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The Journal of Clinical Endocrinology & Metabolism Vol. 92, No. 8 3082-3088
Copyright © 2007 by The Endocrine Society

Heritability of Plasma Adiponectin Levels and Body Mass Index in Twins

Maurizio Cesari, Krzysztof Narkiewicz, Renzo De Toni, Enrico Aldighieri, Christopher J. Williams and Gian Paolo Rossi

Department of Hypertension and Diabetology (K.N.), Medical University of Gdansk, 80-952 Gdansk, Poland; Department of Statistics (C.J.W.), University of Idaho, Moscow, Idaho 83844; and Department of Clinical and Experimental Medicine (M.C., R.D.T., E.A., G.P.R.), Clinica Medica 4, University Hospital, University of Padova Medical School, 35128 Padova, Italy

Address all correspondence and requests for reprints to: Gian Paolo Rossi, M.D., F.A.C.C., F.A.H.A., Department of Clinical and Experimental Medicine, Clinica Medica 4 University Hospital, via Giustiniani, 2, 35126 Padova, Italy. E-mail: gianpaolo.rossi{at}unipd.it.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Context: Adiponectin is suspected to exert antiatherogenic, antiinflammatory, and insulin-sensitizing effects. However, the relative importance of the genetic and environmental factors in influencing plasma adiponectin levels (ADPN) remains unclear.

Objective: The aim of the study was to investigate whether ADPN and body mass index (BMI) are genetically determined.

Design, Setting, Participants, and Main Outcome Measures: In a series of 60 pairs of healthy twins, we estimated genetic variance and heritability of ADPN and BMI using both ANOVA and path analysis methods. Twins were genotyped at two biallelic single nucleotide polymorphisms (SNPs) at the gene encoding adiponectin: the +45 T/G (on exon 2) and the –11377 G/C (on the promoter).

Results: A total of 30 pairs of twins were Monozygotic (MZ), and 30 were dizygotic (DZ). The mean ADPN (±SD) was 10.6 ± 5.7 in MZ and 11.1 ± 4.5 in DZ twins (nonsignificant). Three tests of heritability (within pair = 1.13, P < 0.0001; among components = 1.62, P = 0.005; and intraclass correlation 1.34, P < 0.0001) consistently showed ADPN heritability. The preferred model of a likelihood-based analysis included an additive genetic influence and an individually unique environmental influence for ADPN, accounting for 88% and 12% of ADPN variance, respectively. We found a significantly higher within-pair difference of ADPN in DZ than in MZ pairs, and in +45 T/G SNP discordant compared with concordant DZ twins, indicating a significant effect of this adiponectin gene SNP on ADPN.

Conclusions: ADPN shows significant genetic variance and heritability, which is independent of BMI and partly accounted for by the +45 T/G, but not the –11377 G/C adiponectin gene SNP.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
ADIPONECTIN IS A PEPTIDE that is abundantly expressed in adipocyte (1), and can have antiatherogenic, antiinflammatory, and insulin-sensitizing properties (2). In humans, low-plasma adiponectin levels (ADPN) have been observed in patients with coronary artery disease (3, 4, 5, 6, 7), type 2 diabetes (8), obesity (9), and in smokers (10). Low-ADPN has also been associated with insulin resistance (IR), overweight, and dyslipidemia (11, 12, 13, 14), and has predicted the development of IR and type 2 diabetes in Pima Indians and healthy individuals (15, 16, 17). Therefore, low-ADPN could be a marker for IR, and represent a link between hyperinsulinemia and vascular disease (18).

A genome-wide scan (19, 20) revealed a susceptibility locus for type 2 diabetes and the metabolic syndrome (21) in chromosome 3q27, in which the gene encoding adiponectin (ACDC) sits. Several single nucleotide polymorphisms (SNPs) have been identified in the promoter, and coding and noncoding regions of the ACDC gene (22, 23, 24). Although some might be functional and could influence ADPN, available data are conflicting (23, 24, 25, 26). Therefore, the relative importance of the genetic and environmental factors in influencing ADPN remains unclear. Numerous SNPs (available at http://hgvbase.cgb.ki.se) have been identified, and a strong linkage disequilibrium across the ACDC gene has been reported (23, 27). Two of the most studied entail the –11377 G/C SNP in the promoter, which is in linkage disequilibrium with other SNPs in the promoter (28), and the +45 T/G in exon 2, which is in linkage disequilibrium not only with the promoter SNPs, but also with other SNPs, as the +276 (G/T) in intron 2 that has been associated with plasma ADPN in diabetic men (29).

Studies of monozygotic (MZ) and dizygotic (DZ) twins offer a powerful method of partitioning genetic and environmental sources of covariance of quantitative traits, potentially relevant for the development of phenotypes as the metabolic syndrome and/or diabetes mellitus type 2. Therefore, the comparison of the values of ADPN, between fraternal (DZ) and identical (MZ) twins, represents a streamlined approach to the question of whether this variable is under genetic control. Thus, in identical and fraternal healthy normotensive twins, we investigated whether and to what extent ADPN is genetically determined. Moreover, based on the hypothesis that SNPs in the promoter and the coding sequence could have functional relevance, we elected to genotype our twins at the 11377 G/C (in the promoter) and the +45 T/G SNP (in exon 2) to gather information on their potential role as a determinant of ADPN.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Twins

The twins were enrolled among the students and employees of the high school and University of Gdansk, Poland, and from an Italian association of twins based in Padua, Italy, as already detailed (30, 31). Monozygosity and dizygosity were assessed by the analysis of highly discriminating variable number of tandem repeats microsatellites and minisatellites systems. Three amplified fragment length polymorphisms (ApoB, D1S80, and YNZ22) and four short tandem repeats (HUMACTPB2, HUMTH01, HUMFES/FPS, and HUMMVWA31) were assessed by separate PCR amplification of genomic DNA, followed by PAGE and silver staining. Anthropometric data and information on cohabitation were gathered by a predefined form. Smokers were asked to refrain from smoking for at least 12 h before blood sampling. All subjects were healthy, nondiabetic, normotensive, with normal diet and mostly normal body mass index (BMI) (30, 31). They gave informed written consent, and the Institutional Human Subjects Review Committees of both universities approved the study protocol.

Blood sampling and ADPN assessment

Blood was drawn at about 0900 h after an overnight fast and 15-min. supine rest from an antecubital vein without stasis. Five milliliters of whole blood with 100 µl 6% Na2EDTA were immediately put on ice. After centrifugation at 3000 x g (at 4 C for 10 min), separated aliquots of plasma and buffy coat were stored at –40 C. ADPN concentration was evaluated by an ELISA method (Quantikine Human Adiponectin; R&D Systems, Inc., Minneapolis, MN), in which the monoclonal antibodies recognize the globular domain of adiponectin (essentially the C terminus of the protein) as well as full-length adiponectin. Interarray and intraarray coefficients of variation were 6.9% and 4.7%, respectively.

Genotyping

Genotyping at the –11377 C/G (on the promoter) and +45 T/G (on exon 2) SNPs of the ACDC gene was performed by real-time PCR, followed by melting curve analysis using fluorescence resonance energy transfer probes. Primer and probe sequences are available from the corresponding author (G.P.R.) upon request. Genotyping at both SNPs was confirmed by sequencing.

Statistical analysis

Results are expressed as mean and range, as appropriate (32). Because subjects in each twin pair cannot be regarded as independent unrelated individuals, either twin 1 or twin 2 of each pair was considered for the purpose of statistical comparison. Comparison of MZ and DZ twins was performed with a Mann-Whitney U test or (for the variables normally distributed) with Student’s t test for independent samples. Analysis was performed with the SPSS for Windows statistical package (version 14.0; SPSS, Inc., Chicago, IL). A P value < 0.05 was considered statistically significant.

Analyses of twin data were performed with TWINAN90, a program specifically developed for this purpose (33). The consistency of ADPN with the normal distribution assumption and with the hypothesis of equal variance between zygosity was verified beforehand. Because we previously found that age, gender, and BMI predict ADPN, we used for this genetic both raw and age-, gender-, and BMI-adjusted ADPN values (34). Estimates of genetic variance, including the within pair (WP) and among components (ACs), were thereafter attained, and a test for genetic variance based on the average absolute difference between twins (33), which is standardized to yield an approximate t test for the null hypothesis of no genetic variance, was performed. Three estimates of heritability were also calculated; the first two are derived from the WP and AC genetic variance test statistics, respectively, according to the following equations:

Formula

Formula
where AC indicates the AC estimate of genetic variance, and SMZ and SDZ indicate the total variance in MZ and DZ, respectively.

The third estimate of heritability (h2) is based on intrapair correlation coefficients calculated from the MZ and DZ twins as follows: h2 = 2(rMZ – rDZ). A maximum-likelihood-based approach yields estimates of the proportion of variance of ADPN accounted for by additive genetic influences (A), nonadditive genetic influences (D), environmental influences shared by cotwins within a family (S), and unique to individuals (E) (35). Finally, the results of the likelihood-based analyses were obtained for the following models that allow combinations of effects: the ADE model, the ASE model, the AE model, the SE model, and the E model. A similar analysis was performed for BMI.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
All individuals were healthy, nondiabetic, normotensive, with normal BMI and normal ADPN. Ascertainment of zygosity showed that 30 twins were MZ and 30 DZ. Table 1Go shows their demographic and biochemical characteristics, including ADPN. The MZ and DZ were similar for gender, age, BMI, blood pressure values, cigarette smoking habit, and family history of hypertension. There were 41 pairs (19 MZ and 22 DZ) of cohabitant twins and 19 pairs (11 MZ and eight DZ) of noncohabitant twins.


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  		TABLE 1. Anthropometric, biochemical features, and allele frequencies of twins classified by zygosity

 
A Kolmogorov-Smirnov goodness of fit test applied separately to the grouped pairs for each of the two zygosities showed that neither ADPN nor BMI followed a normal distribution; therefore, log and a Box-Cox power transformation of the ADPN and BMI variables, respectively, were required.

Heritability of ADPN

The relationship between ADPN in each pair of identical and fraternal twins, after log transformation and adjustment for the predictors of ADPN (age, gender, and BMI), is shown as a scatter plot (Fig. 1Go, top). The correlation coefficient of covariate-adjusted ADPN in MZ (r = 0.904; P < 0.0001) was almost 2-fold higher than in DZ [r = 0.233; nonsignificant (NS)] twins. The null hypothesis that the mean of the MZ and DZ twins absolute difference did not differ from zero was accepted (t = 1.36, with 52.3 df; P = 0.18; NS), and the equal variance hypothesis was not rejected (F = 0.78; P = 0.41; NS). Estimates of genetic variance and intraclass correlation coefficients for both MZ and DZ twins were obtained, and a test for genetic variance was performed (Table 2Go). Both the preferred tests of genetic variance (WP and average absolute difference test) and also the AC estimate were statistically significant. The three estimates of heritability (WP, AC, and intraclass correlations) (Table 2Go) also showed significant results, thus leading to accept the hypothesis that ADPN is heritable (33).


Figure 1
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  		FIG. 1. Top, Scatter plot of the relationship between ADPN (Ln) adjusted for the predictors (age, gender, and BMI) with regression lines and 95% confidence intervals, in each pair of MZ (open squares) and DZ (open circles) twins. The correlation was significant (P < 0.0001) in MZ but not in DZ twins. The similar relationship for BMI is shown in the bottom panels. The correlation was highly significant in both MZ and DZ twins.

 

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  		TABLE 2. Output of a path analysis performed with TWINAN90 jointly on MZ and DZ twins to assess the genetic variance and the heritability of ADPN

 
To investigate the components of ADPN variance, the results of the likelihood-based analyses for different models (ADE, ASE, and the AE, SE, and E) were compared. Because the MZ intraclass correlation was about twice the DZ intraclass correlation, and neither term (S in the ASE model, or D in the ADE model) was significant based on the ratio of the estimate to the SE of the estimate, we concluded that the AE model was the best fitting model. In this case the heritability from the AE model was 0.88, which is remarkably close to the heritability estimate from the ANOVA analyses (0.80), while the proportion of variance of ADPN explained by E was 12%. Thus, these two separate analyses results indicate that plasma ADPN is highly heritable.

To explore further the impact of shared environment and lifestyle on ADPN, we performed separate analyses for cohabitant and noncohabitant twins. Results showed that the WP correlation coefficient of cohabitant twins was 0.979 (P < 0.001) in MZ and 0.390 (P = 0.031) in DZ; in noncohabitant twins, corresponding values were 0.165 and 0.074 (both NS).

We also calculated the WP difference in ADPN in MZ and DZ twins. Moreover, we could compare plasma ADPN between DZ twins who were concordant or discordant for the exon 2 + 45 T/G and the promoter –11377 G/C SNP in the ACDC gene. We found a significantly higher WP difference in DZ than in MZ. Although there was no effect of the –11377 G/C SNP, a significantly higher WP difference was observed in DZ twins who were discordant than in those who were concordant for the +45 T/G SNP (Fig. 2Go).


Figure 2
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  		FIG. 2. WP difference of ADPN in MZ and DZ twins who were concordant and discordant for the exon 2 + 45 T/G SNP (A) and for the promoter –11377 G/C SNP (B).

 
Heritability of BMI

The vast majority of our twins had a normal BMI, and only a few had a BMI in the overweight-obesity range (Table 1Go and Fig. 1Go, bottom). Because BMI is a predictor of ADPN, we also examined the relationship between BMI in each pair of identical and fraternal twins. This analysis, shown in Fig. 1Go (bottom), also highlighted a higher correlation coefficient in MZ (r = 0.843; P < 0.0001) than in DZ (r = 0.471; P = 0.003) twins (Table 3Go), thus suggesting strong genetic variance of BMI. The null hypothesis that the mean of the MZ and DZ twins absolute difference did not differ from zero was thereafter accepted (t = –1.54, with 72.8 df; P = 0.13; NS), and the equal variance hypothesis was not rejected (F = 0.76; P = 0.33; NS). Both the tests of genetic variance WP and average absolute difference tests and also the AC estimate were statistically significant (Table 3Go). The three estimates of heritability (WP, AC, and intraclass correlations) were significant, thereby indicating that BMI levels are heritable. The results of the likelihood-based analyses for different models (ADE, ASE, and the AE, SE, and E) identified the AE as the best fitting model (Table 3Go). The heritability from the AE model was 0.814, and the proportion of variance of BMI explained by E was about 18%, thus indicating that BMI is highly heritable. Because BMI derives from two components, e.g. body weight and height, we examined the heritability of each of these components. We found evidence that both height [WP correlation coefficient 0.955 (P < 0.001) in MZ, and 0.406 (P = 0.011) in DZ] and weight [WP correlation coefficient 0.880 (P < 0.001) in MZ, and 0.324 (P = 0.036) in DZ] were highly heritable.


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  		TABLE 3. Output of a path analysis performed with TWINAN90 jointly on MZ and DZ twins to assess the genetic variance and the heritability of BMI

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
To gain insight into the relative effects of genes and environment on ADPN and BMI, we compared these variables within pairs of fraternal (DZ) and identical (MZ) young, healthy, normotensive, mostly nonobese twins who had normal plasma levels of ADPN. Data were analyzed with a strategy that considers the contribution of shared environment and allowing investigation of the proportion of variance of ADPN and BMI accounted for by an additive genetic influence (A), nonadditive genetic influences (D) (i.e. allelic interactions at the same locus), environmental influences shared by cotwins within a family (common, S), and unique to individuals (E). We used TWINAN90, a program that conducts both ANOVA-based and path-analysis-maximum-likelihood-based analyses, so that several different estimators of genetic variance and heritability could be compared (33, 35). By this strategy we found strong evidence of genetic variance and heritability of both ADPN and BMI in these subjects (Table 2Go). When phenotypic covariance was partitioned into additive genetic effects (A), dominant genetic components (D), environmental effects common to cotwins (S), and error variance (E) components, the estimates indicated that about 80% was accounted for by A (Tables 2Go and 3Go), indicating that most of ADPN and BMI variance is genetically determined.

We separately analyzed cohabitant and noncohabitant twins to explore further the impact of shared environment and lifestyle on ADPN. This analysis does not seem to suggest strong heritability because the significant WP correlation disappeared in noncohabitant twins. In DZ twins, cohabitants showed significant relationship (r = 0.390; P = 0.031), but cohabitants (r = 0.074; NS) did not. Cohabitant MZ twins also exhibited a much higher WP correlation (r = 0.979; P < 0.001) than DZ twins (r = 0.390; P = 0.031), suggesting that more similar lifestyle and/or more similar hereditary personality contributes to ADPN. The loss of statistical power might explain the loss of statistical significance in the less numerous noncohabitant twins. However, regardless of the cohabitation status, the WP resemblance of covariate-adjusted ADPN was much higher in the MZ than the DZ twins, suggesting again a genetic component to ADPN.

Because BMI is a determinant of ADPN, one might argue that the heritability of ADNP was only, or predominantly due to that of BMI. However, at least two considerations should be made. First, while a significant WP correlation exists for BMI in both MZ and DZ twins, for age-, gender-, and BMI-adjusted ADPN, such significant correlation was observed only in MZ, and not in DZ (Fig. 1Go). Second, our twin analysis yielded even stronger estimates of heritability when age-, gender-, and BMI-adjusted ADPN rather than raw ADPNs were examined. Thus, this contention does not seem to be supported by our results.

Moreover, the observation that the WP difference of ADPN was lower in MZ than in DZ twins (Fig. 2Go) also supports the conclusion that ADPN is under strong genetic control. To gather insight into the genetic factors underlying this association, we genotyped our twins at two widely investigated SNPs, the +45 T/G (on exon 2) and the –11377 G/C (on the promoter) in the ACDC gene, whose influence on ADPN is conflicting. Overall, studies in non-Caucasian populations of nondiabetics and nonobese subjects were negative (25, 36), while positive results for both SNPs were described in Caucasians (23, 37). We calculated the WP difference of ADPN in the MZ and DZ twins who were concordant or discordant for each genotype. We found a significant effect of the +45 T/G SNP, but no evidence for an effect of the –11377 G/C SNP. Although some caution is advised because of the small number of our twins who had the rarer alleles, this result favors the contention that the +45 T/G but not the –11377 G/C SNP affects ADPN. Noteworthy the finding that in the STOP-NIDDM Study the +45 T/G SNP was associated with the risk of conversion from impaired glucose tolerance to type 2 diabetes (38) accords with our suggestion. However, other SNPs exist in the ACDC gene that were not determined in this study but might be in linkage disequilibrium with those investigated here. Therefore, our finding of strong heritability of ADPN warrants further investigation of other variants in the ACDC gene.

The relatively small sample size might also suggest some caution; however, we would like to point out that this sample size was adequate to show heritability for other variables, as plasminogen activator inhibitor type 1, homocysteine, and angiotensin-converting enzyme (30, 31), which are known to exhibit a higher biological and within-assay variability than ADPN. Moreover, it could be that the shared family environment of most of our twins might have led to some inflation of the estimates of heritability. Therefore, to explore the impact of shared environment and lifestyle on ADPN, we performed a subanalysis of cohabitant and noncohabitant twins. Despite the obvious limitations of post hoc analyses and the inherent loss of statistical power, this disclosed a much higher WP correlation coefficient of ADPN in MZ than in DZ twins, regardless of the cohabitation status, thus suggesting the predominant effect of genetic identity over cohabitation in determining ADPN.

In conclusion, our twins’ data provide novel information on the relative effects of environmental and genetic factors on ADPN and BMI, two quantitative traits that are relevant for IR, the metabolic syndrome, and diabetes mellitus type 2. They indicate that both ADPN and BMI are genetically determined, and that minor influences of environmental factors unique to individuals also exist. Moreover, they suggest that the +45 T/G SNP, but not the –11377 G/C SNP, has an effect on plasma levels of adiponectin. Whether other SNPs in the ACDC gene, which might be in linkage disequilibrium with the +45 T/G but were not assessed in this study, contribute to ADPN remains to be investigated.


   Footnotes
 
This study was supported by research grants from The Foundation for Advanced Research in Hypertension and Cardiovascular Diseases (F.O.R.I.C.A.) and from Unindustria of Treviso (to G.P.R.).

Disclosure Statement: The authors have nothing to declare.

First Published Online May 29, 2007

Abbreviations: A, Additive genetic influence; AC, among component; ADPN, plasma adiponectin levels; BMI, body mass index; D, nonadditive genetic influence; DZ, dizygotic; E, environmental influence unique to individuals; IR, insulin resistance; MZ, monozygotic; NS, nonsignificant; S, environmental influences shared by cotwins within a family; SNP, single nucleotide polymorphism; WP, within pair.

Received February 22, 2007.

Accepted May 18, 2007.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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G. Maiolino, M. Cesari, D. Sticchi, M. Zanchetta, L. Pedon, K. Antezza, A. C. Pessina, and G. P. Rossi
Plasma Adiponectin for Prediction of Cardiovascular Events and Mortality in High-Risk Patients
J. Clin. Endocrinol. Metab., September 1, 2008; 93(9): 3333 - 3340.
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