regarding obesity (Wang and Beydoun, 2007). Thus, we performed a subgroup analysis in term of mean age and gender in present meta-analysis. There were significant associations between 825TT and overweight/obesity among those studies mean age under 30 years or males. In addition, the critical assessment of each original study quality is essential because the meta-analysis is a secondary analysis and evaluation of the original study. We adopted the NOS scale to assess the quality of each study. In subgroup analysis of study quality, we found the pooled effect of high quality studies was closer to the overall pooled effect in all inherited model.

Currently, there are several different definitions for overweight and obesity. BMI is the most widely used measures, which provide a useful population-level measure of overweight and obesity. However, BMI cutoff points for overweight and obesity differ in different regions. And in children and adolescents, BMI 85th and 95th percentiles was defined as overweight (Must et al., 1991, Himes and Dietz, 1994). We separately analyzed the association of the GNB3 c.825C > T polymorphism with overweight or extremely obese alone and the results indicated the impact by the genetic factor of GNB3 c.825C > T polymorphism was greater in overweight than extremely obese. The potential reasons maybe that the extremely obese could be affected more significantly than other factors or other genetic factors.

Heterogeneity between included studies is a common problem in meta-analysis. In present meta-analysis, moderate heterogeneity was found in four inherited models. To investigate this problem in greater depth, subgroup analysis and meta-regression analysis showed that quality scores of included studies were the sources of heterogeneity in the four inherited model. And the P value of HWE and publication year were the sources of heterogeneity in the allelic model, homozygous model, and recessive model. Furthermore, the funnel plot and Egger's linear regression test and Begg's test indicate no apparent publication bias in the present meta-analysis. “Leave one out” sensitivity analyses indicated that the association was more significant after excluding some studies.

A previous meta-analysis by Souza et al. showed a trend (P = 0.053) associating CC and lower BMI under a fixed model (Souza et al., 2008). This suggests that the GNB3 c.825C > T polymorphism was thought to play a key role in overweight/obesity. Our analysis confirmed the association of the GNB3 c.825C > T polymorphism with overweight/obesity. More recently, a meta-analysis involving 10 studies by Tang et al. failed to observe association of GNB3 c.825C > T polymorphism and overweight/obesity in a fixed effect model under a homozygous model (TT versus CC) (Tang et al., 2014). Their results were inconsistent with the present meta-analysis. The difference could be explained by that they included a review published in 2000 by Siffert et al. (2000), of which data overlapped with the study published in 1999 by Siffert et al. (1999). Moreover, they did not assess the association under other inherited models and perform the stratification analysis by the potential confounding variables. Several limitations in the current meta-analysis should be mentioned. Firstly, some included studies do not comply with HWE due to the fact that a very limited amount of studies reported the association between GNB3 c.825C > T and the overweight/obesity, which may influence the cause–effect relationship. Fortunately, the overall pooled effects were in line with the subgroup analysis consistent with the P value of HWE, which illustrates that our results were stable. Secondly, we had insufficient information regarding genotypes and allelic frequency by different sex. In addition, some authors were contacted via email with respect to missing information, but data was not available.

In conclusion, our results suggested that the presence of GNB3 825TT homozygote might be one of the genetic factors susceptible to overweight/obesity and that male or aged less than 30 years increases the genetic susceptibility. We also demonstrated that GNB3 825TT and overweight alone were significantly associated. Additional large sample size and well-studied association studies are needed to provide powerful evidence to the conclusions.

Conflict of interest

The authors declare that they have no conflict of interest.

Acknowledgments

This project was supported by the Chinese National Science Foundation (No. 81422052)

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