Genetic Variations in ADIPOQ Gene Are Associated with Chronic Obstructive Pulmonary Disease

Background Adiponectin is reported to be related to the development of chronic obstructive pulmonary disease (COPD). Genetic variants in the gene encoding adiponectin (ADIPOQ) have been reported to be associated with adiponectin level in several genome–wide linkage and association studies. However, relatively little is known about the effects of ADIPOQ gene variants on COPD susceptibility. We determined the frequencies of single-nucleotide polymorphisms (SNPs) in ADIPOQ in a Chinese Han population and their possible association with COPD susceptibility. Methods We conducted a case–control study of 279 COPD patients and 367 age- and gender-distribution-matched control subjects. Seven tagging SNPs in ADIPOQ, including rs710445, rs16861205, rs822396, rs7627128, rs1501299, rs3821799 and rs1063537 were genotyped by SNaPshot. Association analysis of genotypes/alleles and haplotypes constructed from these loci with COPD was conducted under different genetic models. Results The alleles or genotypes of rs1501299 distributed significantly differently in COPD patients and controls (allele: P = 0.002, OR = 1.43 and 95%CI = 1.14–1.79; genotype: P = 0.008). The allele A at rs1501299 was potentially associated with an increased risk of COPD in all dominant model analysis (P = 0.009; OR: 1.54; 95%CI: 1.11–2.13), recessive model analyses (P = 0.015; OR: 1.75; 95% CI: 1.11–2.75) and additive model analyses (P = 0.003; OR: 2.11; 95% CI: 1.29–3.47). In haplotype analysis, we observed haplotypes AAAAACT and GGACCTC had protective effects, while haplotypes AGAACTC, AGGCCTC, GGAACTC, GGACACT and GGGCCTC were significantly associated with the increased risk of COPD. Conclusions We conducted the first investigation of the association between the SNPs in ADIPOQ and COPD risk. Our current findings suggest that ADIPOQ may be a potential risk gene for COPD. Further studies in larger groups are warranted to confirm our results.


Introduction
Chronic obstructive pulmonary disease (COPD) is a major global disease that has been predicted to be the third leading cause of mortality worldwide by the year 2020 [1], and it is estimated to affect nearly 8.2% of the Chinese adult population [2]. Cigarette smoking is the major environmental risk factor for COPD; however, only approximately 15% of smokers develop clinically relevant airflow obstruction [3] . The variation in the susceptibility to cigarette smoke, in combination with the familial inheritance pattern of COPD, suggests that there may be a genetic component to the development of COPD [4]. The associations between COPD and polymorphisms in genes with potential importance in COPD pathogenesis have been investigated [5]; however, only a1antitrypsin has been unequivocally identified as relevant to the development of COPD. Recently, polymorphisms in the CHRNA3-CHRNA5-IREB2, HHIP, and FAM13A loci have been found to be associated with COPD by genome-wide association studies (GWAS) [6][7][8].
There is increasing evidence of systemic inflammation in patients with COPD. Several disease biomarkers have been found to be helpful in assessing systemic and local inflammation, including interleukin (IL) 6, IL-8 and C-reactive protein (CRP) [9,10]. Adiponectin is a secretary 30 kD protein synthesized by adipocytes in healthy subjects. Its role in inflammation is controversial, as its plasma concentration decreases in diseases such as metabolic syndrome and type II diabetes [11], but increases in some inflammatory diseases such as rheumatoid arthritis and systemic lupus erythematosus [12,13]. Animal studies, for instance, suggest that reduced expression of adiponectin is associated with development of emphysema and is associated with the pathogenesis of cachexia and osteoporosis [14]. However, human studies indicate that circulating adiponectin levels are raised in COPD and associated with poor health outcomes including increased risk of exacerbations [15][16][17]. Thus, the relationship of adiponectin to COPD outcomes remains uncertain. Genetic factor accounts for about 40-70% of the variation in adiponectin levels [18]. Genetic variants in the gene encoding adiponectin (ADIPOQ) have been reported to be associated with adiponectin levels in several genome-wide linkage and association studies [18][19][20]. However, inconsistent findings on the association of genetic variants of ADIPOQ with adiponectin levels have been reported [18][19][20][21][22], which could be due to a difference in ethnic populations, single nucleotide polymorphism (SNP) selection, and study power.
With these considerations in mind, we hypothesized that polymorphisms in the ADIPOQ gene might modulate susceptibility to COPD. To test this hypothesis, we investigated the association of common genetic variants in the ADIPOQ gene with the risk of COPD in a Chinese Han population.

Ethics Statement
The use of human tissue and the protocol in this study were strictly conformed to the principles expressed in the Declaration of Helsinki and were approved by the Ethical Committee of the West China Hospital, Sichuan University, and written informed consent was obtained from all subjects before their participation in the study. The investigator explained the nature, purpose and risks of the study and provided the subject with a copy of the information sheet.

Subjects
As described previously [23], 279 patients with COPD and 367 age-matched non-COPD control subjects were recruited for this study. The subjects in both groups were unrelated ethnic Han Chinese individuals recruited from Chengdu city or surrounding regions in the Sichuan province of western China. All subjects underwent physical examinations including chest x-ray, anthropometric measurements including body mass index (BMI), assessment of lung function, and blood sampling. The recruitment and the clinical analyses were conducted at the department of respiratory medicine in West China Hospital of Sichuan University; clinical analyses were performed according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria [24]. COPD patients were enrolled when they suffered from cough, sputum production and dyspnea at least upon exertion and showed chronic irreversible airflow limitation defined by an FEV 1 (forced expiratory volume in 1s) to FVC (forced vital capacity) ratio ,70%, and FEV1 predicted ,80% after the inhalation of a b2-agonist. Patients were excluded from this study if they had other significant respiratory diseases, such as bronchial asthma, bronchiectasis, lung cancer, or pulmonary tuberculosis based on their chest x-ray test.
The age-matched non-COPD control subjects were volunteers who came to the West China Hospital of Sichuan University for physical examination only. The inclusion criteria for controls were as follows: (1) FEV1/FVC ratio .70%, FEV1% and FVC% predicted .80% and (2) without pulmonary disease. Individuals were excluded if they had a history of chronic lung disease, atopy, an acute pulmonary infection in the 4 weeks before assessment for this study, or a family history of COPD.

Biochemical Measurements
Blood samples were collected at baseline from patients and controls after an overnight fast. Plasma separated from cells by centrifugation at 500 g for 10 min at room temperature was used for lipid, glucose and adiponectin analyses. The plasma levels of total cholesterol, triglycerides and glucose were determined with an enzymatic kit (Boehringer Mannheim) and calibrated with a plasma calibrator. Circulating total adiponectin level was measured by the enzyme-linked immunosorbent assay method (Quantikine, R&D Systems, Minneapolis, MN, USA).

SNP Selection and Genotyping
Genotype data of the Chinese population for the ADIPOQ region were obtained from the HapMap website (http://www. hapmap.org/), and tag SNPs were selected using the Tagger software implemented in the Haploview software [25], with an r 2 threshold of 0.8 and minor allele frequencies (MAF) of 0.1. There were seven tagging SNPs (rs710445, rs16861205, rs822396, rs7627128, rs1501299, rs3821799 and rs1063537), which captured all the fifteen SNPs from 3-kb region upstream to 1-kb downstream of the gene (position 188,040,157-188,059,946 bp, GenBank accession number NM_004797.3, NCBI build 36).
Genomic DNA was extracted from peripheral blood leukocytes using a commercial extraction kit (Bioteke Corporation, Beijing, China) according to the manufacturer's instructions. SNPs were genotyped using the ABI SNaPshot method (Applied Biosystems, CA, USA). The genomic regions of interest were amplified by primers shown in Table 1. The PCR products were then purified by incubating with shrimp alkaline phosphatase (SAP) and exonuclease I (Exo I). The purified PCR products were used as the templates for SNaPshot reaction using the specific SNaPshot primers (Table 1). 3 ml of pooled PCR products, 1 ml of pooled SNaPshot primers and 1 ml of deionized water were incubated in a GeneAmp 9600 thermal cycler by 25 cycles at 96uC for 10 s, 50uC for 5 s, and 60uC for 30 s, and finally 60uC for 30 s. Then, 1 U of SAP was added to SNaPshot product and incubated at 37uC for an hour to deactivate the enzyme. The SNaPshot reaction products were mixed with Hi-Di formamide and GeneScan-120 LIZ internal size standard (Applied Biosystems), and analyzed on an ABI 3130 Genetic Analyzer (Applied Biosystems, CA, USA). The data were analyzed by the software of GeneMapper 4.0. Genotype analysis was performed in a blinded manner so that the staff was unaware of the cases or control status. For quality control, a 10% masked random sample of cases and controls was tested repetitively by different investigators and all the results were completely concordant.

Statistical Analyses
The demographic and clinical data between the COPD patients and the control subjects were compared using the x2 test and Student's t-test. A two-sided significance level of a ,0.05 was used for all significant tests. Statistical analyses were performed in SPSS version 17.0 and Microsoft Excel. A multiple logistic regression analysis using BMI and glucose as covariates was done to correct the significant p-value of adiponectin.
The Hardy-Weinberg equilibrium (HWE) test using two-sided x2 analysis was done for each SNP among cases and controls. Differences in the distribution of genotypes or alleles under different genetic models (including dominant, recessive and additive models) between the COPD patients and the controls were estimated by using the x2 test. Odds ratios (OR) and 95% CIs were calculated by unconditional logistic regression analyses [26,27]. Correction for multiple testing was performed by the SNP spectral decomposition method (SNPSpD) [28]. Under this method, the effective number of independent marker loci (MeffLi) was 7, and the experimental-wide significance threshold to keep type 1 error rate at 5% was 0.0098.
Pairwise linkage disequilibrium (LD) estimation and haplotype reconstruction were performed using SHEsis (http://analysis.bio-x.cn) [29]. For haplotype analysis, only haplotypes with a frequency .3% in at least one group were tested. We also used Haploview 4.2 [25] to estimate LD.

General Characteristics of the Subjects
The baseline characteristics, biochemical features and the results of the pulmonary function tests for the 279 patients with COPD and 367 control subjects were presented in Table 2. All patients had FEV 1 values ,80% of predicted and thus were diagnosed with moderate-to-severe COPD according to the Global Initiative for Chronic Obstructive Lung Disease [24] (classification of severity: mild = FEV 1 $80% of predicted; moderate = FEV 1 $50% to ,80% of predicted; severe = FEV 1 $30% to ,50% of predicted; and very severe = FEV 1 ,30% of predicted). The COPD cases and control subjects did not significantly differ in sex, age or smoking history. The FEV1, FEV1/predicted and FEV1/FVC were significantly lower in the COPD patients compared with the controls (P,0.01). Compared to control subjects, the COPD patients showed statistically higher glucose concentrations (p,0.01), however, they were still within the normal range. In addition, the COPD patients showed a significantly lower BMI (22.0362.27 kg/m 2 vs. 23.9162.48 kg/ m 2 , p,0.01) and a 1.4-fold higher adiponectin levels (8.5460.66 mg/ml vs 6.1260.57 mg/ml; p,0.01). The adiponectin levels remained significantly higher in COPD patients after further adjusting for BMI and glucose levels (p,0.01).

Distribution of the SNPs in ADIPOQ between COPD Patients and Controls
Seven SNPs in ADIPOQ, including rs710445, rs16861205, rs822396, rs7627128, rs1501299, rs3821799 and rs1063537, were screened in all 279 patients with COPD and 367 controls using the SNaPshot method. The genotype and allele frequencies of each SNP in both COPD patients and controls were presented in Table 3. All of the tested SNPs didn't significantly deviate from that expected for a Hardy-Weinberg equilibrium (HWE) in the COPD patients and controls (Table 3, all P values were higher than 0.05), illustrating that our subjects presented the source population well.
We compared the differences in frequency distributions of genotypes or alleles of every SNP between COPD patients and controls by x 2 test. As shown in Table 3, significant differences in allele or genotype frequencies were observed between COPD patients and controls at rs1501299 (allele: P = 0.002, OR = 1.43 and 95%CI = 1.14-1.79; genotype: P = 0.008).

Association of Genotypes with COPD under Different Genetic Models
For each SNP, if one allele frequency is relatively lower compared to another one, it is recognized as the minor allele (Table 3). We assumed that the minor allele of each SNP was a risk allele compared to the wild type allele. We compared the genotype frequencies of every polymorphism between groups under the dominant, recessive and additive genetic models, respectively. As shown in Table 4, the rs1501299 was observed to be associated with COPD risk by all dominant model analysis (P = 0.009; OR: 1.54; 95%CI: 1.11-2.13), recessive model analyses (P = 0.015; OR: 1.75; 95% CI: 1.11-2.75) and additive model analyses (P = 0.003; OR: 2.11; 95% CI: 1.29-3.47).

rs1501299 is Associated with Plasma Adiponectin Levels
We investigated the relationship between rs1501299 and plasma adiponectin levels in both COPD patients and control subjects (Figure 1). Compared to subjects with homozygote CC at rs1501299, the subjects with homozygote AA had significantly higher adiponectin levels after adjusting for BMI and glucose levels (6.8260.52 mg/ml vs. 5.8560.57 mg/ml in controls, 9.1360.49 mg/ml vs. 8.2760.53 mg/ml in COPD patients, P,0.05 in both groups).

Linkage Disequilibrium (LD) between SNPs and Haplotype Analysis
The extent of linkage disequilibrium in pairwise combinations of alleles in different SNP was estimated by means of maximum likelihood from the genotype frequency in the COPD and control groups. Pairwise LD between the seven SNPs was shown in Table 5 and Figure 2. Based on LD determinations, two blocks with moderate LD were detected: block 1 is composed of rs710445 and rs16861205, block 2 is composed of rs1501299, rs3821799 and rs1065537.
We estimated the frequencies of haplotypes constructed from phased multi-locus genotypes in ADIPOQ. The haplotypes with a frequency higher than 3% in at least one group were involved in the haplotype analysis ( Table 6). The global result for block 1 (rs710445 and rs16861205) was: x 2 = 22.99 while df = 3, P = 4.16610 25 . The global result for block 2 (rs1501299, rs3821799 and rs1065537) was: x 2 = 79.33 while df = 5, P = 7.66610 215 . The overall frequency distribution of haplotype composed of all seven SNPs was significantly different between cases and controls (total global x 2 = 159.35 while df = 10, P = 4.40610 229 ).
The results of the association between the ADIPOQ haplotype and the risk of COPD were listed in Table 6. Haplotype GA in block 1 was found to be associated with an increased risk of COPD (OR = 11.54, 95%CI = 3.20-41.57, P = 2.61610 26 ). In block 2, two haplotypes were observed to be associated with the risk of COPD (ACT: OR = 1.62, 95%CI = 1.20-2.10, P = 2.

Discussion
Adiponectin, an adipose tissue-derived cytokine, has important roles in insulin sensitization, cardioprotection, and anti-inammatory processes. Plasma adiponectin level is negatively correlated with body mass index (BMI), glucose, insulin and triglyceride levels and is positively associated with high-density lipoprotein cholesterol (HDL-C) concentration and insulin-stimulated glucose disposal [30]. Although its action on the respiratory system is not fully known, expression of adiponectin and its functional receptors on airway epithelium have been reported [31]. There have been several clinical studies reporting on the relationship between circulating adiponectin and COPD, and elevation of plasma adiponectin level was found in patients with stable and acute exacerbation of COPD [15][16][17]32]. The most bioactive form of adiponectin is a 400-kDa high molecular weight (HMW) complex [33], which was also found to be increased dramatically in COPD patients in a recent report [34].
In this case-control study in a Han Chinese population, we evaluated the possible association of ADIPOQ polymorphisms with susceptibility to COPD. To the best of our knowledge, this study was the first investigation of the association between the SNPs in ADIPOQ and COPD risk. Our current findings suggested that rs1501299 associated with the risk of COPD. In comparison with allele G at rs1501299, the allele A could increase the risk of COPD under all dominant, recessive and additive genetic models. The polymorphism rs1501299, also known as C276A, in ADIPOQ has been found to be in association with adiponectin levels in diverse population [35][36][37], and its A allele is associated with higher   adiponectin. One report showed that serum adiponectin levels were higher in COPD patients than in control subjects and were associated with weight loss and systemic inflammation as assessed by circulating tumor necrosis factor-a (TNF-a) levels [15]. A recently study showed that serum adiponectin was associated with all-cause mortality in COPD patients [38]. However, this association is conflicted in animal models. In one study, hypoadiponectinemia in ADIPOQ gene-knockout mice was associated with emphysema-like changes in the lungs and extrapul-monary features including systemic inflammation, obesity, and osteoporosis [14], while another study reported the opposite effect in which mice deficient in adiponectin were protected from emphysema when they were exposed to cigarette smoke [39]. In our current study, the association of rs1501299 in ADIPOQ and COPD might be modulated by its effects on the adiponection levels. However, rs1501299 is in intron 2 of the ADIPOQ gene and does not have a known function. It is probably a marker of some other variant affecting adiponectin expression. We assessed the   Figure S1), which indicated that rs1501299 was in a LD block encompassing the whole 3'UTR of the ADIPOQ gene, but whether and how far such block extends beyond the gene boundaries remain to be determined [40]. In addition to the genotype analysis, our study also adopted a haplotype based approach. Haplotype analysis, in which several SNPs within the same gene are evaluated simultaneously, can provide more information than a single SNP and thus elevates the statistical power of the analysis [41]. Using this approach, we provided strong support that ADIPOQ variations contributed to the susceptibility to COPD. LD analysis showed that some SNPs in ADIPOQ gene were in moderate LD, and some haplotypes with low frequency were found to affect the risk of COPD dramatically. Haplotypes AAAAACT and GGACCTC were associated with a reductive risk of COPD, while haplotypes AGAACTC, AGGCCTC, GGAACTC, GGACACT and GGGCCTC increased the risk of developing COPD, indicating the complexity of ADIPOQ gene in the development of COPD. This might be attributable to the complex genetic determinants of plasma adiponectin levels. In addition to the SNP in ADIPOQ gene, other gene loci such as 14q13 have been reported to affect plasma adiponectin value and play a much bigger role [40]. Recently, Genome wide association study (GWAS) for genetic markers in determining plasma adiponectin value in Korean population reported that genetic variants in CDH13 on chromosome 16, but not genetic variants in the ADIPOQ gene, influence adiponectin levels [42]. However, another GWAS using plasma adiponectin as a quantitative trait demonstrated the ADIPOQ gene as the only major gene for plasma adiponectin in Caucasian population [20].
We are aware that the significant results in this study could prove to be false positives because of the relatively small sample size. 279 COPD patients and 367 control subjects were not relatively large among COPD association studies published to date, further studies using larger populations are needed. But even with a larger sample, the functional and biological impacts of the described polymorphisms would require further study. Possible gene-gene and gene-environment interactions also pose a challenge for genetic analysis of COPD association studies. We selected SNPs with MAF higher than 10% in the Han Chinese population (CHB) using HapMap project data, this is not suited for situations where genetic architecture is such that multiple rare disease-causing variants contribute significantly to disease risk. Recent studies demonstrate that identification of rare variants may lead to critically important insights about disease etiology through implication of new genes and/or pathways [43,44]. The rare variants in the ADIPOQ gene should be investigated to clarify their susceptibility to the development of COPD.
In conclusion, our comprehensive analysis of SNPs in the ADIPOQ gene suggests that ADIPOQ genotypes and haplotypes are associated with COPD risk. These findings indicate that the genetic variants of ADIPOQ gene play a complex role in the development of COPD, and that interactions of loci in ADIPOQ gene may be more important than a single locus. Our findings in this study provided new evidence for the association between SNPs and haplotypes of ADIPOQ gene and the risk of COPD. Figure S1 Relative position of SNPs and LD map for ADIPOQ in the Han Chinese population (CHB) using HapMap project data. This figure shows that strong LD was observed between rs1501299 and the SNPs in the 39UTR of ADIPOQ.