Abstract
The authors examined whether the adiponectin gene (ADIPOQ) variant was associated with blood pressure and arterial stiffness in Chinese adults. A genome‐wide association study of the adiponectin variant rs864265 in the ADIPOQ gene was genotyped in a total of 2364 participants. After adjustment for sex, age, body mass index (BMI), fasting glucose, and lipids, participants carrying the T allele of rs864265 showed a greater increase in carotid‐femoral pulse wave velocity (cfPWV) and systolic blood pressure (SBP). Further adjustment for blood pressure did not appreciably change the association with cfPWV. The authors found significant interactions between rs864265 and BMI, waist circumference, body fat percentage, and SBP in relation to cfPWV (P for interaction = .035, .001, .003, .013, respectively). The T allele of rs864265 was associated with high blood pressure and arterial stiffness. BMI, body fat percentage, waist circumference, and SBP might modify the effects of genetic polymorphism on arterial stiffness.
Adiponectin is a hormone produced predominantly by adipocytes, which exerts antiatherogenic effects via action on endothelium, inhibition of lipid accumulation, and vascular smooth muscle cell proliferation and anti‐inflammatory effects.1, 2, 3 Indeed, emerging evidence has shown that hypoadiponectinemia may play an important role in the pathogenesis of cardiovascular diseases.4, 5, 6, 7 Recent genome‐wide association studies identified genetic variants in the adiponectin gene (ADIPOQ) associated with blood levels of adiponectin.8, 9, 10, 11, 12 The ADIPOQ variants have also been related to other metabolic disorders such as diabetes and insulin resistance.13, 14, 15 Among these genetic variants, one SNP rs864265, located 5 kb upstream of the widely discussed ADIPOQ promoter region and 16 kb upstream of the translation start site (chr3: 188 036 986), showed the strongest association with plasma adiponectin, also associated with insulin resistance and diabetes‐related metabolic traits.11, 12 However, data are scarce on the relationship between ADIPOQ variants and markers of early‐stage atherosclerosis.16
In the present study, we examined the associations of ADIPOQ polymorphism rs864265 with measure of early‐stage atherosclerosis (arterial stiffness) in Chinese adults. Since adiponectin level is determined by adiposity and correlated with blood pressure (BP),17, 18 a factor closely related to arterial stiffness19, 20 we particularly assessed the interactions between rs864265 and these factors in relation to arterial stiffness.
Methods
Participants
This study was designed as part of the Cardiometabolic Risk in Chinese (CRC) study, in which 2364 Chinese participants with genotype data available at baseline were sequentially recruited. All participants (aged 29–79 years) were randomly selected from residents living in central China who completed a community‐based health examination survey. All participants completed the baseline assessments, which included age, body weight, body mass index (BMI), body fat percentage (BFP), neck circumference, waist circumference, pulse wave velocity (PWV), BP, and other metabolic markers.
The exclusion criteria included a history of diabetes mellitus, vascular disease, hyperlipidemia, and treatment with medication. Individuals who had a serum creatinine level >1.5 mg/dL were also excluded. There was no significant difference between the basic characteristics of the participants with and without genotype data.
The study was reviewed and approved by the ethics committee of the Central Hospital of Xuzhou, Affiliated Hospital of Medical School of Southeast University, China, and participants provided written informed consent for participation, including consent for genetic analysis.
Assessment of Biomarkers and Covariates
Biomarkers were measured for all participants. Venous blood sampling from all participants was performed after fasting overnight (8–12 hours). After blood was drawn, samples were allowed to clot at room temperature for 1 to 3 hours. Following clotting, serum was immediately separated by centrifugation for 15 minutes at 3000 rpm. The blood samples were collected for measurement of serum uric acid, fasting blood glucose, serum insulin, total cholesterol, total triglyceride, high‐density lipoprotein cholesterol (HDL‐C), and low‐density lipoprotein cholesterol (LDL‐C) levels using an autoanalyzer (Type 7600; Hitachi Ltd, Tokyo, Japan). Glycosylated hemoglobin (HbA1c) was measured using high‐performance liquid chromatography (HPLC; HLC‐723G7 hemoglobin HPLC analyzer; Tosoh Corp, Minato, Tokyo) according to the standardized method. Participants underwent a 75‐g oral glucose tolerance test. Blood samples were drawn at 120 minutes after glucose or carbohydrate load. Urinary microalbumin level was measured by the Bromcresol green photometric method (IatroFine ALB II).
Body weight was recorded to the nearest 0.1 kg with the participants wearing light indoor clothing and no shoes. Height was recorded to the nearest 0.5 cm without shoes using a standardized wall‐mounted height board. BMI was calculated as weight (in kilograms) divided by height (in meters) squared. Waist circumference, an index of total abdominal fat, was measured at the mid‐point between the lowest rib margin and the iliac crest.21 Neck circumference, an index of upper‐body fat, was measured with a flexible tape in a standardized manner horizontally above the cricothyroid cartilage to 1‐mm accuracy.22 BP was measured by trained doctors using a mercury sphygmomanometer on the dominant arm after a resting period of at least 5 minutes in the supine position.23, 24 The participant's arm was placed at the heart level, and BP values were taken as the mean of three measurements. Individuals who had a sitting BP ≥140/90 mm Hg or who were taking antihypertensive drugs regularly were defined as hypertensive.25
Assessment of PWVs
Carotid‐femoral PWV (cfPWV), a marker for central aortic stiffness, was measured using an automatic waveform analyzer (Complior System; Artech Medical Corp, Pantin, Franc). Following 5 minutes of rest in a supine position, occlusion and BP monitoring cuffs were placed around both arms and ankles of the participant. Carotid and femoral artery pressure wave forms were recorded with multi‐array tonometry sensors at the left femoral artery and the left carotid arteries. Electrocardiography was monitored by electrodes on both wrists. Carotid artery dorsalis pedis PWV (cdPWV) and carotid‐radial PWV (crPWV), the markers for peripheral arterial stiffness, were obtained in a similar manner, with the pulse wave measured simultaneously in the right radial dorsum of the foot and right carotid arteries. Sixteen PWVs were measured for each participant, and the average PWV was calculated after removing the three highest and three lowest measurements. PWV was based on the distance/time ratio (meters/second), and was calculated as the path length between arterial sites of interest divided by the time delay between the foot of the respective waveforms.26, 27
Genotype Determination
Genomic DNA was extracted from the buffy coat fraction of centrifuged blood by using a QIAamp Blood Mini Kit (Qiagen, Chatsworth, CA). We selected the SNP rs864265 in the ADIPOQ gene that is associated with plasma adiponectin levels.5 The rs864265 genotypes were determined by TaqMan SNP allelic discrimination with an ABI 7900HT (Applied Biosystems, Foster City, CA) and the genotype success rate was 99%. Replicate quality‐control samples (10%) were included and genotyped with greater than 99% concordance.
Statistical Analysis
Data management and statistical analysis were performed using SAS statistical software (version 9.1; SAS Institute, Inc, Cary, NC). The Hardy‐Weinberg equilibrium of rs864265 genotypes was examined by chi‐square test. Differences in continuous variables at baseline were tested using one‐way analysis of variance. Data are presented as means±standard deviations (SDs). The relationship between rs864265 and PWVs or BPs were examined using multivariate linear regression models. We adjusted for multiple potential confounding variables in the models. Multivariate linear regression models were used to test the potential interactions of genetic variation with BMI, BFP, waist circumference, and systolic BP (SBP) in relation to arterial stiffness. All reported P values are two‐tailed. Variables with P<.05 were considered statistically significant.
Results
Baseline Characteristics of the Study Participants
The risk allele frequency of rs864265 (T allele) was 13.9% in all participants. Table 1 shows the baseline characteristics of the participants according to rs864265 genotype. There was significant difference in adiposity measures including body weight, BMI, neck circumference, and waist circumference across the genotypes of rs864265. In addition, the genotypes were correlated with systolic BP (P<.05). We also did not observe significant differences between rs864265 genotypes and other metabolic markers.
Table 1.
Baseline Characteristics of the Study Participants According to rs864265 Genotypes
| rs864265 | ||||
|---|---|---|---|---|
| GG(0) | GT(1) | TT(2) | P Value | |
| No. | 1789 | 492 | 83 | |
| Age, y | 46.5±9.35 | 46.0±9.61 | 48.4±11.38 | .092 |
| Male, No. (%) | 961 (53.7) | 274 (55.7) | 46 (55.4) | .720 |
| Weight, kg | 70.9±11.48 | 71.6±11.49 | 75.1±10.34 | .003 |
| BMI, kg/m2 | 24.7±3.06 | 25.0±3.15 | 26.1±3.02 | <.001 |
| NC, cm | 36.6±3.59 | 37.0±4.13 | 37.8±3.03 | .01 |
| WC, cm | 87.0±9.49 | 87.8±9.80 | 91.4±8.44 | <.001 |
| BFP, % | 26.84±5.00 | 26.76±4.99 | 26.44±1.78 | .749 |
| FBG, mmol/L | 5.28±1.27 | 5.28±1.19 | 5.37±1.20 | .817 |
| FINS, μ/L | 9.62±6.90 | 10.2±8.05 | 9.7±5.79 | .234 |
| HbA1c, % | 5.86±0.86 | 5.82±0.81 | 5.9±0.88 | .666 |
| OGTT2 hours, mmol/L | 7.6±3.16 | 7.5±2.92 | 7.8±3.47 | .813 |
| U‐mALB, mg/L | 31.7±39.4 | 32.5±42.06 | 40.3±48.71 | .164 |
| TC, mmol/L | 5.07±0.91 | 5.12±0.95 | 5.06±0.86 | .613 |
| TG, mmol/L | 1.78±1.80 | 1.95±1.84 | 1.98±1.64 | .113 |
| HDL‐C, mmol/L | 1.23±0.30 | 1.21±0.30 | 1.17±0.27 | .147 |
| LDL‐C, mmol/L | 3.02±0.78 | 3.01±0.82 | 2.98±0.78 | .869 |
| SBP, mm Hg | 123.51±16.16 | 125.97±14.8 | 126.36±18.05 | .024 |
| DBP, mm Hg | 80.29±11.81 | 79.24±10.43 | 79.18±11.78 | .167 |
Abbreviations: BFP, body fat percentage; BMI, body mass index; DBP, diastolic blood pressure; FBG, fasting blood glucose; FINS, fasting insulin; HbA1c, glycosylated hemoglobin; HDL‐C, high density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; NC, neck circumference; OGTT2 hours, 2 hours blood glucose of oral glucose tolerance test; SBP, systolic blood pressure; SUA, serum uric acid; TC, total cholesterol; TG, triglycerides; U‐mALB, urinary microalbumin; WC, waist circumference. Data are presented as mean±standard deviations or number (percentage).
Associations Between rs864265 and PWVs
Table 2 displays the associations between PWVs and genotypes of rs864265. After adjustment for sex and age, participants with the T allele were associated with higher cfPWV values than those without this allele (P for trend = .001), while no significant associations were found between rs864265 and crPWV or cdPWV. Further adjustment for BMI did not significantly change the associations. Additional adjustment for SBP and other biomarkers did not appreciably change the results (P=.012).
Table 2.
Associations of rs864265 With Arterial Stiffness
| GG(0) | GT(1) | TT(2) | P for Trend | |
|---|---|---|---|---|
| cfPWV, m/s | ||||
| Model 1 | 10.7±1.78 | 11.0±1.95 | 11.3±2.53 | .001 |
| Model 2 | 10.6±1.78 | 11.0±1.95 | 11.3±2.58 | .002 |
| Model 3 | 10.8±1.79 | 11.1±1.95 | 11.3±2.58 | .012 |
| crPWV, m/s | ||||
| Model 1 | 10.5±1.55 | 10.6±1.58 | 10.6±1.56 | .604 |
| Model 2 | 10.4±1.55 | 10.6±1.58 | 10.6±1.51 | .641 |
| Model 3 | 10.5±1.55 | 10.8±1.54 | 10.6±1.56 | .796 |
| cdPWV, m/s | ||||
| Model 1 | 9.7±1.51 | 10.0±1.43 | 9.8±1.27 | .070 |
| Model 2 | 9.8±1.51 | 10.0±1.45 | 9.8±1.23 | .126 |
| Model 3 | 9.7±1.52 | 10.0±1.43 | 9.9±1.27 | .342 |
Abbreviations: cdPWV, carotid artery dorsalis pedis pulse wave velocity; cfPWV, carotid‐femoral pulse wave velocity; crPWV, carotid‐radial pulse wave velocity. Data are presented as means±standard deviations. Model 1: adjusted for sex and age. Model 2: adjusted for sex, age, and body mass index. Model 3: adjusted for sex, age, body mass index, blood pressure, fasting glucose, total cholesterol, triglycerides, high‐density lipoprotein cholesterol, and low‐density lipoprotein cholesterol, when they were not the strata variables.
Interaction Between rs864265 and Metabolic Risk Factors on Central Arterial Stiffness and SBP
We further examined the interactions between the rs864265 genotypes and other metabolic risk factors in relation to cfPWV (Table 3). Considering the study power for the stratified analyses, we grouped the strata factors (BMI, BFP, waist circumference, SBP, and diastolic BP) into three categories (tertiles): low, median, and high. We found significant interaction between rs864265 and all the adiposity measures in relation to cfPWV. Participants with the T allele showed a greater increase in cfPWV in the groups with median and high adiposity measures (BMI, BFP, and waist circumference) than in those with low levels of these measures.
Table 3.
Interactions Between rs864265 Polymorphism and Adiposity in Relation to cfPWV
| GG(0) | GT(1) | TT(2) | P for Trend | P for Interaction | |
|---|---|---|---|---|---|
| cfPWV | |||||
| BMI, kg/m2 | |||||
| Low (<23.5) | 10.6±1.96 | 10.6±1.85 | 10.7±3.03 | .505 | .035 |
| Median (23.5–26.1) | 10.7±1.77 | 10.9±1.56 | 11.3±2.72 | .018 | |
| High (>26.1) | 10.9±1.58 | 11.3±2.28 | 11.4±2.36 | .009 | |
| BFP (%) | |||||
| Low (<24.8) | 10.7±1.64 | 10.8±1.56 | 10.6±1.21 | .377 | .001 |
| Median (24.8–28.7) | 10.8±1.78 | 10.8±1.53 | 10.8±1.72 | .700 | |
| High (>28.7) | 10.7±1.93 | 11.1±2.57 | 12.1±3.68 | .001 | |
| WC, cm | |||||
| Low (<84) | 10.4±1.71 | 10.3±1.51 | 9.6±0.99 | .191 | .003 |
| Median (84–91) | 10.8±1.78 | 11.1±1.76 | 10.8±1.80 | .005 | |
| High (>91) | 11.1±1.84 | 11.4±2.24 | 11.8±2.78 | .012 | |
Abbreviations: BFP, body fat percentage; BMI, body mass index; cfPWV, carotid‐femoral pulse wave velocity; WC, waist circumference. Data are expressed as means±standard deviations.
In addition, we found that SBP significantly interacted with rs864265 (P=.013) and these two markers showed an additive effect on cfPWV (Figure). Compared with the group with the G allele and lowest SBP, individuals in the group with the T allele and highest SBP had nearly 3 m/s higher cfPWV. We did not find an interaction between rs864265 and diastolic BP on cfPWV.
Figure 1.
The joint effect of rs864265 genotypes and systolic blood pressure (SBP; low, median, and high levels) on the risk of arterial stiffness. The mean values of carotid femoral pulse wave velocity (cfPWV) are presented.
Discussion
In the present study of Chinese adults, ADIPOQ polymorphism rs864265 was found to be significantly related to arterial stiffness, independent of other cardiovascular risk factors. Furthermore, the deleterious effect of the T allele in rs864265 polymorphism was modified by body adiposity and SBP.
Several previous studies have shown that hypoadiponectinemia is associated with hypertension.5, 28 Recent genome‐wide association studies identified genetic variants in the adiponectin gene (ADIPOQ) associated with blood levels of adiponectin. Our results indicate that the adiponectin‐associated ADIPOQ SNP rs864265 was related to arterial stiffness, a subclinical measure of early‐stage atherosclerosis. Because genetic markers are less likely affected by confounding and free of reverse causation, the genetic association may provide evidence supporting the potential causal relationship between adiponectin and arterial stiffness.29 In a recent cohort study, adiponectin was found to be independently related to brachial‐ankle PWV,16 which is an index of both central and peripheral arterial stiffness index. However, it is not clear whether adiponectin levels specifically affect central or peripheral arterial stiffness.
Intriguingly, we found that the association between the rs864265 polymorphism and cfPWV was stronger among adults with higher BMI, BFP, and waist circumference, which are markers of obesity. Recent studies demonstrate that obesity represents a major independent risk factor for cardiovascular diseases, including arterial stiffness.30, 31 In adults, a previous study reported a positive relationship between obesity and PWV.32 Adiponectin, a 244‐amino acid protein, is secreted by adipocytes.33 Genome‐wide association studies reported that the T allele of ADIPOQ SNP rs864265 was associated with lower adiponectin level.11, 12 Evidence has also shown that BMI,31 BFP,34 and waist circumference31 are significantly positively correlated with arterial stiffness. These findings as well as our results suggest that rs864265 and obesity synergistically affect central arterial stiffness. The T allele of rs864265 was associated with significantly higher arterial stiffness risk in obese patients.
Another interesting finding of our study was that the adverse effects of the T allele of rs864265 on central arterial stiffness appeared more evident in participants with higher SBP. Arterial stiffness is strongly associated with BP. A recent study demonstrated that PWV was positively related to peripheral or central SBP and that for 1 standard deviation of peripheral SBP, PWV increases 0.329 m/s.32 Samar and colleagues also found that lower levels of adiponectin and higher levels and annual changes of SBP were associated with greater progression in aortic stiffness.35 The mechanisms underlying such an additive effect between rs864265 and BP remain unclear; however, some studies have shown that PWV reflects functional or even structural changes in the vascular wall structure as part of the evolving hypertension process and is therefore a harbinger of further BP elevation in the future.32 Therefore, one possibility is that the T allele of rs864265 affects arterial stiffness through an increase in BP. Further investigations are needed to examine this postulation.
Study Limitations
Several limitations of this study warrant consideration. First, we did not measure plasma adiponectin levels in the study participants. This prevented the potential analysis of the relationship of genetic variants and plasma adiponectin levels and the roles of plasma adiponectin levels in the observed associations. However, according to the Mendelian randomization principle, a genetic marker could be a surrogate for the biomarker in yielding a causal relationship.36, 37 Still, we acknowledge that measuring the plasma adiponectin levels would provide additional evidence and strengthen our conclusion. Second, only the ADIPOQ rs864265 polymorphism was genotyped in the study. A more comprehensive coverage of SNPs in and around this region would provide more information regarding the role of ADIPOQ genetic polymorphisms in arterial stiffness risk. Third, we did not collect information on dietary habits and all lifestyle information. Therefore, it is possible that residual confounding of these unmeasured variables might influence the associations. Last, as all participants were Chinese, the results may not be generalized to other ethnic groups. Further studies in other populations of different ethnicities are warranted to verify our findings.
Conclusions
We demonstrated that the T allele of rs864265 was associated with increased arterial stiffness, independent of conventional cardiovascular risk factors, in Chinese adults. Body adiposity and SBP might modify these genetic effects.
Acknowledgments
The authors would like to thank Huaidong Song for excellent technical support and Ben Wang and Fei Teng for critically reviewing the manuscript.
Sources of Funding
This work was sponsored by Jiangsu Provincial Bureau of Health Foundation (H201356), the International Exchange Program, and the Jiangsu Six Talent Peaks Program (2013‐WSN‐013). It was also supported by the Xuzhou Outstanding Medical Academic Leader project and a Xuzhou Science and Technology Grant (XM13B066).
Conflict of Interest
None declared.
J Clin Hypertens (Greenwich). 2015;17:395–400. DOI: 10.1111/jch.12516. © 2015 Wiley Periodicals, Inc.
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