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Keywords:

  • Adiponectin;
  • resistin;
  • polymorphisms;
  • type 2 diabetes;
  • metabolic syndrome

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and discussion
  6. Author Contributions
  7. Disclosures
  8. Funding/Support
  9. Acknowledgements
  10. References
  11. Supporting Information

Single nucleotide polymorphisms (SNPs) at the adiponectin and resistin loci are strongly associated with hypoadiponectinemia and hyperresistinemia, which may eventually increase risk of insulin resistance, type 2 diabetes (T2DM), metabolic syndrome (MS), and cardiovascular disease. Real-time PCR was used to genotype SNPs of the adiponectin (SNP+45T>G, SNP+276G>T, SNP+639T>C, and SNP+1212A>G) and resistin (SNP-420C>G and SNP+299G>A) genes in 809 Malaysian men (208 controls, 174 MS without T2DM, 171 T2DM without MS, 256 T2DM with MS) whose ages ranged between 40 and 70 years old. The genotyping results for each SNP marker was verified by sequencing. The anthropometric clinical and metabolic parameters of subjects were recorded. None of these SNPs at the adiponectin and resistin loci were associated with T2DM and MS susceptibility in Malaysian men. SNP+45T>G, SNP+276G>T, and SNP+639T>C of the adiponectin gene did not influence circulating levels of adiponectin. However, the G-allele of SNP+1212A>G at the adiponectin locus was marginally associated (P= 0.0227) with reduced circulating adiponectin levels. SNP-420C>G (df = 2; F= 16.026; P= 1.50×10−7) and SNP+299G>A (df = 2; F= 22.944; P= 2.04×10−10) of the resistin gene were strongly associated with serum resistin levels. Thus, SNP-420C>G and SNP+299G>A of the resistin gene are strongly associated with the risk of hyperresistinemia in Malaysian men.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and discussion
  6. Author Contributions
  7. Disclosures
  8. Funding/Support
  9. Acknowledgements
  10. References
  11. Supporting Information

Adiponectin is an adipocyte-secreted polypeptide hormone with molecular weight 30kDa (244 amino acids), which modulates a number of metabolic processes, and regulates insulin sensitivity and energy homeostasis, as well as glucose and lipid metabolism (Yamauchi et al., 2010). The hormone plays a principal role in the suppression of the metabolic derangements that may result in insulin resistance, type 2 diabetes (T2DM), metabolic syndrome (MS), and cardiovascular disease (Kusminski & Scherer, 2009; Li et al., 2009; Zhuo et al., 2010). Adiponectin is encoded by the ADIPOQ gene (gene ID 9370) on chromosome 3q27.3 that spans 15,790bp with three exons and two introns (Suppl. Fig. S1A).

Resistin is a macrophage-derived signalling polypeptide hormone with molecular weight 12.5kDa and its length is 108 amino acids in humans (Nogueiras et al., 2010). In contrast with adiponectin, resistin has low circulating levels (Galic et al., 2010). However, the blood circulating levels of resistin have been shown to be upregulated in subjects with insulin resistance, T2DM, MS, and cardiovascular disease (Chen et al., 2009; Momiyama et al., 2010). The human resistin gene (RETN) (gene ID 56729) is located on chromosome 19p13.2 and spans 1,369bp with four exons and three introns (Suppl. Fig. S1B).

The MS drives the twin global epidemics of T2DM and cardiovascular disease (Alberti et al., 2009). T2DM itself is accompanied by increased risk for cardiovascular disease that is aggravated by the concomitant risk factors of the MS (Alberti et al., 2009). Single nucleotide polymorphisms (SNPs) at the ADIPOQ (Menzaghi et al., 2007; Henneman et al., 2010) and RETN (Miyamoto et al., 2009; Menzaghi & Trischitta, 2010) loci are associated with risk of T2DM and MS in several prospective epidemiological studies across a variety of population groups, but often with conflicting results. SNPs in the ADIPOQ (SNP+45T>G, SNP+276G>T, SNP+639T>C and SNP+1212A>G) and RETN (SNP-420C>G and SNP+299G>A) genes were chosen as tags because of their relatively high minor allelic frequencies (Suppl. Table S1) in parallel to the common disease-common variant (CD-CV) hypothesis (Peng & Kimmel, 2007). Moreover, recent large-scale fine-mapping of SNPs and meta-analysis of genome-wide linkage and association studies have shown that these SNPs at the ADIPOQ and RETN loci were strongly associated with hypoadiponectinemia (Menzaghi et al., 2007; Hivert et al., 2008; Ling et al., 2009; Dallinga-Thie & Dullaart, 2010; Heid et al., 2010) and hyperresistinemia (Hivert et al., 2009; Asano et al., 2010; Onuma et al., 2010). Thus, adiponectin and resistin gene polymorphisms were investigated for their aetiological links to T2DM and MS risk, as well as to their respective adipokine levels.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and discussion
  6. Author Contributions
  7. Disclosures
  8. Funding/Support
  9. Acknowledgements
  10. References
  11. Supporting Information

Subjects

All subjects were native to Malaysia and were males. The ages for all subjects were restricted to 40–70 years old. Subjects comprised three primary ethnic groups that were Malay, Chinese and Indian (Suppl. Fig. S2). Ethical clearance (reference number of Ethical Approval Letter was 612.17) to undertake this study was obtained from the University Malaya Medical Centre (UMMC) Ethics Committee. Informed consent was obtained from each subject, to whom possible consequences of the studies were explained. Each subject received a detailed questionnaire about the personal and family disease history and demographic data.

The subjects were classified into 208 controls, 174 MS without T2DM, 171 T2DM without MS, 256 T2DM with MS for a total 809 subjects (Suppl. Fig. S2). The controls were nondiabetic subjects who had no personal and family history and had no first degree relatives such as parent and sibling with T2DM and MS. The fasting plasma glucose levels for a control was in the normal range (<5.60 mmol/L) according to the American Diabetes Association (ADA) 2003 diagnostic criteria (American Diabetes Association, 2008). T2DM individuals were identified as diabetic subjects who had fasting plasma glucose levels of ≥7.0 mmol/L and had been diagnosed by a diabetic physician with T2DM or had been taking diabetic medication. MS was defined according to the International Diabetes Federation (IDF) 2005 diagnostic criteria (Alberti et al., 2005). The controls and subjects with T2DM or MS were selected from those attending the UMMC for routine medical check-up or treatment. All subjects had not been diagnosed with other hereditary (e.g., cancer) and infectious diseases (e.g., hepatitis) (Suppl. Methods).

Determination of Anthropometric Clinical and Metabolic Parameters

The metabolic parameters including fasting serum total adiponectin (Suppl. Fig. S3A), resistin (Suppl. Fig. S3B), insulin, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, plasma glucose, and whole blood HbA1C levels were tested. The anthropometric parameters including the blood pressure, body mass index (BMI), waist circumference, and waist-to-hip ratio (WHR), were also measured or calculated. The insulin resistance was assessed by Quantitative Insulin Sensitivity Check index (QUICKI) (Suppl. Methods).

Genotyping of SNPs Adiponectin and Resistin

Genomic DNA was extracted from whole blood using a Wizard Genomic DNA Purification kit (Promega Corporation, Madison, USA) according to the manufacturer's protocol. SNPs were genotyped using the Custom TaqMan SNP Genotyping Assays kit (Applied Biosystems, California, USA) according to the manufacturer's standard protocol (Suppl. Table S2 and Suppl. Methods). The allelic discrimination analysis was conducted with StepOnePlus Real-Time PCR System Version 2.0 (Applied Biosystems, California, USA) (Suppl. Fig. S4). Primers (Suppl. Table S3) and PCR (Suppl. Table S4, Suppl. Table S5 and Suppl. Fig. S5) were designed to amplify the DNA region for each SNP marker. 30 samples were randomly selected for forward and reverse sequencing (AITBiotech Pte Ltd, Singapore, Singapore) in order to verify the genotyping results for each SNP marker (Suppl. Methods, Suppl. Fig. S6, Suppl. Fig. S7, Suppl. Fig. S8, Suppl. Fig. S9, Suppl. Fig. S10, and Suppl. Fig. S11).

Statistical Analysis

Skewed variables, outliers, and heterogeneity of variances were addressed by using Johnson transformation or Box-Cox Power transformation with Minitab 15 Program (Minitab Inc, Pennsylvania, USA) before analysis. Significant differences in continuous variables among subject groups were confirmed by univariate analysis of covariance (ANCOVA) with PASW Statistics 18 Program (SPSS Inc, Chicago, Illinois, USA). Then, 1,000 stratified bootstrap samples with bias corrected and accelerated (BCa) 95% confident interval were used for the pair wise comparisons (Suppl. Notes).

Marker Check in Haploview 4.1 Program (Broad Institute Cambridge, Massachusetts, USA) (http://www.broad.mit.edu/mpg/haploview/) was used to test whether the samples for each SNP marker are in Hardy–Weinberg proportions. The differences in genotypic and allelic frequencies of each SNP between subject groups were assessed by Pearson's χ2 or Fisher's exact tests. Binary logistic regression was used to adjust the covariate ethnicity with Minitab 15 Program (Minitab Inc). Then, 10,000 × Monte Carlo permutations were used to generate an empirical P-value (multiple testing bias corrections) with the SHEsis Program (Bio-X Life Science Research Center, Shanghai, China) (http://analysis.bio-x.cn/SHEsisMain.htm) (Shi & He, 2005).

The quantitative traits analysis for alleles was conducted with the HAPSTAT Version 3.0 Program (Chapel Hill, Carolina, USA) (http://www.bios.unc.edu/~lin/hapstat/). The additive model was used, in which each SNP was included as a fixed effect. The models included ages, ethnicity, T2DM, and MS as environment (random) effects to account for their covariates effect (covariates adjustment). All P-values were two-tailed, and P-values below 0.05 were considered statistically significant (Suppl. Notes).

Results and discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and discussion
  6. Author Contributions
  7. Disclosures
  8. Funding/Support
  9. Acknowledgements
  10. References
  11. Supporting Information

Clinical Features of T2DM and MS Subjects

The clinical characteristics of the controls, T2DM, and MS subjects are shown in Supplementary Table S6 and Supplementary Table S7, which reflect the criteria used to define the subject groups. The case-control groups were well classified for reliable and accurate association. There was homogeneity for the covariate in terms of ages and ethnicity to match the case-control groups (Suppl. Fig. S2). Circulating serum insulin levels were higher in the subjects with T2DM (df = 3; H= 126.58; P < 0.00001) and MS (df = 3; H= 137.01; P < 0.00001) compared to the healthy subjects. Insulin resistance was strongly associated with increased risk of T2DM (df = 3; F= 106.73; P < 0.00001) and MS (df = 3; F= 123.23; P < 0.00001).

In addition, hypoadiponectinemia was strongly associated with increased risk of T2DM (df = 3; F= 21.801; P < 0.00001) and MS (df = 3; F= 26.505; P < 0.00001). Hyperresistinemia was also strongly associated with increased risk of T2DM (df = 3; F= 52.651; P < 0.00001) and MS (df = 3; F= 51.727; P < 0.00001). Hypoadiponectinemia and hyperresistinemia become more severe (P < 0.00001) in subjects presenting with both T2DM and MS. Thus, hypoadiponectinemia and hyperresistinemia were predisposing subjects with insulin resistance to T2DM and MS which eventually increase risk of cardiovascular disease. These findings were consistent with previous reports on adiponectin (Li et al., 2009; Zhuo et al., 2010) and resistin (Chen et al., 2009; Momiyama et al., 2010) in most epidemiological studies and meta-analyses.

Evaluation of Hardy–Weinberg Equilibrium for Each SNP Marker

All the SNP markers at the ADIPOQ (SNP+45T>G, SNP+276G>T, SNP+639T>C and SNP+1212A>G) and RETN (SNP-420C>G and SNP+299G>A) loci were in Hardy–Weinberg equilibrium (Suppl. Table S8).

Association of Adiponectin Gene Polymorphisms with T2DM and MS Susceptibility

SNP+45T>G, SNP+276G>T, SNP+639T>C and SNP+1212A>G at the ADIPOQ locus were not associated with T2DM and MS susceptibility in Malaysian men (Suppl. Table S9, Suppl. Table S10, Suppl. Table S11). This could be explained by a lack of statistical power to detect variants that are likely to confer only a modest effect on T2DM and MS risk. Thus, larger sample sizes coupled with adequate statistical power are required to detect the association between T2DM or MS risk with these SNPs in Malaysian men.

The area of chromosome 3q27 where the ADIPOQ gene is located has been identified by genome-wide linkage studies (GWLSs) to be a susceptibility locus for risk for the T2DM (Lillioja & Wilton, 2009), MS (Edwards et al., 2008), and cardiovascular disease (Bowden et al., 2006). However, this was not shown in recently published genome-wide association studies (GWASs) (Adeyemo et al., 2009; Hiura et al., 2009; Hiura et al., 2010; Tsai et al., 2010; Zabaneh & Balding, 2010) and the large-scale meta-analyses of GWASs (Dupuis et al., 2010; Franklin et al., 2010; Tan et al., 2010; Voight et al., 2010).

The ADIPOQ gene is very polymorphic since its associations with T2DM and MS have been reported for genetic variants in many populations, but often with conflicting results (Menzaghi et al., 2007; Henneman et al., 2010). SNP+45T>G and SNP+276G>T at the ADIPOQ locus were the most widely studied (Zacharova et al., 2005; Mousavinasab et al., 2006; Szopa et al., 2009; Vendramini et al., 2010). In the STOP-NIDDM trial, it was shown that the G-allele of SNP+45T>G was a predictor for the conversion from impaired glucose tolerance to T2DM (Zacharova et al., 2005). The G-allele of SNP+45T>G and the G/G genotype of SNP+276G>T were associated with impaired glucose tolerance in unrelated Spanish subjects (Gonzalez-Sanchez et al., 2005). Furthermore, it had been reported that the combined effect of SNP+45T>G and SNP+276G>T on the development of T2DM was stronger than that of each SNP alone (Zacharova et al., 2005).

SNP+276G>T may have an independent role in the determination of MS in Korean patients with T2DM (Hwang et al., 2010). In the Health Professionals Follow-up Study, the T/T genotype of SNP+276G>T was significantly associated with decreased cardiovascular risk in diabetic men (Qi et al., 2005). However, neither SNP+45T>G nor SNP+276G>T were associated with risk of T2DM in Han Chinese (Wang et al., 2009), Japanese Brazilian (Vendramini et al., 2010), and African American (Bostrom et al., 2009) populations. Moreover, results from a meta-analysis demonstrated a large heterogeneity among the studies both on SNP+45T>G and SNP+276G>T (Xu et al., 2008). In a meta-analysis consisting 20 case-control studies for SNP+45T>G and 9 case-control studies for SNP+276G>T, it demonstrated a correlation between the SNP+45T>G and the occurrence of T2DM in Chinese population, which was different from the findings that such an association with SNP+276G>T could not be demonstrated in the same ethnic population (Xu et al., 2008).

To our knowledge, only one previous study had demonstrated an association between SNP+639T>C and diabetic nephropathy (Bostrom et al., 2009). Meanwhile, the association of SNP+1212A>G with T2DM or MS risk had not been reported in any other population study so far. Therefore, the findings associated with the adiponectin locus may represent considerable heterogeneity among populations.

Association of Adiponectin Gene Polymorphisms with Serum Adiponectin Levels

SNP+45T>G, SNP+276G>T and SNP+639T>C at the ADIPOQ locus did not influence circulating serum adiponectin levels in Malaysian men (Tables 1 and 2). However, the G-allele of SNP+1212A>G at the ADIPOQ locus was marginally associated with reduced circulating serum adiponectin levels (P= 0.0227) in this study (Table 2). It was estimated that a substantial proportion (between 30% and 80%) of the variability in circulating adiponectin levels was accounted for by genetic factors (Rasmussen-Torvik et al., 2009; Henneman et al., 2010; Menzaghi et al., 2010). Moreover, it had also been suggested that circulating adiponectin levels were highly heritable with estimates ranging from 46% up to 65% for women and 54% for men (Heid et al., 2010).

Table 1.  Association between SNPs of ADIPOQ and serum adiponectin levels.
SNP markersAdiponectin (μg/mL) (n= 809)(df; F; P) Bootstrap pairwise comparisons
Original Mean (95% CI)Transformed with adjusted Mean (BCa 95% CI)
  1. Johnson transformation was performed before analysis. ANCOVA test was used, followed by pair wise comparisons using 1,000 Stratified Bootstrap Samples with Bias Corrected and Accelerated (BCa) 95% CI for multiple testing bias corrections with adjusted covariate ages and stratified ethnicity, T2DM and MS status. Significant levels: P*< 0.05, P**< 0.01, P***< 0.001. Note: 0 = common allele; 1 = rare allele; NA = not applicable.

SNP+45T>G
- T/T genotype (0/0) (n= 501)7.54 (7.14, 7.95)−0.025 (−0.108, 0.059)(2; 0.175; 0.839)
- T/G genotype (0/1) (n= 264)7.78 (7.27, 8.29)0.069 (−0.045, 0.182)NA
- G/G genotype (1/1) (n= 44)8.35 (6.40, 10.30)0.104 (−0.184, 0.392) 
SNP+276G>T
- G/G genotype (0/0) (n= 405)7.40 (6.98, 7.82)−0.042 (−0.123, 0.040)(2; 1.134; 0.322)
- G/T genotype (0/1) (n= 330)7.88 (7.39, 8.36)0.074 (−0.025, 0.172)NA
- T/T genotype (1/1) (n= 74)8.14 (6.68, 9.61)0.062 (−0.152, 0.276) 
SNP+639T>C
- T/T genotype (0/0) (n= 265)8.16 (7.52, 8.79)0.113 (0.002, 0.224)(2; 1.181; 0.308)
- T/C genotype (0/1) (n= 382)7.47 (7.02, 7.92)−0.044 (−0.132, 0.045)NA
- C/C genotype (1/1) (n= 162)7.31 (6.74, 7.88)−0.018 (−0.163, 0.128) 
SNP+1212A>G
- A/A genotype (0/0) (n= 227)8.22 (7.52, 8.92)0.125 (−0.007, 0.256)(2; 1.674; 0.188)
- A/G genotype (0/1/) (n= 375)7.62 (7.16, 8.07)0.007 (−0.089, 0.102)NA
- G/G genotype (1/1) (n= 207)7.14 (6.63, 7.65)−0.098 (−0.233, 0.038) 
Table 2.  Estimative effect of allelic ADIPOQ on serum adiponectin levels.
SNP markersOriginal variableTransformed and adjusted variableP-Value
  1. Data are expressed as estimative effect (standard error). Johnson transformation was conducted before analysis. The quantitative trait analysis for alleles was conducted with adjusted covariate ages, ethnicity, T2DM and MS. The estimative effect value is the change in mean of serum adiponectin levels according to allele groups. Significant levels: P*< 0.05, P**< 0.01, P***< 0.001. Note: 0 = common allele, 1 = rare allele.

SNP+45T>G
- T allele (0) (n= 1265)−0.3261 (0.2715)−0.0747 (0.0558)0.1806
- G allele (1) (n= 353)+0.3261 (0.2715)+0.07474 (0.0558) 
SNP+276G>T
- G allele (0) (n= 1141)−0.4032 (0.2479)−0.0653 (0.0516)0.2058
- T allele (1) (n= 477)+0.4032 (0.2479)+0.0653 (0.0516) 
SNP+639T>C
- T allele (0) (n= 912)+0.4556 (0.2257)+0.0781 (0.0467)0.0946
- C allele (1) (n= 706)−0.4556 (0.2257)−0.0781 (0.0467) 
SNP+1212A>G
- A allele (0) (n= 828)+0.5469 (0.2205)+0.1054 (0.0463)0.0227*
- G allele (1) (n= 790)−0.5469 (0.2205)−0.1054 (0.0463) 

The association between the G-allele of SNP+1212A>G and low circulating serum adiponectin levels was also reported in the Framingham Offspring Study (Hivert et al., 2008) and recently in the large-scale meta-analyses of GWASs (Ling et al., 2009; Heid et al., 2010). The G-allele of SNP+1212A>G may downregulate the gene expression of the ADIPOQ gene due to its location at the 3′-untranslated region (3′UTR) of the ADIPOQ gene, a region known for playing a pivotal role in the control of gene expression by binding proteins that regulate mRNA processing, localisation, translation, stability, or degradation (Suppl. Fig. S1A) (Thorrez et al., 2010).

Another possibility was that SNP+1212A>G was in linkage disequilibrium with other causative polymorphisms of the ADIPOQ gene or other genes, which had an effect on adiponectin expression, secretion, structure, or action. For instance, a novel variant in the ARL15 (ADP-ribosylation factor-like 15) (Richards et al., 2009) and CDH13 (a newly identified receptor for high-molecular-weight species of adiponectin) (Jee et al., 2010) genes were reported to influence circulating adiponectin levels in the recent GWASs. In the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Study, a genome-wide linkage scan had shown the potential for linkage of chromosome 1p35.2 (region of T2DM susceptibility) and 3q28 (nearby the ADIPOQ gene) to circulating adiponectin levels (Rasmussen-Torvik et al., 2009). Moreover, recent genome-wide linkage and association scans have shown that peak evidence for linkage occurred at chromosome 8p23 in Northern and Western European subjects and at chromosome 3q28 near ADIPOQ in Turkish and Southern European subjects (Ling et al., 2009). However, it has been reported that SNPs at the ADIPOQ locus were the most strongly associated with circulating adiponectin levels variation throughout the entire genome (Ling et al., 2009; Richards et al., 2009; Heid et al., 2010). Thus, ADIPOQ is a major gene influencing circulating adiponectin levels from the genome-wide perspective (Ling et al., 2009; Heid et al., 2010).

A recent large-scale meta-analysis of GWASs has also supported numerous genetic variations at the ADIPOQ locus including SNP+639T>C and SNP+1212A>G in modulating serum adiponectin levels (Heid et al., 2010). In the Genetic Epidemiology of Metabolic Syndrome (GEMS) Study, haplotypes of SNP-199A>G (rs3774261) and SNP+1212A>G at the ADIPOQ locus were strongly associated with circulating adiponectin levels (Ling et al., 2009). Moreover, many strong and highly significant associations of other SNPs at the ADIPOQ locus with circulating adiponectin levels have been reported in large-scale fine-mapping SNP studies (Hivert et al., 2008; Heid et al., 2010; Ong et al., 2010; Wassel et al., 2010). In addition to the SNPs in our study, other polymorphisms including rs17366568, rs3774261, rs822387, rs17300539, rs16861209, and rs16861210 were very strongly associated with circulating adiponectin levels in the previous large-scale GWASs (Heid et al., 2010).

Additionally, it is possible that much of the genetic effect on adiponectin is attributable to multiple rare variants, each associated with a relatively large effect. Taking these studies together, it seems likely that simple sequence variances at a single locus capture only a small part of the total genetic contribution to trait variation. Thus, the associated SNP+1212A>G is likely to be in linkage disequilibrium with a true functional SNP in the region and this functional variant has a larger effect on circulating adiponectin levels. In addition, an extensive bioinformatics analysis revealed that the ADIPOQ region might be a high copy number variable region which potentially influences circulating adiponectin levels (Heid et al., 2010).

In the large-scale meta-analysis of GWASs, it was shown that haplotype 2 containing SNP+45T>G, SNP+276G>T, SNP+639T>C, and SNP+1212A>G at the ADIPOQ locus was strongly associated with hypoadiponectinemia (Heid et al., 2006; Heid et al., 2010). Several prior studies had also reported associations of the SNP+45T>G and SNP+276G>T at the ADIPOQ locus with circulating adiponectin levels (Gu, 2009; Dallinga-Thie & Dullaart, 2010). In the previous meta-analysis (Menzaghi et al., 2007) and a large prospective cohort study (Qi et al., 2005), a significant association with circulating adiponectin levels was observed for SNP+276G>T but no association was observed for SNP+45T>G. This may be due to the relatively low minor allelic frequency of SNP+45T>G compared to SNP+276G>T (Suppl. Table S1).

It was shown that patients with the T/T genotype of SNP+276G>T had adiponectin levels significantly lower than those with the G/T genotype in the control group and had significantly lower adiponectin than those with the G/G genotype in the MS group (Hwang et al., 2010). Moreover, it had been reported that G/G genotype of SNP+276G>T exhibited higher adiponectin levels compared to the T-allele carriers under conditions of lower fibre intake (Ntalla et al., 2009). In contrast, the T-allele of SNP+276G>T was associated with significantly higher circulating adiponectin levels in a dose-dependent pattern (GG<GT<TT) in unrelated Spanish subjects (Gonzalez-Sanchez et al., 2005), young Finnish men (Mousavinasab et al., 2006) and in a large prospective cohort study of American diabetic men (Qi et al., 2005).

Therefore, the findings associated with these SNPs on metabolic status may represent considerable heterogeneity among populations. In addition, these conflicting results may relate to the different levels of circulating adiponectin reported in different studies, reflecting disparities in the criteria used to recruit the study populations and/or in the assays used to measure adiponectin concentrations. The confounding factors such as environment (lifestyles or diet), genetics (specific linkage structure or epigenetics), or gene–environment interaction effects may also account for the discrepancy between the present report and previous studies.

Association of Resistin Gene Polymorphisms with T2DM and MS Susceptibility

SNP-420C>G and SNP+299G>A at the RETN locus were not associated with T2DM and MS susceptibility in Malaysian men (Suppl. Table S9, Suppl. Table S10, Suppl. Table S12). Although the RETN gene was not detected as a susceptibility locus for T2DM (Dupuis et al., 2010; Franklin et al., 2010; Tan et al., 2010; Tsai et al., 2010; Voight et al., 2010) and MS (Adeyemo et al., 2009; Hiura et al., 2009; Hiura et al., 2010; Zabaneh & Balding, 2010) in a series of GWASs published so far, the RETN gene variations were associated with the T2DM (Ochi et al., 2007) and MS (Miyamoto et al., 2009) risk in Japanese subjects. In the meta-analyses of GWLSs, suggestive evidence for linkage was observed for LDL cholesterol (Heijmans et al., 2005; Malhotra et al., 2007), Apolipoprotein B (Heijmans et al., 2005), total cholesterol (Malhotra et al., 2007), and HDL cholesterol (Malhotraet al., 2007) on chromosome 19p13 (location of the RETN gene). These provide compelling evidence that the region of chromosome 19p13 harbour important determinants of lipid levels in individuals with T2DM (Malhotraet al., 2007).

Among other polymorphisms at the RETN locus, SNP-420C>G had attracted much attention (Menzaghi & Trischitta, 2010; Onuma et al., 2010). A meta-analysis had shown that those homozygous for the G-allele of SNP-420C>G have a 30% increase in the odds of being affected by T2DM (Osawa et al., 2004). Ochi et al. found that the frequency of the G/G genotype of SNP-420C>G appeared to be increased in younger-onset T2DM subjects (Ochi et al., 2007). Miyamoto et al. also found that the G-allele of SNP-420C>G was significantly associated with an increased risk of MS (Miyamoto et al., 2009). Moreover, it had been reported that individuals with the G/G or C/G genotypes were significantly more likely to have had a stroke than individuals with the C/C genotype (Tsukahara et al., 2009). However, the association of T2DM and MS with SNP-420C>G has been controversial, suggesting that a variety of factors such as heterogeneity of population could affect the results. Moreover, most of the studies showed that SNP-420C>G was a major determinant for circulating serum resistin levels but was not directly associated with T2DM or MS risk (Osawa et al., 2007; Hivert et al., 2009; Onuma et al., 2010). The direct association between T2DM or MS risk, and SNP-420C>G might be more difficult to detect if SNP-420C>G had little effect on T2DM (Ochi et al., 2007) or MS (Miyamoto et al., 2009) especially if the sample sizes were small.

In a study on subjects in Thailand, the G/A or A/A genotype of SNP+299G>A at the RETN locus was associated with increased risk of T2DM (Suriyaprom et al., 2009) but this was not reflected in a study conducted on Caucasians (Ma et al., 2002). Moreover, the G/A or A/A genotype of SNP+299G>A was associated with increased risk of MS in a Japanese cohort study (Miyamoto et al., 2009), but had a protective effect against hypertension in a Finnish population-based cohort study (Ukkola et al., 2008). Conflicting findings between these studies could be due to true differences in allelic association with the disease phenotype in different populations. In agreement with this notion were the differences in allele frequencies of these SNPs in various populations (Suppl. Table S1).

Association of Resistin Gene Polymorphisms with Serum Resistin Levels

Our results showed that SNP-420C>G (df = 2; F= 16.026; P= 1.50 × 10−7) and SNP+299G>A (df = 2; F= 22.944; P= 2.04 × 10−10) at the RETN locus were strongly associated with serum resistin levels (Table 3). For SNP-420C>G, serum resistin levels were the highest in carriers with the G/G genotype, followed by the C/G and C/C genotypes (Table 3). For SNP+299G>A, serum resistin levels were the highest in carriers with the A/A genotype, followed by the G/A and G/G genotypes (Table 3). Moreover, the G-allele of SNP-420C>G (P= 3.44 × 10−8) and the A-allele of SNP+299G>A (P= 1.82 × 10−11) at the RETN locus were strongly associated with elevated circulating serum resistin levels (Table 4). These findings were consistent with a previous report of SNP-420C>G in Japanese (Osawa et al., 2007; Makino et al., 2009; Tsukahara et al., 2009; Asano et al., 2010; Onuma et al., 2010), Finnish (Ukkola et al., 2008), and Korean (Cho et al., 2004) subjects. Similar results for SNP+299G>A had been reported in Japanese (Asano et al., 2010; Onuma et al., 2010), Thai (Suriyaprom et al., 2009), and Finnish (Ukkola et al., 2008) studies. Genetic variants in RETN had been examined by many groups, and it was estimated that up to 70% of the variation in circulating resistin levels could be explained by genetic factors (Menzaghi et al., 2006; Hivert et al., 2009). In the San Antonio Family Heart Study, the maximum linkage signal for the RETN expression was found on chromosome 19p13 (location of the RETN gene) (Tejero et al., 2008). This suggests that RETN expression may be cis-regulated, meaning there are variants in or near the RETN gene that influence the abundance of its mRNA (Tejero et al., 2008).

Table 3.  Association between SNPs of RETN and serum resistin levels.
SNP markersResistin (ng/mL) (n= 809)(df; F; P) Bootstrap pairwise comparisons
Original Mean (95% CI)Transformed with adjusted Mean (BCa 95% CI)
  1. Johnson transformation was performed before analysis. ANCOVA test was used, followed by pair wise comparisons using 1,000 Stratified Bootstrap Samples with Bias Corrected and Accelerated (BCa) 95% CI for multiple testing bias corrections with adjusted covariate ages and stratified ethnicity, T2DM and MS status. Significant levels: P*< 0.05, P**< 0.01, P***< 0.001. Note: Note: 0 = common allele, 1 = rare allele.

SNP-420C>G
- C/C genotype (0/0) (n= 276)17.43 (16.14, 18.72)−0.149 (−0.248, −0.049)(2; 16.026; 1.50 × 10−7)
- C/G genotype (0/1) (n= 397)20.71 (19.40, 22.02)0.037 (−0.059, 0.133)C/C vs. C/G *
- G/G genotype (1/1) (n= 136)26.77 (23.67, 29.88)0.344 (0.157, 0.530)C/C vs. G/G ***
   C/G vs. G/G **
SNP+299G>A
- G/G genotype (0/0) (n= 338)17.56 (16.28, 18.85)−0.197 (−0.295, −0.099)(2; 22.944; 2.04 × 10−10)
- G/A genotype (0/1) (n= 357)21.81 (20.33, 23.29)0.124 (0.022, 0.225)G/G vs. G/A ***
- A/A genotype (1/1) (n= 114)25.88 (22.98, 28.79)0.358 (0.173, 0.542)G/G vs. A/A ***
   G/A vs. A/A *
Table 4.  Estimative effect of allelic RETN on serum resistin levels.
SNP markersOriginal variableTransformed and adjusted variableP-Value
  1. Data are expressed as estimative effect (standard error). Johnson transformation was conducted before analysis. The quantitative trait analysis for alleles was conducted with adjusted covariate ages, ethnicity, T2DM and MS. The estimative effect value is the change in mean of serum resistin levels according to allele groups. Significant levels: P*<0.05, P**< 0.01, P***< 0.001. Note: 0 = common allele; 1 = rare allele.

SNP-420C>G
- C allele (0) (n= 949)−4.4247 (0.6860)−0.2480 (0.0449)3.44 × 10−8***
- G allele (1) (n= 669)+4.4247 (0.6860)+0.2480 (0.0449) 
SNP+299G>A
- G allele (0) (n= 1033)−4.1815 (0.6859)−0.2973 (0.0443)1.82 × 10−11***
- A allele (1) (n= 585)+4.1815 (0.6859)+0.2973 (0.0443) 

In the meta-analysis, a significant association was observed between SNP-420C>G and resistin levels mainly in Asian populations, thus suggesting some heterogeneity caused by ethnic-specific genetic effects (Menzaghi & Trischitta, 2010). A recent pilot study has shown that the G/G genotype of SNP-420C>G may be an independent predictor of the reduction of fasting plasma glucose and insulin resistance by pioglitazone treatment in T2DM patients (Makino et al., 2009). The absolute value of resistin after pioglitazone treatment remained higher in the G/G genotype than the others, suggesting that the enhanced resistin gene expression in subjects with the G/G genotype may have remained higher (Makino et al., 2009). In the study by Cho et al., electrophoretic mobility shift assay (EMSA) showed that the G-allele of SNP-420C>G was specific for binding of nuclear proteins from adipocytes and monocytes (Cho et al., 2004). Chung et al. also demonstrated that the DNA element of SNP-420C and SNP-420G at the RETN gene had different binding affinities for stimulatory protein 1 (Sp1) and stimulatory protein 3 (Sp3) (Chung et al., 2005). Sp1 and Sp3 transcription factors specifically bound to the DNA element of SNP-420G with high affinity and enhanced expression of the RETN gene (Chung et al., 2005).

Recently, there was debate as to whether SNP-638G>A, SNP-420C>G, or SNP-358G>A is a more promising variant for determining serum resistin levels (Asano et al., 2010; Onuma et al., 2010; Osawa et al., 2010). Asano et al. suggested that other transcription factors, such as SRE-binding protein 1c (SREBP1c), that bind to the RETN gene promoter in the vicinity of the SNP-638G>A polymorphisms (in complete linkage disequilibrium with SNP-420C>G and SNP-358G>A) might play a greater role in determining the serum resistin levels than Sp1 and Sp3 (Asano et al., 2010). However, a study by Onuma et al., showed that SNP-358G>A was a more functional SNP than SNP-638G>A (Onuma et al., 2010). Moreover, the A-allele at SNP-358G>A was required for the G-allele at SNP-420C>G to confer the highest serum resistin levels in the general Japanese population (Onuma et al., 2010). Thus, whether SNP-420C>G modulates circulating resistin levels independently or functions in concert with other causative polymorphisms in a haplotype block remains to be elucidated.

In addition to SNP-420C>G and SNP+299G>A, it had been reported that other polymorphisms (rs34861192, rs34124816, rs3219175, rs10402265, rs3833230, rs3745368, rs1477341, rs4804765, rs1423096, and rs10401670) of the RETN gene were strongly associated with circulating resistin levels (Hivertet al., 2009; Asanoet al., 2010; Onuma et al., 2010). Moreover, it had been reported that SNP-638G>A (rs34861192) and SNP-358G>A (rs3219175) had the strongest association with circulating resistin levels among the SNPs at the RETN locus (Asano et al., 2010; Onuma et al., 2010). Indeed, the combination of SNP-638G>A and SNP-358G>A accounted for 37.9% of the variation in circulating resistin level among all study participants in a population-based prospective cohort study (KING study) (Asano et al., 2010). Taking together of these studies, it seems likely that SNP-420C>G and SNP+299G>A at the RETN locus capture only a small part of the total genetic contribution to circulating resistin variation. Due to linkage disequilibrium, the effects of these SNPs should only be seen in association studies of other causative polymorphisms located in the same region.

Our study demonstrated that although SNP-420C>G, and SNP+299G>A at the RETN locus seemed to affect the serum resistin concentration, their association with the risk of T2DM and MS as well as several metabolic risk factors might not be exclusively due to alterations in resistin level. Thus, mechanisms other than an effect on serum resistin levels could be involved in the association with susceptibility to T2DM and MS. Resistin circulates in two distinct assembly states, namely, trimers and hexamers, of which the latter is formed by intertrimer disulfide bonds (Patel et al., 2004). The ability to multimerise could play a role in resistin action and would be modulated by ethnicity, and metabolic changes (Patel et al., 2004).

Strengths and Limitations

Our samples comprised Malay, Chinese, and Indian subjects from Malaysia, which represented a major segment of the Asian population. Most potential confounders were carefully controlled for, which limits the possibility of residual confounding effect. The covariates in term of ages and ethnicity were in homogeneity and were matched with the case-control groups (Suppl. Fig. S2, Suppl. Table S6 and Suppl. Table S7). Given the well-established difference in circulating adiponectin (Heid et al., 2010; Zhuo et al., 2010) and resistin (Chen et al., 2009) levels between men and women, our samples were only comprised male subjects to avoid the confounding effect of gender. Clinical measurements were taken under standardised protocol and biomarkers were measured using assays with good precision.

Nevertheless, this study had limitations. The findings apply mainly to Malaysian men and may not be widely generalisable because of the homogeneity of the study population. Since this was not a prospective study, this study may have reverse causation due to possible effects of T2DM and MS on adiponectin and resistin levels. The results were based on single measurements of the adipokines and therefore may not reflect long-term exposure to these hormones. Although adjusted for known confounding factors, residual confounders imperfectly measured or unmeasured cannot be excluded. We measured total adiponectin and not the high molecular weight fraction, which had been proposed to have substantially more potent effects on hepatic insulin sensitivity compared with total adiponectin (Simpson & Whitehead, 2010). However, a recent study showed that total and high molecular weight adiponectin have similar utility for the identification of insulin resistance and metabolic disturbances (Almeda-Valdes et al., 2010). It had been reported that the low molecular weight form of resistin displays significantly increased bioactivity (Patel et al., 2004). However, the high molecular weight hexamer of resistin is predominant in the human circulation (Patel et al., 2004). Moreover, the measurement of total adiponectin and resistin are better standardised, cheaper, and more accessible than the high molecular weight adiponectin and the low molecular weight resistin. Thus, our study may stimulate the use of adiponectin and resistin in clinical and epidemiological settings.

Furthermore, different variants in the same gene might be more related to biomarker levels but not sufficiently to bring about associated disease states if they are likely to confer only a modest effect on the defect. Our study did not attempt to cover the upstream and downstream variations in both ADIPOQ and RETN regions. Further functional study may be needed to explain the significance of the variants at the RETN locus, for example, functional interaction between SNP-420C>G and SNP+299G>A in-cis. However, the degree to which DNA sequence variants at the ADIPOQ and RETN loci influence health and disease remains to be seen. Thus, fine-mapping and functional studies are needed to identify the true causal variants of these genes. Further replication studies and comprehensive meta-analysis are required.

Author Contributions

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and discussion
  6. Author Contributions
  7. Disclosures
  8. Funding/Support
  9. Acknowledgements
  10. References
  11. Supporting Information

Principle investigator: Cia-Hin Lau. Carried out the molecular genetic studies and laboratory works, collected the data and samples, performed the genotype and statistical analyses, and wrote the manuscript; Adviser and manuscript editor: Sekaran Muniandy. Participated in the design and coordination of the study and helped to edit the manuscript.

Funding/Support

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and discussion
  6. Author Contributions
  7. Disclosures
  8. Funding/Support
  9. Acknowledgements
  10. References
  11. Supporting Information

This work was supported by University of Malaya postgraduate research grants (PS102–2009A and PS201–2010A), short-term research university grant (FS232–2008C), and e-science fund grant (12–02-03–2044).

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and discussion
  6. Author Contributions
  7. Disclosures
  8. Funding/Support
  9. Acknowledgements
  10. References
  11. Supporting Information

We thank all the participants in the project. We are grateful to our lab members for support and helpful discussion throughout the project, and to the nurses of UMMC particulary Madam Farahwahidah for helping in blood sample collection.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and discussion
  6. Author Contributions
  7. Disclosures
  8. Funding/Support
  9. Acknowledgements
  10. References
  11. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results and discussion
  6. Author Contributions
  7. Disclosures
  8. Funding/Support
  9. Acknowledgements
  10. References
  11. Supporting Information

Supplementary Methods

Table S1 Minor allelic frequencies of SNP markers for each population.

Table S2 TaqMan probes sequences and labeling.

Table S3 PCR primers.

Table S4 PCR reaction mixtures.

Table S5 PCR program.

Table S6 Comparison of anthropometric clinical and metabolic parameters between the control and T2DM subjects.

Table S7 Comparison of anthropometric clinical and metabolic parameters between the control and MS subjects.

Table S8 Evaluation of Hardy–Weinberg equilibrium for each SNP marker.

Table S9 Association of adiponectin and resistin gene polymorphisms with T2DM risk.

Table S10 Association of adiponectin and resistin gene polymorphisms with MS risk.

Table S11 Association of adiponectin gene polymorphisms with T2DM and MS risk for each ethnic group.

Table S12 Association of resistin gene polymorphisms with T2DM and MS risk for each ethnic group.

Figure S1 Genetic architecture and location of the SNPs at the ADIPOQ and RETN loci.

Figure S2 Checking for homogeneity of covariates to match the subject groups.

Figure S3 Standard curve for ELISA adiponectin and resistin.

Figure S4 Allelic discrimination plots for TaqMan Real-Time PCR.

Figure S5 UV illumination photos of gel electrophoresis of purified PCR products.

Figure S6 Chromatogram of sequencing results for SNP+45T>G at the ADIPOQ locus.

Figure S7 Chromatogram of sequencing results for SNP+276G>T at the ADIPOQ locus.

Figure S8 Chromatogram of sequencing results for SNP+639T>C at the ADIPOQ locus.

Figure S9 Chromatogram of sequencing results for SNP+1212A>G at the ADIPOQ locus.

Figure S10 Chromatogram of sequencing results for SNP-420C>G at the RETN locus.

Figure S11 Chromatogram of sequencing results for SNP+299G>A at the RETN locus.

Supplementary Notes

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