Abstract
Background: Nuclear receptor coactivator 2 (NCOA2) gene plays an important role in adipogenesis and lipid metabolism. NCOA2 gene null mice exhibited less fat accumulation and lower serum lipid levels, and were protected against obesity. Few studies are known to have analyzed the association of NCOA2 gene single nucleotide polymorphisms with obesity and serum lipid profile. Our study aimed to evaluate the association of NCOA2 gene polymorphisms with the risk of obesity and dyslipidemia in the Chinese Han population. Methods: Two NCOA2 gene polymorphisms (rs41391448 and rs10504473) were selected and genotyped in a Chinese Han cohort with 529 participants. The effect of different genotypes on BMI and serum lipid levels (TG, TC, LDL-C and HDL-C) was performed by the analysis of covariance. Association of NCOA2 polymorphisms with obesity and dyslipidemia was assessed by odds ratios (OR) and 95% confidence intervals (CI) under the unconditional logistic regression analysis. Results: Significant association was observed between rs10504473 polymorphism and obesity under the recessive model (OR = 1.88, 95% CI 1.02-3.45, P = 0.047; adjusted OR = 1.87, 95% CI 1.02-3.44, P = 0.048). However, no association remained significant after Bonferroni correction. Conclusion: Our study suggests a possible association between NCOA2 rs10504473 polymorphism and obesity, and this SNP may influence the susceptibility of obesity in the Chinese Han population.
Keywords: Nuclear receptor coactivator 2, single nucleotide polymorphism, obesity, dyslipidemia
Introduction
Obesity and dyslipidemia are two important risk factors for the development of metabolic syndrome and cardiovascular diseases in the Chinese population [1,2]. The results of Chinese national chronic non-communicable diseases surveillance in 2010 showed that the prevalence of obesity was 12.0% [3]. Luo et al. [4] reported that the prevalence of dyslipidemia was 52.7% among adults in Northwestern China. Owing to genome-wide association studies (GWAS), the total number of susceptible genes implicated in the development of obesity and dyslipidemia has increased substantially. For instance, common variation in the fat mass and obesity associated gene (FTO) was found to be associated with body mass index and adult obesity [5,6]. The lipoprotein lipase gene (LPL) polymorphisms were reported to play a key role in lipid metabolism in the Chinese Han population [7,8]. Peroxisome proliferator-activated receptor gamma (PPARγ) gene variants were found to be associated with serum lipid levels and to increase the risk of dyslipidemia [9,10].
Nuclear receptor coactivator 2 (NCOA2) is a member of the nuclear hormone receptor coactivator family [11,12]. The function of NCOA2 is mainly to enhance transcriptional activity of nuclear hormone receptors and certain other transcription factors, which modulate cell growth, differentiation and homeostasis by regulating the expression of target genes. NCOA2 mediates the stimulation of transcriptional initiation and regulates the transcription of specific genes in a ligand-dependent manner [13-15]. NCOA2 can impact metabolic homeostasis. Altered expression of NCOA2 may result in metabolic diseases [16-18]. The role of NCOA2 as a regulator of fat absorption was revealed in animal models [19]. Genetic ablation of the NCOA2 gene demonstrated its critical roles in lipid metabolism and energy balance [17,20]. Researches also showed that NCOA2 promote adipogenesis by interacting with PPARγ, a key regulator of adipocyte proliferation and differentiation. Thus, NCOA2 participates in conversion of preadipocytes into mature fat cells [21-23].
No published studies have examined the effect of NCOA2 single nucleotide polymorphisms (SNPs) on obesity and dyslipidemia. The present study was conducted to investigate the association of two NCOA2 SNPs (rs41391448 and rs10504473) with obesity and dyslipidemia in the Chinese Han population. The findings would enhance our understanding about the molecular and genetic mechanisms of metabolic diseases and advance new approaches for prevention and treatment of obesity and dyslipidemia.
Materials and methods
Study population
A total of 529 participants were enrolled in this study. The study group included 192 subjects from the health examination center of the Petroleum Jilin Chemical General Hospital during the year 2009-2010 and 337 subjects from the Survey of Chronic Diseases and Risk Factors among Adults in the Jilin Province of China in 2012. They were unrelated Han population in Northeastern China and aged from 40 to 84 years old. Individuals who had family history of obesity, dyslipidemia, and other metabolic diseases were excluded.
Ethical statements
Our experiments comply with the current laws of China. The study was approved by the Ethics Committee of the School of Public Health, Jilin University, and complied with the 1964 Helsinki declaration of ethical principles for medical research involving human subjects. Informed written consents were obtained from all the participants.
Epidemiological survey and biochemical examination
Demographic data and clinical information, including age, sex, ethnicity, and family medical history were collected with standardized questionnaires. In addition, the body height and weight of each participant were obtained by anthropometric measurements for calculating standard body mass index (BMI). Fasting blood samples of each subject were inspected by MODULE P800 automated biochemistry analyzer (ROCHE, USA) to measure serum triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels.
Dyslipidemia was defined according to the Guidelines on Prevention and Treatment of Dyslipidemia in Chinese Adults (2007) [24]: TG ≥ 2.26 mmol/L as high; TC ≥ 6.22 mmol/L as high; LDL-C ≥ 4.14 mmol/L as high; and HDL-C < 1.04mmol/L as low. In our study, high TG, high TC, high LDL-C, or low HDL-C was regarded as dyslipidemia. BMI was calculated as weight (kg) divided by height (m2). According to the Guidelines for Prevention and Control of Overweight and Obesity in Chinese Adults [25], obesity was defined as BMI ≥ 28 kg/m2.
SNP selection
Two SNPs in the NCOA2 gene were selected using the HapMap website (http://snp.cshl.org/cgi-perl/gbrowse/hapmap24_B36/) and Haploview 4.2 software (Daly Lab. at the Broad Institute, USA). The rs41391448 and rs10504473 reside in the intron region of NCOA2 gene. The minor allele frequencies (MAF) of the two SNPs were all greater than 1% in this study.
DNA extraction
We obtained 5 ml peripheral blood sample from each subject for genotyping. The blood samples were collected in non-anticoagulant, plexiglass tubes, and then stored at -20°C until DNA extraction. Genomic DNA of each subject was extracted from peripheral blood lymphocytes using a DNA extraction kit (ClotBlood DNA kit, Cwbio, Beijing, China), according to the manufacturer’s instructions. The extracted DNA was then stored at 4°C for the following analysis. Purity and concentration of the isolated DNA were detected by ultraviolet spectrophotometer (Beckman, USA).
SNP genotyping
The DNA specimens were amplified by standard polymerase chain reaction (PCR) protocols. PCR reaction was performed on 384-well plates using a MassARRAY Nanodispenser (Sequenom). The primers for PCR reaction were designed using the Assay Design 3.1 software (Sequenom Inc., San Diego, CA, USA). SNP genotyping was detected by Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS) [26] with the MassARRAY system (Sequenom, San, Diego, CA, USA).
Statistical analysis
Continuous variables were presented as mean ± standard deviation (SD) when agreed with normal distribution, otherwise described as median ± interquartile range. Categorical variables were presented using percentages instead. Variables that did not have a normal distribution were transformed into natural logarithms before the statistical tests were applied. Departure from Hardy-Weinberg equilibrium (HWE) [27] and genotype distribution between the groups were all evaluated using the Chi-square test. Analysis of covariance (ANCOVA) was applied to evaluate the effect of different genotypes on BMI and serum lipid levels (TG, TC, LDL-C, and HDL-C). Association between genotypes and the risk of obesity and dyslipidemia was assessed by odds ratios (OR) and 95% confidence intervals (CI) under the unconditional logistic regression analysis. Bonferroni correction was applied for multiple testing to reduce Type I error in association analysis. The Akaike information criterion (AIC) was used to determine the best model of inheritance for each SNP. All statistical analyses were performed using the SNPStats program (http://bioinfo.iconcologia.net/SNPStats) [28] and SPSS 17.0 software, and adjusted for age and sex when appropriate. Two-sided test with P < 0.05 was considered statistically significant.
Results
Characteristics of the study population
Table 1 shows the characteristics of all the subjects included in this study. The study included 252 males and 277 females. The average age was 58.94 ± 9.69 years. The mean concentrations of TG, TC, LDL-C, and HDL-C were 1.62 ± 1.51 mmol/L, 5.21 ± 1.13 mmol/L, 3.16 ± 1.02 mmol/L, and 1.22 ± 0.50 mmol/L, respectively. The average value of BMI was 24.26 ± 3.16 kg/m2. 52.9% and 9.6% of the subjects were diagnosed as dyslipidemia and obesity, respectively.
Table 1.
Characteristics of study subjects
| Characteristics | rs41391448 (n = 489) | rs10504473 (n=505) | ||
|---|---|---|---|---|
|
| ||||
| n (%) | Mean ± SD | n (%) | Mean ± SD | |
| Males | 231 (47.2) | 242 (47.9) | ||
| Age (years) | 58.83 ± 9.68 | 58.93 ± 9.73 | ||
| BMI (kg/m2) | 24.30 ± 3.21 | 24.27 ± 3.19 | ||
| < 28 | 438 (89.6) | 23.65 ± 2.66 | 454 (89.9) | 23.64 ± 2.66 |
| ≥ 28 | 51 (10.4) | 29.92 ± 1.51 | 51 (10.1) | 29.92 ± 1.51 |
| TG (mmol/L)* | 1.63 ± 1.52 | 1.62 ± 1.51 | ||
| < 2.26 | 337 (68.9) | 1.27 ± 0.76 | 350 (69.3) | 1.28 ± 0.76 |
| ≥ 2.26 | 152 (31.1) | 3.19 ±1.72 | 155 (30.7) | 3.11 ± 1.68 |
| TC (mmol/L) | 5.20 ± 1.13 | 5.20 ± 1.13 | ||
| < 6.22 | 398 (81.4) | 4.81 ± 0.75 | 412 (81.6) | 4.81 ± 0.75 |
| ≥ 6.22 | 91 (18.6) | 6.92 ± 0.84 | 93 (18.4) | 6.93 ± 0.85 |
| LDL-C (mmol/L) | 3.14 ± 1.01 | 3.16 ± 1.01 | ||
| < 4.14 | 413 (84.5) | 2.85 ± 0.73 | 425 (84.2) | 2.86 ± 0.73 |
| ≥ 4.14 | 76 (15.5) | 4.74 ± 0.76 | 80 (15.8) | 4.77 ± 0.77 |
| HDL-C (mmol/L)* | 1.24 ± 0.50 | 1.23 ± 0.50 | ||
| < 1.04 | 142 (29.0) | 0.90 ± 0.15 | 150 (29.7) | 0.90 ± 0.15 |
| ≥ 1.04 | 347 (71.0) | 1.39 ± 0.46 | 355 (70.3) | 1.39 ± 0.45 |
Data was shown as median ± interquartile range;
Genotyping data of 40 subjects for rs41391448 and 24 subjects for rs10504473 were missing.
Genotype distribution and MAF of the two SNPs
The MAF and genotype distributions of the two SNPs are shown in Table 2. Genotyping rates of SNPs rs41391448 and rs10504473 in our study were 92.4% and 95.5%, respectively. All SNP genotype distributions were in HWE. The distribution of genotypes for each SNP was not significantly different between groups (P > 0.05).
Table 2.
MAF and genotype distributions of two NCOA2 polymorphisms
| rs41391448 (A > G) | rs10504473 (A > C) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
|||||||||||
| Genotypes, n (%) | MAF | P | Genotypes, n (%) | MAF | P | ||||||
|
|
|
||||||||||
| AA | AG | GG | AA | AC | CC | ||||||
| Obesity | Yes | 40 (78.4) | 10 (19.6) | 1 (2.0) | 0.12 | 0.85 | 12 (23.5) | 20 (39.2) | 19 (37.3) | 0.57 | 0.12 |
| No | 328 (74.9) | 99 (22.6) | 11 (2.5) | 0.14 | 122 (26.9) | 223 (49.1) | 109 (24.0) | 0.49 | |||
| Dyslipidemia | Yes | 198 (77.0) | 54 (21.0) | 5 (2.0) | 0.12 | 0.55 | 74 (27.7) | 132 (49.4) | 61 (22.8) | 0.48 | 0.39 |
| No | 170 (73.3) | 55 (23.7) | 7 (3.0) | 0.15 | 60 (25.2) | 111 (46.6) | 67 (28.2) | 0.51 | |||
Genotype distributions for each polymorphism shown separately for individuals with and without obesity or dyslipidemia; P values are derived from Chi-square test; MAF, minor allele frequency.
Association of two NCOA2 polymorphisms with BMI and serum lipid levels
The effect of different genotypes on BMI and serum lipid levels was performed using ANCOVA. As shown in Table 3, none of the two SNPs was significantly associated with BMI or serum lipid levels (TG, TC, LDL-C, and HDL-C) (P > 0.05).
Table 3.
Association between NCOA2 polymorphisms and BMI or serum indices
| rs41391448 | F | P * | rs10504473 | F | P * | |||||
|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|||||||||
| AA (n = 368) | AG (n = 109) | GG (n = 12) | AA (n = 134) | AC (n = 243) | CC (n = 128) | |||||
| BMI (kg/m2) | 24.37 ± 3.18 | 24.09 ± 3.25 | 24.08 ± 3.64 | 0.35 | 0.71 | 24.13 ± 3.14 | 24.35 ± 3.02 | 24.26 ± 3.56 | 0.31 | 0.73 |
| TG (mmol/l) | 1.69 ± 1.57 | 1.51 ± 1.30 | 1.63 ± 2.01 | 0.07 | 0.93 | 1.77 ± 1.50 | 1.68 ± 1.63 | 1.53 ± 1.17 | 1.51 | 0.22 |
| TC (mmol/l) | 5.20 ± 1.13 | 5.21 ± 1.08 | 5.05 ± 1.36 | 0.18 | 0.83 | 5.18 ± 0.93 | 5.26 ± 1.24 | 5.11 ± 1.08 | 0.71 | 0.49 |
| HDL-C (mmol/l) | 1.24 ± 0.51 | 1.23 ± 0.45 | 1.13 ± 0.69 | 0.22 | 0.81 | 1.30 ± 0.55 | 1.21 ± 0.49 | 1.22 ± 0.50 | 0.77 | 0.47 |
| LDL-C (mmol/l) | 3.14 ± 1.03 | 3.21 ± 0.92 | 2.72 ± 1.17 | 1.37 | 0.25 | 1.34 ± 0.47 | 1.28 ± 0.40 | 1.29 ± 0.40 | 0.09 | 0.91 |
BMI, TC, LDL-C were presented as mean ± standard deviation; TG and HDL-C were presented as median ± interquartile range; Bold values are statistically significant (P < 0.05);
P value was calculated with analysis of covariance adjusted for age and sex.
Association of two NCOA2 polymorphisms with obesity and dyslipidemia
For each SNP, the risk of obesity and dyslipidemia was estimated under different models of inheritance (codominant, dominant, recessive, overdominant and multiplicative models). As shown Table 4, promoting effect on obesity was observed for the rs10504473 polymorphism. Genotype CC was found to increase the risk of obesity in the recessive model (OR = 1.88, 95% CI 1.02-3.45, P = 0.047; adjusted OR = 1.87, 95% CI 1.02-3.44, P = 0.048). However, no association remained statistically significant after Bonferroni correction. The results indicate that SNP rs10504473 may play a critical role in the susceptibility of obesity, and need to be confirmed in further studies with larger sample size. While, SNP rs41391448 was not significantly associated with obesity and dyslipidemia before or after Bonferroni correction.
Table 4.
Association of NCOA2 polymorphisms with obesity and dyslipidemia
| SNPs | Models | OR (95% CI) | P * | Adjusted OR (95%CI)# | Adjusted P # | AIC | |
|---|---|---|---|---|---|---|---|
| Obesity | rs41391448 | Codominant | 0.83 (0.40-1.72) | 0.61 | 0.83 (0.40-1.73) | 0.62 | 336.0 |
| 0.75 (0.09-5.92) | 0.78 | 0.70 (0.09-5.64) | 0.74 | ||||
| Dominant | 0.82 (0.41-1.65) | 0.57 | 0.82 (0.41-1.65) | 0.57 | 334.0 | ||
| Recessive | 0.78 (0.10-6.14) | 0.80 | 0.73 (0.09-5.83) | 0.76 | 334.2 | ||
| Overdominant | 0.84 (0.40-1.73) | 0.62 | 0.84 (0.41-1.74) | 0.64 | 334.1 | ||
| Multiplicative | 0.84 (0.45-1.56) | 0.57 | 0.84 (0.45-1.55) | 0.56 | 334.0 | ||
| rs10504473 | Codominant | 0.91 (0.43-1.93) | 0.81 | 0.94 (0.44-2.01) | 0.88 | 335.7 | |
| 1.77 (0.82-3.82) | 0.14 | 1.81 (0.84-3.90) | 0.13 | ||||
| Dominant | 1.19 (0.61-2.36) | 0.60 | 1.23 (0.62-2.45) | 0.54 | 337.3 | ||
| Recessive | 1.88 (1.02-3.45) | 0.047 | 1.87 (1.02-3.44) | 0.048 | 333.8 | ||
| Overdominant | 0.67 (0.37-1.21) | 0.18 | 0.68 (0.38-1.24) | 0.21 | 336.1 | ||
| Multiplicative | 1.38 (0.92-2.07) | 0.12 | 1.39 (0.93-2.09) | 0.11 | 335.1 | ||
| Dyslipidemia | rs41391448 | Codominant | 0.84 (0.55-1.29) | 0.43 | 0.84 (0.55-1.29) | 0.43 | 683.8 |
| 0.61 (0.19-1.97) | 0.41 | 0.61 (0.19-1.98) | 0.42 | ||||
| Dominant | 0.82 (0.54-1.23) | 0.34 | 0.82 (0.54-1.23) | 0.33 | 682.1 | ||
| Recessive | 0.64 (0.20-2.04) | 0.44 | 0.64 (0.20-2.05) | 0.45 | 682.4 | ||
| Overdominant | 0.86 (0.56-1.31) | 0.47 | 0.85 (0.55-1.31) | 0.47 | 682.5 | ||
| Multiplicative | 0.82 (0.57-1.18) | 0.28 | 0.82 (0.57-1.18) | 0.28 | 681.8 | ||
| rs10504473 | Codominant | 0.96 (0.63-1.47) | 0.87 | 0.97 (0.63-1.49) | 0.90 | 703.9 | |
| 0.74 (0.45-1.20) | 0.22 | 0.74 (0.45-1.20) | 0.23 | ||||
| Dominant | 0.88 (0.59-1.31) | 0.52 | 0.88 (0.59-1.32) | 0.54 | 703.4 | ||
| Recessive | 0.76 (0.51-1.13) | 0.17 | 0.75 (0.50-1.13) | 0.17 | 701.9 | ||
| Overdominant | 1.12 (0.79-1.59) | 0.53 | 1.13 (0.79-1.61) | 0.50 | 703.3 | ||
| Multiplicative | 0.86 (0.67-1.10) | 0.22 | 0.86 (0.67-1.10) | 0.23 | 702.3 |
Data was analyzed with unconditional logistic regression analysis adjusted for age and sex;
P values were not corrected by Bonferroni correction;
AIC, Akaike information criterion; OR, odds ratio; CI, confidence interval; Codominant, wild homozygote serves as the reference; Dominant, heterozygote and minor allele homozygote vs. wild homozygote; Recessive, minor allele homozygote vs. wild homozygote and heterozygote; Overdominant, heterozygote vs. wild homozygote and minor allele homozygote; Multiplicative, heterozygote and minor allele homozygote were weighted 1 and 2 respectively to wild homozygote.
Discussion
It has been confirmed that body weight gain in adults is associated with an increased risk for the development of type 2 diabetes mellitus and other obesity comorbidities [29,30]. The prevalence of obesity in the Chinese population has greatly increased over the past decade with the changes of lifestyle and diet [1,31]. Dyslipidemia is a major pathogenic factor for atherosclerosis and one of the independent risk factors for cardiovascular diseases in the Chinese population, particularly in middle-aged and elder subjects [4,32,33]. Therefore, we recruited individuals aged at least 40 years old from Northeast of China. Results from family and twin studies have demonstrated that genetic factors play a significant role in the development of obesity and dyslipidemia in response to a particular environment [5,34]. Genome-wide association and linkage studies have identified more than 50 loci that were associated with obesity and BMI [35], such as variants in the FTO gene [5]. Polymorphisms of CREB-regulated transcription coactivator 3 gene (CRTC3) were confirmed to play an important role in lipid metabolism and obesity in the Chinese Han population [36].
NCOA2 gene is located at chromosome 8q13 in humans. NCOA2 is a transcriptional coregulatory protein which expressed in a variety of hormone responsive tissues and implicated in adipogenesis, lipid metabolism, cancer, fertility, bone morphogenesis, and other pathological conditions [12,17,20,37,38]. NCOA2 has several nuclear receptor interacting domains and an intrinsic histone acetyltransferase activity. After binding to ligand-bound nuclear receptors, NCOA2 recruit histone acetyltransferases and methyltransferases to specific DNA enhancer or promoter regions, which mediates the stimulation of transcription initiation and makes downstream gene more accessible to transcription [12]. Therefore, the function of NCOA2 is mainly to enhance transcriptional activity of nuclear receptors and certain other transcription factors by facilitating chromatin remodeling, assembly of transcription factors, and target genes transcription [11,14].
NCOA2 plays a critical role in metabolic homeostasis due to its complex biological functions, and thus it is supposed to be a potential contributor to metabolic diseases [17,18]. A recent study by Duteil et al. [16] confirmed the metabolic functions of NCOA2 in white and brown adipose tissues as well as the liver. In NCOA2 null mice, white adipose tissue (WAT) showed a down-regulated expression of PPARγ gene, causing a higher level of lipolysis and a lower potential for fatty acid storage. Therefore, the ablation of NCOA2 gene could stimulate energy expenditure which protected against excessive fat accumulation induced by high fat diet or hyperphagia. The absence of NCOA2 in the brown adipose tissue (BAT) causes higher energy expenditure due to the enhanced fatty acid oxidation and uncoupling of respiration. Consequently, NCOA2 null mice showed enhanced adaptive thermogenesis and increased energy expenditure rendering them a protection against obesity [12,20,39,40]. Several knock out studies conducted on animal models have revealed the role of NCOA2 as a regulator of fat absorption. The expression of NCOA2 results in activation of the genes that trigger bile production in the liver. Mice lacking the NCOA2 gene were unable to absorb fat properly, suggesting that the NCOA2 gene is involved in fat absorption [19]. Whole body ablation of NCOA2 led to a reduced capacity to absorb dietary fat from the gut. Hepatocyte-specific ablation of NCOA2 resulted in intestinal fat malabsorption and reduced entry of fat into the circulation, which can be restored by replenishing the bile acid levels. Moreover, NCOA2 positively regulates bile acid secretion into the gut. It was ascertained by studies on animal models that the expression of major bile acid transporter was significantly decreased in NCOA2 null mice [19]. Jeong et al. [17] observed that several key regulatory enzymes involved in fatty acid and cholesterol biosynthesis were decreased in the NCOA2 null mice as compared with wild types.
According to the previous studies conducted on animal models and human adipocytes above, it was hypothesized that NCOA2 was associated with obesity and dyslipidemia. The association of NCOA2 variations with obesity and dyslipidemia has not ever been reported. Hence, genetic association studies on NCOA2 polymorphisms would promote a further understanding of NCOA2 with obesity and dyslipidemia. In our study, it showed possible relevance of NCOA2 rs10504473 polymorphism to the risk of obesity though it became insignificant after Bonferroni correction. It supposed that SNP rs10504473 may have a promoting effect on the risk of obesity. NCOA2 functions as a transcriptional coactivator for PPARγ and stimulates the fat uptake under high fat feeding conditions. It plays a key role in promoting human adipogenesis by interacting with PPAR γ, a regulator of adipogenesis which converts human preadipocyte into a mature fat cell [21-23]. SNP rs10504473 reside in the intron region of NCOA2 gene. Variation in that locus may change the expression of NCOA2. Accordingly, it would influence the function of PPARγ which may cause higher potential for fatty acids storage and increase the risk of obesity.
The limitations of our analysis include but not limit to the relatively small sample size and genetic heterogeneity of the study population. Moreover, two SNPs can hardly reveal the effect of NCOA2 gene on obesity and dyslipidemia. Therefore, studying a larger number of SNPs within NCOA2 gene would possibly obtain a more reliable result. Modified studies encompassing gene-gene and gene-environment interactions are also required to thoroughly ascertain the contribution of NCOA2 variants to the risk of obesity and dyslipidemia. In addition, the variation of environmental exposure should be taken into account in our study.
In conclusion, NCOA2 rs10504473 polymorphism is likely to influence the susceptibility of obesity, but it may not be associated with dyslipidemia in the Chinese Han population. Considering the critical role of NCOA2 in adipogenesis and lipid metabolism, further genetic association studies with large sample size and different ethnic population are needed to confirm our observation, and the findings would help developing new approaches for prevention and treatment of obesity and dyslipidemia.
Acknowledgements
This study was supported by the Scientific Research Foundation of the Health Bureau of Jilin Province in China (No. 2011Z116), the National Natural Science Foundation of China (No. 30870952), the Science and Technology Department of Jilin Province (No. 20080735), and the Health Department of Jilin Province (No. 2001R009).
Disclosure of conflict of interest
None.
References
- 1.Gu D, Reynolds K, Wu X, Chen J, Duan X, Reynolds RF, Whelton PK, He J. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet. 2005;365:1398–1405. doi: 10.1016/S0140-6736(05)66375-1. [DOI] [PubMed] [Google Scholar]
- 2.Xi B, He D, Hu Y, Zhou D. Prevalence of metabolic syndrome and its influencing factors among the Chinese adults: the China Health and Nutrition Survey in 2009. Prev Med. 2013;57:867–871. doi: 10.1016/j.ypmed.2013.09.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Li XY, Jiang Y, Hu N, Li YC, Zhang M, Huang ZJ, Zhao WH. [Prevalence and characteristic of overweight and obesity among adults in China, 2010] . Zhonghua Yu Fang Yi Xue Za Zhi. 2012;46:683–686. [PubMed] [Google Scholar]
- 4.Luo JY, Ma YT, Yu ZX, Yang YN, Xie X, Ma X, Liu F, Li XM, Chen BD. Prevalence, awareness, treatment and control of dyslipidemia among adults in northwestern China: the cardiovascular risk survey. Lipids Health Dis. 2014;13:4. doi: 10.1186/1476-511X-13-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JR, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW, Barrett JC, Ellard S, Groves CJ, Knight B, Patch AM, Ness AR, Ebrahim S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin MR, Sovio U, Bennett AJ, Melzer D, Ferrucci L, Loos RJ, Barroso I, Wareham NJ, Karpe F, Owen KR, Cardon LR, Walker M, Hitman GA, Palmer CN, Doney AS, Morris AD, Smith GD, Hattersley AT, McCarthy MI. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316:889–894. doi: 10.1126/science.1141634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Scuteri A, Sanna S, Chen WM, Uda M, Albai G, Strait J, Najjar S, Nagaraja R, Orru M, Usala G, Dei M, Lai S, Maschio A, Busonero F, Mulas A, Ehret GB, Fink AA, Weder AB, Cooper RS, Galan P, Chakravarti A, Schlessinger D, Cao A, Lakatta E, Abecasis GR. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007;3:e115. doi: 10.1371/journal.pgen.0030115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Mo X, Liu X, Wang L, Li H, Lu X, Huang J, Chen J, Cao J, Li J, Chen S, Tang Y, Peng X, Gu D. Lipoprotein lipase gene polymorphism rs1059611 functionally influences serum lipid concentrations. Atherosclerosis. 2013;229:511–516. doi: 10.1016/j.atherosclerosis.2013.05.005. [DOI] [PubMed] [Google Scholar]
- 8.Mo X, Liu X, Wang L, Lu X, Chen S, Li H, Huang J, Chen J, Cao J, Li J, Tang Y, Gu D. Association of lipoprotein lipase polymorphism rs2197089 with serum lipid concentrations and LPL gene expression. J Hum Genet. 2013;58:160–164. doi: 10.1038/jhg.2012.151. [DOI] [PubMed] [Google Scholar]
- 9.Gu SJ, Guo ZR, Zhou ZY, Hu XS, Wu M, Zhang N. Peroxisome proliferatoractivated receptor gamma polymorphisms as risk factors for dyslipidemia. Mol Med Rep. 2014;10:2759–2763. doi: 10.3892/mmr.2014.2553. [DOI] [PubMed] [Google Scholar]
- 10.Li Q, Chen R, Bie L, Zhao D, Huang C, Hong J. Association of the variants in the PPARG gene and serum lipid levels: a meta-analysis of 74 studies. J Cell Mol Med. 2015;19:198–209. doi: 10.1111/jcmm.12417. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Mangelsdorf DJ, Thummel C, Beato M, Herrlich P, Schutz G, Umesono K, Blumberg B, Kastner P, Mark M, Chambon P, Evans RM. The nuclear receptor superfamily: the second decade. Cell. 1995;83:835–839. doi: 10.1016/0092-8674(95)90199-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Xu J, Li Q. Review of the in vivo functions of the p160 steroid receptor coactivator family. Mol Endocrinol. 2003;17:1681–1692. doi: 10.1210/me.2003-0116. [DOI] [PubMed] [Google Scholar]
- 13.Hong H, Kohli K, Garabedian MJ, Stallcup MR. GRIP1, a transcriptional coactivator for the AF-2 transactivation domain of steroid, thyroid, retinoid, and vitamin D receptors. Mol Cell Biol. 1997;17:2735–2744. doi: 10.1128/mcb.17.5.2735. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Onate SA, Tsai SY, Tsai MJ, O’Malley BW. Sequence and characterization of a coactivator for the steroid hormone receptor superfamily. Science. 1995;270:1354–1357. doi: 10.1126/science.270.5240.1354. [DOI] [PubMed] [Google Scholar]
- 15.Yore MA, Im D, Webb LK, Zhao Y, Chadwick JG Jr, Molenda-Figueira HA, Haidacher SJ, Denner L, Tetel MJ. Steroid receptor coactivator-2 expression in brain and physical associations with steroid receptors. Neuroscience. 2010;169:1017–1028. doi: 10.1016/j.neuroscience.2010.05.053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Duteil D, Chambon C, Ali F, Malivindi R, Zoll J, Kato S, Geny B, Chambon P, Metzger D. The transcriptional coregulators TIF2 and SRC-1 regulate energy homeostasis by modulating mitochondrial respiration in skeletal muscles. Cell Metab. 2010;12:496–508. doi: 10.1016/j.cmet.2010.09.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Jeong JW, Kwak I, Lee KY, White LD, Wang XP, Brunicardi FC, O’Malley BW, DeMayo FJ. The genomic analysis of the impact of steroid receptor coactivators ablation on hepatic metabolism. Mol Endocrinol. 2006;20:1138–1152. doi: 10.1210/me.2005-0407. [DOI] [PubMed] [Google Scholar]
- 18.Wang Z, Qi C, Krones A, Woodring P, Zhu X, Reddy JK, Evans RM, Rosenfeld MG, Hunter T. Critical roles of the p160 transcriptional coactivators p/CIP and SRC-1 in energy balance. Cell Metab. 2006;3:111–122. doi: 10.1016/j.cmet.2006.01.002. [DOI] [PubMed] [Google Scholar]
- 19.Chopra AR, Kommagani R, Saha P, Louet JF, Salazar C, Song J, Jeong J, Finegold M, Viollet B, DeMayo F, Chan L, Moore DD, O’Malley BW. Cellular energy depletion resets whole-body energy by promoting coactivator-mediated dietary fuel absorption. Cell Metab. 2011;13:35–43. doi: 10.1016/j.cmet.2010.12.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Picard F, Gehin M, Annicotte J, Rocchi S, Champy MF, O’Malley BW, Chambon P, Auwerx J. SRC-1 and TIF2 control energy balance between white and brown adipose tissues. Cell. 2002;111:931–941. doi: 10.1016/s0092-8674(02)01169-8. [DOI] [PubMed] [Google Scholar]
- 21.Hartig SM, He B, Long W, Buehrer BM, Mancini MA. Homeostatic levels of SRC-2 and SRC-3 promote early human adipogenesis. J Cell Biol. 2011;192:55–67. doi: 10.1083/jcb.201004026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Kodera Y, Takeyama K, Murayama A, Suzawa M, Masuhiro Y, Kato S. Ligand type-specific interactions of peroxisome proliferator-activated receptor gamma with transcriptional coactivators. J Biol Chem. 2000;275:33201–33204. doi: 10.1074/jbc.C000517200. [DOI] [PubMed] [Google Scholar]
- 23.Janani C, Ranjitha Kumari BD. PPAR gamma gene-A review. Diabetes Metab Syndr. 2015;9:46–50. doi: 10.1016/j.dsx.2014.09.015. [DOI] [PubMed] [Google Scholar]
- 24.Committee CADP. China Adult Dyslipidemia Prevention Guide. Chinese Journal of Cardiology. 2007:390–419. [Google Scholar]
- 25.Chen C, Lu FC Department of Disease Control Ministry of Health, PR China. The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed Environ Sci. 2004;17(Suppl):1–36. [PubMed] [Google Scholar]
- 26.Buetow KH, Edmonson M, MacDonald R, Clifford R, Yip P, Kelley J, Little DP, Strausberg R, Koester H, Cantor CR, Braun A. High-throughput development and characterization of a genomewide collection of gene-based single nucleotide polymorphism markers by chip-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Proc Natl Acad Sci U S A. 2001;98:581–584. doi: 10.1073/pnas.021506298. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet. 2006;7:781–791. doi: 10.1038/nrg1916. [DOI] [PubMed] [Google Scholar]
- 28.Sole X, Guino E, Valls J, Iniesta R, Moreno V. SNPStats: a web tool for the analysis of association studies. Bioinformatics. 2006;22:1928–1929. doi: 10.1093/bioinformatics/btl268. [DOI] [PubMed] [Google Scholar]
- 29.James WP. The epidemiology of obesity: the size of the problem. J Intern Med. 2008;263:336–352. doi: 10.1111/j.1365-2796.2008.01922.x. [DOI] [PubMed] [Google Scholar]
- 30.Visscher TL, Heitmann BL, Rissanen A, Lahti-Koski M, Lissner L. A break in the obesity epidemic? Explained by biases or misinterpretation of the data? Int J Obes (Lond) 2015;39:189–198. doi: 10.1038/ijo.2014.98. [DOI] [PubMed] [Google Scholar]
- 31.Wang Y, Mi J, Shan XY, Wang QJ, Ge KY. Is China facing an obesity epidemic and the consequences? The trends in obesity and chronic disease in China. Int J Obes (Lond) 2007;31:177–188. doi: 10.1038/sj.ijo.0803354. [DOI] [PubMed] [Google Scholar]
- 32.Deng B, Luo T, Huang Y, Shen T, Ma J. Prevalence and determinants of hyperlipidemia in moderate altitude areas of the Yunnan-Kweichow plateau in Southwestern China. High Alt Med Biol. 2012;13:13–21. doi: 10.1089/ham.2011.1037. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Li JH, Wang LM, Mi SQ, Zhang M, Li YC, Jiang Y, Xu Y, Dai M, Wang LH. [Awareness rate, treatment rate and control rate of dyslipidemia in Chinese adults, 2010] . Zhonghua Yu Fang Yi Xue Za Zhi. 2012;46:687–691. [PubMed] [Google Scholar]
- 34.Maes HH, Neale MC, Eaves LJ. Genetic and environmental factors in relative body weight and human adiposity. Behav Genet. 1997;27:325–351. doi: 10.1023/a:1025635913927. [DOI] [PubMed] [Google Scholar]
- 35.Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Lango Allen H, Lindgren CM, Luan J, Magi R, Randall JC, Vedantam S, Winkler TW, Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, Weedon MN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segre AV, Estrada K, Liang L, Nemesh J, Park JH, Gustafsson S, Kilpelainen TO, Yang J, Bouatia-Naji N, Esko T, Feitosa MF, Kutalik Z, Mangino M, Raychaudhuri S, Scherag A, Smith AV, Welch R, Zhao JH, Aben KK, Absher DM, Amin N, Dixon AL, Fisher E, Glazer NL, Goddard ME, Heard-Costa NL, Hoesel V, Hottenga JJ, Johansson A, Johnson T, Ketkar S, Lamina C, Li S, Moffatt MF, Myers RH, Narisu N, Perry JR, Peters MJ, Preuss M, Ripatti S, Rivadeneira F, Sandholt C, Scott LJ, Timpson NJ, Tyrer JP, van Wingerden S, Watanabe RM, White CC, Wiklund F, Barlassina C, Chasman DI, Cooper MN, Jansson JO, Lawrence RW, Pellikka N, Prokopenko I, Shi J, Thiering E, Alavere H, Alibrandi MT, Almgren P, Arnold AM, Aspelund T, Atwood LD, Balkau B, Balmforth AJ, Bennett AJ, Ben-Shlomo Y, Bergman RN, Bergmann S, Biebermann H, Blakemore AI, Boes T, Bonnycastle LL, Bornstein SR, Brown MJ, Buchanan TA, Busonero F, Campbell H, Cappuccio FP, Cavalcanti-Proenca C, Chen YD, Chen CM, Chines PS, Clarke R, Coin L, Connell J, Day IN, den Heijer M, Duan J, Ebrahim S, Elliott P, Elosua R, Eiriksdottir G, Erdos MR, Eriksson JG, Facheris MF, Felix SB, Fischer-Posovszky P, Folsom AR, Friedrich N, Freimer NB, Fu M, Gaget S, Gejman PV, Geus EJ, Gieger C, Gjesing AP, Goel A, Goyette P, Grallert H, Grassler J, Greenawalt DM, Groves CJ, Gudnason V, Guiducci C, Hartikainen AL, Hassanali N, Hall AS, Havulinna AS, Hayward C, Heath AC, Hengstenberg C, Hicks AA, Hinney A, Hofman A, Homuth G, Hui J, Igl W, Iribarren C, Isomaa B, Jacobs KB, Jarick I, Jewell E, John U, Jorgensen T, Jousilahti P, Jula A, Kaakinen M, Kajantie E, Kaplan LM, Kathiresan S, Kettunen J, Kinnunen L, Knowles JW, Kolcic I, Konig IR, Koskinen S, Kovacs P, Kuusisto J, Kraft P, Kvaloy K, Laitinen J, Lantieri O, Lanzani C, Launer LJ, Lecoeur C, Lehtimaki T, Lettre G, Liu J, Lokki ML, Lorentzon M, Luben RN, Ludwig B, Manunta P, Marek D, Marre M, Martin NG, McArdle WL, McCarthy A, McKnight B, Meitinger T, Melander O, Meyre D, Midthjell K, Montgomery GW, Morken MA, Morris AP, Mulic R, Ngwa JS, Nelis M, Neville MJ, Nyholt DR, O’Donnell CJ, O’Rahilly S, Ong KK, Oostra B, Pare G, Parker AN, Perola M, Pichler I, Pietilainen KH, Platou CG, Polasek O, Pouta A, Rafelt S, Raitakari O, Rayner NW, Ridderstrale M, Rief W, Ruokonen A, Robertson NR, Rzehak P, Salomaa V, Sanders AR, Sandhu MS, Sanna S, Saramies J, Savolainen MJ, Scherag S, Schipf S, Schreiber S, Schunkert H, Silander K, Sinisalo J, Siscovick DS, Smit JH, Soranzo N, Sovio U, Stephens J, Surakka I, Swift AJ, Tammesoo ML, Tardif JC, Teder-Laving M, Teslovich TM, Thompson JR, Thomson B, Tonjes A, Tuomi T, van Meurs JB, van Ommen GJ, Vatin V, Viikari J, Visvikis-Siest S, Vitart V, Vogel CI, Voight BF, Waite LL, Wallaschofski H, Walters GB, Widen E, Wiegand S, Wild SH, Willemsen G, Witte DR, Witteman JC, Xu J, Zhang Q, Zgaga L, Ziegler A, Zitting P, Beilby JP, Farooqi IS, Hebebrand J, Huikuri HV, James AL, Kahonen M, Levinson DF, Macciardi F, Nieminen MS, Ohlsson C, Palmer LJ, Ridker PM, Stumvoll M, Beckmann JS, Boeing H, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Collins FS, Cupples LA, Smith GD, Erdmann J, Froguel P, Gronberg H, Gyllensten U, Hall P, Hansen T, Harris TB, Hattersley AT, Hayes RB, Heinrich J, Hu FB, Hveem K, Illig T, Jarvelin MR, Kaprio J, Karpe F, Khaw KT, Kiemeney LA, Krude H, Laakso M, Lawlor DA, Metspalu A, Munroe PB, Ouwehand WH, Pedersen O, Penninx BW, Peters A, Pramstaller PP, Quertermous T, Reinehr T, Rissanen A, Rudan I, Samani NJ, Schwarz PE, Shuldiner AR, Spector TD, Tuomilehto J, Uda M, Uitterlinden A, Valle TT, Wabitsch M, Waeber G, Wareham NJ, Watkins H, Wilson JF, Wright AF, Zillikens MC, Chatterjee N, McCarroll SA, Purcell S, Schadt EE, Visscher PM, Assimes TL, Borecki IB, Deloukas P, Fox CS, Groop LC, Haritunians T, Hunter DJ, Kaplan RC, Mohlke KL, O’Connell JR, Peltonen L, Schlessinger D, Strachan DP, van Duijn CM, Wichmann HE, Frayling TM, Thorsteinsdottir U, Abecasis GR, Barroso I, Boehnke M, Stefansson K, North KE, McCarthy MI, Hirschhorn JN, Ingelsson E, Loos RJ. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42:937–948. doi: 10.1038/ng.686. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Ou Z, Wang G, Li Q, Ma Z, Dai M, Zou F. CRTC3 polymorphisms were associated with the plasma level of total cholesterol and the risks of overweight and hypertriglyceridemia in a Chinese Han population. Mol Biol Rep. 2014;41:125–130. doi: 10.1007/s11033-013-2844-4. [DOI] [PubMed] [Google Scholar]
- 37.Fenne IS, Helland T, Flageng MH, Dankel SN, Mellgren G, Sagen JV. Downregulation of steroid receptor coactivator-2 modulates estrogen-responsive genes and stimulates proliferation of mcf-7 breast cancer cells. PLoS One. 2013;8:e70096. doi: 10.1371/journal.pone.0070096. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Modder UI, Monroe DG, Fraser DG, Spelsberg TC, Rosen CJ, Gehin M, Chambon P, O’Malley BW, Khosla S. Skeletal consequences of deletion of steroid receptor coactivator-2/transcription intermediary factor-2. J Biol Chem. 2009;284:18767–18777. doi: 10.1074/jbc.M109.000836. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Lin J, Handschin C, Spiegelman BM. Metabolic control through the PGC-1 family of transcription coactivators. Cell Metab. 2005;1:361–370. doi: 10.1016/j.cmet.2005.05.004. [DOI] [PubMed] [Google Scholar]
- 40.Puigserver P, Wu Z, Park CW, Graves R, Wright M, Spiegelman BM. A cold-inducible coactivator of nuclear receptors linked to adaptive thermogenesis. Cell. 1998;92:829–839. doi: 10.1016/s0092-8674(00)81410-5. [DOI] [PubMed] [Google Scholar]