Abstract

Background: Obesity is a common heritable trait and a major risk factors of chronic and metabolic diseases. Insulin-induced gene 1 (INSIG1) is known to play important roles in cholesterol and triacylglycerol (TAG) metabolism. In the present study, our primary objective was to explore whether the single nucleotide polymorphisms (SNPs) in INSIG1 gene were associated with obesity in Uygur subjects, in Xinjiang, China.

Methods: We designed a case–control study including 516 obese patients and 463 age- and sex-matched control subjects. Three SNPs (rs2721, rs9767875 and rs9719268) were genotyped using TaqMan SNP genotyping assays.

Results: For rs2721, the distribution of genotypes, dominant model (GT + TT vs GG), recessive model (TT vs GT + GG) showed significant differences between obese patients and the controls (P = 0.008, P = 0.005 and P = 0.035, respectively). For rs9719268, the distribution of genotypes showed significant differences between obese patients and the controls (P = 0.004). The dominant model (GT + TT vs GG) of rs2721 and rs9719268 GT genotype remain significantly associated with obesity after adjustment for confounders (OR = 1.393, 95% CI = 1.047–1.853, P = 0.023; OR = 1.631, 95% CI = 1.059–2.512, P = 0.026). The TG levels were significantly higher in rs2721 GT/TT genotypes than that in GG genotypes (P<0.05).

Conclusions: Rs2721 and rs9719268 of INSIG1 gene are associated with obesity in Uygur subjects. Subjects with GT/TT genotype or T allele of rs2721 and GT genotype of rs9719268 were associated with an increased risk of obesity.

Introduction

Obesity is one of the major risk factors of chronic and metabolic diseases and the prevalence of obesity is increasing worldwide [1–4]. Globally, the proportion of obesity increased from 28.8% in 1980 to 36.9% in 2013 for men and from 29.8% to 38.0% for women in 2013 [5]. Besides, it is estimated that 3.4 million deaths, 3.9% of years of life lost and 3.8% of disability adjusted life years (DALYs) globally were caused by obesity in 2010 [6]. The high prevalence of obesity, together with its severe prognosis reveals it to be an increasing burden.

The etiology and pathogenesis of obesity are likely to comprise a multifactorial disorder, besides the conventional and modifiable risk factors, a large number of studies have demonstrated that obesity is a complicated polygenic disease and the genetic influence accounts for 40–70% of the individual differences [7–10]. In 2007, Frayling et al. performed the first genome-wide association study (GWAS) about BMI, a measure commonly used to define obesity and assess adiposity, and found that the common variants of the fat mass and obesity associated (FTO) gene may affect obesity in the general population and reflect a specific increase in fat mass [11]. Recent GWAS study of body mass index (BMI) have identified 97 genetic variants and all these loci only explained 2.7% of the variance in BMI [12]. While genome-wide estimates suggest that common variation accounts for >20% of BMI variation [12]. Further studies are needed to enrich the genetic knowledge of obesity.

Recently, a multicenter linkage study using a large number of dizygotic twins from six countries reported a linkage susceptibility loci for BMI at 7q36.3 [13]. This region has also been replicated in other studies and been demonstrated to be associated with obesity and triglyceridemia level [14]. The genomic interval on chromosome 7q36.3 includes 22 known genes, including insulin-induced gene 1 (INSIG1), the only biological candidate [15]. INSIG gene, including insig-1 and insig-2, play a lot of important roles in regulating cholesterol or (TAG) synthesis, mainly in the liver [16,17]. However, little is known about the association between insig1 gene and obesity. The present case–control study aimed to explore the association of insig1 gene polymorphisms with obesity in Chinese Uygur subjects.

Methods

Subjects

We conducted a case–control study involving Uygur subjects aged 18 years old and above. Obese patients and non-obese controls were all recruited from the Cardiovascular Risk Survey (CRS) [18]. On the basis of BMI, the subjects were classified as normal weight (18.5–22.99 kg/m2) and obese (>25 kg/m2), according to Asia Pacific population criteria [19]. Individuals with the following conditions were excluded from recruitment: pregnancy; history of cancer, renal or hepatic disease; severe coronary artery disease; substance abuse; diabetes mellitus; corticosteroids or thyroid dosages above replacement dose; and individuals receiving lipid-lowering medications. We finally included 516 obese subjects and 463 normal weight controls in the present study.

Anthropometric measurements (height, weight and waist circumference) were performed using standardized protocols. BMI was calculated as the weight divided by square of height (kg/m2). Hypertension was defined as a systolic blood pressure ≥140 mmHg and/or a diastolic blood pressure ≥90 mmHg at least on two distinct occasions. Briefly, diabetes mellitus was defined as two fasting plasma glucose level ≥7.0 mmol/L, the use of insulin or oral hypoglycemic agents, or a self-reported history of diabetes. The following information was collected: age, gender, hypertension, diabetes, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C).

Genotyping

The blood samples were drawn into a 5 ml ethylene diamine tetraacetic acid (EDTA) tube and centrifuged at 4000 × g for 5 min to separate the plasma content. Genomic DNA was extracted from the peripheral leukocytes using the standard phenol–chloroform method. The DNA samples were stored at -80°C until use. For use, the DNA was diluted to a concentration of 50 ng/μl. Using Haploview 4.2 software and International HapMap Project website phase I and II database (http://www.hapmap.org), we obtained three tag single nucleotide polymorphisms (SNPs) of INSIG1: rs2721, rs9767875 and rs9719268, by using minor allele frequency (MAF) ≥0.05 and linkage disequilibrium patterns with r2≥0.8 as a cutoff. Genotyping was undertaken using the TaqMan® SNP Genotyping Assay (Applied Biosystems). The primers and probes used for the TaqMan® SNP Genotyping Assays (ABI) were chosen based on information on ABI’s website (http://appliedbiosystems.com.cn/). Thermal cycling was conducted using the Applied Biosystems 7900HT Standard Real-Time PCR System. Plates were read using the Sequence Detection Systems (SDS) automation controller software v2.4 (ABI). PCR amplification was performed using 2.5 μl of TaqMan Universa Master Mix, 0.15 μl probes and 1.85 ddH2O in a 6 μl final reaction volume containing 1 μl DNA. Thermal cycling conditions were as follows: 95°C for 5 min; 40 cycles of 95°C for 15 s and 60°C for 1 min. All 96-well plates were read using SDS automation controller software v2.4 (ABI).

Statistical analysis

The data analysis was performed using SPSS version 17.0 for Windows (SPSS Inc., Chicago, IL, U.S.A.). The Hardy–Weinberg equilibrium (HWE) was assessed via chi-square analysis. The measurement data are shown as the means ± SD, and the differences between the obese subjects and the control subjects were assessed using an independent-sample t-test. Differences in the enumeration data, such as the frequencies of smoking, drinking, hypertension between the obese patients and the control subjects were analyzed using the chi-square test. Additionally, logistic regression analyses with effect ratios (odds ratio [OR] and 95% CI) were used to assess the contribution of the major risk factors. Then we examined an additive scale interaction by calculating the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP) and the synergy index (SI). A P value < 0.05 was considered to be statistically significant

Result

Characteristics of subjects

The baseline characteristics of 516 obese patients and 463 control subjects were shown in Table 1. All the measurement data compared by T-test are normally distributed. The mean age, HDL-C, LDL-C levels and the prevalence of male, smoking and drinking were similar between obese patients and the controls (all P>0.05). The obese patients and controls differed significantly with regards to BMI(P < 0.001), hypertension (P<0.001), waist-to-hip ratio (WHR) (P<0.001), TC (P<0.001) and TG (P<0.001).

Table 1
Clinical and metabolic characteristics of subjects
Characteristic Control (n=463) Case (n=516) P value 
Age (years) 53.37 ± 12.23 53.06 ± 10.64 0.670 
Male/female 215/248 233/283 0.688 
BMI 21.21 ± 1.23 28.95 ± 3.13 <0.001 
Hypertension 115 (24.8) 208 (40.3) <0.001 
Smoking 92 (19.9) 83 (16.1) 0.123 
Drinking 42 (9.1) 46 (8.9) 0.932 
WHR 0.87 ± 0.07 0.92 ± 0.07 <0.001 
TC 4.08 ± 0.97 4.49 ± 1.10 <0.001 
TG 1.15 ± 0.65 1.81 ± 1.17 <0.001 
LDL-C 2.88 ± 0.89 2.89 ± 0.97 0.768 
HDL-C 1.28±0.42 1.25±0.42 0.173 
Characteristic Control (n=463) Case (n=516) P value 
Age (years) 53.37 ± 12.23 53.06 ± 10.64 0.670 
Male/female 215/248 233/283 0.688 
BMI 21.21 ± 1.23 28.95 ± 3.13 <0.001 
Hypertension 115 (24.8) 208 (40.3) <0.001 
Smoking 92 (19.9) 83 (16.1) 0.123 
Drinking 42 (9.1) 46 (8.9) 0.932 
WHR 0.87 ± 0.07 0.92 ± 0.07 <0.001 
TC 4.08 ± 0.97 4.49 ± 1.10 <0.001 
TG 1.15 ± 0.65 1.81 ± 1.17 <0.001 
LDL-C 2.88 ± 0.89 2.89 ± 0.97 0.768 
HDL-C 1.28±0.42 1.25±0.42 0.173 

Continuous variables are expressed as mean ± SD. Categorical variables are expressed as percentages.

The P value of the continuous variables was calculated by the independent samples t-test. The P value of the categorical variables was calculated by χ2 test.

Association between INSIG1 gene polymorphisms and risk of obesity

Table 2 shows the distribution of genotypes for the three SNPs (rs2721, rs9767875 and rs9719268) of the INSIG1 gene. The genotype distributions of the three SNPs were in accordance with the HWE in all cases and controls (all P>0.05). For rs2721, the distribution of the genotypes, the dominant model (GT + TT vs GG), the recessive model (TT vs GT + GG) showed significant differences between obese patients and the controls (P = 0.008, P = 0.005 and P = 0.035, respectively). For rs9719268, the distribution of the genotypes showed significant differences between obese patients and the controls (P = 0.004). While, there were no significant differences between obese patients and controls in the distribution of rs9767875 genotypes, dominant model, recessive model and over-dominant model (P = 0.974, P = 0.833, P = 0.877 and P = 0.888, respectively).

Table 2
Distribution of SNPs of INSIG1 gene in obese patients and non-obese controls
Genotype or allele Control [n(%)] Case [n(%)] P value 
rs2721    
GG 307(66.3) 297(57.6)  
GT 138(29.8) 183(35.4)  
TT 18(3.9) 36(7.0) 0.008 
PHWE 0.615 0.287  
Dominant    
GT + TT 156(33.7) 219(42.4)  
GG 307(66.3) 297(57.6) 0.005 
Recessive    
TT 18(3.9) 36(7.0)  
GG + GT 445(96.1) 480(93.0) 0.035 
Additive    
GT 138(29.8) 183(35.4)  
GG + TT 325(70.2) 333(64.6) 0.060 
rs9767875    
GG 285(61.6) 321(62.2)  
GT 150(32.4) 165(32.0)  
TT 28(6.0) 30(5.8) 0.974 
PHWE 0.172 0.158  
Dominant    
GT + TT 178(38.4) 195(37.8)  
GG 285(61.6) 321(62.2) 0.833 
Recessive    
TT 28(6.0) 30(5.8)  
GG + GT 435(94.0) 486(94.2) 0.877 
Additive    
GT 150(32.4) 165(32.0)  
GG + TT 313(67.6) 351(68.0) 0.888 
rs9719268    
GG 421(90.9) 438(84.9)  
GT 42(9.1) 78(15.1) 0.004 
PHWE 0.307 0.063  
Genotype or allele Control [n(%)] Case [n(%)] P value 
rs2721    
GG 307(66.3) 297(57.6)  
GT 138(29.8) 183(35.4)  
TT 18(3.9) 36(7.0) 0.008 
PHWE 0.615 0.287  
Dominant    
GT + TT 156(33.7) 219(42.4)  
GG 307(66.3) 297(57.6) 0.005 
Recessive    
TT 18(3.9) 36(7.0)  
GG + GT 445(96.1) 480(93.0) 0.035 
Additive    
GT 138(29.8) 183(35.4)  
GG + TT 325(70.2) 333(64.6) 0.060 
rs9767875    
GG 285(61.6) 321(62.2)  
GT 150(32.4) 165(32.0)  
TT 28(6.0) 30(5.8) 0.974 
PHWE 0.172 0.158  
Dominant    
GT + TT 178(38.4) 195(37.8)  
GG 285(61.6) 321(62.2) 0.833 
Recessive    
TT 28(6.0) 30(5.8)  
GG + GT 435(94.0) 486(94.2) 0.877 
Additive    
GT 150(32.4) 165(32.0)  
GG + TT 313(67.6) 351(68.0) 0.888 
rs9719268    
GG 421(90.9) 438(84.9)  
GT 42(9.1) 78(15.1) 0.004 
PHWE 0.307 0.063  

The P value of the categorical variables was calculated by χ2 test.

Tables 3 and 4 show the multivariable logistic regression analyses of the major confounding factors for obesity. Following the multivariate adjustments for the confounders, such as age, gender, TG, TC, HDL-C, LDL-C and prevalence of hypertension, smoking and drinking, the dominant model (GT + TT vs GG) of rs2721 remain significantly associated with obesity (OR = 1.393, 95% CI = 1.047–1.853, P = 0.023). In addition, carriers of rs9719268 GT genotype had a significantly elevated obesity risk compared with those of GG genotype (OR = 1.631, 95% CI = 1.059–2.512, P = 0.026).

Table 3
Results of logistic analysis (rs2721)
 OR 95% CI P value 
rs2721 (GT + TT vs GG) 1.393 1.047–1.853 0.023 
Age 0.982 0.970–0.995 0.007 
Gender 0.782 0.565–1.082 0.138 
Smoking 0.477 0.290–0.787 0.004 
Drinking 1.158 0.628–2.135 0.638 
Hypertension 2.138 1.559–2.932 <0.001 
TG 2.510 2.013–3.130 <0.001 
TC 1.208 1.039–1.404 0.014 
HDL-C 0.924 0.653–1.306 0.654 
LDL-C 1.079 0.923–1.263 0.339 
 OR 95% CI P value 
rs2721 (GT + TT vs GG) 1.393 1.047–1.853 0.023 
Age 0.982 0.970–0.995 0.007 
Gender 0.782 0.565–1.082 0.138 
Smoking 0.477 0.290–0.787 0.004 
Drinking 1.158 0.628–2.135 0.638 
Hypertension 2.138 1.559–2.932 <0.001 
TG 2.510 2.013–3.130 <0.001 
TC 1.208 1.039–1.404 0.014 
HDL-C 0.924 0.653–1.306 0.654 
LDL-C 1.079 0.923–1.263 0.339 

OR, odds ration; CI, confidence interval.

Table 4
Results of logistic analysis (rs9719268)
 OR 95% CI P value 
rs9719268 (GT vs GG) 1.631 1.059–2.512 0.026 
Age 0.983 0.970–0.995 0.008 
Gender 0.773 0.558–1.070 0.121 
Smoking 0.49 0.297–0.807 0.005 
Drinking 1.09 0.591–2.010 0.782 
Hypertension 2.048 1.494–2.807 <0.001 
TG 2.54 2.040–3.163 <0.001 
TC 1.199 1.032–1.393 0.017 
HDL-C 0.925 0.654-1.309 0.66 
LDL-C 1.09 0.931-1.276 0.283 
 OR 95% CI P value 
rs9719268 (GT vs GG) 1.631 1.059–2.512 0.026 
Age 0.983 0.970–0.995 0.008 
Gender 0.773 0.558–1.070 0.121 
Smoking 0.49 0.297–0.807 0.005 
Drinking 1.09 0.591–2.010 0.782 
Hypertension 2.048 1.494–2.807 <0.001 
TG 2.54 2.040–3.163 <0.001 
TC 1.199 1.032–1.393 0.017 
HDL-C 0.925 0.654-1.309 0.66 
LDL-C 1.09 0.931-1.276 0.283 

OR, odds ration; CI, confidence interval.

Stratified analysis between INSIG1 gene polymorphisms and obesity risk

We performed stratification analyses in terms of gender, hypertension, smoking, drinking and WHR status to evaluate how these variables modified the association between the SNPs (rs2721, rs9767875 and rs9719268) and obesity risk (Table 5). The rs2721 GT/TT genotypes were shown to significantly increase obesity risk in smokers (AOR = 2.406, 95% CI = 1.194–4.850, P = 0.014) and drinkers (AOR = 2.710, 95% CI = 1.575–5.710, P = 0.008). A significant interaction between rs2721 and smoking (RERI = 0.61, AP = 0.40 and SI = 5.1, P<0.05), drinking (RERI = 1.4, AP = 0.56 and SI = 12.67, P<0.05) were also observed. The rs9719268 GT genotype were shown to significantly increase obesity risk in females (AOR = 2.234, 95% CI = 1.240–4.025, P = 0.007), nonsmokers (AOR = 2.085, 95% CI = 1.276–3.408, P = 0.003), nondrinkers (AOR = 1.806, 95% CI = 1.137–2.870, P = 0.012) and high WHR (AOR = 1.747, 95% CI = 1.030–2.961, P = 0.038).

Table 5
Stratifed analysis between INSIG1 gene polymorphisms and obesity risk
Variables rs2721 (control/case) Adjusted Ora P1 RERI AP SI rs9767875 (control/case) Adjusted Ora P1 RERI AP SI rs9719268 (control/case) Adjusted Ora P1 RERI AP SI 
 GG GT + TT 95% CI     GG GT + TT 95% CI     GG GT      
Gender     0.04 0.026 0.93     0.36 0.34 0.16     0.91 0.40 3.68 
  Females 165/171 83/112 1.399(0.949–2.062) 0.090    153/165 95/118 1.148(0.784–1.680) 0.479    228/236 20/47 2.234(1.240–4.025) 0.007    
  Males 142/136 73/97 1.327(0.868–2.027) 0.191    132/156 83/77 0.784(0.511–1.204) 0.267    193/202 22/31 1.085(0.564–2.087) 0.808    
Hypertension     1.23 0.34 1.89     0.17 0.08 1.2     1.47 0.37 1.97 
  No 221/176 127/132 1.274(0.907–1.790) 0.162    216/198 132/110 0.968(0.686–1.366) 0.854    317/267 31/41 1.471(0.859–2.522) 0.16    
  Yes 86/131 29/77 1.536(0.881–2.676) 0.130    69/123 46/85 0.926(0.551–1.555) 0.771    104/171 11/37 1.916(0.890–4.127) 0.097    
Smoking status     0.61 0.40 5.1*     0.25 0.29 0.36     1.83 4.16 0.79 
  Never 240/262 131/171 1.206(0.881–1.651) 0.243    224/270 147/163 0.932(0.683–1.272) 0.655    342/361 29/72 2.085(1.276–3.408) 0.003    
  Ever 67/45 25/38 2.348(1.163–4.742) 0.017    61/51 31/32 1.232(0.613–2.477) 0.558    79/77 13/6 0.584(0.197–1.731) 0.332    
Drinking status     1.40 0.56 12.67*     0.22 0.2 0.57     1.47 2.01 0.23 
  Never 273/281 148/189 1.222(0.908–1.643) 0.186    256/291 165/179 0.941(0.701–1.264) 0.688    387/398 34/72 1.806(1.137–2.870) 0.012    
  Ever 34/26 8/20 2.710(1.575–5.710) 0.008    29/30 13/16 1.738(0.554–5.456) 0.344    34/40 8/6 0.973(0.225–4.211) 0.971    
WHR     1.22 0.18 1.26     0.15 -0.04 0.95     3.56 0.42 1.91 
  Normal 125/36 61/30 1.512(0.798–2.865) 0.205    121/46 65/20 0.781(0.395–1.541) 0.476    168/57 18/9 1.518(0.565–4.076) 0.408    
  High 182/271 95/179 1.256(0.892–1.769) 0.192    164/275 113/175 0.911(0.652–1.274) 0.587    253/381 24/69 1.747(1.030–2.961) 0.038    
Variables rs2721 (control/case) Adjusted Ora P1 RERI AP SI rs9767875 (control/case) Adjusted Ora P1 RERI AP SI rs9719268 (control/case) Adjusted Ora P1 RERI AP SI 
 GG GT + TT 95% CI     GG GT + TT 95% CI     GG GT      
Gender     0.04 0.026 0.93     0.36 0.34 0.16     0.91 0.40 3.68 
  Females 165/171 83/112 1.399(0.949–2.062) 0.090    153/165 95/118 1.148(0.784–1.680) 0.479    228/236 20/47 2.234(1.240–4.025) 0.007    
  Males 142/136 73/97 1.327(0.868–2.027) 0.191    132/156 83/77 0.784(0.511–1.204) 0.267    193/202 22/31 1.085(0.564–2.087) 0.808    
Hypertension     1.23 0.34 1.89     0.17 0.08 1.2     1.47 0.37 1.97 
  No 221/176 127/132 1.274(0.907–1.790) 0.162    216/198 132/110 0.968(0.686–1.366) 0.854    317/267 31/41 1.471(0.859–2.522) 0.16    
  Yes 86/131 29/77 1.536(0.881–2.676) 0.130    69/123 46/85 0.926(0.551–1.555) 0.771    104/171 11/37 1.916(0.890–4.127) 0.097    
Smoking status     0.61 0.40 5.1*     0.25 0.29 0.36     1.83 4.16 0.79 
  Never 240/262 131/171 1.206(0.881–1.651) 0.243    224/270 147/163 0.932(0.683–1.272) 0.655    342/361 29/72 2.085(1.276–3.408) 0.003    
  Ever 67/45 25/38 2.348(1.163–4.742) 0.017    61/51 31/32 1.232(0.613–2.477) 0.558    79/77 13/6 0.584(0.197–1.731) 0.332    
Drinking status     1.40 0.56 12.67*     0.22 0.2 0.57     1.47 2.01 0.23 
  Never 273/281 148/189 1.222(0.908–1.643) 0.186    256/291 165/179 0.941(0.701–1.264) 0.688    387/398 34/72 1.806(1.137–2.870) 0.012    
  Ever 34/26 8/20 2.710(1.575–5.710) 0.008    29/30 13/16 1.738(0.554–5.456) 0.344    34/40 8/6 0.973(0.225–4.211) 0.971    
WHR     1.22 0.18 1.26     0.15 -0.04 0.95     3.56 0.42 1.91 
  Normal 125/36 61/30 1.512(0.798–2.865) 0.205    121/46 65/20 0.781(0.395–1.541) 0.476    168/57 18/9 1.518(0.565–4.076) 0.408    
  High 182/271 95/179 1.256(0.892–1.769) 0.192    164/275 113/175 0.911(0.652–1.274) 0.587    253/381 24/69 1.747(1.030–2.961) 0.038    

OR, odds ration; CI, confidence interval.

1

Adjusted for the confounders, such as age, gender, TG, TC, HDL-C, LDL-C and prevalence of hypertension, smoking and drinking.

*

P<0.05.

Associations between haplotypes of INSIG1 gene polymorphisms and obesity risk

In the present study, we investigated the interaction of INSIG1 polymorphisms in obesity through linkage disequilibrium analysis and the results showed the strong linkage disequilibrium among INSIG1 rs2721, rs9767875 and rs9719268 polymorphisms. The associations between possible haplotypes and risk of obesity were estimated and listed in Table 6. The INSIG1 rs2721/ rs9767875/ rs9719268 haplotype G-G-T and T-G-G were significantly associated with increased risk of obesity (OR = 1.984, 95% CI = 1.186-3.320, P = 0.008; OR = 1.512, 95% CI = 1.170–1.953, P = 0.001). However, the haplotype G-G-G was associated with lower risk of obesity (OR = 0.751, 95% CI = 0.624–0.903, P = 0.002).

Table 6
Associations between haplotypes of INSIG1 gene polymorphisms and obesity risk
Haplotype Case Control OR(95% CI) P 
GGG 571.37(0.554) 579.09(0.625) 0.751(0.624–0.903) 0.002 
GGT 47.06(0.046) 21.98(0.024) 1.984(1.186–3.320) 0.008 
GTG 150.27(0.146) 137.78(0.149) 0.985(0.766–1.266) 0.904 
TGG 176.20(0.171) 112.16(0.121) 1.512(1.170–1.953) 0.001 
TTG 56.17(0.054) 54.96(0.059) 0.920(0.627–1.350) 0.671 
Haplotype Case Control OR(95% CI) P 
GGG 571.37(0.554) 579.09(0.625) 0.751(0.624–0.903) 0.002 
GGT 47.06(0.046) 21.98(0.024) 1.984(1.186–3.320) 0.008 
GTG 150.27(0.146) 137.78(0.149) 0.985(0.766–1.266) 0.904 
TGG 176.20(0.171) 112.16(0.121) 1.512(1.170–1.953) 0.001 
TTG 56.17(0.054) 54.96(0.059) 0.920(0.627–1.350) 0.671 

Haplotype frequency < 0.03 in both obese patients and controls has been dropped; OR, odds ratio; 95% CI, 95% confidence interval.

Genotypes and serum lipid levels

As shown in Figure 1, the TG levels were significantly higher in rs2721 GT/TT genotypes than that in GG genotypes (P<0.05).

Association between rs2721 and lipid parameters

Figure 1
Association between rs2721 and lipid parameters

(A) The TG levels were significantly higher in rs2721 GT/TT genotypes than that in GG genotypes (P<0.05). (B) There exists no significant difference of TC between rs2721 GT/TT genotypes and GG genotypes (P>0.05). (C) There exists no significant difference of HDL-C between rs2721 GT/TT genotypes and GG genotypes (P>0.05). (D) There exists no significant difference of LDL-C between rs2721 GT/TT genotypes and GG genotypes (P>0.05).

Figure 1
Association between rs2721 and lipid parameters

(A) The TG levels were significantly higher in rs2721 GT/TT genotypes than that in GG genotypes (P<0.05). (B) There exists no significant difference of TC between rs2721 GT/TT genotypes and GG genotypes (P>0.05). (C) There exists no significant difference of HDL-C between rs2721 GT/TT genotypes and GG genotypes (P>0.05). (D) There exists no significant difference of LDL-C between rs2721 GT/TT genotypes and GG genotypes (P>0.05).

Discussion

In the present study, we investigated associations between three SNPs in the INSIG1 gene and obesity risk in a Chinese Uygur population. Our results show that rs2721 and rs9719268 are significantly associated with obesity susceptibility.

Obesity is a common heritable trait and a major risk factor for the development of hypertension, diabetes, coronary heart disease and stroke [20–25]. Long-term trends of overweight indicate a strong impact of lifestyle on the risk of obesity, despite this, many studies that aim to evaluate family heredity have proven the influence of genetic factors on BMI [13,26]. Recently, Kettunen performed a large multicenter linkage study including 4401 twin families from six countries (Australia, Denmark, Holland, Finland, Sweden and the United Kingdom) on BMI and found suggestive evidence for a quantitative trait locus on 7q36.3 [13]. It is very interesting that a recent genome-wide linkage analysis on BMI in 126 Chinese dizygotic twins identified a genome-wide significant linkage peak on 7q36.3 too [26]. According to bioinformatics analysis, we found that there exists genes directly related to glycolipid metabolism in the 7q36 region, such as INSIG1, the only biological candidate.

Insulin-induced genes are newly characterized genes in human and encode two isoform membrane proteins in the endoplasmic reticulum (ER): INSIG1 and INSIG2. Previous studies have shown that INSIG1 genes can affect intracellular cholesterol synthesis through sterol regulatory element binding proteins (SREBP) pathway and sterol-regulated degradation of HMG-CoA reductase (HMGCR) pathway, two of the most important mechanisms governing the negative feedback regulation of de novo cholesterol biosynthesis. SREBP is a transcription factor located in the ER and its main function is to control the synthesis of cholesterol and fatty acids [27,28]. When the cellular sterol levels rise, INSIG1 binds to the sterol-sensing domain (SSD) of sterol regulatory element-binding protein (SREBP) cleavage-activating protein (SCAP), blocking the binding of SCAP to SREBP and preventing the transport of the SCAP/SREBP complex from ER to the Golgi, causing transcriptional rates of SREBP target genes to decline and ultimately leading to a reduction in cholesterol synthesis and uptake. When cells are depleted of sterols, INSIG1 expressed at a low level, the binding of INSIG1 and SCAP is reduced and SREBPs are transported by SCAP from ER to Golgi, where they are processed and released into the cytosol on their way to the nucleus to activate transcription of lipogenic genes, and finally leading to the increasing of cholesterol synthesis [29,30]. HMGCR, the enzyme that catalyzes the rate-determining step of the cholesterol biosynthetic pathway, is the rate-limiting enzyme in the biosynthesis of cholesterol. Binding of HMGCR to insig proteins leads to the ubiquitination and degradation of the reductase. The degradation slows down the reductase-catalyzed conversion of HMG-CoA to mevalonate, a rate-limiting step in cholesterol synthesis [31–32]. Through these two mechanisms, INSIG1 protein cause coordinated association in both transcription of relevant genes and sterol pathway activity.

Studies conducted in animals have demonstrated the critical roles of INSIG1 gene in lipid homeostasis. In vivo, the mRNA level of INSIG1 gene in fat tissue of diet-induced obese rats increased significantly [32]. Takaishi et al. infected Zucker diabetic fatty (ZDF) (fa/fa) rats with recombinant adenovirus containing insig-1 cDNA to overexpress the INSIG1 gene and found that the increase in endogenous insig-1 expression was associated with augmented lipogenesis. Overexpression of INSIG1 gene was shown to reduce high levels of TG in both liver and plasma of Zucker diabetic fatty rats [33]. Moreover, single knockout of either INSIG1 or INSIG2 gene alone in mouse may result in the increasing of cholesterol and TG level in mouse livers [34]. All these suggested that INSIG1 gene may affect the homeostasis of lipid.

The association between INSIG1 gene polymorphisms and obesity was poor. Liu et al. performed a case–control study including 705 obese cases and 1325 non-obese controls to explore the association of genetic variants in INSIG-SCAP-SREBP pathway with obesity in Chinese children [35]. And the result revealed that rs2721 and rs13223383 have no association with obesity [35]. However, Le Hellard et al. found that INSIG1 rs13223383 was related to BMI change in Germans [36]. In addition, Smith analyzed a quantitative trait locus (QTL) for plasma TG levels [logarithm of odds (LOD) = 3.7] on human chromosome 7q36.3 and examined 29 SNPs across INSIG1 gene [15]. They found that the INSIG1 rs2721 was associated with TG levels. Compared with GG genotype, carriers of INSIG1 rs2721 TT genotype had 9% higher TG levels and 2-fold lower expression of INSIG1 in surgical liver biopsy samples [15].

In the present study, we genotyped polymorphisms of rs2721, rs9767875 and rs9719268 in INSIG1 gene and found that rs2721 and rs9719268 were associated with obesity. The rs2721 GT/TT genotype has a higher frequency in obese patients than that in controls. After adjustments for several confounders, this association remained exist, indicating that the rs2721 GT/TT were independent risk factors for obesity and the risk of obesity was increased in the subjects with the T allele in rs2721. The rs9719268 GT genotype has a higher frequency in obese patients than that in controls. After adjustments for confounders, this association remained exist, indicating that the rs9719268 GT were independent risk factors for obesity and the risk of obesity was increased in the subjects with the T allele in rs9719268.

Furthermore, we also analyzed the relationship between genotypes and lipid levels. We found that T allele (GT/TT) carriers in rs2721 have higher levels of TG when compared with T allele non-carriers. This finding is consistent with our previous conclusions. We drew the inference that variation at rs2721 in INSIG1 gene may influence lipid homeostasis and lead to the increased total content of TG. Th e mechanism may be that INSIG1 genes can affect intracellular cholesterol synthesis through SREBP pathway and sterol-regulated degradation of HMGCR pathway, two of the most important mechanisms governing the negative feedback regulation of de novo cholesterol biosynthesis. Despite the promising findings in the present study, several limitations should be mentioned. First of all, we only draw conclusions based on the present observational association study, we failed to get a cause-and-effect relationship between risk factors and obesity. Secondly, the sample size of present study is relatively moderate. The association between rs2721 and rs9719268 and obesity should be confirmed by studies with larger sample size. Thirdly, the present study lacked functional validation. Additional studies need to be undertaken to clarify the underlying molecular mechanism between INSIG1 gene polymorphisms and obesity.

Conclusion

This study revealed that rs2721 and rs9719268 of INSIG1 gene are associated with obesity in Uygur subjects. Subjects with GT/TT genotype or T allele of rs2721 and GT genotype of rs9719268 were associated with an increased risk of obesity.

Availability to Data and Materials

All data generated or analyzed during this study are included in this published article.

Ethics Approval and Consent to Participate

This study was approved by the Ethics Committee of the People’s Hospital of Xinjiang Uygur Autonomous Region (Urumqi, China). It was conducted according to the standards of the Declaration of Helsinki. All of the patients provided written informed consent and explicitly provided permission for DNA analyses, as well as for the collection of relevant clinical data.

Author Contribution

JT, MA, GQL, ZW and YTM conceived and designed the experiments. JT and MA performed the experiments. XS, JL, FXW, JY and PPG analyzed the data. WZ and XTZ contributed reagents/materials/analysis tools. JT and MA wrote the paper.

Competing Interests

The authors declare that there are no competing interests associated with the manuscripts.

Funding

This work was supported by the Natural Science Fund Project of Xinjiang Uygur Autonomous Region [grant number 2018D01C116].

Abbreviations

     
  • BMI

    body mass index

  •  
  • ER

    endoplasmic reticulum

  •  
  • FTO

    fat mass and obesity associated

  •  
  • GWAS

    genome-wide association study

  •  
  • HDL-C

    high-density lipoprotein cholesterol

  •  
  • HMGCR

    HMG-CoA reductase

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • INSIG

    insulin-induced gene

  •  
  • LDL-C

    low-density lipoprotein cholesterol

  •  
  • SCAP

    SREBP cleavage-activating protein

  •  
  • SNPs

    single nucleotide polymorphisms

  •  
  • SREBP

    sterol regulatory element binding proteins

  •  
  • TAG

    triacylglycerol

  •  
  • TC

    total cholesterol

  •  
  • TG

    triglyceride

  •  
  • WHR

    waist-to-hip ratio

References

References
1.
Stevens
G.A.
,
Singh
G.M.
,
Lu
Y.
,
Danaei
G.
,
Lin
J.K.
,
Finucane
M.M.
et al.
(
2012
)
National, regional, and global trends in adult overweight and obesity prevalences
.
Popul. Health Metr.
10
,
22
[PubMed]
2.
Finucane
M.M.
,
Stevens
G.A.
,
Cowan
M.J.
,
Danaei
G.
,
Lin
J.K.
,
Paciorek
C.J.
et al.
(
2011
)
National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants
.
Lancet North Am. Ed.
377
,
557
567
3.
De Onis
M.
,
Blössner
M.
and
Borghi
E.
(
2010
)
Global prevalence and trends of overweight and obesity among preschool children
.
Am. J. Clin. Nutr.
92
,
1257
1264
[PubMed]
4.
Popkin
B.M.
,
Adair
L.S.
and
Ng
S.W.
(
2012
)
Global nutrition transition and the pandemic of obesity in developing countries
.
Nutr. Rev.
70
,
3
21
[PubMed]
5.
Ng
M.
,
Fleming
T.
,
Robinson
M.
,
Thomson
B.
,
Graetz
N.
,
Margono
C.
et al.
(
2014
)
Global, regional and national prevalence of overweight and obesity in children and adults 1980–2013: a systematic analysis
.
Lancet
384
,
766
781
[PubMed]
6.
Lim
S.S.
,
Vos
T.
,
Flaxman
A.D.
,
Danaei
G.
,
Shibuya
K.
,
Adair-Rohani
H.
et al.
(
2012
)
A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010
.
Lancet North Am. Ed.
380
,
2224
2260
7.
Visscher
P.M.
,
Brown
M.A.
,
McCarthy
M.I.
and
Yang
J.
(
2012
)
Five years of GWAS discovery
.
Am. J. Hum. Genet.
90
,
7
24
[PubMed]
8.
Zaitlen
N.
,
Kraft
P.
,
Patterson
N.
,
Pasaniuc
B.
,
Bhatia
G.
,
Pollack
S.
et al.
(
2013
)
Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits
.
PLoS Genet.
9
,
e1003520
[PubMed]
9.
Fall
T.
and
Ingelsson
E.
(
2014
)
Genome-wide association studies of obesity and metabolic syndrome
.
Mol. Cell. Endocrinol.
382
,
740
757
[PubMed]
10.
Speliotes
E.K.
,
Willer
C.J.
,
Berndt
S.I.
,
Monda
K.L.
,
Thorleifsson
G.
,
Jackson
A.U.
et al.
(
2010
)
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index
.
Nat. Genet.
42
,
937
948
[PubMed]
11.
Frayling
T.M.
,
Timpson
N.J.
,
Weedon
M.N.
,
Zeggini
E.
,
Freathy
R.M.
,
Lindgren
C.M.
et al.
(
2007
)
A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity
.
Science
316
,
889
894
[PubMed]
12.
Locke
A.E.
,
Kahali
B.
,
Berndt
S.I.
,
Justice
A.E.
,
Pers
T.H.
,
Day
F.R.
et al.
(
2015
)
Genetic studies of body mass index yield new insights for obesity biology
.
Nature
518
,
197
206
[PubMed]
13.
Kettunen
J.
,
Perola
M.
,
Martin
N.G.
,
Cornes
B.K.
,
Wilson
S.G.
,
Montgomery
G.W.
et al.
(
2009
)
Multicenter dizygotic twin cohort study confirms two linkage susceptibility loci for body mass index at 3q29 and 7q36 and identifies three further potential novel loci
.
Int. J. Obes. (Lond.)
33
,
1235
1242
[PubMed]
14.
Zhang
D.F.
,
Pang
Z.
,
Li
S.
,
Thomassen
M.
,
Wang
S.
,
Jiang
W.
et al.
(
2012
)
High-resolution genome-wide linkage mapping identifies susceptibility loci for BMI in the Chinese population
.
Obesity (Silver Spring)
20
,
830
833
[PubMed]
15.
Smith
E.M.
,
Zhang
Y.
,
Baye
T.M.
,
Gawrieh
S.
,
Cole
R.
,
Blangero
J.
et al.
(
2010
)
INSIG1 influences obesity-related hypertriglyceridemia in humans
.
J. Lipid Res.
51
,
16.
Yang
T.
,
Espenshade
P.J.
,
Wright
M.E.
,
Yabe
D.
,
Gong
Y.
,
Aebersold
R.
et al.
(
2002
)
Crucial step in cholesterol homeostasis: sterols promote binding of SCAP to INSIG-1, a membrane protein that facilitates retention of SREBPs in ER
.
Cell
110
,
489
500
[PubMed]
17.
Yabe
D.
,
Brown
M.S.
and
Goldstein
J.L.
(
2002
)
Insig-2, a second endoplasmic reticulum protein that binds SCAP and blocks export of sterol regulatory element binding proteins
.
Proc. Natl. Acad. Sci. U.S.A.
99
,
12753
12758
[PubMed]
18.
Tao
J.
,
Ma
Y.-t.
,
Xiang
Y.
,
Xie
X.
,
Yang
Y.N.
,
Li
X.M.
et al.
(
2013
)
Prevalence of major cardiovascular risk factors and adverse risk profiles among three ethnic groups in the Xinjiang Uygur Autonomous Region, China
.
Lipids Health Dis.
12
,
185
[PubMed]
19.
Ningombam
S.S.
,
Chhunqi
V.
,
Newmei
M.K.
,
Rajkumar
S.
,
Devi
N.K.
,
Mondal
P.R.
et al.
. (
2018
)
Differential distribution and association of FTO rs9939609 gene polymorphism with obesity: A cross-sectional study among two tribal populations of India with East-Asian ancestry
.
Gene
20
,
198
204
20.
Antza
C.
,
Stabouli
S.
,
Natsis
M.
,
Doundoulakis
I.
and
Kotsis
V.
(
2017
)
Obesity-induces hypertension: new insight
.
Curr. Pharm. Des.
23
,
4620
4625
21.
Kershaw
K.N.
and
Pender
A.E.
(
2016
)
Racial/ethnic residential segregation, obesity, and diabetes mellitus
.
Curr. Diab. Rep.
16
,
[PubMed]
22.
Polsky
S.
and
Ellis
S.L.
(
2015
)
Obesity, insulin resistance, and type 1 diabetes mellitus
.
Curr. Opin Endocrinol. Diabetes Obes.
22
,
277
282
[PubMed]
23.
Shen
C.
,
Zhou
Z.
,
Lai
S.
,
Tao
X.
,
Zhao
D.
,
Dong
W.
et al.
Urban-rural-specific trend in prevalence of general and central obesity, and association with hypertension in Chinese adults, aged 18-65 years
.
BMC Public Health
19
,
153
160
24.
Jokinen
E.
(
2015
)
Obesity and cardiovascular disease
.
Minerva Pediatr.
67
,
25
32
[PubMed]
25.
Mitchell
A.B.
,
Cole
J.W.
,
McArdle
P.F.
,
Cheng
Y.C.
,
Ryan
K.A.
,
Sparks
M.J.
et al.
(
2015
)
Obesity increases risk of ischemic stroke in young adults
.
Stroke
46
,
1690
1692
26.
Wu
Y.
,
Wang
W.
,
Jiang
W.
,
Yao
J.
and
Zhang
D.
(
2017
)
An investigation of obesity susceptibility genes in Northern Han Chinese by targeted resequencing
.
Medicine (Baltimore)
96
,
e6117
27.
Brown
M.S.
and
Goldstein
J.L.
(
1997
)
The SREBP pathway: regulation of cholesterol metabolism by proteolysis of a membrane-bound transcription factor
.
Cell
89
,
331
340
[PubMed]
28.
Horton
J.D.
,
Goldstein
J.L.
and
Brown
M.S.
(
2002
)
SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver
.
J. Clin. Invest.
109
,
1125
1131
[PubMed]
29.
Yang
T.
,
Espenshade
P.J.
,
Wright
M.E.
,
Yabe
D.
,
Gong
Y.
,
Aebersold
R.
et al.
(
2002
)
Crucial step in cholesterol homeostasis: sterols promote binding of SCAP to INSIG-1, a membrane protein that facilitates retention of SREBPs in ER
.
Cell
110
,
489
500
[PubMed]
30.
Ye
J.
and
DeBose-Boyd
R.A.
(
2011
)
Regulation of cholesterol and fatty acid synthesis
.
Cold Spring Harb. Perspect. Biol.
3
,
1220
1231
[PubMed]
31.
Jo
Y.
and
Debose-Boyd
R.A.
(
2010
)
Control of cholesterol synthesis through regulated ER-associated degradation of HMG CoA reductase
.
Crit. Rev. Biochem. Mol. Biol.
45
,
185
198
[PubMed]
32.
Sever
N.
,
Song
B.L.
,
Yabe
D.
,
Goldstein
J.L.
,
Brown
M.S.
and
DeBose-Boyd
R.A.
(
2003
)
Insig-dependent ubiquitination and degradation of mammalian 3-hydroxy-3-methylglutaryl-CoA reductase stimulated by sterols and geranylgeraniol
.
J. Biol. Chem.
278
,
52479
52490
[PubMed]
33.
Takaishi
K.
,
Duplomb
L.
,
Wang
M.Y.
,
Li
J.
and
Unger
R.H.
(
2004
)
Hepatic insig-1 or -2 overexpression reduces lipogenesis in obese Zucker diabetic fatty rats and in fasted/refed normal rats
.
Proc. Natl. Acad. Sci. U.S.A.
101
,
7106
7111
,
[PubMed]
34.
Engelking
L.J.
,
Liang
G.
,
Hammer
R.E.
,
Takaishi
K.
,
Kuriyama
H.
,
Evers
B.M.
et al.
(
2005
)
Schoenheimer effect explained–feedback regulation of cholesterol synthesis in mice mediated by Insig proteins
.
J. Clin. Invest.
115
,
2489
[PubMed]
35.
Liu
F.-H.
,
Song
J.-Y.
,
Shang
X.-R.
,
Meng
X.-R.
,
Ma
J.
and
Wang
H.-J.
(
2014
)
The gene–gene interaction of INSIG-SCAP-SREBP pathway on the risk of obesity in Chinese children
.
Biomed. Res. Int.
2014
36.
Le Hellard
S.
,
Theisen
F.M.
,
Haberhausen
M.
,
Raeder
M.B.
,
Fernø
J.
and
Gebhardt
S.
(
2009
)
Association between the insulin-induced gene 2 (INSIG2) and weight gain in a German sample of antipsychotic-treated schizophrenic patients: perturbation of SREBP-controlled lipogenesis in drug-related metabolic adverse effects?
Mol. Psychiatry
14
,
308
317
[PubMed]

Author notes

*

These authors have contributed equally to this work and should be considered as co-first authors.

This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).