Heritability of Obesity-related Phenotypes and Association with Adiponectin Gene Polymorphisms in the Chinese National Twin Registry

Authors

  • Juan Lee,

    1. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 100191, Beijing, China; Key Laboratory of Epidemiology, Ministry of Education, 100191, Beijing, China
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  • Li Chen,

    1. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 100191, Beijing, China; Key Laboratory of Epidemiology, Ministry of Education, 100191, Beijing, China
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  • Harold Snieder,

    1. Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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  • Da F. Chen,

    1. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 100191, Beijing, China; Key Laboratory of Epidemiology, Ministry of Education, 100191, Beijing, China
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  • Li M. Lee,

    1. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 100191, Beijing, China; Key Laboratory of Epidemiology, Ministry of Education, 100191, Beijing, China
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  • Gai F. Liu,

    1. Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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  • Ting Wu,

    1. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 100191, Beijing, China; Key Laboratory of Epidemiology, Ministry of Education, 100191, Beijing, China
    2. Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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  • Xun Tang,

    1. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 100191, Beijing, China; Key Laboratory of Epidemiology, Ministry of Education, 100191, Beijing, China
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  • Si Y. Zhan,

    1. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 100191, Beijing, China; Key Laboratory of Epidemiology, Ministry of Education, 100191, Beijing, China
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  • Wei H. Cao,

    1. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 100191, Beijing, China; Key Laboratory of Epidemiology, Ministry of Education, 100191, Beijing, China
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  • Jun Lv,

    1. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 100191, Beijing, China; Key Laboratory of Epidemiology, Ministry of Education, 100191, Beijing, China
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  • Wen J. Gao,

    1. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 100191, Beijing, China; Key Laboratory of Epidemiology, Ministry of Education, 100191, Beijing, China
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  • Yong H. Hu

    Corresponding author
    1. Department of Epidemiology and Biostatistics, Peking University Health Science Center, 100191, Beijing, China; Key Laboratory of Epidemiology, Ministry of Education, 100191, Beijing, China
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*Corresponding author: Yong-Hua Hu, Department of Epidemiology and Biostatistics, Peking University Health Science Center, 38 Xueyuan Rd, Haidian District, Beijing, 100191 China. Tel.: 010-8280-5644; Fax: 010-8280-5644; E-mail: yhhu@bjmu.edu.cn.

Summary

The purpose of this study was to estimate the heritability of obesity-related phenotypes and investigate the association of adiponectin gene polymorphisms +45T>G and +276G>T with these measures in Chinese twins. 1260 twin pairs were recruited from two cities through the Chinese National Twin Registry System from 2001 to 2005. Two SNPs at the adiponectin locus (+45T>G and +276G>T) were genotyped. Structural equation modeling (SEM) was used to estimate heritability and the best-fitting variance component model. The regular association among all twins was analysed with generalised estimating equations (GEE). Sib-transmission/disequilibrium test (TDT) within dizygotic (DZ) twin pairs discordant for their genotype was performed using SEM. Additive genetic, common and unique environmental (ACE) model-based heritability of body mass index (BMI) was 61%, while additive genetic and unique environmental (AE)-model-based heritability of waist circumference (WC) and waist-hip ratio (WHR) were 75% and 61%, respectively. There was no association of adiponectin gene +45T>G and +276G>T genotypes with obesity-related phenotypes in all twins or discordant DZ twins. Our twins data did not support that there was an association between adiponectin gene polymorphisms +45T>G and +276G>T and the obesity-related phenotypes. Further studies are required to better understand the role of adiponectin gene polymorphisms in obesity.

Introduction

The Chinese National Twin Registry is the first and largest population-based twin registry in China (Li et al., 2006). Twins, due to their unique genetic and environmental relationships, have provided crucial insight in our understanding of genetic contributions to numerous etiologically complex disorders. The overall goal of this program is to develop a resource for genetic epidemiologic studies of the common and complex diseases in China. Obesity is a disorder resulting from excess adipose tissue mass. Large-scale epidemiological studies have established that obesity is an independent risk factor for several major diseases including coronary heart disease, type 2 diabetes, hypertension, certain malignancies and musculoskeletal disorders (Jeffery & Sherwood, 2008). Our focus was to study genetic contributions to general and abdominal obesity and associations of adiponectin gene polymorphisms with related phenotypes in a relatively large twin population.

Obesity is a complex disease resulting from the combined effects of behavioural and lifestyle factors, as well as genes and their interactions. A number of studies demonstrated that family history, dietary habits and lifestyle behaviours must play important roles in the current epidemic of obesity (Fiore et al., 2006; Kosti et al., 2008). Genetic epidemiology has been helpful in defining the magnitude of the genetic contribution to obesity from a population perspective. The level of heritability has been considered in several twin, adoption and family studies. Studies on heritability of BMI, WC and WHR have indeed been conducted previously to determine the genetic and environmental effect on the measures of obesity in other ethnic groups (Maes et al., 1997; Faith et al., 1999). However, only one previous study was conducted in Chinese twins. This study assessed heritability of metabolic syndrome related characters including BMI in only 260 pair twins collected from 2001 to 2003 through this Chinese Twin Registry System (Ren et al., 2003; Chen et al., 2005). Our current study is the first heritability report to consider BMI, WC and WHR simultaneously in a larger Chinese population based on a twin cohort of adults with a wide distribution of ages.

If the heritability estimation of obesity phenotypes indicated important genetic factors in the etiology of general and abdominal obesity, the obvious next work is to explore which genes might be responsible for the high heritability. Studies of candidate genes indicated that some obesity related genes controlled important functions of adipose tissue, and that structural variance in these genes may alter adipose tissue function in a way that promotes obesity. Adiponectin is expressed exclusively in differentiated adipocytes (Maeda et al., 1996) and regulates energy metabolism and endothelial activation. Since the initial description of adiponectin, numerous animal and human studies have examined the relation of adiponectin to obesity. The adiponectin gene consists of three exons and two introns spanning a 17-kb region and has been located on chromosome 3q27 (Takahashi et al., 2000). In recent years, there have been many reports about the association of variations or mutations in the adiponectin gene with obesity-related diseases (Kondo et al., 2002; Gonzalez-Sanchez et al., 2005; Yang et al., 2005). These studies provided the genetic evidence that adiponectin influences obesity, which was consistent with the findings in a recent genetic study of a Chinese population with type 2 diabetic obesity (Liu et al., 2007). More than 10 common SNPs have been identified after screening for this gene in Japanese (Hara et al., 2002) and Caucasian populations (Vasseur et al., 2002). However, here we focus on function of these allelic variants and the inconsistent results of previous studies. +45T>G is in exon 2 of adiponectin gene, while +276G>T is in intron 2. It is noteworthy that variations of these two loci have been shown to affect plasma adiponectin levels (Kyriakou et al., 2008). Reduced plasma levels of adiponectin have been found in individuals with obesity (Shand et al., 2003). The inverse association between visceral adipose tissue and adiponectin was stronger in African Americans compared with Hispanics, a finding that suggested possible ethnic differences in the effects of visceral obesity (Hanley et al., 2007). Several studies demonstrated that +45T>G and +276G>T variants of the adiponectin gene were significantly associated with obesity and type 2 diabetes in the Japanese and German populations (Hara et al., 2002; Stumvoll et al., 2002), but these associations have not been seen in other ethnic populations (Schaffler et al., 2000; Xu et al., 2008). These observations give us a clue that adiponectin gene variants +45T>G and +276G>T may play different roles in the pathogenesis of obesity in various ethnic groups.

The World Health Organization has agreed on an international standard for identifying overweight and obesity in adult populations using the body mass index (weight/height2). BMI, which relates weight to height, is the most widely used and simple measure of body size, and is frequently used to estimate the prevalence of obesity within a population. Yet this measurement does not account for variation in body fat distribution and abdominal fat mass, which can differ greatly across populations and can vary substantially within a narrow range of BMI. Excess intra-abdominal fat is associated with greater risk of obesity-related morbidity than is overall adiposity (Ho et al., 2001). Thus, measurements of WC and WHR have been viewed as alternatives to BMI, with both measures regularly used in the clinical and research settings. WC and WHR are simple and effective ways of measuring abdominal obesity (Pouliot et al., 1994; Taylor et al., 2000) and may be better predictors of cardiovascular disease risk than BMI in adults (Zhu et al., 2002).

Twin studies are widely used as a perfect natural experiment to determine heritability. A comparison of similarities of phenotypes between monozygotic (MZ) and dizygotic (DZ) twins allows for the estimation of heritability when the pair-wise variation of environmental factors is assumed to be very similar between MZ and DZ groups (He et al., 2008). The aim of the present study was to estimate the heritability of obesity-related phenotypes (BMI, WC and WHR) and investigate their association with two adiponectin gene polymorphisms, +45T>G(rs2241766) and +276G>T(rs1501299), in a large sample of Chinese twins.

Methods

Study Site and Population

All twins in this study were recruited from two cities that are parts of the Chinese twin registry system, Qingdao city in Shandong province (north China) and Lishui city in Zhejiang province (south China), from 2001 to 2005. Twins were initially recruited through local disease control networks and the mass media. From the initial recruitment, medical examination (including blood samples) and questionnaire data were collected from more than 6500 individual female and male twins aged 15 years or older. Questionnaire assessment included a standard battery of twin similarity questions, demographic information, measures of personality, cigarette smoking and alcohol use (Li et al., 2006). By the end of 2005, a total of 688 twin pairs from Qingdao city and 572 twin pairs from Lishui city were enrolled for the detailed phenotype studies. 1260 pairs were included in this study based on the following criteria: (1) aged 25 years or older; (2) signed informed consent; (3) both members were healthy. Study protocols were approved by the Ethics Committee for Human Subject Studies of the Peking University Health Science Center.

Body Measurements

Height was measured to the nearest 0.5 cm without shoes using a stadiometer. Each participant stood with heels, buttocks and shoulders resting lightly against the backing board so that the Frankfort plane (a line connecting the superior border of the external auditory meatus with the infraorbital rim) was horizontal. Weight was measured after removal of shoes and when wearing light clothing only, using a mechanical beam balance, and was recorded to the nearest 0.1 kg. BMI was calculated as weight (kg)/height (m)2. Waist circumference was measured using a steel measuring tape, with measurements made halfway between the lower border of the ribs, and the iliac crest in a horizontal plane. Hip circumference was measured at the widest point over the buttocks. For both waist and hip circumference, two measurements to the nearest 0.5 cm were recorded. If the variation between the measurements was greater than 2 cm, a third measurement was taken. The mean of the two closest measurements was calculated. WHR was obtained by dividing the mean waist circumference by the mean hip circumference.

Zygosity Determination

The gender and ABO blood type were used for the initial screen of zygosity. For same-sex twin pairs, determination of zygosity was made by PCR-amplified short tandem repeat (STR) analysis with a commercially available panel, comprising 10 autosomal, codominant, unlinked loci (including D3S1358, vWA, D16S539, D2S1338, D8S1179, D21S11, D18S51, D19S433, TH01, FGA) and the gender-determining marker, amelogenin. MZ twins were determined when all these unlinked loci and the gender-determining marker were identical. DZ twins were established by more than two discordant alleles. For the remaining twin pairs, zygosity was based on questionnaire items, filled by the mother, about physical similarity and frequency of confusion of the twins by family and strangers. The probability of MZ determined by identity of 10 STRs is estimated to be at least 99.9% (Gao et al., 2006).

SNP Genotyping

Genotypes were determined at positions 45 and 276 relative to the translation start site by PCR followed by dot blotting and allele-specific hybridisation. DNA fragments containing each SNP (250 bp for SNP 45T→G and 196 bp for 276G→T) were amplified by PCR from genomic DNA using primers ’-TCTCTCCATGGCTGACAGTG-3′ and 5′-CCTTTCTCACCCTTCTCACC-3′ for SNP 45 T→G and 5′-GGCCTCTTTCATCACAGACC-3′ and 5′-AGATGCAGCAAAGCCAAAGT-3′ for SNP276G→T. PCR was performed on 30 ng DNA in 20 μl containing Tris HCl 10 mmol/l, pH 8.3, KCl 50 mmol/l, MgCl2 1.5 mmol/l, each dNTPs 0.2 mmol/l, forward and reverse primers 0.4 μmol/l,Taq polymerase 0.035 U/μl (Applied biosystems, Foster City, CA) for 30 cycles (60 s at 95°C, 45 s at 58°C, 45 s at 72°C) in an MJ Research thermal cycler. PCR fragments were dot-blotted on Nylon membranes in duplicate, hybridised with 32P-labeled allele-specific 17mers according to standard protocols, and autoradiographed overnight. Genotypes were inferred by comparing the autoradiograms of membranes hybridised with different allele probes. Because of PCR failure, genotypes could not be determined for 61 individuals at position 45 and 71 individuals at position 276.

Statistical Methods

Normality for each continuous variable was checked and values >3 standard deviations from the mean were set as missing before analysis. Descriptive analysis included mean and standard deviation for the continuous phenotypes and the percentage for categorical phenotypes.

Twin studies are based on the assumption that MZ twin pairs share all their segregating genes while DZ pairs share on average 50%. Thus, differences between MZ cotwins are due to environmental effects, while differences between DZ cotwins are due to genetic effects. Similarity was assessed by intraclass correlations in MZ (rMZ) and DZ (rDZ), which were calculated from a random-effects one-way analysis of variance. Structural equation modeling (SEM) was used to estimate heritability. Univariate twin analyses were performed, where the phenotypic variance can be decomposed into additive genetic (A, additive effects of genes on multiple loci), non-additive genetic (D, interactions between alleles at the same locus [dominance] or on different loci [epistasis]), common environmental (C, environmental effects shared by twins reared in the same family) and unique environmental effects (E, environmental effects unique to the individual). MZ pairs are assumed to share the same A and D genetic variance; DZ pairs are assumed to share one-half of the A variance and one-quarter of the D variance. The C variance is assumed to be the same for both MZ and DZ twin pairs. The broad sense heritability (h2), which estimated the extent to which variation of a trait in a population can be explained by genetic variation, was defined as the proportion of genetic variance to total phenotypic variance. As non-additive genetic (D) and shared environmental effects (C) cannot be identified simultaneously in data from twins reared together, ACE and additive and dominance genetic, unique environmental (ADE) models were fitted separately. If inline image rMZ < rDZ, ACE models were fitted. If inline image rMZ > rDZ, ADE models were fitted. The significance of variance components A, C or D in the model was tested by dropping these parameters and comparing the fit of the models. The model fit was based on χ2 tests, where a small χ2 and a high p value indicated a good fit. Parsimony was assessed by means of the Akaike information criterion, which corresponded to χ2–2df. The model with the lowest Akaike information criterion was preferred. A likelihood ratio test was used to test whether a significantly poorer fit was obtained when removing parameters. Estimates of variance components were derived from the best-fitting model and presented with 95% confidence intervals (CIs) (McCaffery et al., 2007).

Sib-TDTs for quantitative traits were performed using structural equation modeling. Parameters were estimated by normal-theory maximum-likelihood, where the models were fitted to the raw data. Only DZ pairs discordant for their genotype were informative for the sib-TDTs. The locus effect on the quantitative trait was modeled using a parameterisation in which a score a is assigned to A1A1 subjects, d to A1A2 subjects, and -a to A2A2 subjects. Codominant (a and d estimated), additive (only a estimated, d = 0) and completely dominant (d equals a) models were tested for each locus. For both tests, the effect of the locus was tested by comparing the full model, in which the parameters a and d were estimated, with the reduced model in which the effect of a or d was set to zero, which gave a χ2 test with 2 df. for the codominant and a χ2 test with 1 df. for the additive and dominant models (Dong et al., 2004).

All regular association analyses were performed within a regression framework using generalised estimating equations, which takes the non-independency of twin data into account and estimates the parameter coefficients, 95% CI and unbiased p-values after controlling for possible confounders (age, sex, region, education, job, smoking and alcohol drinking). To further assess gene-gene interaction, we examined the combined association of adiponectin genotypes with obesity-related continuous variables in four subgroups defined by SNP 45(T/T, T/G+G/G) and SNP 276(G/G, G/T+T/T), with adjustment for covariates.

The SAS System (SAS Institute Inc., Cary, NC, USA) was used to perform statistical analysis. Twin model fitting and Sib-TDTs were performed with the statistical software Mx. All statistical tests were two sided, and a value of p < 0.05 was considered statistically significant.

Results

The demographic characteristics of twins are displayed in Table 1. The average age of twins was 38 years in males and 37 years in females (p= 0.004). The zygosity distribution was similar in twins of different gender. Less than 25% of twins were illiterate or had elementary education. More than 70% of twins were currently working. More than half of twins reported being non-smokers, and more than 70% of twins were not an active user of alcohol. The distribution of region, education, job, smoking and alcohol drinking had significant differences between male and female twins (p < 0.001).

Table 1.  Demographic characteristics of the study subjects.
VariablesaMaleFemalep
  1. aContinuous variables were shown as mean ± s.d.; Categorical variables were shown as n(%).

n12681252 
Age (years)38.72 ± 10.5737.57 ± 9.070.004
Zygosity
 MZ760 (59.9)798 (63.7) 
 DZ508 (40.1)454 (36.3)0.050
Region
 Qingdao602 (47.5)774 (61.8)<0.001
 Lishui666 (52.5)478 (38.2) 
Education  <0.001
 Illiterate or elementary405 (31.9)302 (24.1) 
 Middle school534 (42.1)461 (36.8) 
 High school199 (15.7)283 (22.6) 
 Higher education130 (10.3)206 (16.5)<0.001
Currently working1176 (92.7)811 (64.8)<0.001
Current smoking732 (57.7)3 (0.2)<0.001
Current alcohol drinking407 (32.1)19 (1.5)<0.001

The results of means and standard deviations for BMI, WC and WHR measures and analyses of intraclass correlation coefficients in MZ and DZ twins are presented in Table 2. The difference of WC and WHR were significant between MZ and DZ (p < 0.05). For all measures, these were reflected in higher intraclass correlations for MZ twins compared to DZ twins, indicating the importance of genetic influences for the variables.

Table 2.  Descriptive statistics of obesity-related phenotypes and intraclass correlation in MZ and DZ twins.
Zygosityn (pairs)BMIWCWHR
mean ± s.d.rpmean ± s.d.rpmean ± s.d.rp
  1. r is the intraclass correlation coefficient of MZ and DZ respectively.

MZ77922.73 ± 3.260.790.09775.91 ± 9.140.750.0050.82 ± 0.060.620.018
DZ48122.95 ± 3.170.47 76.96 ± 9.190.35 0.82 ± 0.060.28 

SEM of obesity-related phenotypes is shown in Table 3. As DZ correlations for BMI were greater than half of the MZ correlations, the ACE model was taken as the starting point of genetic modeling. While DZ correlations for WC and WHR were less than half of the MZ correlations, the ADE model was taken as the starting point of genetic modeling. We found an ACE model to provide the best fit for BMI and AE models for WC and WHR, because they were the most parsimonious (according to the AIC). Based on these models, the heritability of BMI, WC and WHR were 61% (95% CI, 48% to 76%), 75% (95% CI, 72% to 77%) and 61% (95% CI, 57% to 65%), respectively.

Table 3.  Results of SEM of obesity-related phenotypes.
VariablesModelVariance ComponentFit Statistics 
a2d2/ c2e2χ2pAICh2(%)
  1. Figures in parentheses were 95% CIs; AIC = Akaike information criterion.

  2. a2, d2,c2 and e2 were the squared regression coefficients, representing the proportion of the variance explained by addictive genetic, nonadditive genetic, shared environmental, and non-shared environmental effects including error.

  3. h2 was heritability, defined as the proportion of genetic variance (a2+ d2) to total phenotypic variance.

  4. *indicates best fitting model for variables.

BMIACE*0.61 (0.48,0.76)0.17 (0.03,0.29)0.22 (0.20,0.25)4.2110.240−1.78961%
AE0.78 (0.76,0.80)0.00 (0.00, 0.00)0.22 (0.20,0.24)9.6040.0481.604 
CE0.00 (0.00, 0.00)0.67 (0.64,0.70)0.33 (0.30,0.36)126.0560118.056 
E0.00 (0.00, 0.00)0.00 (0.00, 0.00)1.00 (1.00,1.00)867.3110857.311 
WCADE0.65 (0.33,0.77)0.10 (0.00, 0.42)0.25 (0.23,0.28)17608.860012582.86075%
AE*0.75 (0.72,0.77)0.00 (0.00, 0.00)0.25 (0.23,0.28)0.4540.500−1.546 
DE0.00 (0.00, 0.00)0.75 (0.72,0.77)0.25 (0.23,0.28)14.883012.883 
E0.00 (0.00, 0.00)0.00 (0.00, 0.00)1.00 (1.00,1.00)698.4520694.452 
WHRADE0.49 (0.15,0.65)0.13 (0.00, 0.47)0.39 (0.35,0.43)−6827.8810−11857.88161%
AE*0.61 (0.57,0.65)0.00 (0.00, 0.00)0.38 (0.35,0.43)0.5840.445−1.416 
DE0.00 (0.00, 0.00)0.62 (0.58,0.66)0.38 (0.34,0.42)7.8300.0055.830 
E0.00 (0.00, 0.00)0.00 (0.00, 0.00)1.00 (1.00,1.00)408.7150404.715 

In Table 4, obesity-related continuous variables are compared in GEE models across the adiponectin genotypes. There was no association of adiponectin gene +45T>G and +276G>T genotypes with BMI,WC and WHR in twins. The results of sib-TDT within DZ twin pairs discordant for their genotype are shown in Table 5. The adiponectin gene +45T>G and +276G>T genotypes had no association with BMI, WC and WHR in both the full model (codominant model) and reduced models (additive and dominant model), which was consistent with the observed associations in all twins.

Table 4.  Adjusted association of adiponectin gene polymorphisms +45T>G and +276G>T with obesity-related phenotypes in Chinese twins.
GenotypenBMIWCWHR
mean ± s.d.β(95% CI)pmean ± s.d.β(95% CI)pmean ± s.d.β(95% CI)p
  1. Adjusted for age, sex, region, education, job, smoking, alcohol drinking.

+45T>G
 T/T124422.81 ± 3.180 76.20 ± 9.120 0.82 ± 0.060 
 T/G101722.87 ± 3.240.16 (−0.15,0.47)0.31076.42 ± 9.120.62 (−0.23,1.47)0.4330.82 ± 0.060 (0,0.01)0.270
 G/G19822.54 ± 3.42−0.09 (−0.70,0.52)0.77576.14 ± 9.770.61(−1.06,2.29)0.8650.82 ± 0.060.01 (0,0.02)0.189
+276G>T
 G/G127322.81 ± 3.210 76.26 ± 9.370 0.82 ± 0.06  
 G/T98122.84 ± 3.28−0.03 (−0.34,0.29)0.16276.11 ± 8.98−0.40 (−1.27,0.47)0.4420.82 ± 0.06−0.01 (−0.01,0)0.109
 T/T19522.75 ± 2.92−0.13 (−0.61,0.34)0.24377.36 ± 8.750.47(−0.94,1.88)0.7190.82 ± 0.060 (−0.01,0.01)0.690
Table 5.  Results for the sib-transmission disequilibrium test(sib-TDT) between obesity-related phenotypes and adiponectin gene polymorphisms +45T>G and +276G>T.
 nGenotypeAdditive modelDominant modelCodominant model
χ2pχ2pχ2p
  1. n is the total number of informative dizygotic twin pairs included in the sib-TDT analysis.

+45T>G T/TT/GG/G      
 BMI14623.08 ± 3.2223.40 ± 3.2022.14 ± 2.861.5520.2130.0080.9302.0400.361
 WC14677.38 ± 9.2277.99 ± 9.0774.38 ± 9.531.4970.2210.0180.8921.6520.438
 WHR1460.82 ± 0.060.83 ± 0.070.81 ± 0.071.9820.1590.9580.3282.0980.350
+276G>T G/GG/TT/T      
 BMI13523.39 ± 3.4723.90 ± 3.2323.33 ± 3.001.8330.1760.0160.9002.4510.294
 WC13576.68 ± 10.2776.13 ± 9.1380.12 ± 8.921.5290.2160.0010.9731.9840.371
 WHR1350.81 ± 0.070.82 ± 0.070.84 ± 0.060.3910.5320.0690.7920.3920.822

As shown in Table 6, we assessed association of the combined genotypes with obesity-related phenotypes in twins. None of the adiponectin combined genotypes was significantly associated with obesity-related phenotypes in twins. We further illustrated the interaction of adiponectin genotype. There was no significant gene-gene interaction with any of the variables in twins.

Table 6.  Combined associations of multiple gene polymorphisms with obesity-related phenotypes in Chinese twins.
Genotype BMIWCWHR
+45T>G+276G>Tnβ(95%CI)pβ(95%CI)pβ(95%CI)p
  1. Adjusted for age, sex, region, education, job, smoking, alcohol drinking.

T/TG/G4820 0 0 
T/TG/T+T/T7520.05 (−0.37,0.47)0.8210.30 (−0.87,1.47)0.6200 (−0.01,0.01)0.742
T/G+G/GG/G7840.18 (−0.25,0.60)0.4131.00 (−0.17,2.16)0.0930(0,0.01)0.267
T/G+G/GG/T+T/T4120.09 (−0.40,0.57)0.7320.46 (−0.84,1.75)0.4920 (−0.01,0.01)0.983
Interaction
 Crude  −0.09 (−0.76,0.57)0.782−1.10 (−2.93,0.73)0.240−0.01 (−0.02,0.01)0.215
 Adjusted  −0.14 (−0.76,0.48)0.655−0.84 (−2.51,0.83)0.3250 (−0.01,0.01)0.596

Discussion

The current study was conducted to investigate the heritability of measures of general and central adiposity and the association of adiponectin gene variants +45T>G and +276G>T with those obesity-related phenotypes, which was the first study of its kind in Chinese twins.

The results of our large, population-based twin study strongly supported a genetic contribution to the measures of general and central adiposity. Intraclass correlations were consistently larger in MZ than in DZ twins. Our SEM analyses indicated that the individual differences in central obesity were fully explained by genetic and non-shared environmental factors, and that general obesity was primarily influenced by genetic and non-shared environmental factors and, to a lesser degree, by shared environmental factors.

The results proposed that especially genetic factors were important in the etiology of general and abdominal obesity. The heritability estimate of 75% and 61% for WC and WHR demonstrated the notion of a major genetic influence on the development of central obesity, while the heritability of 61% for BMI also confirmed that genetic factors played a significant role in general obesity. This was the first study that reported heritability of BMI, WC and WHR in a larger Chinese population based on a twin cohort of adults. Our results were in line with previous studies. The heritability of BMI ranged from 45% to 85% in a comparative study of twin cohorts in eight countries (Maes et al., 1997; Schousboe et al., 2003) and that for WC varied between 45% and 77% (Nelson et al., 2002, 2006; Schousboe et al., 2004; Benyamin et al., 2007). The heritability of BMI in only 260 Chinese twins was 88%, which also indicated that BMI was more influenced by inheritance than by enviroment (Ren et al., 2003).

The heritability estimates of obesity phenotypes indicated important genetic factors in the etiology of general and abdominal obesity. Then we further explored which genes might contribute to the high heritability. Our study did not find any association of adiponectin gene +45T>G and +276G>T genotypes with BMI, WC and WHR in twins. The results were consistent with one report in the Chinese Han population, which found that the means of BMI and WHR did not differ among genotypes of +45T>G and +276G>T in the adiponectin gene (Yan et al., 2006). However, our findings were clearly at variance with several other reports. A study from a Swedish Obese Subjects (SOS) cohort indicated that adiponectin gene variant +45T>G was associated with WC (Ukkola et al., 2003). A study in a Czech population found that BMI was influenced by T allele presence in 276 position of the adiponectin gene in lean women only. BMI in GG genotype subjects was significantly higher relative to T allele carriers of the +276G>T allele of the adiponectin gene (23.48 ± 0.85 kg/m2 vs. 19.7 ± 0.95 kg/m2, p < 0.05) (Dolinkova et al., 2006). Conflicting findings between these studies could be due to true differences in allelic association with the obesity phenotype by different measures in different populations. The differences in allele frequencies of SNPs in the adiponectin gene in various populations provided evidence for the above idea (Vasseur et al., 2002). The SOS study consisted of 96 unrelated female subjects with severe obesity (mean BMI, 42.3 kg/m2) and 96 non-obese female controls (mean BMI, 23.0 kg/m2). Adiponectin gene +45T>G substitution was found in equal frequencies among obese and control subjects (13.5% vs. 14.6%, p > 0.05). WC was lower (p= 0.043) in TG (112.5 ± 2.0 cm) than in TT (117.0 ± 0.8 cm) genotype subjects. In the study of the Czech population cited above, 28 female patients with anorexia nervosa (BMI 15.72 ± 0.36 kg/m2), 77 obese female patients (BMI 43.48 ± 1.12 kg/m2) and 38 age- and sex-matched healthy controls (BMI 22.32 ± 0.40 kg/m2) were included. The G allele in locus 276 of the adiponectin gene was present more frequently than minor alleles T in corresponding loci. The G allele in +276G>T occurred in 83% of the control group, 71% of the anorexia nervosa and 72% of the obesity group. In the present study, the mean WC of twins was 76.37 ± 9.23 cm in TG+GG genotype subjects, while that in TT genotype subjects was 76.20 ± 9.12 cm. The mean BMI in overall subjects (22.73 ± 3.26 kg/m2 in MZ and 22.95 ± 3.17 kg/m2 in DZ) was lower than that in the previous studies. The prevalence of obesity was 19.6% in males and 22.4% in females, respectively (not shown in tables), which was much lower than in prior samples. The genotypic frequencies of +45T>G TT and +276G>T GG homozygotes were 50.6% and 52.0% in this twin population, which was different from the allele frequency of +45T>G in the Swedish Obese Subjects (SOS) cohort and +276G>T in the Czech population.

Another possible reason for different findings could be the presence of population stratification and admixture. Thus we employed sib-TDT to solve this problem and found that the association was still not significant, making it unlikely that the negative association is due to population stratification and/or admixture in our twin population. Furthermore, the present study showed no significant association of adiponectin combined genotypes and gene-gene interaction with obesity-related phenotypes in twins.

One of the strengths of this study was that it was the first attempt to use large-scale, population-based twin subjects to investigate the association of adiponectin gene variants +45T>G and +276G>T with abdominal obesity. Meanwhile, two kinds of statistical analyses, a GEE model for all twin pairs and sib-TDT analysis for only DZ pairs discordant for their genotype, were used to test the association and gene-gene interaction. However, despite our evidence that variation in the adiponection gene was not associated with obesity, the following possible shortcoming must be considered; we only investigated two polymorphisms in the adiponectin gene and did not cover other important polymorphisms or candidate genes. Besides, if the circulating levels of adiponectin had been measured, we would have been able to estimate the heritability of adiponectin and the association with +45T>G and +276G>T. Future investigation may provide more evidence of genetic effects on adiponectin levels.

In summary, the results from our large, population-based sample of twins suggested that genetic factors had a noticeable impact on general and abdominal obesity. Our data showed some evidence for strong heritability of BMI, WC and WHR. However, our findings failed to support a relationship between the adiponectin gene variants +45T>G and +276G>T and obesity-related phenotypes. Further studies are warranted in order to draw a more definitive conclusion.

Acknowledgements

This work was partly supported by the China Medical Board of New York (01–746), the National Natural Science Foundation of China (30070672) and the National Basic Research Program of China (973 Program) (No. 2006CB503903). We gratefully acknowledge the assistance and cooperation of the staff of the Center for Disease Control and Prevention in Qingdao city and Lishui city. We are thankful to all the participants in our study and their families.

Disclosure

The authors declared no conflict of interest.

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