GENE-40303; No. of pages: 5; 4C: Gene xxx (2015) xxx–xxx
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Gene journal homepage: www.elsevier.com/locate/gene
Association of RBP4 gene variants with adverse lipid profile and obesity Mansour Shajarian a, Laleh Rafiee a, Hajar Naji-Esfahani a, Shaghayegh Haghjooy-Javanmard a,⁎, Sarrafzadegan Nizal b a b
Applied Physiology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran Cardiovascular Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
a r t i c l e
i n f o
Article history: Received 9 November 2014 Received in revised form 17 December 2014 Accepted 20 December 2014 Available online xxxx Keywords: Retinol-binding protein4 (RBP4) Obesity Polymorphism High-density lipoprotein (HDL) Low-density lipoprotein (LDL) Triglyceride
a b s t r a c t Obesity is currently a worldwide public health problem. Retinol-binding protein4 (RBP4) is a recently discovered adipokine, which is potentially associated with insulin resistance and obesity. We aimed to investigate whether genetic variation within the RBP4 gene is correlated with the obesity and lipid profile in Iranian population. 321 samples were randomly selected from participants of Isfahan Healthy Heart Program (IHHP). Genomic DNA was isolated from peripheral blood cells (PBCs) and HRM-PCR was performed in order to investigate the presence of SNPs, and further sequencing analysis was done from selected subjects according to the differences of HRM curve pattern. Statistical analyses were performed using SPSS v16.00. The difference of the presence of rs3758539 polymorphism between controls and obese patients was significant, but not about rs10882280. We found noticeable association among genetic polymorphisms and biomedical and physical characteristics within investigated population. Our findings suggested that variations in the RBP4 gene were correlated with BMI and polymorphisms more likely could contribute to the development of obesity in our population. Also appraisal of obesity risk factors within each group might be helpful for preventing obesity initiation and could have a possible role in a predisposition to obesity in the Iranians. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Obesity is currently a worldwide public health problem (Kelishadi, 2007). Increasing burden of obesity leads to increased morbidity and mortality due to metabolic syndrome, insulin resistance, type-2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) (Mensah et al., 2004). There is a progressive increase in obesity in both children and adolescent groups in the last decade in Iran (Sarrafzadegan et al., 2010). The underlying mechanism of obesity is very complex, including interactions among behavioral, environmental, and genetic factors. Increasing prevalence of obesity can be attributed to highly calorific food intake and relatively sedentary lifestyle of modern times. Several studies have shown important role of genetic components in the risk of becoming obese (Lyon and Hirschhorn, 2005; Xia and Grant, 2013). Retinol-binding protein4 (RBP4) is a recently discovered adipokine that belongs to the lipocalin family of proteins, which is potentially in Abbreviations: RBP, retinol binding protein; T2DM, type-2 diabetes mellitus; CVD, cardiovascular disease; BMI, body mass index; SNP, single nucleotide polymorphism; HRM, high-resolution melt; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, triglyceride; FBS, fasting blood sugar; SBP, systolic blood pressure; DBP, diastolic blood pressure. ⁎ Corresponding author. E-mail address: [email protected] (S. Haghjooy-Javanmard).
association with insulin resistance (Yang et al., 2005). RBP4 is released from the liver as a primary source, and also from the adipose tissue as the alternate source (Tsutsumi et al., 1992). RBP4 acts as a carrier protein for retinol (one of the animal form of vitamin A), and other small hydrophobic molecules (Newcomer and Ong, 2000). Overexpression of RBP4 in visceral adipose tissue would cause elevated level of serum RBP4 and consequently leads to the accumulation of visceral fat and causes insulin resistance (Kloting et al., 2007). Although, the association between RBP4 and level of insulin (Kovacs et al., 2007) and insulin sensitiveness (Craig et al., 2007) was reported in previous researches, further studies couldn't find a significant association (Hu et al., 2008). Serum RBP4 levels are associated with body mass index (BMI) in diabetic or non-diabetic obese patients (Yang et al., 2005; Graham et al., 2006). There are several RBP4 gene variants that are related to the adiposity level and adipose tissue accumulation (Kotnik et al., 2011). The RBP4 maps to chromosome 10q23–q24 in humans, closely to a region that has been linked with increased risk for type-2 diabetes in other populations (Ping et al., 2012). Since pivotal role of RBP4 gene in obesity progresses, we therefore aimed to investigate whether genetic variation within the RBP4 gene correlates with the obesity in participants of Isfahan Healthy Heart Program (IHHP). We screened the RBP4 gene for 2 specific sequence variants rs3758539 and rs10882273 that have been previously linked
http://dx.doi.org/10.1016/j.gene.2014.12.071 0378-1119/© 2015 Elsevier B.V. All rights reserved.
Please cite this article as: Shajarian, M., et al., Association of RBP4 gene variants with adverse lipid profile and obesity, Gene (2015), http:// dx.doi.org/10.1016/j.gene.2014.12.071
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M. Shajarian et al. / Gene xxx (2015) xxx–xxx
with the development of T2DM and obesity in humans (Craig et al., 2007; Kovacs et al., 2007) and hypothesized that variations of these SNPs are associated with Iranian obese subjects. 2. Materials and methods 2.1. Patients and setting This project was conducted as a sub-study of the Isfahan Healthy Heart Program (IHHP), which is a comprehensive action-oriented integrated community-based intervention program for the prevention and control of non-communicable diseases (NCDs), which was implemented in central Iran. For the current study, we randomly selected 321 samples from participants of IHHP with the age range of 19 to 60 years. The participants were divided in 2 subgroups according to the body mass index (BMI) including 129 obese cases (66 female, 63 male, age: 38.5 ± 11.030) and 192 healthy controls (66 female, 126 male, age: 29.84 ± 9.079). Details of data collection and sampling are published previously (Sarraf-Zadegan et al., 2003). Written informed consent was obtained from all participants. The ethics committee of the Isfahan University of Medical Sciences approved the study. 2.2. DNA extraction and genotyping 2 ml of peripheral blood was recruited from each participant. The genomic DNA was isolated from the blood using Genomic DNA Isolation Kit (GeNetBio, Korea) according to the manufacturer's protocol. The investigated single nucleotide polymorphisms (rs3758539 & rs10882280) were identified by the NCBI data bank. The primers were designed by Beacon Designer version7.5 (PREMIER Biosoft International, USA) and synthesized by TIB MOLBIOL (Germany). The sequences of primers are demonstrated in Table 1. In order to genotype, the high-resolution melt (HRM) was performed. Our tests were done by Rotor-gene 6000 instrument (Corbet Life Science, Australia). PCR reactions were performed in duplicate in 20 μL of final volume using the type-it HRM Kit (Feldan), HRM PCR buffer, HotStarTaq Plus DNA Polymerase, nucleotides and EvaGreen dye, and 30 ng DNA. Program of PCR was an initial denaturation activation step at 95 °C for 5 min, followed by a 40-cycle program (denaturation at 95 °C for 15 s, annealing conditions 55 °C for 5 s, 72 °C for 15 s; and HRM step from 70 to 95 °C rising at 0.1 °C per second). The HRM curves were investigated and selected samples are chosen for further sequence analysis. The two investigated SNPs were rs3758539 (G/A: 5′ flanking region) and rs10882280 (C/A: intron). 2.3. Statistical analysis All statistical analyses were performed using SPSS v16.00 (SPSS Inc., Chicago) statistical analysis software. A χ2 test was used to compare the genotype and allele frequencies between the case and control participants. Hardy–Weinberg equilibrium was tested by chi-squared test. The associations between allele frequencies of SNPs and cases were estimated by computing the odds ratios and their 95% confidence intervals with logistic regression analyses controlling for age and gender. Independent T-test was used to compare three genotypes of each SNP in numerous obesity markers. A P-value of less than 0.05 was considered statistically significant.
Table 1 The primer sequences of RBP4 gene polymorphism. SNP
Sequences (5′–3′)
rs3758539
F: TTTCAAAGTGGTTTCAGGGAAGT R: CTCTCTTTCAGGAGCGTTGTG F: GGACACATATCATGGAATCTTAG R: GCTGGTCTGGATTAGGAG
rs10882280
Table 2 Biochemical and physical characteristics of subjects. Variables
Cases (BMI ≥ 25) (n = 129)
Controls (BMI b 25) (n = 192)
P-value
Age (years) Sex Female Male Weight (kg) Waist circumference (cm) FBS (mg/dl) Total cholesterol (mg/dl) Triglyceride (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) SBP (mm Hg) DBS (mm Hg)
38.5 ± 11.030
29.84 ± 9.079
b0.001
66 (51.2%) 63 (48.8%) 88.24 ± 12.49 103.81 ± 11.39 88.90 ± 10.38 201.68 ± 37.81 182.92 ± 111.26 43.18 ± 9.80 122.08 ± 30.40 119.05 ± 16.56 77.77 ± 9.41
66 (34.4%) 126 (65.6%) 64.70 ± 7.69 81.25 ± 6.48 85.49 ± 8.90 169.57 ± 29.56 87.71 ± 29.52 52.45 ± 8.29 99.26 ± 26.86 106.53 ± 12.61 71.25 ± 8.04
0.004 b0.001 b0.001 0.004 b0.001 b0.001 b0.001 b0.001 b0.001 b0.001
Values are expressed as mean ± S.D. BMI: Body mass index, FBS: fasting blood sugar, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, SBP: systolic blood pressure, DBP: diastolic blood pressure.
3. Results In the present study, the relationship among rs3758539 and rs10882280 SNPs and level of various factors of investigated subjects were performed. Demographic characteristic of all participants is demonstrated in Table 2. The summary of SNP description with minor allele frequency is shown in Table 3. Table 4 provided the allele frequencies and also genotype profile of studied population. As demonstrated in Table 4, there was a significant association between G allele and A allele in rs3758539 within the case and control groups. We observed a higher frequency of A allele as minor allele in the case group compared to the healthy subjects. Furthermore the frequency of GA and AA genotype in the case group was increased compared to the healthy subjects. Moreover the ratio of GA + AA genotype in comparison with GG genotype in patients was significantly higher than the control group. We did not see any significant differences between allele and genotype frequency within both groups. The relationship between biomedical and physical characteristics across genotype rs3758539 and rs10882280 is shown in Tables 5 and 6 respectively. In rs3758539, data showed that the level of BMI was increasing from GG to AA genotype (32.35 vs. 32.44 vs. 34.63 kg/m2 for GG, GA, and AA respectively); it means that minor allele had an effect on BMI increasing. Also comparison of GA + AA with GG in the control group is significant for waist.c and BMI. Although BMI level did not follow the specific increasing or decreasing pattern in the control group, overlay of the difference means of BMI level within GG, GA, and AA genotype was significant. Table 6 as in previous table, provided the effect of rs10882280 on biomedical and physical characteristics of investigated population. Like the previous table for the case group, results showed that BMI levels of the case group have increased from CC to AA genotype (31.37 vs. 32.88 vs. 38.84 kg/m2 for CC, CA, and AA respectively). Moreover, the BMI level mean was higher in individuals who carry CA + AA compared with CC genotype in the case group. Also FBS mean level in controls with CA was higher than CC genotype, and the difference of FBS mean level in controls that carry CA + AA was significantly higher than individuals with CC genotype.
Table 3 Summary of SNPs and allele frequencies. RS number
Position
Minor allele
Location
Minor allele frequency (MAF)
rs3758539(G/A) rs10882280(C/A)
−803 +1473
A A
5′ Flanking Intron 4
0.16 0.08
Please cite this article as: Shajarian, M., et al., Association of RBP4 gene variants with adverse lipid profile and obesity, Gene (2015), http:// dx.doi.org/10.1016/j.gene.2014.12.071
M. Shajarian et al. / Gene xxx (2015) xxx–xxx Table 4 Genotype and allele frequencies in the study population. Cases (BMI ≥ 25) (n = 129)
Controls (BMI b 25) (n = 192)
OR (95% CI)
P-value
rs3758539(G/A) G A GG GA AA GG GA + AA
100(77.5%) 29(22.5%) 81(62.8%) 40(31%) 8(6.2%) 81(62.8%) 48(37.2%)
163(84.9%) 29(15.1%) 146(76%) 36(18.8%) 10(5.2%) 146(76%) 46(24%)
0.61(0.34–1.08)
0.092
– 0.49(0.29–0.84) 0.69(0.26–1.82) 0.53(0.32–0.86)
0.031
rs10882280(C/A) C A CC CA AA CC CA + AA
104(80.6%) 25(19.4%) 82(63.6%) 46(35.7%) 1(0.8%) 82(63.6%) 47(36.4%)
161(84%) 31(16%) 129(67.2%) 63(32.8%) 0(0%) 129(67.2%) 63(32.8%)
0.80(0.44–1.43)
0.454
– 0.87(0.54–1.39) 0.98(0.96–1.01) 0.85(0.53–1.36)
0.401
Polymorphism
0.012
0.503
Genotype distributions are shown as number (%). OR: odds ratio, 95% CI: 95% confidence interval, SNP: single nucleotide polymorphism. P values are two tailed.
Furthermore, independent association of various factors with BMI was assessed. Multiple regressions were carried out using baseline characteristics plus genetic variables. The results in the case group showed that weight, waist.c, HDL, LDL, and rs10882280 were independently associated with BMI. In the control group, results and outcomes demonstrated that sex, age, weight, FBS, and rs3758539 were autonomously correlated with BMI (Table 7). 4. Discussion Many studies have reported the impact of genetic variations of RBP4 gene on BMI and obesity. The results of the present study have shown the association between variants in RBP4 gene and BMI level in our population. Although in rs3758539 we found the
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significant direct proportions of BMI mean level with minor allele frequency in the case group, in the control group minor allele had reverse effect on BMI mean level. In the current study in agreement with the previous investigation, it has been shown that rs3758539 risk allele (A) resulted in increased susceptibility for obesity (Munkhtulga et al., 2010). It seems that patients who carry the A allele in rs3758539 were significantly more likely to develop obesity. Also it has been reported that serum RBP4 levels positively correlated with BMI in obese patients (Yang et al., 2005; Graham et al., 2006). In addition to increased BMI and whole-body fat content, increased circulating RBP4 levels are linked to increased visceral adipose tissue content (Cho et al., 2006; Gavi et al., 2007; Jia et al., 2007; Lee et al., 2007; Kelly et al., 2010). RBP4 is preferentially expressed in omental adipose tissue which is an important source of RBP4 in severe obese patients (Kelly et al., 2010). In recent studies, higher waist circumference and waist-to-hip ratio were associated with higher RBP4 levels and markers of systemic inflammation (Cho et al., 2006; Graham et al., 2006; Gavi et al., 2007; Jia et al., 2007; Kloting et al., 2007; Kovacs et al., 2007; Lee et al., 2007; Kelly et al., 2010; Munkhtulga et al., 2010; Nair et al., 2010; Hermsdorff et al., 2011). One the other hand various studies have reported the strong association of SNPs on RBP4 serum and plasma levels (Yang et al., 2005; Munkhtulga et al., 2007; Wu et al., 2009; Munkhtulga et al., 2010). Our finding also showed the relationship between RBP4 gene variant (rs3758539) and waist.c was significant in the control group, but not for case individuals. The A allele of rs3758539 has been associated with the increasing risk of type 2 diabetes in Mongolians (Munkhtulga et al., 2007) as well as white (van Hoek et al., 2008). This gene variant was known as the cause of increased BMI in Japanese men and women and also Mongolian women (Kelishadi, 2007). Like them, our results showed that A allele of rs3758539 in cases related with higher levels of BMI. Although we couldn't find any significant association between rs3758538 and triglyceride level, SNP rs3758538 was significantly associated with hypertriglyceridemia in a population-based sample of 3210 Chinese Hans (Wu et al., 2009). Shea et al. showed that rs10882280, which is a
Table 5 Biochemical and physical characteristics across genotype rs3758539(G/A) in two groups. Variables
Cases (BMI ≥ 25) (n = 129) GG
GA
AA
GA + AA
Triglyceride (mg/dl) Cholesterol (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) FBS (mg/dl) Waist.c (cm) SBP (mm Hg) DBP (mm Hg) BMI (kg/m2)
177.92 ± 106.97 205.16 ± 39.75 44.41 ± 10.30 125.08 ± 32.24 90.76 ± 9.62 103.36 ± 9.49 117.72 ± 15.41 78.24 ± 9.14 32.35 ± 2.08
192.90 ± 123.34 198.10 ± 32.23 41.17 ± 8.08 119.14 ± 26.18 85.75 ± 12.17 104.35 ± 14.62 119.56 ± 15.28 77.50 ± 10.34 32.44 ± 3.04
183.62 ± 99.88 184.50 ± 41.75 41.00 ± 11.67 106.47 ± 27.83 85.42 ± 7.56 105.62 ± 11.56 118.43 ± 12.67 74.37 ± 7.28 34.63 ± 1.89
191.35 ± 118.83 195.83 ± 33.88 41.14 ± 8.63 117.02 ± 26.58 85.66 ± 11.02 104.56 ± 14.05 119.37 ± 4.76 76.97 ± 0.90 32.80 ± 2.98
Variables
Controls (BMI b 25) (n = 192)
Triglyceride (mg/dl) Cholesterol (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) FBS (mg/dl) Waist.c (cm) SBP (mm Hg) DBP (mm Hg) BMI (kg/m2)
GG
GA
AA
GA + AA
85.92 ± 27.66 167.56 ± 29.82 52.89 ± 8.36 97.07 ± 26.84 85.24 ± 9.06 81.58 ± 6.60 107.21 ± 12.57 71.18 ± 8.24 22.52 ± 1.39
93.08 ± 33.34 179.05 ± 29.33 51.83 ± 7.95 108.60 ± 27.57 85.45 ± 8.44 79.78 ± 5.96 103.23 ± 12.95 70.75 ± 7.33 21.76 ± 1.28
94.50 ± 35.07 164.90 ± 20.62 48.30 ± 7.94 97.70 ± 18.07 91.25 ± 8.30 81.70 ± 6.41 108.50 ± 10.94 74.00 ± 7.65 22.27 ± 1.44
93.39 ± 33.33 175.97 ± 28.08 51.06 ± 8.00 106.23 ± 26.02 86.28 ± 8.52 80.23 ± 6.05 104.38 ± 12.61 71.45 ± 7.44 21.87 ± 1.32
P-valuea
P-valueb
0.334 0.343 0.190 0.222 0.219 0.381 0.766 0.663 0.004
0.158 0.230 0.069 0.149 0.088 0.239 0.480 0.706 0.621
P-valuea
P-valueb
0.523 0.128 0.167 0.108 0.345 0.150 0.212 0.430 0.013
0.258 0.120 0.198 0.057 0.616 0.005 0.185 0.968 0.006
Values are expressed as mean ± SEM. HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBS: fasting blood sugar, waist.c: waist circumference, SBP: systolic blood pressure, DBP: diastolic blood pressure, BMI: body mass index. a Used of Kruskal Wallis test to compare GG, GA, and AA. b Used of Mann–Whitney test to compare GG with GA + AA.
Please cite this article as: Shajarian, M., et al., Association of RBP4 gene variants with adverse lipid profile and obesity, Gene (2015), http:// dx.doi.org/10.1016/j.gene.2014.12.071
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M. Shajarian et al. / Gene xxx (2015) xxx–xxx
Table 6 Biochemical and physical characteristics across genotype rs10882280(C/A) in two groups. Cases (BMI ≥ 25) (n = 129)
Variables
CC
CA
AA
CA + AA
Triglyceride (mg/dl) Cholesterol (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) FBS (mg/dl) Waist.c (cm) SBP (mm Hg) DBP (mm Hg) BMI (kg/m2)
184.68 ± 110.77 206.68 ± 36.96 44.08 ± 9.71 125.75 ± 29.77 88.87 ± 11.47 103.96 ± 10.69 119.11 ± 15.71 78.23 ± 9.48 31.37 ± 2.12
181.47 ± 113.95 193.10 ± 38.50 41.37 ± 9.86 115.70 ± 31.09 88.96 ± 8.34 103.65 ± 12.74 117.34 ± 14.06 77.11 ± 9.38 32.88 ± 2.90
105 ± 0 187 ± 0 51 ± 0 115 ± 0 88.17 ± 0 100 ± 0 100 ± 0 70 ± 0 36.84 ± 0
179.85 ± 113.25 192.97 ± 38.09 41.58 ± 9.85 115.68 ± 30.75 88.96 ± 8.34 103.57 ± 12.61 116.97 ± 14.14 76.96 ± 9.33 32.96 ± 2.93
Variables
Controls (BMI b 25) (n = 192)
Triglyceride (mg/dl) Cholesterol (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) FBS (mg/dl) Waist.c (cm) SBP (mm Hg) DBP (mm Hg) BMI (kg/m2)
CC
CA
AA
CA + AA
89.27 ± 30.09 169.44 ± 28.94 52.86 ± 8.28 98.71 ± 25.66 84.40 ± 9.53 80.78 ± 6.33 106.19 ± 13.25 70.83 ± 7.99 22.35 ± 1.42
84.52 ± 27.24 169.85 ± 31.03 51.59 ± 8.30 100.39 ± 29.35 87.32 ± 7.49 82.24 ± 6.73 107.23 ± 11.26 72.10 ± 8.13 22.40 ± 1.38
– – – – – – – – –
84.52 ± 27.24 169.85 ± 31.03 51.59 ± 8.30 100.39 ± 29.35 87.32 ± 7.49 82.24 ± 6.73 107.23 ± 11.26 72.10 ± 8.13 22.40 ± 1.38
P-valuea
P-valueb
0.359 0.197 0.183 0.207 0.996 0.909 0.393 0.401 0.026
0.414 0.072 0.130 0.076 0.996 0.974 0.533 0.363 0.429
P-valuea
P-valueb
0.303 0.823 0.267 0.606 0.017 0.159 0.466 0.222 0.822
0.303 0.823 0.267 0.606 0.017 0.159 0.466 0.222 0.822
Values are expressed as mean ± SEM. HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBS: fasting blood sugar, waist.c: waist circumference, SBP: systolic blood pressure, DBP: diastolic blood pressure, BMI: body mass index. a Used of Kruskal Wallis test to compare CC, CA, and AA. b Used of Mann–Whitney test to compare CC with CA + AA.
noncoding SNP, was associated with high-density lipoprotein cholesterol (HDL-C). In line of our results, they did not find any association among rs3758538 and cholesterol, HDL, LDL, and triglyceride. Similar to our finding, they also did not find any correlation among rs10882280 with cholesterol, LDL, and triglyceride (Shea et al., 2010). One the other hand, other studies have reported significant associations between RBP4 and serum lipids including triglyceride levels and high-density lipoprotein cholesterol (HDL-C) (Graham et al., 2006; von Eynatten et al., 2007). Previous studies have shown that elevated serum RBP4 is associated with metabolic abnormalities such as glucose intolerance, and dyslipidemia (Basualdo et al., 1997; Yang et al., 2005; Cho et al., 2006; Erikstrup et al., 2006; Graham et al., 2006; Takashima et al., 2006; Mallat et al., 2009; Solini et al., 2009; Usui et al., 2009; Aust et al., 2011; Choi et al., 2011). Interestingly, increasing evidences suggested that RBP4 plays a role in lipid metabolism to an even greater extent than insulin resistance. In fact, many human studies have found a strong relationship between RBP4 and triglycerides, while some failing to do so (Takashima et al., 2006; Verges et al., 2012). Based on the primary function of RBP4 as a binding protein to retinol, it is reasonable to assume that RBP4 serves as a link between retinol
Table 7 Risk factors of obesity. Groups
Case
Control
Variable
Weight Waist.c HDL LDL rs10882280(C/A) Sex Age Weight FBS rs3758539(G/A)
Unstandardized coefficients
Standardized coefficients
B
Standard error
β
0.057 0.113 0.065 0.026 0.794 1.32 0.022 0.144 0.027 −0.429
0.016 0.021 0.020 0.006 0.379 0.271 0.010 0.017 0.011 0.231
0.323 0.481 0.256 0.302 0.153 0.459 0.151 0.820 0.173 −0.132
t
P-value
3.495 5.347 3.262 4.018 2.093 4.89 2.14 8.49 2.44 2.86
0.001 b0.001 0.002 b0.001 0.040 b0.001 0.034 b0.001 0.016 0.011
metabolism and activation of nuclear receptors and may be involved in the regulation of lipid homeostasis (Staels, 2001). Verges et al. have reported that RBP4 is involved in VLDL catabolism (Verges et al., 2012). A recently published study in morbid obese patients has shown that systemic RBP4 levels could play an important role in lipid metabolism in morbid obesity, increasing triglyceride levels and contributing to the formation of small (Rocha et al., 2013). Another large-scale population study showed that elevated RBP4 levels are strongly and independently associated with MetS (Qi et al., 2007). In an interesting study, Broch et al. found that RBP4 was mainly correlated with systemic lipoprotein concentrations and the reduction of RBP4 level was associated with the TG level decrease and increase of HDL cholesterol level, in patients with a substantial weight reduction due to gastric bypass surgery (Broch et al., 2010). In addition, Sopher et al. showed that RBP4 might be an early marker of dyslipidemia, which may herald future onset of hepatic insulin resistance and metabolic syndrome (Sopher et al., 2011). Further studies are needed to elucidate the role of variants within RBP4 and lipid metabolism. In summary, our results provide further evidence that RBP4 is involved in different components of the metabolic syndrome including obesity, increased level of TG and decreased HDL. We should point out that our study has several limitations. First, causality could not be assessed due to the cross-sectional nature. Therefore, a longitudinal study needs to be performed in the future in order to explore further independent association among triglycerides, low HDL and RBP4 levels. Second, our results cannot be applied to the worldwide human population, since our population was exclusively Iranian.
Conflicts of interest statement The authors have declared no conflicts of interest.
Please cite this article as: Shajarian, M., et al., Association of RBP4 gene variants with adverse lipid profile and obesity, Gene (2015), http:// dx.doi.org/10.1016/j.gene.2014.12.071
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Effects of genetic variation in the human retinol binding protein-4 gene (RBP4) on insulin resistance and fat depot-specific mRNA expression. Diabetes 56, 3095–3100. Lee, J.W., Im, J.A., Lee, H.R., Shim, J.Y., Youn, B.S., Lee, D.C., 2007. Visceral adiposity is associated with serum retinol binding protein-4 levels in healthy women. Obesity (Silver Spring) 15, 2225–2232. Lyon, H.N., Hirschhorn, J.N., 2005. Genetics of common forms of obesity: a brief overview. Am. J. Clin. Nutr. 82, 215S–217S. Mallat, Z., Simon, T., Benessiano, J., Clement, K., Taleb, S., Wareham, N.J., Luben, R., Khaw, K.T., Tedgui, A., Boekholdt, S.M., 2009. Retinol-binding protein 4 and prediction of incident coronary events in healthy men and women. J. Clin. Endocrinol. Metab. 94, 255–260. Mensah, G.A., Mokdad, A.H., Ford, E., Narayan, K.M., Giles, W.H., Vinicor, F., Deedwania, P.C., 2004. Obesity, metabolic syndrome, and type 2 diabetes: emerging epidemics and their cardiovascular implications. Cardiol. Clin. 22, 485–504. Munkhtulga, L., Nakayama, K., Utsumi, N., Yanagisawa, Y., Gotoh, T., Omi, T., Kumada, M., Erdenebulgan, B., Zolzaya, K., Lkhagvasuren, T., Iwamoto, S., 2007. Identification of a regulatory SNP in the retinol binding protein 4 gene associated with type 2 diabetes in Mongolia. Hum. Genet. 120, 879–888. Munkhtulga, L., Nagashima, S., Nakayama, K., Utsumi, N., Yanagisawa, Y., Gotoh, T., Omi, T., Kumada, M., Zolzaya, K., Lkhagvasuren, T., Kagawa, Y., Fujiwara, H., Hosoya, Y.,
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Hyodo, M., Horie, H., Kojima, M., Ishibashi, S., Iwamoto, S., 2010. Regulatory SNP in the RBP4 gene modified the expression in adipocytes and associated with BMI. Obesity (Silver Spring) 18, 1006–1014. Nair, A.K., Sugunan, D., Kumar, H., Anilkumar, G., 2010. Case–control analysis of SNPs in GLUT4, RBP4 and STRA6: association of SNPs in STRA6 with type 2 diabetes in a South Indian population. PLoS One 5, e11444. Newcomer, M.E., Ong, D.E., 2000. Plasma retinol binding protein: structure and function of the prototypic lipocalin. Biochim. Biophys. Acta 1482, 57–64. Ping, F., Xiang, H.D., Li, M., Li, W., Liu, J.T., Nie, M., Hui, Y.C., 2012. Effects of variation in retinol binding protein 4 gene and adipose specific expression of gestational diabetes in Beijing, China. Diabetes Res. Clin. Pract. 97, 283–289. Qi, Q., Yu, Z., Ye, X., Zhao, F., Huang, P., Hu, F.B., Franco, O.H., Wang, J., Li, H., Liu, Y., Lin, X., 2007. Elevated retinol-binding protein 4 levels are associated with metabolic syndrome in Chinese people. J. Clin. Endocrinol. Metab. 92, 4827–4834. Rocha, M., Banuls, C., Bellod, L., Rovira-Llopis, S., Morillas, C., Sola, E., Victor, V.M., Hernandez-Mijares, A., 2013. Association of serum retinol binding protein 4 with atherogenic dyslipidemia in morbid obese patients. PLoS One 8, e78670. Sarrafzadegan, N., Kelishadi, R., Siadat, Z.D., Esmaillzadeh, A., Solhpour, A., Shirani, S., Naderi, G., Asgary, S., Sadri, G., Khosravi, A., Bahonar, A., 2010. Obesity and cardiometabolic risk factors in a representative population of Iranian adolescents and adults in comparison to a Western population: the Isfahan Healthy Heart Programme. Public Health Nutr. 13, 314–323. Sarraf-Zadegan, N., Sadri, G., Malek Afzali, H., Baghaei, M., Mohammadi Fard, N., Shahrokhi, S., Tolooie, H., Poormoghaddas, M., Sadeghi, M., Tavassoli, A., Rafiei, M., Kelishadi, R., Rabiei, K., Bashardoost, N., Boshtam, M., Asgary, S., Naderi, G., Changiz, T., Yousefie, A., 2003. Isfahan Healthy Heart Programme: a comprehensive integrated community-based programme for cardiovascular disease prevention and control. Design, methods and initial experience. Acta Cardiol. 58, 309–320. Shea, J.L., Loredo-Osti, J.C., Sun, G., 2010. Association of RBP4 gene variants and serum HDL cholesterol levels in the Newfoundland population. Obesity (Silver Spring) 18, 1393–1397. Solini, A., Santini, E., Madec, S., Rossi, C., Muscelli, E., 2009. Retinol-binding protein-4 in women with untreated essential hypertension. Am. J. Hypertens. 22, 1001–1006. Sopher, A.B., Gerken, A.T., Lee, E.J., Blaner, W.E., Deeds, S., Gallagher, D., Shungu, D.C., Mao, X., Hassoun, A., Mahon, D.J., Oberfield, S.E., 2011. Retinol-binding protein 4 correlates with triglycerides but not insulin resistance in prepubertal children with and without premature adrenarche. J. Pediatr. Endocrinol. Metab. 24, 683–687. Staels, B., 2001. Regulation of lipid and lipoprotein metabolism by retinoids. J. Am. Acad. Dermatol. 45, S158–S167. Takashima, N., Tomoike, H., Iwai, N., 2006. Retinol-binding protein 4 and insulin resistance. N. Engl. J. Med. 355, 1392 (author reply 1394–5). Tsutsumi, C., Okuno, M., Tannous, L., Piantedosi, R., Allan, M., Goodman, D.S., Blaner, W.S., 1992. Retinoids and retinoid-binding protein expression in rat adipocytes. J. Biol. Chem. 267, 1805–1810. Usui, S., Ichimura, M., Ikeda, S., Okamoto, M., 2009. Association between serum retinolbinding protein 4 and small dense low-density lipoprotein cholesterol levels in young adult women. Clin. Chim. Acta 399, 45–48. van Hoek, M., Dehghan, A., Zillikens, M.C., Hofman, A., Witteman, J.C., Sijbrands, E.J., 2008. An RBP4 promoter polymorphism increases risk of type 2 diabetes. Diabetologia 51, 1423–1428. Verges, B., Guiu, B., Cercueil, J.P., Duvillard, L., Robin, I., Buffier, P., Bouillet, B., Aho, S., Brindisi, M.C., Petit, J.M., 2012. Retinol-binding protein 4 is an independent factor associated with triglycerides and a determinant of very low-density lipoprotein–apolipoprotein B100 catabolism in type 2 diabetes mellitus. Arterioscler. Thromb. Vasc. Biol. 32, 3050–3057. von Eynatten, M., Lepper, P.M., Liu, D., Lang, K., Baumann, M., Nawroth, P.P., Bierhaus, A., Dugi, K.A., Heemann, U., Allolio, B., Humpert, P.M., 2007. Retinol-binding protein 4 is associated with components of the metabolic syndrome, but not with insulin resistance, in men with type 2 diabetes or coronary artery disease. Diabetologia 50, 1930–1937. Wu, Y., Li, H., Loos, R.J., Qi, Q., Hu, F.B., Liu, Y., Lin, X., 2009. 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Please cite this article as: Shajarian, M., et al., Association of RBP4 gene variants with adverse lipid profile and obesity, Gene (2015), http:// dx.doi.org/10.1016/j.gene.2014.12.071
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Association of RBP4 gene variants with adverse lipid profile and obesity Mansour Shajarian a, Laleh Rafiee a, Hajar Naji-Esfahani a, Shaghayegh Haghjooy-Javanmard a,⁎, Sarrafzadegan Nizal b a b
Applied Physiology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran Cardiovascular Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
a r t i c l e
i n f o
Article history: Received 9 November 2014 Received in revised form 17 December 2014 Accepted 20 December 2014 Available online xxxx Keywords: Retinol-binding protein4 (RBP4) Obesity Polymorphism High-density lipoprotein (HDL) Low-density lipoprotein (LDL) Triglyceride
a b s t r a c t Obesity is currently a worldwide public health problem. Retinol-binding protein4 (RBP4) is a recently discovered adipokine, which is potentially associated with insulin resistance and obesity. We aimed to investigate whether genetic variation within the RBP4 gene is correlated with the obesity and lipid profile in Iranian population. 321 samples were randomly selected from participants of Isfahan Healthy Heart Program (IHHP). Genomic DNA was isolated from peripheral blood cells (PBCs) and HRM-PCR was performed in order to investigate the presence of SNPs, and further sequencing analysis was done from selected subjects according to the differences of HRM curve pattern. Statistical analyses were performed using SPSS v16.00. The difference of the presence of rs3758539 polymorphism between controls and obese patients was significant, but not about rs10882280. We found noticeable association among genetic polymorphisms and biomedical and physical characteristics within investigated population. Our findings suggested that variations in the RBP4 gene were correlated with BMI and polymorphisms more likely could contribute to the development of obesity in our population. Also appraisal of obesity risk factors within each group might be helpful for preventing obesity initiation and could have a possible role in a predisposition to obesity in the Iranians. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Obesity is currently a worldwide public health problem (Kelishadi, 2007). Increasing burden of obesity leads to increased morbidity and mortality due to metabolic syndrome, insulin resistance, type-2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) (Mensah et al., 2004). There is a progressive increase in obesity in both children and adolescent groups in the last decade in Iran (Sarrafzadegan et al., 2010). The underlying mechanism of obesity is very complex, including interactions among behavioral, environmental, and genetic factors. Increasing prevalence of obesity can be attributed to highly calorific food intake and relatively sedentary lifestyle of modern times. Several studies have shown important role of genetic components in the risk of becoming obese (Lyon and Hirschhorn, 2005; Xia and Grant, 2013). Retinol-binding protein4 (RBP4) is a recently discovered adipokine that belongs to the lipocalin family of proteins, which is potentially in Abbreviations: RBP, retinol binding protein; T2DM, type-2 diabetes mellitus; CVD, cardiovascular disease; BMI, body mass index; SNP, single nucleotide polymorphism; HRM, high-resolution melt; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, triglyceride; FBS, fasting blood sugar; SBP, systolic blood pressure; DBP, diastolic blood pressure. ⁎ Corresponding author. E-mail address: [email protected] (S. Haghjooy-Javanmard).
association with insulin resistance (Yang et al., 2005). RBP4 is released from the liver as a primary source, and also from the adipose tissue as the alternate source (Tsutsumi et al., 1992). RBP4 acts as a carrier protein for retinol (one of the animal form of vitamin A), and other small hydrophobic molecules (Newcomer and Ong, 2000). Overexpression of RBP4 in visceral adipose tissue would cause elevated level of serum RBP4 and consequently leads to the accumulation of visceral fat and causes insulin resistance (Kloting et al., 2007). Although, the association between RBP4 and level of insulin (Kovacs et al., 2007) and insulin sensitiveness (Craig et al., 2007) was reported in previous researches, further studies couldn't find a significant association (Hu et al., 2008). Serum RBP4 levels are associated with body mass index (BMI) in diabetic or non-diabetic obese patients (Yang et al., 2005; Graham et al., 2006). There are several RBP4 gene variants that are related to the adiposity level and adipose tissue accumulation (Kotnik et al., 2011). The RBP4 maps to chromosome 10q23–q24 in humans, closely to a region that has been linked with increased risk for type-2 diabetes in other populations (Ping et al., 2012). Since pivotal role of RBP4 gene in obesity progresses, we therefore aimed to investigate whether genetic variation within the RBP4 gene correlates with the obesity in participants of Isfahan Healthy Heart Program (IHHP). We screened the RBP4 gene for 2 specific sequence variants rs3758539 and rs10882273 that have been previously linked
http://dx.doi.org/10.1016/j.gene.2014.12.071 0378-1119/© 2015 Elsevier B.V. All rights reserved.
Please cite this article as: Shajarian, M., et al., Association of RBP4 gene variants with adverse lipid profile and obesity, Gene (2015), http:// dx.doi.org/10.1016/j.gene.2014.12.071
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M. Shajarian et al. / Gene xxx (2015) xxx–xxx
with the development of T2DM and obesity in humans (Craig et al., 2007; Kovacs et al., 2007) and hypothesized that variations of these SNPs are associated with Iranian obese subjects. 2. Materials and methods 2.1. Patients and setting This project was conducted as a sub-study of the Isfahan Healthy Heart Program (IHHP), which is a comprehensive action-oriented integrated community-based intervention program for the prevention and control of non-communicable diseases (NCDs), which was implemented in central Iran. For the current study, we randomly selected 321 samples from participants of IHHP with the age range of 19 to 60 years. The participants were divided in 2 subgroups according to the body mass index (BMI) including 129 obese cases (66 female, 63 male, age: 38.5 ± 11.030) and 192 healthy controls (66 female, 126 male, age: 29.84 ± 9.079). Details of data collection and sampling are published previously (Sarraf-Zadegan et al., 2003). Written informed consent was obtained from all participants. The ethics committee of the Isfahan University of Medical Sciences approved the study. 2.2. DNA extraction and genotyping 2 ml of peripheral blood was recruited from each participant. The genomic DNA was isolated from the blood using Genomic DNA Isolation Kit (GeNetBio, Korea) according to the manufacturer's protocol. The investigated single nucleotide polymorphisms (rs3758539 & rs10882280) were identified by the NCBI data bank. The primers were designed by Beacon Designer version7.5 (PREMIER Biosoft International, USA) and synthesized by TIB MOLBIOL (Germany). The sequences of primers are demonstrated in Table 1. In order to genotype, the high-resolution melt (HRM) was performed. Our tests were done by Rotor-gene 6000 instrument (Corbet Life Science, Australia). PCR reactions were performed in duplicate in 20 μL of final volume using the type-it HRM Kit (Feldan), HRM PCR buffer, HotStarTaq Plus DNA Polymerase, nucleotides and EvaGreen dye, and 30 ng DNA. Program of PCR was an initial denaturation activation step at 95 °C for 5 min, followed by a 40-cycle program (denaturation at 95 °C for 15 s, annealing conditions 55 °C for 5 s, 72 °C for 15 s; and HRM step from 70 to 95 °C rising at 0.1 °C per second). The HRM curves were investigated and selected samples are chosen for further sequence analysis. The two investigated SNPs were rs3758539 (G/A: 5′ flanking region) and rs10882280 (C/A: intron). 2.3. Statistical analysis All statistical analyses were performed using SPSS v16.00 (SPSS Inc., Chicago) statistical analysis software. A χ2 test was used to compare the genotype and allele frequencies between the case and control participants. Hardy–Weinberg equilibrium was tested by chi-squared test. The associations between allele frequencies of SNPs and cases were estimated by computing the odds ratios and their 95% confidence intervals with logistic regression analyses controlling for age and gender. Independent T-test was used to compare three genotypes of each SNP in numerous obesity markers. A P-value of less than 0.05 was considered statistically significant.
Table 1 The primer sequences of RBP4 gene polymorphism. SNP
Sequences (5′–3′)
rs3758539
F: TTTCAAAGTGGTTTCAGGGAAGT R: CTCTCTTTCAGGAGCGTTGTG F: GGACACATATCATGGAATCTTAG R: GCTGGTCTGGATTAGGAG
rs10882280
Table 2 Biochemical and physical characteristics of subjects. Variables
Cases (BMI ≥ 25) (n = 129)
Controls (BMI b 25) (n = 192)
P-value
Age (years) Sex Female Male Weight (kg) Waist circumference (cm) FBS (mg/dl) Total cholesterol (mg/dl) Triglyceride (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) SBP (mm Hg) DBS (mm Hg)
38.5 ± 11.030
29.84 ± 9.079
b0.001
66 (51.2%) 63 (48.8%) 88.24 ± 12.49 103.81 ± 11.39 88.90 ± 10.38 201.68 ± 37.81 182.92 ± 111.26 43.18 ± 9.80 122.08 ± 30.40 119.05 ± 16.56 77.77 ± 9.41
66 (34.4%) 126 (65.6%) 64.70 ± 7.69 81.25 ± 6.48 85.49 ± 8.90 169.57 ± 29.56 87.71 ± 29.52 52.45 ± 8.29 99.26 ± 26.86 106.53 ± 12.61 71.25 ± 8.04
0.004 b0.001 b0.001 0.004 b0.001 b0.001 b0.001 b0.001 b0.001 b0.001
Values are expressed as mean ± S.D. BMI: Body mass index, FBS: fasting blood sugar, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, SBP: systolic blood pressure, DBP: diastolic blood pressure.
3. Results In the present study, the relationship among rs3758539 and rs10882280 SNPs and level of various factors of investigated subjects were performed. Demographic characteristic of all participants is demonstrated in Table 2. The summary of SNP description with minor allele frequency is shown in Table 3. Table 4 provided the allele frequencies and also genotype profile of studied population. As demonstrated in Table 4, there was a significant association between G allele and A allele in rs3758539 within the case and control groups. We observed a higher frequency of A allele as minor allele in the case group compared to the healthy subjects. Furthermore the frequency of GA and AA genotype in the case group was increased compared to the healthy subjects. Moreover the ratio of GA + AA genotype in comparison with GG genotype in patients was significantly higher than the control group. We did not see any significant differences between allele and genotype frequency within both groups. The relationship between biomedical and physical characteristics across genotype rs3758539 and rs10882280 is shown in Tables 5 and 6 respectively. In rs3758539, data showed that the level of BMI was increasing from GG to AA genotype (32.35 vs. 32.44 vs. 34.63 kg/m2 for GG, GA, and AA respectively); it means that minor allele had an effect on BMI increasing. Also comparison of GA + AA with GG in the control group is significant for waist.c and BMI. Although BMI level did not follow the specific increasing or decreasing pattern in the control group, overlay of the difference means of BMI level within GG, GA, and AA genotype was significant. Table 6 as in previous table, provided the effect of rs10882280 on biomedical and physical characteristics of investigated population. Like the previous table for the case group, results showed that BMI levels of the case group have increased from CC to AA genotype (31.37 vs. 32.88 vs. 38.84 kg/m2 for CC, CA, and AA respectively). Moreover, the BMI level mean was higher in individuals who carry CA + AA compared with CC genotype in the case group. Also FBS mean level in controls with CA was higher than CC genotype, and the difference of FBS mean level in controls that carry CA + AA was significantly higher than individuals with CC genotype.
Table 3 Summary of SNPs and allele frequencies. RS number
Position
Minor allele
Location
Minor allele frequency (MAF)
rs3758539(G/A) rs10882280(C/A)
−803 +1473
A A
5′ Flanking Intron 4
0.16 0.08
Please cite this article as: Shajarian, M., et al., Association of RBP4 gene variants with adverse lipid profile and obesity, Gene (2015), http:// dx.doi.org/10.1016/j.gene.2014.12.071
M. Shajarian et al. / Gene xxx (2015) xxx–xxx Table 4 Genotype and allele frequencies in the study population. Cases (BMI ≥ 25) (n = 129)
Controls (BMI b 25) (n = 192)
OR (95% CI)
P-value
rs3758539(G/A) G A GG GA AA GG GA + AA
100(77.5%) 29(22.5%) 81(62.8%) 40(31%) 8(6.2%) 81(62.8%) 48(37.2%)
163(84.9%) 29(15.1%) 146(76%) 36(18.8%) 10(5.2%) 146(76%) 46(24%)
0.61(0.34–1.08)
0.092
– 0.49(0.29–0.84) 0.69(0.26–1.82) 0.53(0.32–0.86)
0.031
rs10882280(C/A) C A CC CA AA CC CA + AA
104(80.6%) 25(19.4%) 82(63.6%) 46(35.7%) 1(0.8%) 82(63.6%) 47(36.4%)
161(84%) 31(16%) 129(67.2%) 63(32.8%) 0(0%) 129(67.2%) 63(32.8%)
0.80(0.44–1.43)
0.454
– 0.87(0.54–1.39) 0.98(0.96–1.01) 0.85(0.53–1.36)
0.401
Polymorphism
0.012
0.503
Genotype distributions are shown as number (%). OR: odds ratio, 95% CI: 95% confidence interval, SNP: single nucleotide polymorphism. P values are two tailed.
Furthermore, independent association of various factors with BMI was assessed. Multiple regressions were carried out using baseline characteristics plus genetic variables. The results in the case group showed that weight, waist.c, HDL, LDL, and rs10882280 were independently associated with BMI. In the control group, results and outcomes demonstrated that sex, age, weight, FBS, and rs3758539 were autonomously correlated with BMI (Table 7). 4. Discussion Many studies have reported the impact of genetic variations of RBP4 gene on BMI and obesity. The results of the present study have shown the association between variants in RBP4 gene and BMI level in our population. Although in rs3758539 we found the
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significant direct proportions of BMI mean level with minor allele frequency in the case group, in the control group minor allele had reverse effect on BMI mean level. In the current study in agreement with the previous investigation, it has been shown that rs3758539 risk allele (A) resulted in increased susceptibility for obesity (Munkhtulga et al., 2010). It seems that patients who carry the A allele in rs3758539 were significantly more likely to develop obesity. Also it has been reported that serum RBP4 levels positively correlated with BMI in obese patients (Yang et al., 2005; Graham et al., 2006). In addition to increased BMI and whole-body fat content, increased circulating RBP4 levels are linked to increased visceral adipose tissue content (Cho et al., 2006; Gavi et al., 2007; Jia et al., 2007; Lee et al., 2007; Kelly et al., 2010). RBP4 is preferentially expressed in omental adipose tissue which is an important source of RBP4 in severe obese patients (Kelly et al., 2010). In recent studies, higher waist circumference and waist-to-hip ratio were associated with higher RBP4 levels and markers of systemic inflammation (Cho et al., 2006; Graham et al., 2006; Gavi et al., 2007; Jia et al., 2007; Kloting et al., 2007; Kovacs et al., 2007; Lee et al., 2007; Kelly et al., 2010; Munkhtulga et al., 2010; Nair et al., 2010; Hermsdorff et al., 2011). One the other hand various studies have reported the strong association of SNPs on RBP4 serum and plasma levels (Yang et al., 2005; Munkhtulga et al., 2007; Wu et al., 2009; Munkhtulga et al., 2010). Our finding also showed the relationship between RBP4 gene variant (rs3758539) and waist.c was significant in the control group, but not for case individuals. The A allele of rs3758539 has been associated with the increasing risk of type 2 diabetes in Mongolians (Munkhtulga et al., 2007) as well as white (van Hoek et al., 2008). This gene variant was known as the cause of increased BMI in Japanese men and women and also Mongolian women (Kelishadi, 2007). Like them, our results showed that A allele of rs3758539 in cases related with higher levels of BMI. Although we couldn't find any significant association between rs3758538 and triglyceride level, SNP rs3758538 was significantly associated with hypertriglyceridemia in a population-based sample of 3210 Chinese Hans (Wu et al., 2009). Shea et al. showed that rs10882280, which is a
Table 5 Biochemical and physical characteristics across genotype rs3758539(G/A) in two groups. Variables
Cases (BMI ≥ 25) (n = 129) GG
GA
AA
GA + AA
Triglyceride (mg/dl) Cholesterol (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) FBS (mg/dl) Waist.c (cm) SBP (mm Hg) DBP (mm Hg) BMI (kg/m2)
177.92 ± 106.97 205.16 ± 39.75 44.41 ± 10.30 125.08 ± 32.24 90.76 ± 9.62 103.36 ± 9.49 117.72 ± 15.41 78.24 ± 9.14 32.35 ± 2.08
192.90 ± 123.34 198.10 ± 32.23 41.17 ± 8.08 119.14 ± 26.18 85.75 ± 12.17 104.35 ± 14.62 119.56 ± 15.28 77.50 ± 10.34 32.44 ± 3.04
183.62 ± 99.88 184.50 ± 41.75 41.00 ± 11.67 106.47 ± 27.83 85.42 ± 7.56 105.62 ± 11.56 118.43 ± 12.67 74.37 ± 7.28 34.63 ± 1.89
191.35 ± 118.83 195.83 ± 33.88 41.14 ± 8.63 117.02 ± 26.58 85.66 ± 11.02 104.56 ± 14.05 119.37 ± 4.76 76.97 ± 0.90 32.80 ± 2.98
Variables
Controls (BMI b 25) (n = 192)
Triglyceride (mg/dl) Cholesterol (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) FBS (mg/dl) Waist.c (cm) SBP (mm Hg) DBP (mm Hg) BMI (kg/m2)
GG
GA
AA
GA + AA
85.92 ± 27.66 167.56 ± 29.82 52.89 ± 8.36 97.07 ± 26.84 85.24 ± 9.06 81.58 ± 6.60 107.21 ± 12.57 71.18 ± 8.24 22.52 ± 1.39
93.08 ± 33.34 179.05 ± 29.33 51.83 ± 7.95 108.60 ± 27.57 85.45 ± 8.44 79.78 ± 5.96 103.23 ± 12.95 70.75 ± 7.33 21.76 ± 1.28
94.50 ± 35.07 164.90 ± 20.62 48.30 ± 7.94 97.70 ± 18.07 91.25 ± 8.30 81.70 ± 6.41 108.50 ± 10.94 74.00 ± 7.65 22.27 ± 1.44
93.39 ± 33.33 175.97 ± 28.08 51.06 ± 8.00 106.23 ± 26.02 86.28 ± 8.52 80.23 ± 6.05 104.38 ± 12.61 71.45 ± 7.44 21.87 ± 1.32
P-valuea
P-valueb
0.334 0.343 0.190 0.222 0.219 0.381 0.766 0.663 0.004
0.158 0.230 0.069 0.149 0.088 0.239 0.480 0.706 0.621
P-valuea
P-valueb
0.523 0.128 0.167 0.108 0.345 0.150 0.212 0.430 0.013
0.258 0.120 0.198 0.057 0.616 0.005 0.185 0.968 0.006
Values are expressed as mean ± SEM. HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBS: fasting blood sugar, waist.c: waist circumference, SBP: systolic blood pressure, DBP: diastolic blood pressure, BMI: body mass index. a Used of Kruskal Wallis test to compare GG, GA, and AA. b Used of Mann–Whitney test to compare GG with GA + AA.
Please cite this article as: Shajarian, M., et al., Association of RBP4 gene variants with adverse lipid profile and obesity, Gene (2015), http:// dx.doi.org/10.1016/j.gene.2014.12.071
4
M. Shajarian et al. / Gene xxx (2015) xxx–xxx
Table 6 Biochemical and physical characteristics across genotype rs10882280(C/A) in two groups. Cases (BMI ≥ 25) (n = 129)
Variables
CC
CA
AA
CA + AA
Triglyceride (mg/dl) Cholesterol (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) FBS (mg/dl) Waist.c (cm) SBP (mm Hg) DBP (mm Hg) BMI (kg/m2)
184.68 ± 110.77 206.68 ± 36.96 44.08 ± 9.71 125.75 ± 29.77 88.87 ± 11.47 103.96 ± 10.69 119.11 ± 15.71 78.23 ± 9.48 31.37 ± 2.12
181.47 ± 113.95 193.10 ± 38.50 41.37 ± 9.86 115.70 ± 31.09 88.96 ± 8.34 103.65 ± 12.74 117.34 ± 14.06 77.11 ± 9.38 32.88 ± 2.90
105 ± 0 187 ± 0 51 ± 0 115 ± 0 88.17 ± 0 100 ± 0 100 ± 0 70 ± 0 36.84 ± 0
179.85 ± 113.25 192.97 ± 38.09 41.58 ± 9.85 115.68 ± 30.75 88.96 ± 8.34 103.57 ± 12.61 116.97 ± 14.14 76.96 ± 9.33 32.96 ± 2.93
Variables
Controls (BMI b 25) (n = 192)
Triglyceride (mg/dl) Cholesterol (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) FBS (mg/dl) Waist.c (cm) SBP (mm Hg) DBP (mm Hg) BMI (kg/m2)
CC
CA
AA
CA + AA
89.27 ± 30.09 169.44 ± 28.94 52.86 ± 8.28 98.71 ± 25.66 84.40 ± 9.53 80.78 ± 6.33 106.19 ± 13.25 70.83 ± 7.99 22.35 ± 1.42
84.52 ± 27.24 169.85 ± 31.03 51.59 ± 8.30 100.39 ± 29.35 87.32 ± 7.49 82.24 ± 6.73 107.23 ± 11.26 72.10 ± 8.13 22.40 ± 1.38
– – – – – – – – –
84.52 ± 27.24 169.85 ± 31.03 51.59 ± 8.30 100.39 ± 29.35 87.32 ± 7.49 82.24 ± 6.73 107.23 ± 11.26 72.10 ± 8.13 22.40 ± 1.38
P-valuea
P-valueb
0.359 0.197 0.183 0.207 0.996 0.909 0.393 0.401 0.026
0.414 0.072 0.130 0.076 0.996 0.974 0.533 0.363 0.429
P-valuea
P-valueb
0.303 0.823 0.267 0.606 0.017 0.159 0.466 0.222 0.822
0.303 0.823 0.267 0.606 0.017 0.159 0.466 0.222 0.822
Values are expressed as mean ± SEM. HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, FBS: fasting blood sugar, waist.c: waist circumference, SBP: systolic blood pressure, DBP: diastolic blood pressure, BMI: body mass index. a Used of Kruskal Wallis test to compare CC, CA, and AA. b Used of Mann–Whitney test to compare CC with CA + AA.
noncoding SNP, was associated with high-density lipoprotein cholesterol (HDL-C). In line of our results, they did not find any association among rs3758538 and cholesterol, HDL, LDL, and triglyceride. Similar to our finding, they also did not find any correlation among rs10882280 with cholesterol, LDL, and triglyceride (Shea et al., 2010). One the other hand, other studies have reported significant associations between RBP4 and serum lipids including triglyceride levels and high-density lipoprotein cholesterol (HDL-C) (Graham et al., 2006; von Eynatten et al., 2007). Previous studies have shown that elevated serum RBP4 is associated with metabolic abnormalities such as glucose intolerance, and dyslipidemia (Basualdo et al., 1997; Yang et al., 2005; Cho et al., 2006; Erikstrup et al., 2006; Graham et al., 2006; Takashima et al., 2006; Mallat et al., 2009; Solini et al., 2009; Usui et al., 2009; Aust et al., 2011; Choi et al., 2011). Interestingly, increasing evidences suggested that RBP4 plays a role in lipid metabolism to an even greater extent than insulin resistance. In fact, many human studies have found a strong relationship between RBP4 and triglycerides, while some failing to do so (Takashima et al., 2006; Verges et al., 2012). Based on the primary function of RBP4 as a binding protein to retinol, it is reasonable to assume that RBP4 serves as a link between retinol
Table 7 Risk factors of obesity. Groups
Case
Control
Variable
Weight Waist.c HDL LDL rs10882280(C/A) Sex Age Weight FBS rs3758539(G/A)
Unstandardized coefficients
Standardized coefficients
B
Standard error
β
0.057 0.113 0.065 0.026 0.794 1.32 0.022 0.144 0.027 −0.429
0.016 0.021 0.020 0.006 0.379 0.271 0.010 0.017 0.011 0.231
0.323 0.481 0.256 0.302 0.153 0.459 0.151 0.820 0.173 −0.132
t
P-value
3.495 5.347 3.262 4.018 2.093 4.89 2.14 8.49 2.44 2.86
0.001 b0.001 0.002 b0.001 0.040 b0.001 0.034 b0.001 0.016 0.011
metabolism and activation of nuclear receptors and may be involved in the regulation of lipid homeostasis (Staels, 2001). Verges et al. have reported that RBP4 is involved in VLDL catabolism (Verges et al., 2012). A recently published study in morbid obese patients has shown that systemic RBP4 levels could play an important role in lipid metabolism in morbid obesity, increasing triglyceride levels and contributing to the formation of small (Rocha et al., 2013). Another large-scale population study showed that elevated RBP4 levels are strongly and independently associated with MetS (Qi et al., 2007). In an interesting study, Broch et al. found that RBP4 was mainly correlated with systemic lipoprotein concentrations and the reduction of RBP4 level was associated with the TG level decrease and increase of HDL cholesterol level, in patients with a substantial weight reduction due to gastric bypass surgery (Broch et al., 2010). In addition, Sopher et al. showed that RBP4 might be an early marker of dyslipidemia, which may herald future onset of hepatic insulin resistance and metabolic syndrome (Sopher et al., 2011). Further studies are needed to elucidate the role of variants within RBP4 and lipid metabolism. In summary, our results provide further evidence that RBP4 is involved in different components of the metabolic syndrome including obesity, increased level of TG and decreased HDL. We should point out that our study has several limitations. First, causality could not be assessed due to the cross-sectional nature. Therefore, a longitudinal study needs to be performed in the future in order to explore further independent association among triglycerides, low HDL and RBP4 levels. Second, our results cannot be applied to the worldwide human population, since our population was exclusively Iranian.
Conflicts of interest statement The authors have declared no conflicts of interest.
Please cite this article as: Shajarian, M., et al., Association of RBP4 gene variants with adverse lipid profile and obesity, Gene (2015), http:// dx.doi.org/10.1016/j.gene.2014.12.071
M. Shajarian et al. / Gene xxx (2015) xxx–xxx
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Please cite this article as: Shajarian, M., et al., Association of RBP4 gene variants with adverse lipid profile and obesity, Gene (2015), http:// dx.doi.org/10.1016/j.gene.2014.12.071
