Introduction

Metabolic syndrome (MetS) is a clustering of metabolic risk factors that include abdominal obesity, dyslipidemia [reduced high-density lipoprotein cholesterol (HDL-C), and/or increased triglyceride levels], elevated blood pressure, and insulin resistance (Grundy et al. 2005). Metabolic syndrome has been associated with the increased incidence of diabetes mellitus and cardiovascular disease (CVD) (Wilson et al. 2005). The etiology of the MetS has been attributed to environmental factors such as sedentary lifestyle, western diet, lack of exercise, and stress. Moreover, several genetic factors have been investigated responsible for MetS, for example, FTO, ApoA5, ApoE, CETP, LPL, and LEP (Taylor et al. 2013).

The cholesteryl ester transfer protein (CETP) plays a key role in the metabolism of HDL (Agellon et al. 1990). CETP enables the transfer of cholesteryl esters from high-density lipoprotein (HDL) to very low density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and low-density lipoprotein (LDL). Then, the IDL and LDL are catabolized via the low-density lipoprotein receptor (LDLR) in the liver. The CETP gene is located on chromosome 16q21 (Agellon et al. 1990). Several polymorphisms have been reported in the CETP gene (Drayna and Lawn 1987). The most commonly studied is TaqIB, which is a silent base change affecting the 277th nucleotide in the first intron of the CETP gene (Drayna and Lawn 1987). The B1 allele is associated with low HDL-C levels and increased CETP levels and activity (Kuivenhoven et al. 1997), whereas B2 allele is associated with a decreased risk of CVD (Ordovas et al. 2000; Liu et al. 2002) and MetS (Ozsait et al. 2008). B1 allele has been shown to influence development of Alzheimer’s disease and type 2 diabetes mellitus (Fidani et al. 2004; Kawasaki et al. 2002; Yilmaz et al. 2004). Moreover, the association of B1 allele with MetS has been reported (Ozsait et al. 2008; Sandhofer et al. 2008; Elsammak et al. 2011). However, the association between CETP TaqIB and HDL-C levels may be population specific (Mitchell et al.1994; Yijiang et al. 2008; Lu et al. 2013) and is highly influenced by environmental factors, such as alcohol consumption, tobacco smoking, body mass index (BMI), and dietary fat (Hannuksela et al. 1994; Kauma et al. 1996; Freeman et al. 1994; Li et al. 2007).

Apolipoprotein E (ApoE) is a component of plasma chylomicrons, chylomicron remnants, VLDL, IDL, and HDL. ApoE acts as ligand for LDLR and LDL-related protein (LRP) (Mahley and Rall 2000). ApoE gene is located on chromosome 19q13.2 consisting of 4 exons and 3 introns (Mahley and Rall 2000). There are three common alleles (ε2, ε3, and ε4) in the ApoE gene, which code for six genotypes of ε2/2, ε3/3, ε4/4, ε2/3, ε2/4, and ε3/4 (Mahley and Rall 2000). ε4 allele is associated with higher and ε2 with lower levels of low-density lipoprotein–cholesterol (LDL-C), and total cholesterol compared with ε3 allele (Bennet et al. 2007). In addition, ε4 allele is associated with lower levels of HDL-C (Kataoka et al. 1996). It has been reported that ε4 allele is associated with increased risk of CVD and Alzheimer’s disease (Bennet et al. 2007; Kataoka et al. 1996, 2009). Moreover, the association between ApoE polymorphism and MetS has been evaluated in several studies (Sima et al. 2007; Ferreira et al. 2011; Luptakova et al. 2013; Olivieri et al. 2007; Ranjith et al. 2009; Lai et al. 2014).

Because of the importance of CETP and ApoE on lipid metabolism, it is conceivable that polymorphisms of these genes may influence the susceptibility to MetS. In Southern Thailand, the data concerning the CETP TaqIB and ApoE polymorphisms and MetS are rare. In the present study, we aim at the evaluation of CETP TaqIB and ApoE polymorphisms, in relation to MetS in a Southern Thai population.

Materials and Methods

Study Subjects

The study group included 378 individuals from Southern Thailand. The MetS− group consisted of 257 subjects (91 men and 166 women). The MetS+group consisted of 121 subjects (41 men and 80 women). Anthropometric measurements, such as body mass index (BMI) and waist circumference (WC), were recorded from each participant. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured.

MetS was diagnosed if three or more of the following five factors were present, according to National Cholesterol Education Program Adult Treatment Panel III (NCEP ATPIII) criteria, as follows (Grundy et al. 2005):

  1. 1)

    Central obesity (WC ≥ 90 cm in men and WC ≥ 80 cm in women);

  2. 2)

    High blood pressure (SBP ≥ 130 mmHg and/or DBP ≥ 85 mmHg);

  3. 3)

    High fasting plasma glucose (fasting plasma glucose ≥ 100 mg/dL);

  4. 4)

    Hypertriglyceridemia (triglyceride ≥ 150 mg/dL); and

  5. 5)

    Low high-density lipoprotein cholesterol (HDL-C) (HDL-C < 50 mg/dL in women and HDL-C < 40 mg/dL in men).

Subjects with less than three risk components were considered as MetS− group. Exclusion criteria for subjects were the presence of chronic disease, thyroid disease, renal or hepatic disease, and the use of hormone replacement therapy (HRT), lipid-lowering agents, and drug abuse. The study protocol was approved by the Ethics Committee of Walailak University. Written informed consent was obtained from all the subjects before being included in the study.

Laboratory Analysis

Blood samples were collected from subjects after 12-h fasting. The serum and plasma were separated by centrifugation at 3000 rpm for 10 min. Serum total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) were measured using standard enzymatic method. Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald formula. Fasting plasma glucose (FPG) was measured using glucose oxidase method. All testing was performed using the Beckman Coulter (Unicel DXC 800 Synchron).

Genotyping

DNA was extracted from blood leukocytes using the Genomic DNA Mini kit (GeneAid Biotech Ltd., Taiwan). The quality and quantity of DNA were estimated using NanoDrop 2000 UV–Vis spectrophotometer (Thermo Fisher Scientific Inc., USA). DNA samples were stored at −20 °C until analysis. CETP TaqIB and ApoE polymorphisms were analyzed using polymerase chain reaction and restriction fragment length polymorphism (PCR–RFLP) method. All primers were synthesized by Eurofins MWG Operon (Germany).

CETP TaqIB Polymorphism

CETP TaqIB polymorphism was analyzed using a slightly modified method of Mohrschladt et al. (2005). The forward and reverse primers used were 5'-CAC ACC ACT GCC TGA TAA CC-3' and 5'-GTG ACC CCC AAC ACC AAA TA-3', respectively. PCR reagents included 0.5 μl of 10 μM of each primer, 100 ng genomic DNA, 0.5 μl of 10 mM dNTPs (New England Biolabs (NEB), USA), 2.5 μl of ×10 buffer containing 15 mmol/l MgCl2, and 0.625 U of Taq polymerase (NEB, USA) in a total volume of 25 μl. PCR reaction was carried out in GeneAmp PCR system 9700 thermal cycler (Applied Biosystems, USA) using the following conditions: initial denaturation at 95 °C for 5 min, followed by 30 cycles of 95 °C for 1 min, 58 °C for 1 min, and 72 °C for 2 min, and a final extension at 72 °C for 10 min. The PCR products were digested with TaqI for overnight at 65 °C and the digested samples were run in 2 % agarose and visualized by staining with ethidium bromide. A fragment of 505 bp indicated for uncut B2 allele and 415 and 90 bp for B1 allele.

ApoE Polymorphism

ApoE polymorphism was analyzed using a slightly modified method of Zivelin et al. (Zivelin et al. 1997). The forward and reverse primers used were 5′-TCCAAGGAGCTGCAGGCGGCGCA-3′ and 5′-GCCCCGGCCTGGTACACTGCCA-3′, respectively. PCR reagents included 0.5 μl of 10 μM of each primer, 100 ng genomic DNA, 0.5 μl of 10 mM dNTPs (NEB, USA), 2.5 μl of ×10 buffer containing 15 mmol/l MgCl2, 0.625 U of Taq polymerase (NEB, USA), and 10 % dimethylsulphoxide (DMSO) in a total volume of 25 μl. The PCR reactions were an initial denaturation at 94 °C for 5 min, followed by 40 cycles of denaturation at 94 °C for 0.5 min, annealing at 55 °C for 0.5 min, extension at 70 °C for 1.5 min and final extension at 70 °C for 10 min. The PCR products were digested with AflIII and HaeII for 24 h at 37 °C. The digested samples were run in 4 % agarose and visualized by staining with ethidium bromide. The fragments of sizes 145, 168, and 195 bp represented ε3, ε2, and ε4 alleles, respectively.

Statistical Analysis

All data were analyzed using SPSS (SPSS Inc., Chicago, IL; Version 17). The distribution of CETP TaqIB and ApoE polymorphisms was tested for Hardy–Weinberg equilibrium using chi-square test. Data were tested for normality. Continuous variables were expressed as mean and standard deviation (SD). Differences among the two groups and multiple groups were tested using student’s t test and analysis of variant (ANOVA) followed by Tukey’s multiple comparison test, respectively. The associations of these polymorphisms and MetS were evaluated using logistic regression analyses. A p value < 0.05 was considered statistically significant.

Results

The demographic, anthropometric, and biochemical characteristics of all subjects, MetS−, and MetS+ groups are summarized in Table 1. The individuals with MetS had significantly increased concentrations of TC, TG, LDL-C, FPG, and decreased concentrations of HDL-C in comparison to the MetS− group (p < 0.01). WC, BMI, SBP, and DBP were significantly higher in the MetS+ group in comparison to the MetS− group (p < 0.01).

Table 1 Demographic, anthropometric, and biochemical characteristics of individuals between MetS− and MetS+ groups
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ApoE and CETP polymorphisms of all subjects, MetS− group, and MetS+ group are shown in Table 2. Genotypic distributions of ApoE and CETP were consistent with Hardy–Weinberg equilibrium in the all subjects, MetS− group , and MetS+ group. No significant differences were observed in the frequencies of ApoE and CETP genotypes and alleles between MetS− and MetS+ groups (p > 0.05).

Table 2 Distribution of the frequencies of the CETP TaqIB and ApoE genotypes and alleles between MetS− and MetS+ groups
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The metabolic parameters in all subjects, MetS− group, and MetS+ group according to CETP variants are presented in Table 3. B2B2 genotype was associated with higher HDL-C levels (p < 0.05) in comparison to B1B2 genotype in MetS+ group. There were no statistically significant changes in other parameters in all subjects, MetS− group, and MetS+ group.

Table 3 Differences in metabolic parameters in all subjects, MetS− group, and MetS+ group according to CETP genotypes
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To evaluate the effect of ApoE genotypes, all subjects, MetS− group, and MetS+ group were subdivided into three subgroups; ε2 carriers (ε2/2, ε2/3, ε2/4), ε3 carriers (ε3/3), and ε4 (ε3/4, ε4/4) carriers. Although a previous study has demonstrated that ε2/4 genotype was excluded from analysis due to the presumed counteracting effects of ε2 and ε4 alleles on lipid levels (Niu et al. 2012), several studies have shown that ApoE genotypes have an approximately linear relationship with the LDL-C levels and CVD risk in the following order: ε2/2, ε2/3, ε2/4, ε3/3, ε3/4, and ε4/4 (Bennet et al. 2007; Gustavsson et al. 2012). Therefore, in this study, the ε2/4 genotype was grouped within ε2 carriers.

The metabolic parameters in all subjects, MetS− group, and MetS+ group according to ApoE variants are presented in Table 4. In all subjects, MetS+ group, and MetS− group, the ε2 allele was associated with lower levels of TC and LDL-C in comparison to ε3 allele (p < 0.05). In all subjects and MetS− group, ε4 allele was associated with lower HDL-C levels (p < 0.05) in comparison to ε3 allele. No significant changes in concentrations of other parameters were seen in all three groups.

Table 4 Differences in metabolic parameters in all subjects, MetS − group, and MetS + group according to ApoE genotypes
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After adjustment for age and sex (Table 5), logistic regression analysis revealed that the CETP B1B1 and B1B2 genotypes were not associated with MetS and other metabolic components (p > 0.05) when compared with B2 homozygotes. Similarly, ε2 and ε4 alleles were not associated with MetS compared with ε3 allele. However, ε4 allele had a significantly increased odds ratio (OR) of reduced HDL-C levels when compared with ε3 allele (OR 1.91; 95 % CI 1.11–3.29, p = 0.020).

Table 5 Association of CETP and ApoE genotypes with MetS and metabolic components in all subjects
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Discussion

The prevalence of MetS is rapidly increasing in developing countries. In Thailand, the prevalence of MetS (23.2 %) has been reported. Prevalence of MetS varies with gender (19.5 % in men and 26.8 % in women) and increases with increasing age (Aekplakorn et al. 2011). Several gene polymorphisms are associated with MetS, as well as other components of MetS (obesity and diabetes mellitus) in Thais. ADIPOQ -11377C > G and LEPR Q223R polymorphisms are associated with MetS (Suriyaprom et al. 2014a, b). In addition, ApoE ε4 allele, SNPs rs7895340, and rs11196205 in TCF7L2 gene, as well as ADIPOQ -11377C > G polymorphism are associated with type 2 diabetes mellitus (Chaudhary et al. 2012; Tangjittipokin et al. 2012; Suriyaprom et al. 2010). Moreover, the SNPs rs1421085, rs17817449, and rs8043757 in FTO gene, SNPs rs6234-5 and rs3811951 in PCSK1 gene are associated with the increasing risk of obesity (Chuenta et al. 2015; Kulanuwat et al. 2014). To our knowledge, this is the first study to assess the associations between the common CETP TaqIB and ApoE polymorphisms and MetS in a Southern Thai population. In this study, we found no evidence for any statistically significant association between the CETP TaqIB and ApoE polymorphisms and the MetS defined by the NCEP ATPIII definition, suggesting that, these polymorphisms are not major risk factors for the MetS in the study population.

In the present study, we found that B2B2 genotype was associated with higher HDL-C levels in comparison to B1B2 genotype in MetS + group. However, the CETP TaqIB polymorphism was not significantly associated with HDL-C levels in all subjects and MetS− group. In addition, logistic regression analysis showed no significant association between TaqIB genotype and MetS and other metabolic components. Our results are inconsistent with previous studies. In the Framingham Offspring Study, the B2 allele was associated with decreased CETP activity (or concentrations), increased HDL-C levels, and further associated with a decreased risk of CVD (Ordovas et al. 2000; Liu et al. 2002) and MetS (Ozsait et al. 2008). In type 2 diabetic patients, the B2 allele was associated in a dose-dependent fashion with higher HDL-C and lower CETP concentrations. Then, the prevalence of macrovascular complications was significantly higher in subjects with the B1B1 genotype (Kawasaki et al. 2002). In Turkey, B1B1 genotype had higher in diabetic patients with myocardial infarction than diabetic patients without myocardial infarction (Yilmaz et al. 2004). Nevertheless, our findings are consistent with the results of those studies in North India (Meena et al. 2007), Finland (Tenkanen et al. 1991), Italian migrants to Australia (Mitchell et al. 1994), and healthy French Canadians (Kessling et al. 1992) which found no association between the CETP TaqIB polymorphism and HDL-C levels. In Turkey, CETP TaqIB polymorphism neither plays a role in determining HDL-C levels nor is a useful predictor of the risk of CVD (Tanrikulu-Kucuk et al. 2010). In this study, the frequency of CETP TaqIB genotypes and alleles was not significantly different between MetS− and MetS+ groups, indicating that there was no association of CETP TaqIB polymorphism with MetS. Our results are inconsistent with previous studies that B1 allele was associated with MetS in Turkish, Austria, and Egypt populations (Ozsait et al. 2008; Sandhofer et al. 2008; Elsammak et al. 2011).

Overall, the inconsistent results across various studies may be due to the population specific, gene–gene and/or gene–environmental interactions. It has been found that CETP TaqIB polymorphism is associated with HDL-C levels in Greek samples but not in the Italian samples (Mitchell et al. 1994). Similarly, such association has been found in Hei Yi Zhuang but not in Han populations in China (Yijiang et al. 2008), as well as in Chinese men but not in Malays and Indians in Singapore (Lu et al. 2013), suggesting that, the association between the CETP gene and HDL-C levels may be population or ethnic specific. Moreover, several environmental factors including smoking, dietary fat, alcohol consumption, BMI, and menopausal status have been found to modulate the variability of the HDL-C levels. It has been shown that B2 allele has strongly increased HDL-C levels in alcohol consumers (Hannuksela et al. 1994), nonsmokers (Freeman et al. 1994), and lean participants (Freeman et al. 1994) but weaker in the postmenopausal than that in premenopausal women (Kauma et al. 1996). Many studies have shown that the association of TaqIB with HDL-C levels was slightly weakened in obesity. (Freeman et al. 1994; Ruan et al. 2009; Vohl et al. 1999). The plausible mechanism may be due to the elevated CETP concentration in obesity (Arai et al. 1994; Hayashibe et al. 1997; Dullaart et al. 1994) that may override the up-regulating effect of TaqIB B2 allele on CETP expression, and thus, the association between TaqIB and HDL may not be visible. The inverse association of the B1 allele with HDL-C concentrations has been reported for those with a high consumption of animal fat, saturated fat, and monounsaturated fat (Li et al. 2007). Finally, since TaqIB is non-functional by itself, its effect on HDL-C is in almost complete linkage disequilibrium with the –629C/A promoter polymorphism which directly modulates CETP gene transcriptional activity in vitro (Klerkx et al. 2003). The −629A allele has been associated with 25 % lower in vitro transcription activity and lower in vivo plasma CETP mass but increased in vivo HDL cholesterol levels (Dachet et al. 2000). In this study, we found that ε2 allele was associated with lower TC and LDL-C levels compared to ε3 allele in all subjects, MetS− group, and MetS+ group. Although, ε4 had higher levels of TC and LDL-C than ε3 and ε2 alleles, no significant differences of the TC and LDL-C levels between ε4 and ε3 alleles were observed in all the three groups. We assumed that a low number of the subjects were included in this study, especially in ε4 subgroup. Moreover, we observed the association of ε4 allele with the low levels of HDL-C which is similar to previous study (Kataoka et al. 1996).

There was no association of ApoE polymorphism with the levels of triglyceride in this study. In contrast, previous study has shown that ε2 and ε4 alleles are associated with higher triglyceride levels in comparison to ε3 allele (Novotny et al. 2014). The inconsistencies of the relationship between ApoE polymorphism and triglyceride levels have been examined in the previous studies. Several biological factors including age, BMI, smoking, and geographic location, have been found to associate the inter-individual variability of triglyceride concentrations (Dallongeville et al. 1992). Moreover, Lee et al. reported that overweight/obesity may potentiate the genetic variants of the ApoE4 and ApoA5 ‘T’ alleles on the risk of severe hypertriglyceridemia (Lee et al. 2013). This suggests that there is a close interaction between genetic variants and environmental factors on the risk of high triglyceride levels.

Similar to CETP TaqIB polymorphism, the frequency of ApoE genotypes and alleles was not significantly different between MetS− and MetS+ groups, and the logistic regression analysis showed no significant association between ApoE genotype and MetS and other metabolic components. This suggests that there was no association of ApoE polymorphism with MetS in this study. Our findings are in conflict with the other studies. Olivieri et al. reported that cardiovascular patients without diabetes and lipid-lowering therapy, carrying ε4 allele was found to be positively associated with MetS prevalence (Olivieri et al. 2007). Similarly, Sima et al. found that the frequency of the ε4 allele was higher in the MetS group than in the control group (Sima et al. 2007). Nevertheless, our findings are in agreement with some studies. Ranjith et al. reported that ApoE variant was not associated with MetS defined by either the NCEP or the IDF definition in young Asian Indian patients with acute myocardial infarction (Ranjith et al. 2009). Similarly, Lai et al. showed that there was no difference in ε4 carriers between participants with and those without MetS among Taiwanese Chinese (Lai et al. 2014). Furthermore, Luptakova et al. showed that ApoE genotype did not influence MetS in Slovak adult women (Luptakova et al. 2013). Interestingly, Ferreira et al. revealed that ε4 allele was associated with absence of MetS and hypertension in subjects with extreme obesity (Ferreira et al. 2011). These suggest that the differences of these results are a likely result of the complexity interaction between genetic and environmental factors. The potential mechanism of these factors on MetS occurrence is needed to elucidate.

The limitations of our study result from a small sample size. Because two polymorphisms in two genes were studied, we cannot exclude that other variants in these genes are not associated with the MetS. In addition, other polymorphisms in other genes may be susceptible to MetS. We recommend that further studies on a larger sample are required to confirm the results.

In conclusion, we describe here that there is no significant effect of CETP TaqIB and ApoE genotypes on the presence of MetS in a Southern Thai population. These polymorphisms may not be considered as genetic risk factors for MetS in a Southern Thai population. However, ε4 allele which is associated with one metabolic component, low HDL-C levels, might predispose the subjects to develop metabolic disturbances.