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Keywords:

  • ADIPOQ gene;
  • adiponectin;
  • single nucleotide polymorphism;
  • metabolic syndrome

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure
  9. References

Our aim was to investigate whether the ADIPOQ gene polymorphisms are associated with the metabolic syndrome (MetS). Genotypes of MetS patients (n= 1049) and normal controls (n= 1092) were analysed by TaqMan® assay, and serum adiponectin concentration was measured by ELISA. The variant genotypes rs266729CG; rs1063539GC, GC/CC; rs16861205AA and rs7649121AT, AT/TT (Adjusted P= 0.037, 0.044, 0.025, 0.011, 0.019, 0.020, respectively) of the ADIPOQ gene were associated with MetS. Patients with rs266729CG, CG/GG genotypes (P= 0.034, 0.035) and rs7649121AT, AT/TT genotypes (P= 0.013, 0.022) had higher levels of serum adiponectin than those with the CC and AA genotypes respectively. Furthermore, the prevalence of haplotypes GGAAAATC and GGGTAACC was lower in cases (10.7% and 4.5%) than in controls (12.1% and 5.9%) [Adjusted ORs (95% CIs) = 0.70 (0.54–0.91), 0.65 (0.46–0.92)]. The ADIPOQ gene variants associated with the risk of MetS in this study must be validated by further functional studies to reveal any potential effects on metabolism.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure
  9. References

The metabolic syndrome (MetS), is now well recognised as being associated with an increased risk of cardiovascular disease (Jeremy 2011) as well as obesity, hypertension hyperglycemia, elevated plasma triglycerides and low high-density cholesterol. The prevalence of MetS in the adult population of China has been reported to be 14–18% and 60–80% in diabetes patients (Xu, 2005). The susceptibility of MetS patients to diabetes and cardiovascular disease is four times and two times greater than that of unaffected individuals respectively (Kissebah et al., 2000). Both environmental and genetic factors contribute to the development of MetS. At present, the etiology of MetS has not been clarified completely.

Adiponectin is an adipose tissue derived cytokine with anti-inflammatory and antiatherogenic properties, which was linked to central obesity and was proposed as a major contributor to MetS in addition to contributing the insulin resistance (Okamoto et al., 2006). Reduced plasma adiponectin levels were observed in patients with obesity, diabetes and those with other metabolic diseases (Yang & Chuang, 2006). Genome-wide scans mapped a susceptibility locus for MetS to chromosome 3q27, and the ADIPOQ gene encoding adiponectin was located in its vicinity (Schwarz et al., 2006b). Thus, the adiponectin gene has emerged as a susceptibility gene for MetS. To date, several studies of various single-nucleotide polymorphisms (SNPs) in the ADIPOQ gene and their associations with MetS traits have been reported. ADIPOQ gene SNPs and haplotypes have been found to associate with phenotypes related to obesity (Bouatia-Naji et al., 2006; Warodomwichit et al., 2009), insulin resistance and type 2 diabetes mellitus (T2DM) (Vasseur et al., 2005; Bouatia-Naji et al., 2006; Schwarz et al., 2006a; Tso et al., 2006; Menzaghi et al., 2007; Yang et al., 2008; Rasmussen-Torvik et al., 2009; Wang et al., 2009) and serum adiponectin levels (Pollin et al., 2005; Bouatia-Naji et al., 2006; Heid et al., 2006; Kyriakou et al., 2008; Hivert et al., 2008; Ling et al., 2009; Rasmussen-Torvik et al., 2009; Warodomwichit et al., 2009; Ferguson et al., 2010), whereas few reports focused on the relationship between SNPs of ADIPOQ gene and MetS (AlSaleh et al., 2011; Leu et al., 2011). In the present study, we provide a systematic investigation, including analysis of high-density SNPs of the ADIPOQ gene, to examine the potential association between genetic variants in the ADIPOQ gene and MetS risk in a Chinese Han population.

Materials and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure
  9. References

Subjects

Our study involved a total of 2141 subjects, consisting of 1049 MetS patients and 1092 MetS-free controls who were recruited with informed consent. All subjects were genetically unrelated ethic Han Chinese. The cases were consecutively enrolled between March 2008 and August 2010 from the MetS inpatient or outpatient departments of three affiliated hospitals of Nanjing Medical University (The Affiliated Changzhou 2nd Hospital of NJMU, the 3rd Affiliated Hospital of NJMU and the Affiliated Nanjing1st Hospital of NJMU) without the restrictions of age and sex (509 male and 540 female; aged 55.93 ± 11.02 years). Each subject was administered a standard questionnaire, and physical measurements taken by a trained doctor. The level of physical activity was defined as walking or riding ≥15 min/day and/or lifting or carrying heavy objects at work daily and/or doing sports or physical exercise >2 h/week. Current cigarette smokers were defined as subjects reporting at least one cigarette per day. Total alcohol intake was expressed as the sum of millimetres of alcohol per week from wine, beer, cider and spirits. The definition of MetS was based on clinical criteria from a recent joint interim statement of the International Diabetes Federation (IDF); National Heart, Lung and Blood Institute (NHLBI); American Heart Association (AHA); World Heart Federation; International Atherosclerosis Society and International Association for the Study of Obesity Alberti et al., 2009). In summary, a MetS case should meet any three of the following five conditions: (1) waist circumference (WC) ≥90 cm in males, ≥80 cm in females; (2) triglycerides (TG) ≥150 mg/dl in both genders; (3) high density lipoprotein (HDL) cholesterol <40 mg/dl in males, <50 mg/dl in females; (4) systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥85 mmHg in both genders (whereas antihypertensive drug treatment in a patient with a history of hypertension is an alternate indicator); (5) fasting glucose ≥100 mg/dl in both genders (whereas drug treatment of elevated glucose is an alternate indicator). MetS-free control subjects who had no history of MetS, frequency matched to the cases on age (±5 years) and gender, were randomly selected from outpatient clinics within the same geographical area and the period of the cases. Controls were enrolled with routine annual health examinations (491 male and 601 female; aged 55.74 ± 13.10 years). Between the cases and controls, there were no significant differences in the distribution of age and gender (t= 40.85, P= 0.705; χ2= 2.72, P= 0.100).

Measurements

Weight, height and WC were measured by trained personnel, and body mass index (BMI) was calculated. BP was measured on the right arm with the individual in a sitting position after a 10 min rest using a standard mercury sphygmomanometer of appropriate cuff size. After an overnight fast, venous blood samples were collected and promptly centrifuged, and the serum was stored at –20 °C. Serum adiponectin was measured by ELISA (human adiponectin ELISA kit; RapidBio, West Hills, CA, USA). Fasting plasma glucose (FPG) was measured in the laboratories in the three affiliated hospitals of NJMU with the glucose oxidase method. Total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol and TG were determined in the three affiliated hospitals by an enzymatic colorimetric method (Au5400; Olympus; Japan). DNA was isolated from peripheral blood by standard proteinase K and phenol/chloroform extraction method. This study was approved by the Research Ethics Committee of Nanjing Medical University.

SNP Selection and Genotyping

Two potentially functional SNPs (rs16861194 and rs266729) of the ADIPOQ gene located within the exonic region with minor allele frequency (MAF) ≥0.05 in the Chinese Han population were identified from the NCBI dbSNP database (http://www.ncbi.nlm. nih.gov/).

The selection of tagging SNPs was performed with the tagger programme implemented in Haploview (Version 3.2). Genotype data were downloaded from Hapmap (http://www.hapmap.org). Haploview was used to assess linkage disequilibrium for all possible SNP pairs by determining r2 (Arrett et al., 2005). Eight tagging SNPs were selected based on pair-wise tagging (r2≥ 0.80, MAF ≥ 0.05) using genotype data from the unrelated HapMap CHB individuals (Carlson et al., 2004). Thus, any marker that was not eventually chosen as a tagging SNP was already strongly correlated with at least one of the tagging markers with r2≥ 0.8. The 28 SNPs in the ADIPOQ gene could be tagged by eight tagging SNPs (rs2241767, rs822394, rs1063539, rs12495941, rs16861205, rs182052, rs3821799 and rs7649121), including seven within introns and one in the 3’ untranslated region (UTR); the coverage percentage of the ADIPOQ gene by the eight tagging SNPs genotyped was 28.57%.

Genotyping Assay

A 5′-Nuclease TaqMan® assay was used to genotype the polymorphisms in 384-well plates on ABI PRISM 7900HT Sequence Detection system (Applied BioSystems, Foster City, CA). The primers and probes of TaqMan® assays were designed using Primer Express Oligo Design software v2.0 (ABI PRISM) and are available on request as TaqMan® Pre-Designed SNP Genotyping Assays. The primer sequences used are shown in Table 1. PCR reactions were performed in a 5 μl reaction mixture containing 5 ng DNA, 2.5 μl 2* TaqMan® Universal PCR Master Mix and 0.083 μl 40* Assay Mix. The PCR conditions were: 50 °C for 2 min, 95 °C for 10 min, 95 °C for 15 s and then 60 °C for 1 min; 40 cycles of real-time PCR were performed. Individual genotype identification was performed by SDS software 2.0(ABI). Each plate contained two samples from the same individual as positive controls and two blank samples as negative controls for the genotyping quality confirmation. There was 100% consistency in a 5% sample of duplicate testing.

Table 1.  Primers and probe sequences for the amplification of ADIPOQ gene SNPs.
SNPPrimers and probes
rs266729SenseATTCTGTTTTGGATGTCTTGTTG
AntisenseCTTGGACTTTCTTGGCACG
Probe 1ATCCTGCCCTTCAA
Probe 2TCCTGCGCTTCAA
rs16861194SenseTGTCTTGTTGAAGTTGGTGCTG
AntisenseGACTTTCTTGGCACGCTCAT
Probe 1TGAATTAAATTACGACCCC
Probe 2TGAATTAAACTACGACCC
rs2241767SenseTCTTTCATCACAGACCTCCTACA
AntisenseGCACCATCTACACTCATCCTTG
Probe 1CAACCTGAAGTGATT
Probe 2CAACCTGAGGTGATT
rs822394SenseTCTTTTACAATCAGAGTCCGTTC
AntisenseCCACTAATAGGTGCGATCAGC
Probe 1TGCTAATCACACTCTT
Probe 2TGCTAATCCCACTCTT
rs7649121SenseGAGACATTCTTGGAGTTGAGTATTG
AntisenseCTGGAATCCCACTCCTATGC
Probe 1TATACTACCCTCTTCACGTG
Probe 2TATACTACCCACTTCACGTG
rs3821799SenseCTGCCTTTGGGGAACTCTT
AntisenseCATCAGGTCCACGGTGAGTAT
Probe 1TTTCTTGTGGTAACCAC
Probe 2CTTTCTTGTAGTAACCAC
rs16861205SenseGAGAAAAGCCTGGCATATAGTG
AntisenseTAACCTTCAGCATCCACAGC
Probe 1TGTTTCTAAGGCATCC
Probe 2TGTTTCTGAGGCATC
rs12495941SenseGCAGTGAGGTACCATTATTTCC
AntisenseCTCCTGATACATATCCCCACAT
Probe 1AGGGCATACCTTAACTA
Probe 2AAGGGCATAACTTAACTA
rs182052SenseCAGCCCCAAGAGAGAAAGG
AntisenseGAATTGGACTTCATCTGTGGAC
Probe 1CTGAATTTTACCCAGTTC
Probe 2CTGAATTTTGCCCAGTT
rs1063539SenseCTCTGGGGCAGGGTTATTC
AntisenseTCAAAGCATCACAGGACCATT
Probe 1ACAGAGACAGTCAACT
Probe 2ACAGAGAGAGTCAACT

Statistical Analysis

The distribution of the genotype frequencies between the MetS cases and MetS-free controls was compared using a two-sided χ2-test. Among controls, genotype frequencies for each SNP were tested for Hardy–Weinberg equilibrium. The association between genotypes and MetS was estimated by computing the odds ratios (ORs) and 95% confidence intervals (CIs) from multivariate logistic regression analysis with adjustment for age, gender, smoking, drinking and physical activity. Differences between quantitative traits were compared with the Univariate of General linear model. Haplotypes were predicted using the PHASE (version 2.1) Bayesian algorithm. A two-tailed P-value less than 0.05 was considered statistically significant. A 1000 times permutation test was performed by Stata 10.0 software. All the statistical analyses were carried out by Statistical Analysis System software (Version 9.1.3; SAS Institute, Cary, NC).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure
  9. References

Basic Characteristics

The clinical characteristics of the 2141 subjects divided into cases and controls according to MetS status are shown in Table 2.

Table 2.  Basic characteristics of the cases and controlsa.
VariablesCases (n= 1049)Controls (n= 1092)
  1. aData were presented as means ± S.D. SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; FG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

Gender (male:female)509:540491:601
Age (years) 55.93 ± 11.02 55.74 ± 13.10
SBP (mmHg)137.25 ± 19.54123.95 ± 18.34
DBP (mmHg) 85.10 ± 11.48 77.23 ± 10.07
TC (mmol/l)  5.21 ± 1.25  4.89 ± 0.93
TG (mmol/l)  2.78 ± 2.52  1.23 ± 0.73
FG (mmol/l)  8.98 ± 4.03  5.90 ± 2.81
HDL-C (mmol/l)  1.14 ± 0.41  1.44 ± 0.37
LDL-C (mmol/l)  2.72 ± 0.97  2.55 ± 0.83
Adiponectin(mg/l)  6.75 ± 2.39  6.97 ± 2.55
T2DM (n, %)874 (83.6)302 (27.7)
Obesity (n, %)750 (73.2)213 (19.9)

Logistic Regression Analysis for the Association Between Variant Genotypes of ADIPOQ Gene and MetS

The genotype distribution for all 10 SNPs did not show any deviation from Hardy–Weinberg equilibrium (all P > 0.05 in controls). Among the 2141 subjects, the successfully genotyped rates for 10 SNPs were all more than 95%. Compared with the GG homozygote genotype, the variant genotype GC and the combined genotype GC/CC of rs1063539 were all associated with a significant risk effect for MetS [Adjusted ORs = 1.22, 1.24; 95% CIs = (1.01–1.49), (1.03–1.49)]. The variant genotype CG [Adjusted OR (95% CI) = 0.81 (0.67–0.99)] of rs266729 was associated with a significantly decreased risk of MetS compared with the CC genotype. The variant genotype AA of rs16861205 was associated with a significant protective effect for MetS compared with the GG genotype [Adjusted OR(95% CI) = 0.49(0.28–0.85)]. Similarly, the variant genotype AT and the combined genotype AT/TT [Adjusted ORs = 0.79, 0.80; 95% CIs = (0.64–0.96), (0.66–0.97)] of rs7649121 compared with the AA genotype were all associated with a significantly decreased risk of MetS. After inspection by the 1000 times permutation test, the frequency distribution of those sites genotyped between the case and control group still contained statistical differences (Table 3). Furthermore, the stratified analysis by gender showed that the significantly lower risk of MetS was observed in females who carried the rs16861205AA genotype [Adjusted OR (95% CI) = 0.34 (0.15–0.80)]. Similarly, a decreased risk of MetS was observed in males who carried rs7649121AT genotype [Adjusted OR (95% CI) = 0.68 (0.51–0.91)] (Data not shown).

Table 3.  Distribution of genotypes in case and control groups.
GenotypesCases no.(%)Controls no.(%)Adjusteda OR (95% CI)AdjustedaP-valueP-valueb
  1. The bold values indicate positive results.

  2. aAdjusted for age, gender, smoking, drinking and physical activity.

  3. bThousand times permutation test.

  4. *Dom, dominant model.

rs2667299921022   
CC555 (55.9)530 (51.9)1.00 
CG353 (35.6)410 (40.1)0.81 (0.67–0.99)0.0370.043
GG84 (8.5)82 (8.0)1.03 (0.73–1.47)0.8610.779
CG/GG*437 (44.1)492 (48.1)0.85 (0.70–1.02)0.0790.088
rs22417679891016   
AA468 (47.3)533 (52.5)1.00 
AG425 (43.0)399 (39.3)1.15 (0.94–1.40)0.1660.189
GG96 (9.7)84 (8.3)1.27 (0.91–1.78)0.1650.153
AG/GG*521 (52.7)483 (47.6)1.17 (0.97–1.41)0.0990.147
rs1686119410101024   
AA673 (66.6)686 (67.0)1.00 
AG309 (30.6)293 (28.6)1.00 (0.82–1.23)0.9830.743
GG28 (2.8)45 (4.4)0.64 (0.38–1.07)0.0860.068
AG/GG*337 (33.4)338 (33.0)0.96 (0.79–1.16)0.6540.620
rs8223949981020   
CC779 (78.1)813 (79.7)1.00 
CA207 (20.7)197 (19.3)1.15 (0.91–1.45)0.2340.233
AA12 (1.2)10 (1.0)1.27 (0.54–2.97)0.5890.748
CA/AA*219 (21.9)207 (20.3)1.16 (0.92–1.45)0.2060.278
rs10635399961013   
GG475 (47.7)545 (538)1.00 
GC430 (43.2)390 (38.5)1.22 (1.01–1.49)0.0440.049
CC91 (9.1)78 (7.7)1.32 (0.93–1.87)0.1240.065
GC/CC*521 (52.3)468 (46.2)1.24 (1.03–1.49)0.0250.024
rs124959419811001   
GG333 (33.9)362 (36.2)1.00 
GT466 (47.5)472 (47.2)1.10 (0.87–1.35)0.3930.312
TT182 (18.6)167 (16.7)1.18 (0.89–1.35)0.2480.277
GT/TT*648 (66.1)639 (63.9)1.12 (0.92–1.36)0.2730.290
rs168612059891007   
GG679 (68.7)676 (67.1)1.00 
GA287 (29.0)287 (28.5)0.92 (0.75–1.13)0.4200.484
AA23 (2.3)44 (4.4)0.49 (0.28–0.85)0.0110.006
GA/AA*310 (31.3)331 (32.9)0.86 (0.71–1.05)0.1470.130
rs1820529871005   
GG328 (33.2)318 (31.6)1.00 
GA464 (47.0)471 (46.9)0.91 (0.73–1.13)0.3830.409
AA195 (19.8)216 (21.5)0.84 (0.65–1.10)0.2090.111
GA/AA*659 (66.8)687 (68.4)0.89 (0.73–1.09)0.2500.217
rs38217999831007   
TT798 (81.2)792 (78.6)1.00 
TC21 (2.1)25 (2.5)0.84 (0.46–1.53)0.5620.320
CC164 (16.7)190 (18.9)0.88 (0.69–1.24)0.3050.387
TC/TT*185 (18.8)215 (21.4)0.87 (0.69–1.10)0.2580.676
rs76491219971011   
AA620 (62.2)572 (56.6)1.00 
AT311 (31.2)362 (35.8)0.79 (0.64–0.96)0.0190.031
TT66 (8.6)77 (7.6)0.86 (0.59–1.25)0.4270.481
AT/TT*377 (39.8)439 (43.4)0.80 (0.66–0.97)0.0200.011

Phenotype Serum Adiponectin Level of Different Genotypes of rs266729, rs16861205, rs1063539 and rs7649121 of the ADIPOQ Gene in Case and Control Groups

Because rs266729, rs16861205, rs1063539 and rs7649121 had shown an association with MetS in the logistic regression analysis, we calculated the adiponectin level of different genotypes of the four SNPs. As presented in Table 4, patients with genotype CG and CG/GG of rs266729 had higher levels of serum adiponectin than those with the genotype CC (P= 0.034, 0.035). Patients with genotype AT and AT/TT of rs7649121 also had higher levels of serum adiponectin than those with the AA genotype (P= 0.013, 0.022).

Table 4.  Adiponectin level of the different genotypes of rs266729, rs16861205, rs1063539 and rs7649121 in case and control groups.
 CasesControls
  1. Data are presented as mean ± S.D (mg/l). The bold values indicate positive results.

  2. *Adjusted for age, gender, smoking, drinking and physical activity.

  3. aP= 0.034, versus CC genotype from Univariate of General linear model in cases.

  4. bP= 0.035, versus CC genotype from Univariate of General linear model in cases.

  5. cP= 0.013, versus AA genotype from Univariate of General linear model in cases.

  6. dP= 0.022, versus AA genotype from Univariate of General linear model in cases.

rs2667299921022
CC6.58 ± 2.137.03 ± 2.66
CG6.99 ± 2.80a6.90 ± 2.57
GG6.82 ± 2.546.63 ± 2.53
CG/GG6.96 ± 2.75b6.85 ± 2.56
*P-value0.034a, 0.035bN/A
rs168612059891007
GG6.77 ± 2.497.02 ± 2.66
GA6.66 ± 2.306.67 ± 2.41
AA6.85 ± 2.646.94 ± 3.16
GA/AA6.68 ± 2.296.78 ± 2.52
P-valueN/AN/A
rs10635399961013
GG6.78 ± 2.617.00 ± 2.66
GC6.70 ± 2.256.92 ± 2.45
CC6.69 ± 2.316.70 ± 2.29
GC/CC6.70 ± 2.266.88 ± 2.43
P-valueN/AN/A
rs76491219971011
AA6.59 ± 2.226.89 ± 2.64
AT7.03 ± 2.74c7.07 ± 2.60
TT6.76 ± 2.626.74 ± 2.55
AT/TT6.98 ± 2.72d7.01 ± 2.59
*P-value0.013c, 0.022dN/A

Haplotype Analysis

PHASE (Version 2.1) was used to determine haplotypes. The total number of successfully genotyped subjects was 2009 for two potential functional SNPs and 1815 for eight tagging SNPs. In the haplotypes of potential functional SNPs, there were no halotypes which may increase or decrease the risk of MetS compared with the most common haplotype, even after adjusting for age, gender, smoking, drinking and physical activity (Table 5). In the haplotypes of tagging SNPs, compared with the most common haplotype CTGAGGTC, the haplotypes GGAAAATC and GGGTAACC were less common in cases (10.7% and 4.5%) than in the controls (12.1% and 5.9%). These two haplotypes were associated with a significant protective effect for MetS [Adjusted ORs (95% CIs) = 0.70 (0.54–0.91), 0.65 (0.46–0.92)] (Table 6).

Table 5.  ORs and 95% CIs for the association between two potentially functional SNP haplotypes and MetS in the case-control study.
HaplotypesaCases (n= 989)Controls (n= 1020)Adjusted P-valuebAdjusted OR (95% CI)b
Chromosome no.%Chromosome no.%
  1. aLoci of SNPs are written 5’ to 3’ and include the following SNPs: rs266729, rs16861194.

  2. bAdjusted for age, gender, smoking, drinking and physical activity.

CA110155.7109353.61.00
GA51626.156627.70.3010.91(0.76–1.09)
CG35718.037318.30.2040.90(0.78–1.06)
GG40.280.40.2520.49(0.15–1.16)
Table 6.  ORs and 95% CIs for the association between eight SNPs haplotypes and MetS in the case-control study.
HaplotypesaCases (n= 886)Controls (n= 929)Adjusted P-valuebAdjusted OR (95% CI)b
Chromosome no.%Chromosome no.%
  1. The bold values indicate positive results.

  2. aLoci of SNPs are shown in a 5’ to 3’ orientation and include the following SNPs: rs1063539, rs12495941, rs16861205, rs7649121, rs182052, rs2241767, rs3821799, rs822394.

  3. bAdjusted for age, gender, smoking, drinking and physical activity.

CTGAGGTC35219.930916.61.00
GTGAGATC20211.422712.20.0670.79 (0.61–1.02)
GGAAAATC18910.722412.10.0070.70 (0.54–0.91)
GGGTAATC19511.020210.90.2050.84 (0.65–1.10)
GGGAGATA1186.71075.80.8600.97 (0.71–1.34)
GGGAAATC1186.7975.20.5051.12 (0.81–1.55)
GGGTAACC804.51105.90.0140.65 (0.46–0.92)
others51829.258231.30.0270.79 (0.65–0.97)

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure
  9. References

Adipose tissue has been postulated to play a prominent role in both insulin resistance and MetS traits. Adiponectin is the most quantitatively abundant adipokine secreted by adipocytes. One previous study (Ryo et al., 2004) proposed adiponectin as an important mediator and biomarker of MetS. Adiponectin concentrations also have a strong genetic component, whereas the associations between SNPs of ADIPOQ gene and MetS were reported infrequently. In this study, we studied potential functional SNPs, tagging SNPs and haplotypes, combined with environmental factors, to find whether ADIPOQ gene polymorphisms are associated with the MetS phenotype in a large sample size Chinese Han population (case/control = 1049/1092).

In the single locus analysis, rs266729CG and rs7649121AT, AT/TT contribute an independently decreased risk for MetS compared with rs266729CC and rs7649121AA. The association between lower risk of MetS and rs7649121A/T SNP was more evident in male subjects according to the analysis stratified by sex. Thus far, there has been little focus on the association between these two SNPs and the risk of MetS. However, studies investigating the association between these two SNPs and the risk of T2DM have been reported frequently. The allele C of rs266729 was associated with increased risk of T2DM in a German population (Schwarz et al., 2006a). Similarly, in a cohort of European origin, individuals who carried allele C of rs266729 had lower clamp-derived insulin sensitivity compared with those carrying allele G (Rasmussen-Torvik et al., 2009). In our previous study (Du et al., 2011; Zhao et al., 2011) of the association between ADIPOQ gene variants and T2DM, the results showed that individuals with rs7649121AT and rs7649121AT/TT genotypes were associated with a significantly decreased risk of T2DM after adjusting for age, gender and BMI. Sanghera et al. (2010) found that carrying haplotype GA (with variant allele G of rs182052G/A and A of rs7649121A/T) could decrease the risk for T2DM in the India population. A similar result was obtained in our study. In our subjects, patients whose fasting glucose ≥100 mg/dl were far more frequent in cases (83.6%) than in controls (27.7%) (P < 0.001) suggesting that. hyperglycemia may contribute some effects to the MetS phenotype. Furthermore, in the analysis of phenotype adiponectin level of different genotypes, we found that patients with rs266729CG, CG/GG genotypes (P= 0.034,0.036) and rs7649121AT, AT/TT genotypes (P= 0.013,0.023) had higher levels of serum adiponectin than those with the CC and AA genotypes respectively. It could be tentatively concluded that these two SNPs may be the genetic marker of MetS by their phenotype, i.e. the levels of adiponectin secretion. Our results also showed that MetS patients had lower serum adiponectin levels compared with controls (P= 0.043). Hung's finding (Hung et al., 2008) suggested that higher circulating adiponectin levels may mitigate against adipose-related inflammation, insulin resistance and MetS as much in lean as obese persons. The low serum adiponectin level is a strong risk marker for MetS, which is independent of measures of adiposity, insulin resistance and inflammatory markers. Many studies also focused on the association between ADIPOQ gene SNPs and adiponectin levels (Vasseur et al., 2005; Heid et al., 2006; Hivert et al., 2008; Kyriakou et al., 2008; Ling et al., 2009; Warodomwichit et al., 2009). However, the study of phenotype serum adiponectin levels in individuals with different genotypes of rs266729 and rs7649121 of the ADIPOQ gene has not been reported yet.

Interestingly, our study suggested that the variant genotype AA of rs16861205 was associated with a significant protective effect for MetS, especially in the female subjects. To date, one Finnish study (Siitonen et al., 2011) reported that the A allele of rs16861205 was dependently associated with an increase in serum adiponectin levels and lower body weight compared with the G allele. This may suggest that carriers of the A allele of rs16861205 benefit more from weight loss in terms of a change in adiponectin concentrations. This result was similar to our study. At present, no studies investigating the association between rs1063539 of the ADIPOQ gene and the risk of MetS have yet been reported. Wang, who studied the Chinese Han population in Sichuan province, stated that subjects with allele G of rs1063539 might be at a decreased risk of nonobese diabetes (Wang et al., 2009). In agreement with that study, our results showed that the variant genotype GC of rs1063539 compared with the GG genotype was associated with an increased risk of MetS. However, these findings are preliminary and the mechanisms by which the variants increase the risk of MetS should be clarified in further studies.

Haplotypes of the ADIPOQ gene in the eight tagging SNPs were analysed between case and control subjects. We found that the two kinds of haplotypes GGAAAATC (with variant allele G of rs1063539G/C and A of rs16861205G/A) and GGGTAACC (with variant allele G of rs1063539G/C and T of rs7649121A/T) could decrease the risk of MetS compared with the most common haplotype CTGAGGTC. This result was coincident with the single locus analysis. It is worth pointing out that we failed to find any association between MetS and different genotypes of rs12495941G/T, rs182052G/A, rs2241767A/G and rs822394C/A in the single locus analysis, whereas the four SNPs played a role in the haplotype analysis. This suggests that the haplotype analysis had a significant advantage in locus–locus interactions. To our knowledge, no previous data were available concerning the haplotypes of the ADIPOQ gene in the eight tagging SNPs for the risk of MetS. The exact mechanism of the haplotype effect is not fully clarified. Therefore, potential locus–locus interactions in SNPs of the ADIPOQ gene need to be further elucidated in future studies.

At present, there are some reports (Schwarz et al., 2006b; Tso et al., 2006; Wang et al., 2009) on the association between rs16861194A/G and rs182052G/A of the ADIPOQ gene with the risk of MetS, T2DM and obesity. However, we failed to find any association between these ADIPOQ gene polymorphisms and MetS. This inconsistency may be the result of different genetic background, sample sizes and different sample inclusion criteria or other environmental factors.

Like all other case-control studies, inherent biases existed. Because the cases were from hospitals, the study subjects may not be fully representative of the general population, so this limitation may influence the observed association. Although less than 5% of each loci of the DNA samples failed for genotyping, this may cause some selection bias. Other limitations relate to the lack of confirmation of our findings in other populations.

In conclusion, our case-control study provides evidence that polymorphisms of the ADIPOQ gene are associated with the risk of MetS in a Chinese Han population. However, these preliminary findings are validated with more comprehensive and informative data and the mechanisms by which the variants increase or decrease the risk of MetS should be clarified in further well-designed molecular epidemiological studies.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure
  9. References

We would like to thank the National Natural Science Foundation of China (Grant No. 30771858) and the Jiangsu Provincial Natural Science Foundation (Grant No. BK2007229).

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure
  9. References
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