Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Genetic Polymorphisms of the Main Transcription Factors for Adiponectin Gene Promoter in Regulation of Adiponectin Levels: Association Analysis in Three European Cohorts

  • Lyudmyla Kedenko ,

    Contributed equally to this work with: Lyudmyla Kedenko, Claudia Lamina

    Florian.Kronenberg@i-med.ac.at (FK); l.kedenko@salk.at (LK)

    Affiliation University Clinic for Internal Medicine I, Paracelsus Medical University Salzburg, Austria

  • Claudia Lamina ,

    Contributed equally to this work with: Lyudmyla Kedenko, Claudia Lamina

    Affiliation Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria

  • Tobias Kiesslich,

    Affiliation University Clinic for Internal Medicine I, Paracelsus Medical University Salzburg, Austria

  • Karen Kapur,

    Affiliations Department of Medical Genetics, University of Lausanne, Switzerland, Swiss Institute of Bioinformatics, Lausanne, Switzerland

  • Sven Bergmann,

    Affiliations Department of Medical Genetics, University of Lausanne, Switzerland, Swiss Institute of Bioinformatics, Lausanne, Switzerland

  • Dawn Waterworth,

    Affiliation Genetics, GlaxoSmithKline, King of Prussia, Philadelphia, United States of America

  • Iris M. Heid,

    Affiliations Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany, Institute of Epidemiology I, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany

  • H.-Erich Wichmann,

    Affiliations Institute of Epidemiology I, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany, Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany, Klinikum Grosshadern, Munich, Germany

  • Igor Kedenko,

    Affiliation University Clinic for Internal Medicine I, Paracelsus Medical University Salzburg, Austria

  • Florian Kronenberg ,

    Florian.Kronenberg@i-med.ac.at (FK); l.kedenko@salk.at (LK)

    These authors also contributed equally to this work.

    Affiliation Division of Genetic Epidemiology, Innsbruck Medical University, Innsbruck, Austria

  • Bernhard Paulweber

    These authors also contributed equally to this work.

    Affiliation University Clinic for Internal Medicine I, Paracelsus Medical University Salzburg, Austria

Abstract

Adiponectin serum concentrations are an important biomarker in cardiovascular epidemiology with heritability etimates of 30–70%. However, known genetic variants in the adiponectin gene locus (ADIPOQ) account for only 2%–8% of its variance. As transcription factors are thought to play an under-acknowledged role in carrying functional variants, we hypothesized that genetic polymorphisms in genes coding for the main transcription factors for the ADIPOQ promoter influence adiponectin levels. Single nucleotide polymorphisms (SNPs) at these genes were selected based on the haplotype block structure and previously published evidence to be associated with adiponectin levels. We performed association analyses of the 24 selected SNPs at forkhead box O1 (FOXO1), sterol-regulatory-element-binding transcription factor 1 (SREBF1), sirtuin 1 (SIRT1), peroxisome-proliferator-activated receptor gamma (PPARG) and transcription factor activating enhancer binding protein 2 beta (TFAP2B) gene loci with adiponectin levels in three different European cohorts: SAPHIR (n = 1742), KORA F3 (n = 1636) and CoLaus (n = 5355). In each study population, the association of SNPs with adiponectin levels on log-scale was tested using linear regression adjusted for age, sex and body mass index, applying both an additive and a recessive genetic model. A pooled effect size was obtained by meta-analysis assuming a fixed effects model. We applied a significance threshold of 0.0033 accounting for the multiple testing situation. A significant association was only found for variants within SREBF1 applying an additive genetic model (smallest p-value for rs1889018 on log(adiponectin) = 0.002, β on original scale = −0.217 µg/ml), explaining ∼0.4% of variation of adiponectin levels. Recessive genetic models or haplotype analyses of the FOXO1, SREBF1, SIRT1, TFAPB2B genes or sex-stratified analyses did not reveal additional information on the regulation of adiponectin levels. The role of genetic variations at the SREBF1 gene in regulating adiponectin needs further investigation by functional studies.

Introduction

Adipose tissue secretes a number of peptides referred to as adipocytokines or adipokines [1]. One such adipocytokine is adiponectin encoded by ADIPOQ (also known as APM1) which is involved in regulation of insulin sensitivity, carbohydrate and lipid metabolism, immunological responses, and cardiovascular functions [2], [3]. Several studies indicated that 39%–70% of the variability in adiponectin levels is governed by genetic factors [4][7]. During the last decade, several polymorphisms at the ADIPOQ locus have been tested for association with adiponectin levels [8], [9]. Results of a meta-analysis and genome-wide association (GWA) studies indicated a role of variants in the ADIPOQ gene region in modulation of adiponectin levels [10], [11]. Although this locus explains exceptionally high 2%–8% of the adiponectin levels [8], [11], [12], a major fraction of heritability is still unexplained. The results of different GWA studies and linkage analyses suggested that different genomic regions are likely to be involved in regulation of adiponectin levels – either via a primary influence or through pathways influencing body composition [10], [11], [13][17].

The adiponectin gene promoter region contains binding sites for various types of nuclear receptors (PPARG2, LRH, RXR), transcription factors (CEBPA, SREBP1c, TFAP2B, FOXO1, SP1) and at least three co-regulators of transcription factors (SIRT1, NCOR1 and NCOR2) [18][20]. Kita et al. demonstrated that the adiponectin promoter region from −676 to +41 is sufficient for promoter activity and that the region from −676 to −416 is crucial for basal promoter activity [21]. This region consists of putative SREBP-responsive element (−676 to −416) and CEBP-responsive element (−416 to +41) which were both required for promoter activity. Furthermore, it was shown that FOXO1 up-regulates adiponectin gene transcription through a FOXO1-response element in the adiponectin promoter containing two adjacent FOXO1 binding sites [22]. SIRT1 increases adiponectin transcription in adipocytes by activating FOXO1 and enhancing FOXO1 and CEBPA interaction. Low expression of SIRT1 and FOXO1 can lead to impaired FOXO1-CEBPA complex formation, which might contribute to the diminished adiponectin expression in obesity [22]. PPARG2 may directly bind to the human adiponectin promoter by forming heterodimers with RXR and increase adiponectin promoter activity [18]. Moreover, promoter activity of the adiponectin gene is inhibited by over-expression of TFAP2B and enhanced by knockdown of its endogenous expression [23].

These lines of evidence indicate that several transcription factors and their co-regulators are involved in adiponectin gene expression: some through binding to the adiponectin promoter and increasing promoter activity, others through negative regulation of adiponectin gene expression. Taking this into consideration, we hypothesized that genetic polymorphisms in these gene regions may influence adiponectin gene transcription and adiponectin levels. To evaluate this hypothesis, we performed association analyses on the relation of selected polymorphisms in main adiponectin transcription factors for adiponectin promoter with the adiponectin levels in three different European cohorts.

Materials and Methods

Study Populations

SAPHIR Study.

The SAPHIR study has been initiated in the year 1999 as a population-based prospective study that investigates genetic and environmental factors contributing to atherosclerotic vascular diseases [8]. Study participants were recruited by a health screening programs in large companies in and around the city of Salzburg, Austria. The study comprises 1770 healthy unrelated Caucasian subjects (663 females and 1107 males aged 39–67 years). At baseline, all participants were subjected to a comprehensive examination – detailed personal and family history, physical, instrumental and laboratory investigations. Serum adiponectin levels were measured by an enzyme-linked immunosorbent assay kit from BioCat (Heidelberg, Germany). DNA was available from 1760 participants and all relevant variables for analyses were available for 1742 participants.

KORA F3 Study.

The KORA F3 Study is the 10 year follow-up of the third survey from the KORA-Study (Cooperative Health Research in the Region of Augsburg), a population-based sample from the general population of the South-German city of Augsburg and surrounding counties from 1994/1995. The KORA surveys have been described in detail previously [24]. Genome-wide genotype data were available for a subsample of 1644 individuals with all relevant variables available for 1636 individuals. Serum levels of adiponectin were measured by ELISA from Mercodia (Uppsala, Sweden).

CoLaus Study.

The CoLaus study is a single-center, cross-sectional study which included 6188 Caucasian subjects aged 35 to 75 years from the city of Lausanne in Switzerland [25]. The major goal of the CoLaus study is investigation of prevalence, severity and molecular architecture of cardiovascular risk factors in a Lausanne population. Recruitment began in June 2003 and ended in May 2006. For 5435 participants, genome-wide genotype data are available. The current analysis included 5355 extensively phenotyped participants from this study. Plasma adiponectin levels were measured using the ELISA assay from R&D Systems (MN, USA).

Ethics Statement

All clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki. Participants from all 3 studies provided written informed consent and the studies were approved by the local ethical committees (SAPHIR Study, Ethical Committee of Land Salzburg; KORA F3, local ethical committee of Bayerische Landesärztekammer; CoLaus, Institutional Ethic’s Committee of the University of Lausanne).

Selection of Transcription Factors

Three main transcription factors acting on the adiponectin promoter (FOXO1, SREBP1c, TFAP2B) were selected based on previously published data on their important role in regulation of human adiponectin promoter activity or association of genetic polymorphisms at these genes with adiponectin levels [21], [22], [26], [27]. CEBPA was excluded from our consideration because of controversial data about the CEBPA binding site at the adiponectin gene promoter or intron I [21], [28], [29] and lack of evidence for a role of genetic variation at this locus in control of adiponectin levels. One co-regulator of transcription factors (SIRT1) was added based on its interaction with FOXO1 in regulation of adiponectin expression [22].

SNP Selection

Genotype data of the main transcription factors (FOXO1, SREBF1, TFAP2B) and one co-regulator of the transcription factors (SIRT1) for adiponectin promoter were downloaded from HapMap Data (Phase III/Rel.#2, Feb 09) (http://hapmap.ncbi.nlm.nih.gov/cgi-perl/gbrowse/hapmap3r2_B36/). Then data were transferred to SNP tagger (http://www.broad.mit.edu/mpg/tagger/server.html) to identify haplotype-tagging SNPs.

Using the haplotype block structure we selected a maximally informative subset of validated SNPs with minor allele frequency of ≥5% and pairwise r2≥80% (FOXO1 MAF = 10%, r2 = 80%; SREBF1 MAF = 5%, r2 = 100%; TFAP2B MAF = 5%, r2 = 80%; SIRT1 MAF = 5%, r2 = 80%). Haplotype frequencies in each gene were estimated by implementation of the expectation maximization algorithm. Four SNPs (rs2236319, rs3740051 at SIRT1, rs987237 at TFAP2B and rs1801282 at PPARG) were included additionally based on the previous publications [30][33].

Genotyping

Genomic DNA was isolated from whole blood in all three populations according to manufacturer’s protocols. Genotyping in SAPHIR cohort was performed using 5' nuclease allelic discrimination TaqMan genotyping method and pre-designed assays from Applied Biosystems (Foster City, CA, USA) according to the manufacturer’s instructions.

After the SNPs have been genotyped in SAPHIR, in-silico replication has been performed in the KORA F3 and CoLaus cohorts. For both populations, imputed genome-wide genotypes are available from which the respective SNPs were selected. Original genotyping for KORA F3 and CoLaus studies were performed using the Affymetrix GeneChip Human Mapping 500K Array Set (Affymetrix, Santa Clara, USA). Genotypes were determined using the BRLMM clustering algorithm (Bayesian Robust Linear Modeling using Mahalanobis distance, Affymetrix 500K Array Set) [34]. Imputation of genotypes was performed with the software MACH v1.0.9 [35] in KORA F3 and IMPUTE version 0.2.0 [36] in the CoLaus study.

Statistical Analysis

In each study population, the association of each SNP with log-transformed adiponectin levels using linear regression models adjusted for age, sex and body mass index (BMI) applying an additive as well as recessive genetic model was analyzed. The analyses were also conducted stratified for sex, in this case only adjusting for age and BMI. A pooled effect size for all participants as well as for men and women separately was obtained by meta-analysis assuming a fixed effects model. The possibility of sex-specific effects has been tested on each SNP using a t-test based on the meta-analyzed effect estimates and standard errors on the original scale of adiponectin [37].

Correction for multiple testing was applied on independent number of tests for the main analyses (additive model). This number was calculated using the effective number of loci [38], which accounts for the correlation structure between the SNPs. The percentage of explained variance per SNP and pair-wise linkage disequilibrium (LD) between SNPs (D' and R2) was obtained using data from the SAPHIR study only. Also in SAPHIR, haplotypes were estimated for all genes except PPARG by the expectation maximization algorithm using the haplo.stats package http://CRAN.R-project.org/package=haplo.stats) in the R software environment [39]. Subsequent association analysis of the number of haplotype copies on log-transformed adiponectin was adjusted for age, sex and BMI.

Results

Patient and Genotype Characteristics

Clinical characteristics and adiponectin levels of the three study populations are presented in Table 1. All three study populations were comparable with respect to the phenotype studied and adjusting variables with the exception of the higher mean age and higher rate of type 2 diabetes (T2D) in the KORA F3 Study. There was a higher frequency of male participants in the SAPHIR population (62.7%) compared KORA F3 (49.5%) and CoLaus cohorts (47.3%).

thumbnail
Table 1. Clinical characteristics of the subjects from SAPHIR, KORA F3 and CoLaus studies (means ± SD or numbers (%)), for whom all relevant variables and genotypes are available.

https://doi.org/10.1371/journal.pone.0052497.t001

Initially, we genotyped 24 SNPs at FOXO1, SREBF1, PPARG, TFAP2B and SIRT1 genes in SAPHIR cohort and added in-silico replication of the respective SNPs using the imputed genotypes from KORA F3 and CoLaus. Table 2 shows the corresponding descriptive statistics of the genotypes. The minor allele frequencies were comparable between all three populations. Issues regarding the genotyping quality could only be detected in four SNPs. In TFAP2B gene rs2143079 was poorly imputed (imputation quality score RSQR<60% in both in KORA F3 and CoLaus) and rs1569777 showed a deviation from the Hardy-Weinberg equilibrium in SAPHIR. Also in FOXO1, two SNPs (rs10507486 and rs17446614) had a deviation from the Hardy-Weinberg equilibrium in SAPHIR cohort (Table 2).

thumbnail
Table 2. Characteristics of the 24 SNPs in SAPHIR, KORA F3 and CoLaus, including genotype quality (call rate or imputation quality RSQR).

https://doi.org/10.1371/journal.pone.0052497.t002

Association Analysis

We evaluated the association of genetic variants in the main transcription factors (FOXO1, SREBF1, PPARG, TFAP2B) and their co-regulator (SIRT1) for ADIPOQ promoter with adiponectin level. Meta-analysis of additive linear regression models adjusted for age, sex, and BMI revealed association with log(adiponectin) in all 5 selected SNPs in the SREBF1 gene. After calculating the number of independent SNPs, which is 15 out of the 24 selected SNPs, and correcting for multiple testing (α = 0.05/15 = 0.0033) two SNPs from the SREBF1 gene remained significant: rs1889018 (p = 0.002) and rs2236513 (p = 0.003). Table 3 shows the results for all three populations as well as the combined effects. For rs1889018, for example, each copy of the minor allele leads to a reduction of the adiponectin level of 0.217 µg/ml. This corresponds to an explained variance of ∼0.4% as calculated from the SAPHIR Study. All selected SNPs within SREBF1 are highly correlated (Figure 1).

thumbnail
Figure 1. Linkage disequilibrium structure across the SREBF1 single nucleotide polymorphisms.

The pair wise linkage disequilibrium (R2 and D’) is given for each pair of single nucleotide polymorphisms. Color-coding is based on R2. The diagonal line indicates the physical position of the single nucleotide polymorphisms relative to each other.

https://doi.org/10.1371/journal.pone.0052497.g001

thumbnail
Table 3. Linear model results on the 24 selected SNPs in the SAPHIR, KORA F3 and CoLaus study using an additive genetic model, adjusted for age, sex and BMI, as well as the combined fixed effects meta-analysis results.

https://doi.org/10.1371/journal.pone.0052497.t003

Recessive effect models as well as haplotype analyses did not provide any additional information (data not shown). There was also no significant sex-specific effect in regulation of adiponectin levels for SNPs in FOXO1, SREBF1, TFAP2B and SIRT1 genes. Comparison of men-specific and women-specific data, however, showed a sex-difference in rs1801282 (PPARG, p for sex-difference  = 0.012), though this cannot be deemed significant given the number of tests involved. Nevertheless, it was interesting to see a negative effect in men (β = −0.235, p = 0.212), while the effect was positive in women (β = 0.288, p = 0.177).

Discussion

Considering that transcriptional control of the ADIPOQ is one of the most important factors involved in regulation of adiponectin levels, we hypothesized that genetic variation at the loci encoding the main transcription factors controlling activity of the adiponectin promoter might be involved in regulation of adiponectin levels. We therefore performed association analyses of the 24 selected SNPs from 5 different transcription factors. We observed a modest influence of genetic polymorphisms at the SREBF1 gene on the adiponectin levels in three healthy West-Eurasian populations including 8733 individuals, but not for the other transcription factors (FOXO1, SIRT1, TFAP2B and PPARG). Two of the five investigated polymorphisms (rs1889018 and rs2236513) at the SREBF1 gene locus demonstrated an influence on the adiponectin levels even after adjustment for multiple testing with lower concentrations in carriers of the minor allele. Additionally, our data revealed a sex-specific effect of the Pro12Ala SNP at the PPARG locus on adiponectin levels. The minor allele (Ala) of this gene negatively correlated with adiponectin levels in men, but positively in women. This finding was not significant after correction for multiple testing.

In previous publications the role of genetic polymorphisms of the transcription factors controlling ADIPOQ promoter activity in regulation of adiponectin levels was not investigated in detail – moreover the haplotype structure of these genes and sex-related effects (with the exception of PPARG) were not taken into consideration. Few years ago, Felder et al. using data from the SAPHIR cohort and additionally 446 unrelated patients with T2D discovered an association between one SNP at the SREBF1 gene (rs2297508) and the prevalence of T2D and adiponectin levels [26]. In our study we extended the analysis of genetic polymorphisms at the SREBF1 gene including additionally two cohorts and 4 SNPs, three of them having been selected as haplotype-tagging SNPs and one (rs2236513) based on literature data [40]. In our meta-analysis of 3 cohorts the two SNPs located in the 5'-UTR of the gene showed the strongest association with adiponectin levels (Figure 2). However, it should be noted, that all selected SNPs within SREBF1 are highly correlated (Figure 1). Therefore, it can be assumed that the different hits within this gene refer to the same signal or signals. The search for the effect-triggering variants still requires further investigation. The identified SNPs at the SREBF1 gene locus do not directly change its protein structure. They could, however, change various aspects of mRNA metabolism such as alterations of regulatory RNA-binding protein sites and mRNA secondary structure, that may influence functional properties of SREBP1c mRNA. It cannot be excluded that these SNPs are in linkage disequilibrium with yet unidentified functional mutations, either in the SREBF1 gene or a gene located in that region.

thumbnail
Figure 2. Schematic structure of SREBF1 gene.

Exons are numbered indicating the alternatively spliced -a and -c variants. Genomic location of the analyzed single nucleotide polymorphisms are marked. The single nucleotide polymorphisms highlighted in yellow showed a strongly associated with adiponectin levels in our study. The single nucleotide polymorphism highlighted in grey showed a significant association in a previous study [26] and was only borderline significantly associated in the present study (p = 0.004).

https://doi.org/10.1371/journal.pone.0052497.g002

The PPARG2 is a ligand-activated transcription factor, which acts as a heterodimer with the RXR [41], [42]. PPARG2 has been shown to increase transcription of the ADIPOQ gene and many other genes that are involved in the pathogenesis of insulin resistance [18], [19], [43]. Agonist-induced activation of PPARG is known to cause adipocyte differentiation, improvement in insulin sensitivity and also increased secretion of adiponectin by adipose tissue [44], [45]. The most frequently analysed and most well documented SNP in the PPARG gene is a proline to alanine substitution (Pro12Ala) in codon 12 of exon B (15% frequency among Caucasians). This substitution has a protective effect against the development of T2D. The Ala receptor variant is less efficient in its ability to bind and trans-activate a PPARG2 target gene in vitro [46]. The proline to alanine amino acid change also might affect the secondary structure of the protein and its functionality [47]. Heikkinen et al. (2009) suggested that the Pro12Ala polymorphism might be involved in the function of G protein, in sensitization of adiponectin signalling and altered recruitment of cofactors [48].

Yamamoto et al. investigated the effect of the PPARG Pro12Ala polymorphism on metabolic parameters and adiponectin levels in 598 Japanese people and found that adiponectin levels were significantly lower in subjects with the Ala12 allele [33]. However, a Finnish study demonstrated that adiponectin levels were significantly higher among PPARG-Ala allele carriers after weight loss induced by heavy exercises [32]. In our study, we could not confirm an influence of the Pro12Ala polymorphism at the PPARG gene on adiponectin levels. Nevertheless, sex-specific effects of this SNP on adiponectin levels were observed indicating a positive correlation in women and a negative in men. The mechanism underlying this sex-specific effect of the PPARG gene in regulating adiponectin levels and various metabolic traits is currently not known. Sexual dimorphisms have frequently been reported in relation to fat distribution and have been evidenced for genes that affect BMI. Men are more likely to gain visceral fat and deep subcutaneous fat than women [49][51]. Taking into consideration that women have more subcutaneous fat as compared to men and PPARG2 expression is more pronounced in subcutaneous adipose tissue [52][54], one can speculate that sex-specific effects of this polymorphism on adiponectin levels are related to differences in fat distribution between men and women.

Our data did not reveal any effect of genetic polymorphisms at the FOXO1, TFAP2B and SIRT1 gene loci on adiponectin levels. This might be explained by their minor role in the process of transcriptional regulation of the adiponectin gene or the investigated polymorphisms do not have sufficient influence on the structure of these transcription factor proteins.

Previous linkage studies suggested that different genetic components might be involved in the regulation of adiponectin levels, but their replications were inconsistent across different ethnic populations [6], [13], [15][17], [55][57]. Also the genetic variants detected in GWA studies did not explain the high level of heritability of adiponectin levels. The first GWA study was conducted in a European population and showed strong associations of adiponectin levels with ADIPOQ and CDH13 loci [15]. Later, Richards et al. in a meta-analysis of three GWA studies confirmed ADIPOQ and revealed a new locus - ARL15 (rs4311394) [58]. In 2010, Wu et al. provided a strong evidence of association with adiponectin for three loci: ADIPOQ, CDH13 and KNG1 together explained 7.5% and 8.9% of the variability of log-transformed adiponectin levels in Filipino women and their offspring, respectively. The strongest signal mapped to the CDH13 and explained approximately 4% of the variability of adiponectin levels [59]. The association of CDH13 locus with adiponectin levels was later confirmed in another GWA studies [14], [60], [61]. In a recent meta-analysis of GWA studies, 10 novel loci for adiponectin levels were identified and confirming the associations with variants at the ADIPOQ and CDH13 loci [10]. The genes included in our study were not detected in the previously published GWA studies. Possible explanations for this finding might be the different principles of SNP selection (candidate gene approach in our study vs. use of common genetic determinants across the genome in GWA studies) and the much higher threshold for significance (p<10−8) used in GWA studies.

Strengths and Limitations of the Study

The strength of our study is the selection of candidate genes based on the known regulatory mechanisms of adiponectin gene transcription. Additionally, we used the haplotype structure of the candidate genes for SNP selection with the addition of previously reported SNPs, allowing coverage of the whole gene region instead of single SNPs in the gene region. Finally, we could show direction-consistent significant effects for two SNPs in SREBF1 gene in three European populations, including a total number of 8733 participants.

Nevertheless, several limitations of this work should be noted. The differences in mean age, proportion of male participants and subjects with T2D between the three populations studied might have influenced adiponectin levels [59], [62]. Also we did not take into consideration the genetic polymorphisms of all transcription factors known to be important for the adiponectin promoter, but included only four main transcription factors and one co-regulator based which have been found to be of greatest importance in regulation of adiponectin promoter activity and adiponectin levels. Finally, the study size was too small to performed analyses stratified for T2D or obesity.

Conclusion

From the 24 selected SNPs at the five investigated transcription factors important for regulation of the adiponectin gene promoter, only those at the SREBF1 gene had a modest influence on adiponectin levels in three healthy West-Eurasian populations. The role of genetic variations at the SREBF1 gene and possible sex-related effects of PPARG in regulation of adiponectin levels have to be investigated in functional studies. Understanding the genetic mechanisms regulating adiponectin levels will expand our present knowledge concerning the factors that influence adiponectin levels. This could also lead to new therapeutic strategies to normalize circulating levels of adiponectin in subjects with metabolic disorders and cardiovascular disease.

Acknowledgments

The authors would like to thank the field investigators of the three cohorts for their assistance with data collection. We appreciate the technical assistance of Fabienne Buchsteiner from the University Clinic for Internal Medicine I for TaqMan genotyping in SAPHIR cohort and Barbara Luhan for the measurement of adiponectin in the KORA Study. Above all, the authors thank the study participants.

Author Contributions

Conceived and designed the experiments: LK CL FK BP. Performed the experiments: LK IK FK. Analyzed the data: LK TK IK CL KK SB DW. Contributed reagents/materials/analysis tools: BP LK IK FK IMH HEW KK SB DW. Wrote the paper: LK CL TK FK. Reviewed the manuscript: LK CL TK KK SB DW IMH HEW IK FK BP.

References

  1. 1. Galic S, Oakhill JS, Steinberg GR (2010) Adipose tissue as an endocrine organ. Mol Cell Endocrinol 316: 129–139.
  2. 2. Brochu-Gaudreau K, Rehfeldt C, Blouin R, Bordignon V, Murphy BD, et al. (2010) Adiponectin action from head to toe. Endocrine 37: 11–32.
  3. 3. Nishida M, Funahashi T, Shimomura I (2007) Pathophysiological significance of adiponectin. Med Mol Morphol 40: 55–67.
  4. 4. Comuzzie AG, Funahashi T, Sonnenberg G, Martin LJ, Jacob HJ, et al. (2001) The genetic basis of plasma variation in adiponectin, a global endophenotype for obesity and the metabolic syndrome. J Clin Endocrinol Metab 86: 4321–4325.
  5. 5. Henneman P, Aulchenko YS, Frants RR, Zorkoltseva IV, Zillikens MC, et al. (2010) Genetic architecture of plasma adiponectin overlaps with the genetics of metabolic syndrome-related traits. Diabetes Care 33: 908–913.
  6. 6. Lindsay RS, Funahashi T, Krakoff J, Matsuzawa Y, Tanaka S, et al. (2003) Genome-wide linkage analysis of serum adiponectin in the Pima Indian population. Diabetes 52: 2419–2425.
  7. 7. Vasseur F, Helbecque N, Dina C, Lobbens S, Delannoy V, et al. (2002) Single-nucleotide polymorphism haplotypes in the both proximal promoter and exon 3 of the APM1 gene modulate adipocyte-secreted adiponectin hormone levels and contribute to the genetic risk for type 2 diabetes in French Caucasians. Hum Mol Genet 11: 2607–2614.
  8. 8. Heid IM, Wagner SA, Gohlke H, Iglseder B, Mueller JC, et al. (2006) Genetic architecture of the APM1 gene and its influence on adiponectin plasma levels and parameters of the metabolic syndrome in 1,727 healthy Caucasians. Diabetes 55: 375–384.
  9. 9. Hivert MF, Manning AK, McAteer JB, Florez JC, Dupuis J, et al. (2008) Common variants in the adiponectin gene (ADIPOQ) associated with plasma adiponectin levels, type 2 diabetes, and diabetes-related quantitative traits: the Framingham Offspring Study. Diabetes 57: 3353–3359.
  10. 10. Dastani Z, Hivert MF, Timpson N, Perry JR, Yuan X, et al. (2012) Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet 8: e1002607.
  11. 11. Heid IM, Henneman P, Hicks A, Coassin S, Winkler T, et al. (2010) Clear detection of ADIPOQ locus as the major gene for plasma adiponectin: results of genome-wide association analyses including 4659 European individuals. Atherosclerosis 208: 412–420.
  12. 12. Vozarova de Courten B, Hanson RL, Funahashi T, Lindsay RS, Matsuzawa Y, et al. (2005) Common Polymorphisms in the Adiponectin Gene ACDC Are Not Associated With Diabetes in Pima Indians. Diabetes 54: 284–289.
  13. 13. Hicks C, Zhu X, Luke A, Kan D, Adeyemo A, et al. (2007) A genome-wide scan of loci linked to serum adiponectin in two populations of African descent. Obesity (Silver Spring) 15: 1207–1214.
  14. 14. Jee SH, Sull JW, Lee JE, Shin C, Park J, et al. (2010) Adiponectin concentrations: a genome-wide association study. Am J Hum Genet 87: 545–552.
  15. 15. Ling H, Waterworth DM, Stirnadel HA, Pollin TI, Barter PJ, et al. (2009) Genome-wide linkage and association analyses to identify genes influencing adiponectin levels: the GEMS Study. Obesity (Silver Spring) 17: 737–744.
  16. 16. Rasmussen-Torvik LJ, Pankow JS, Peacock JM, Borecki IB, Hixson JE, et al. (2009) Suggestion for linkage of chromosome 1p35.2 and 3q28 to plasma adiponectin concentrations in the GOLDN Study. BMC Med Genet 10: 39.
  17. 17. Ruchat SM, Despres JP, Weisnagel SJ, Chagnon YC, Bouchard C, et al. (2008) Genome-wide linkage analysis for circulating levels of adipokines and C-reactive protein in the Quebec family study (QFS). J Hum Genet 53: 629–636.
  18. 18. Iwaki M, Matsuda M, Maeda N, Funahashi T, Matsuzawa Y, et al. (2003) Induction of adiponectin, a fat-derived antidiabetic and antiatherogenic factor, by nuclear receptors. Diabetes 52: 1655–1663.
  19. 19. Liu M, Liu F (2010) Transcriptional and post-translational regulation of adiponectin. Biochem J 425: 41–52.
  20. 20. Schaffler A, Langmann T, Palitzsch KD, Scholmerich J, Schmitz G (1998) Identification and characterization of the human adipocyte apM-1 promoter. Biochim Biophys Acta 1399: 187–197.
  21. 21. Kita A, Yamasaki H, Kuwahara H, Moriuchi A, Fukushima K, et al. (2005) Identification of the promoter region required for human adiponectin gene transcription: Association with CCAAT/enhancer binding protein-beta and tumor necrosis factor-alpha. Biochem Biophys Res Commun 331: 484–490.
  22. 22. Qiao L, Shao J (2006) SIRT1 regulates adiponectin gene expression through Foxo1-C/enhancer-binding protein alpha transcriptional complex. J Biol Chem 281: 39915–39924.
  23. 23. Maeda S, Tsukada S, Kanazawa A, Sekine A, Tsunoda T, et al. (2005) Genetic variations in the gene encoding TFAP2B are associated with type 2 diabetes mellitus. J Hum Genet 50: 283–292.
  24. 24. Wichmann HE, Gieger C, Illig T (2005) KORA-gen–resource for population genetics, controls and a broad spectrum of disease phenotypes. Gesundheitswesen 67 Suppl 1S26–30.
  25. 25. Firmann M, Mayor V, Vidal PM, Bochud M, Pecoud A, et al. (2008) The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc Disord 8: 6.
  26. 26. Felder TK, Oberkofler H, Weitgasser R, Mackevics V, Krempler F, et al. (2007) The SREBF-1 locus is associated with type 2 diabetes and plasma adiponectin levels in a middle-aged Austrian population. Int J Obes (Lond) 31: 1099–1103.
  27. 27. Ikeda K, Maegawa H, Ugi S, Tao Y, Nishio Y, et al. (2006) Transcription factor activating enhancer-binding protein-2beta. A negative regulator of adiponectin gene expression. J Biol Chem 281: 31245–31253.
  28. 28. Qiao L, Maclean PS, Schaack J, Orlicky DJ, Darimont C, et al. (2005) C/EBPalpha regulates human adiponectin gene transcription through an intronic enhancer. Diabetes 54: 1744–1754.
  29. 29. Segawa K, Matsuda M, Fukuhara A, Morita K, Okuno Y, et al. (2009) Identification of a novel distal enhancer in human adiponectin gene. J Endocrinol 200: 107–116.
  30. 30. Helisalmi S, Vepsalainen S, Hiltunen M, Koivisto AM, Salminen A, et al. (2008) Genetic study between SIRT1, PPARD, PGC-1alpha genes and Alzheimer’s disease. J Neurol 255: 668–673.
  31. 31. Yeung E, Qi L, Hu FB, Zhang C (2011) Novel abdominal adiposity genes and the risk of type 2 diabetes: findings from two prospective cohorts. Int J Mol Epidemiol Genet 2: 138–144.
  32. 32. Mousavinasab F, Tahtinen T, Jokelainen J, Koskela P, Vanhala M, et al. (2005) Effect of the Pro12Ala polymorphism of the PPARg2 gene on serum adiponectin changes. Endocrine 27: 307–309.
  33. 33. Yamamoto Y, Hirose H, Miyashita K, Nishikai K, Saito I, et al. (2002) PPAR(gamma)2 gene Pro12Ala polymorphism may influence serum level of an adipocyte-derived protein, adiponectin, in the Japanese population. Metabolism 51: 1407–1409.
  34. 34. Sandhu MS, Waterworth DM, Debenham SL, Wheeler E, Papadakis K, et al. (2008) LDL-cholesterol concentrations: a genome-wide association study. Lancet 371: 483–491.
  35. 35. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34: 816–834.
  36. 36. Marchini J, Howie B, Myers S, McVean G, Donnelly P (2007) A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39: 906–913.
  37. 37. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, et al. (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466: 707–713.
  38. 38. Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (Edinb) 95: 221–227.
  39. 39. Sinnwell JP, Schaid DJ (2009) Haplo.stats: Statistical Analysis of Haplotypes with Traits and Covariates when Linkage Phase is Ambiguous. R package version 1.4.4..
  40. 40. Harding AH, Loos RJ, Luan J, O’Rahilly S, Wareham NJ, et al. (2006) Polymorphisms in the gene encoding sterol regulatory element-binding factor-1c are associated with type 2 diabetes. Diabetologia 49: 2642–2648.
  41. 41. Kersten S, Desvergne B, Wahli W (2000) Roles of PPARs in health and disease. Nature 405: 421–424.
  42. 42. Stumvoll M, Haring H (2002) The peroxisome proliferator-activated receptor-gamma2 Pro12Ala polymorphism. Diabetes 51: 2341–2347.
  43. 43. Rangwala SM, Lazar MA (2004) Peroxisome proliferator-activated receptor gamma in diabetes and metabolism. Trends Pharmacol Sci 25: 331–336.
  44. 44. Kawada T, Goto T, Hirai S, Kang MS, Uemura T, et al. (2008) Dietary regulation of nuclear receptors in obesity-related metabolic syndrome. Asia Pac J Clin Nutr 17 Suppl 1126–130.
  45. 45. Spiegelman BM, Flier JS (1996) Adipogenesis and obesity: rounding out the big picture. Cell 87: 377–389.
  46. 46. Sharma AM, Staels B (2007) Review: Peroxisome proliferator-activated receptor gamma and adipose tissue–understanding obesity-related changes in regulation of lipid and glucose metabolism. J Clin Endocrinol Metab 92: 386–395.
  47. 47. Gouda HN, Sagoo GS, Harding AH, Yates J, Sandhu MS, et al. (2010) The association between the peroxisome proliferator-activated receptor-gamma2 (PPARG2) Pro12Ala gene variant and type 2 diabetes mellitus: a HuGE review and meta-analysis. Am J Epidemiol 171: 645–655.
  48. 48. Heikkinen S, Argmann C, Feige JN, Koutnikova H, Champy MF, et al. (2009) The Pro12Ala PPARgamma2 variant determines metabolism at the gene-environment interface. Cell Metab 9: 88–98.
  49. 49. Bjorntorp P (1996) The regulation of adipose tissue distribution in humans. Int J Obes Relat Metab Disord 20: 291–302.
  50. 50. Blaak E (2001) Gender differences in fat metabolism. Curr Opin Clin Nutr Metab Care 4: 499–502.
  51. 51. Smith SR, Lovejoy JC, Greenway F, Ryan D, deJonge L, et al. (2001) Contributions of total body fat, abdominal subcutaneous adipose tissue compartments, and visceral adipose tissue to the metabolic complications of obesity. Metabolism 50: 425–435.
  52. 52. Gonzalez Sanchez JL, Serrano Rios M, Fernandez Perez C, Laakso M, Martinez Larrad MT (2002) Effect of the Pro12Ala polymorphism of the peroxisome proliferator-activated receptor gamma-2 gene on adiposity, insulin sensitivity and lipid profile in the Spanish population. Eur J Endocrinol 147: 495–501.
  53. 53. Vidal H (2001) Gene expression in visceral and subcutaneous adipose tissues. Ann Med 33: 547–555.
  54. 54. Vidal-Puig AJ, Considine RV, Jimenez-Linan M, Werman A, Pories WJ, et al. (1997) Peroxisome proliferator-activated receptor gene expression in human tissues. Effects of obesity, weight loss, and regulation by insulin and glucocorticoids. J Clin Invest 99: 2416–2422.
  55. 55. Bowden DW, An SS, Palmer ND, Brown WM, Norris JM, et al. (2010) Molecular basis of a linkage peak: exome sequencing and family-based analysis identify a rare genetic variant in the ADIPOQ gene in the IRAS Family Study. Hum Mol Genet 19: 4112–4120.
  56. 56. Chuang LM, Chiu YF, Sheu WH, Hung YJ, Ho LT, et al. (2004) Biethnic comparisons of autosomal genomic scan for loci linked to plasma adiponectin in populations of Chinese and Japanese origin. J Clin Endocrinol Metab 89: 5772–5778.
  57. 57. Guo X, Saad MF, Langefeld CD, Williams AH, Cui J, et al. (2006) Genome-wide linkage of plasma adiponectin reveals a major locus on chromosome 3q distinct from the adiponectin structural gene: the IRAS family study. Diabetes 55: 1723–1730.
  58. 58. Richards JB, Waterworth D, O’Rahilly S, Hivert MF, Loos RJ, et al. (2009) A genome-wide association study reveals variants in ARL15 that influence adiponectin levels. PLoS Genet 5: e1000768.
  59. 59. Wu Y, Li Y, Lange EM, Croteau-Chonka DC, Kuzawa CW, et al. (2010) Genome-wide association study for adiponectin levels in Filipino women identifies CDH13 and a novel uncommon haplotype at KNG1-ADIPOQ. Hum Mol Genet 19: 4955–4964.
  60. 60. Chung CM, Lin TH, Chen JW, Leu HB, Yang HC, et al. (2011) A genome-wide association study reveals a quantitative trait locus of adiponectin on CDH13 that predicts cardiometabolic outcomes. Diabetes 60: 2417–2423.
  61. 61. Morisaki H, Yamanaka I, Iwai N, Miyamoto Y, Kokubo Y, et al. (2012) CDH13 gene coding T-cadherin influences variations in plasma adiponectin levels in the Japanese population. Hum Mutat 33: 402–410.
  62. 62. Kadowaki T, Yamauchi T, Kubota N, Hara K, Ueki K, et al. (2006) Adiponectin and adiponectin receptors in insulin resistance, diabetes, and the metabolic syndrome. J Clin Invest 116: 1784–1792.