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To investigate the association of four common single nucleotide polymorphisms (SNPs) at ABCG5 (i7892A>G, i18429C>T, Gln604GluC>G, i11836G>A) and five at ABCG8 (5U145T>G, Tyr54CysA>G, Asp19HisG>C, i14222T>C, and Thr400LysG>T) with plasma lipids concentrations and to explore the interaction between those SNPs and smoking in patients with FH.
ABCG5/G8 SNPs were genotyped in 500 subjects with genetic diagnosis of FH. Carriers of the minor A allele at the ABCG5_i11836G>A SNP displayed significantly higher HDL-C concentrations (P=0.023) than G/G subjects. In addition, carriers of the minor G allele at the ABCG5_Gln604GluC>G SNP had significantly lower VLDL-C (P=0.011) and lower TG (P=0.017) concentrations than homozygous C/C. Interestingly, a significant gene-smoking interaction was found, in which carriers of the minor alleles at ABCG5 (i7892A>G, i18429C>T, i11836G>A) SNPs displayed significantly lower HDL-C, higher TC and higher TG respectively, only in smokers. On the other hand, non-smokers carriers of the minor alleles at ABCG5 (i18429C>T and Gln604GluC>G) SNPs had significantly lower TG concentrations (P=0.012 and P=0.035) compared with homozygous for the major allele.
Our data support the notion that ABCG5/G8 genetic variants modulate plasma lipids concentrations in patients with FH and confirm that this effect could be influenced by smoking. Therefore, these results suggest that gene-environmental interactions can affect the clinical phenotype of FH.
Familial hypercholesterolemia is an autosomal codominant inherited disorder of lipoprotein metabolism associated with the development of severe and premature coronary heart disease (CHD) (1). It is caused by mutations in the gene encoding the low-density lipoprotein receptor (LDLR) located in the short arm of the chromosome 19, and it is transmitted as an autosomal dominant character with a high penetrance. To date more than 800 mutations have been identified, and it has been estimated that there are 10,000,000 people with FH worldwide.
It is well known that despite being a monogenic disorder, the clinical expression of FH is highly variable in terms of the age of onset and severity, even in cases that share the same mutation (2). This fact suggests that other metabolic, environmental and genetic factors could play an important role in the development of atherosclerosis in FH.
ABCG5 and ABCG8 are cholesterol half-transporters that have been identified to form heterodimers and function together to regulate cholesterol kinetics in humans (3–5). Furthermore, it has been recently shown the crucial role of ABCG5 and ABCG8 in promoting cholesterol excretion in vivo through the reverse cholesterol transport (RCT) pathway and their up-regulation by liver X receptor (LXR) agonists (6,7). Single nucleotide polymorphisms (SNPs) in these transporters have been associated with hypercholesterolemia (8–10). However, to date not many studies have examined the associations between ABCG5/G8 polymorphisms with plasma lipids in patients with severe hypercholesterolemia. In this context, Miwa et al. (11) did not find significant associations with lipids in 100 Japanese patients with hypercholesterolemia. Another study examined the relationship between SNPs in the ABCG8 gene (D19H and T400K) and plasma cholesterol concentrations in patients with heterozygous FH (12). Interestingly, although they did not find significant differences in plasma lipid concentrations, they showed that these genetic variants in the ABCG8 gene may affect variation in cardiovascular risk.
On the other hand, cigarette smoking increases plasma LDL-C and TG levels and decreases HDL-C, and several studies have suggested gene-smoking interactions modifying these effects (13). In this regard, we have recently demonstrated that genetic variation at the ABCG5/G8 genes modulates the effect of cigarette smoking on plasma HDL-C concentrations (14). This gene-environmental interaction could affect the clinical phenotype of FH but this effect has never been studied in patients with severe hypercholesterolemia. Therefore, our primary aim was to examine the association between nine ABCG5/G8 polymorphisms with plasma lipids in patients with FH. In a next step, we explored the interaction between those SNPs and smoking status in the same population.
The study population consisted of 500 unrelated subjects (244 women and 256 men) randomly selected from a Spanish FH longitudinal cohort study, supported by the “Fundación Española Hipercolesterolemia Familiar” (http//www.colesterolfamiliar.com) (15). All patients included in the study were heterozygous carriers for known functional mutations in the LDL receptor (LDLR) gene. The mean age (± SD) was 43 ± 16.2 years. In order to homogenize the sample, patients with familial defective apolipoprotein B disorder were excluded from the analysis. A written informed consent was obtained from all participants before their inclusion in the cohort and the protocol was approved by the ethic committee of the CEIC Fundación Jiménez Díaz (Madrid).
Blood samples were obtained after 12 h fasting. Total cholesterol (TC) and TG were determined by enzymatic techniques (16, 17). Very low density lipoprotein cholesterol (VLDL-C) was determined from plasma TG (18). HDL-C was determined after precipitation with phosphotungstate (19). LDL-C concentration was calculated using the Friedewald formula (20). ApoA-I and B levels were determined by immunoturbidimetry (21).
The genotyping study was carried out in the Nutrition and Genomics Laboratory, at the Jean Mayer–US Department of Agriculture Human Nutrition Research Center on Aging. Genomic DNA was isolated from whole blood samples using standard methods. All subjects in this study were heterozygous carriers for known mutations in the LDLR gene associated with FH. The genetic diagnosis was made using a DNA-microarray (Progenika, Bilbao, Spain) as previously described (22). Four ABCG5 SNPs (i7892A>G, i18429C>T, Gln604GluC>G, i11836G>A) and five ABCG8 SNPs (5U145T>G, Tyr54CysA>G, Asp19HisG>C, i14222T>C, and Thr400LysG>T) were genotyped. SNPs were selected using two criteria: bioinformatics functional assessment and linkage disequilibrium (LD) structure. Computational analysis of ABCG5/G8 SNPs (http://www.ncbi.nlm.nih.gov/SNP/buildhistory.cgi) ascribed potential functional characteristics to each variant allele. Given that SNP rs3806471 maps to the 5´-UTR of ABCG8 but also lies around 216 bp upstream of the ABCG5 mRNA start, this SNP sequence was analyzed by MAPPER (23), which identified an allele-specific farnesoid X receptor (FXR) (NR1H4) transcription factor binding site. Intronic SNPs were also analyzed with MAPPER and manually checked for altered mRNA splice donor and acceptor sites and transversions affecting the polypyrimidine tract near splice acceptors. Assessing LD structure at the ABCG5/G8 loci facilitated the selection of tag SNPs representing different LD blocks.
Genotyping was performed using a TaqMan® assay with allele-specific probes on the ABIPrism 7900 HT Sequence Detection System (Applied Biosystems) according to routine laboratory protocols (24). The description of ABCG5/G8 SNPs, probes, and sequences, as well as ABI assay-on-demand ID is presented in Table 1. Standard good laboratory practices were undertaken to ensure the accuracy of genotype data, including the inclusion of dummy duplicates.
Statistical Package for the Social Sciences (SPSS v 15.0, Chicago, IL, USA) was used for the statistical comparisons. A chi-square test was used to determine whether the genotype distribution followed Hardy-Weinberg equilibrium. Data were presented as means ± SD or means ± standard error for continuous variables and as frequencies or percentages for categorical variables. A logarithmic transformation was applied to measures of plasma TG to normalize the distribution of the data. Differences in mean values were assessed by using analysis of variance and unpaired t-tests. Furthermore, comparisons of frequencies between qualitative variables were carried out using the Chi-squared test. Potential confounding factors were age, gender, BMI, physical activity, smoking habits (current vs. never and past smokers), diabetes, HTA, lipid lowering drugs and cardiovascular disease (CVD). Potential interactions between ABCG5/G8 and smoking in determining lipid values (as continuous variables) were tested using the variance test. In this regard, the nonsmokers were patients who reported that they had never smoked, not even a puff or reported that they had stopped smoking. In this context, past smokers were considered those patients who took more than one year without smoking. On the other hand smokers were considered those patients who smoked more than ten cigarettes per day. Thus, we considered smoking habit into two categories: current vs never or past smokers. All the analyses were adjusted for potential confounders and P<0.05 was considered to be significant.
Analysis were performed using different R packages implementing statistical methods an algorithms for the analysis of genetic data (GNU) (25). The SNPs pairwise LD was evaluated by the measures of D´ and r2, and the results plotted by using the LDheatmap package. For haplotype analysis, we estimated haplotype frequencies using maximum likelihood algorithm for a subset of SNPs selected on the basis of individual association with the phenotypes to ensure reasonable statistical power. Haplotype based hypothesis tests of generalised linear models (GLM) were conducted by use of prospective likelihood implemented in the haplo.glm function of the haplo.stats R package (26). The regression coeficient (β) represents the effect of the haplotype by phenotype interaction in which inferred haplotypes were considered as predictors, and the aforementioned confounding factors as covariates. Analyses were adjusted for gender, age, BMI, HDL-C levels, exercise, diabetes, treatment, CVD and smoking conditions as potential confounders. P values were further adjusted for multiple tests by a permutation test.
A total of 500 unrelated subjects (256 men and 244 women) with conclusive genetic diagnosis for FH were genotyped. In order to homogenize the analysis, only subjects with all lipid parameters available were included. Thus, complete demographic, biochemical and genotype data were available in 465 subjects.
Genotype distributions did not deviate from Hardy-Weinberg expectations for all ABCG5/G8 SNPs (P>0.05). Given the low genotype frequencies of individuals homozygous for the minor alleles, and as the analysis did not suggest a recessive mode of action, we analyzed all SNPs using two genotype categories.
We found significant and novel association between genotypes ABCG5_i11836G>A SNP and HDL-C concentrations. Thus, carriers of the minor A allele showed significantly higher HDL-C (P=0.023) than G/G subjects (Figure 1a). In addition, significantly lower plasma VLDL-C (P=0.011) and TG (P=0.017) concentrations were also observed in carriers of the minor G allele at ABCG5_Gln604GluC>G than C/C subjects (Figure 1b).
No other significant associations were found between all these SNPs explored and other plasma lipid variables. However, a similar trend was observed for ABCG8_Asp19HisG>C by which carriers of the minor C allele had lower VLDL-C (P=0.089) and TG (P=0.089) concentrations than G/G subjects (data not shown).
In the next step, we examined wether the interaction between smoking and the polymorphisms at the ABCG5/G8 genes determines plasma lipid response. Demographic and biochemical characteristics according to the smoking status are shown in Table 2.
As expected, smokers displayed higher TC, LDL-C, TG, VLDL-C and ApoB and lower HDL-C and ApoA-I concentrations than nonsmokers. However, smokers had a lower prevalence of CVD with respect to nonsmokers (11/128, 8.1% vs 54/337, 14.8%) (Table 2). In this context, it is essential to note that smokers were younger than nonsmokers (40 vs 44; P=0.005).
For ABCG8 SNPs a significant interaction between ABCG8_5U145T>G and smoking was found for TG (P=0.033) and VLDL-C (P=0.020) (data not shown). In this context, smokers carriers of the minor G allele had significantly higher VLDL-C (P=0.04) concentrations and a trend toward higher values of TG (4.67 ± 0.05 vs. 4.60 ± 0.04, respectively; P=0.11) compared to T/T subjects. Conversely, no significant differences across genotypes were found in nonsmokers (P=0.796 and P=0.867, respectively). No other significant gene-smoking interactions were found in other examined ABCG8 SNPs.
For ABCG5 (i7892A>G, i18429C>T, i11836G>A, Gln604GluC>G) SNPs strong interactions were found between genetic variants and smoking (Table 3). Interestingly, smokers carriers of the minor G, T and A alleles at ABCG5 (i7892A>G, i18429C>T, i11836G>A) SNPs displayed significantly lower HDL-C (P=0.042), and higher TC (P=0.040) and TG (P=0.035) respectively, compared with smokers homozygous for the major allele. In contrast, it is essential to note that no significant differences were demonstrated in nonsmokers (Figures 2a, and 2b).
On the other hand, non-smokers carriers of the minor T and G alleles at ABCG5 (i18429C>T and Gln604GluC>G) SNPs had a protective effect towards significantly lower TG concentrations (P=0.012 and P=0.032, respectively) than homozygous for the major allele. However, this effect was lost if they were smokers (Figure 2c and 2d).
It is essential to note that of the five significant interactions, four ABCG5 (i7892A>G, i18429C>T, i11836G>A and Gln604GluC>G) SNPs (table 3) and ABCG8_5U145T>G were related with TG concentrations. To understand the combined effects of genetic variants at ABCG5/G8, we conducted haplotype analysis using a subset of ABCG5/G8 SNPs according to their association with the phenotypes as individual variants. The pattern of pairwise LD between the nine SNPs is presented in Supplemental Data 1. Given that all pairwise linkage desequilibria correlation coefficient (r2) were < 0.80, we concluded that no LD block was found on SNP structure of our population. For further haplotype analysis, we selected four SNPs (i7892A>G, i18429C>T, i11836G>A and Gln604GluC>G) which showed a statistical association with TG plasma levels in single marker analysis, and 5U145T>G because of the moderate LD linkage desequilibria observed with i7892A>G.
There were eight haplotypes with frequencies ranking from 1.9 % to 13 % accounting for 45.5 % of all haplotypes in this population (Supplemental Data 2). After adjustment for covariates, carriers of the haplotype G-T-A-G-G showed significantly lower TG concentrations (β=-1.8374, P=0.0007).
We selected the aforementioned SNPs for haplotype analysis in relation to smoking status (Supplemental Data 3). In smokers, there were eight haplotypes with frequencies from 3 % to 14 % accounting for 67 % of all haplotypes. For individual haplotypes, carriers of C-T-A-G-T showed significantly higher plasma TG levels (β=0.8166, P=0.0026). Among nonsmokers, there were seven haplotypes with frequencies ranking from 2 % to 16 % accounting for 64 % of all haplotypes. For individual haplotypes, carriers of C-T-G-G-G showed significantly lower plasma TG levels (β=0.2432, P=0.0016). All associations reached significance even after correcting for multiple testing.
This study provides novel evidences supporting that genetic variation at ABCG5/G8 genes modulates plasma lipids in FH patients. Moreover, this study confirms previous data suggesting that the effect of these genetic variants on plasma lipids is influenced by smoking status.
The ABCG5 and ABCG8 proteins play an essential role in hepatobiliary cholesterol transport (3–6). In this regard, the current study showed a significant and new association between a common ABCG5_i11836G>A SNP and plasma HDL-C, in which carriers of the minor A allele displayed significantly higher concentrations than G/G subjects. In addition, carriers of the minor G allele at ABCG5_Gln604GluC>G SNP displayed lower VLDL-C and lower TG concentrations than C/C subjects (similar trend was observed for carriers of the minor C allele at ABCG8_Asp19HisG>C SNP for VLDL-C and TG). This pattern of associations has not been previously described.
Despite the importance of ABCG5/G8 transporters in the regulation of cholesterol kinetics in humans, not many studies have examined the associations between ABCG5/G8 polymorphisms with plasma lipids in patients with familial hypercholesterolemia. In this context, Koeijvoets et al. (12) explored the effect of two common ABCG8 (D19H and T400K) SNPs on lipids and CVD in 2,012 patients with heterozygous FH but did not find significant associations with plasma lipid concentrations. Interestigly, they showed that variants at the ABCG8 gene may affect cardiovascular risk. In our study, although the prevalence of CVD was not the main purpose, there were no significant differences in CVD across ABCG5/ABCG8 SNPs. In this context, it is noteworthy that our population is still too young for suffer cardiovascular disease (mean age was 43 years). This is consistent with previously reported findings in other populations with heterozygous familial hypercholesterolemia in which has been demonstrated that age is an important risk factor for CVD (27–29). On the other hand, Miwa et al. (11) reported no significant associations between three ABCG5/G8 (Q604E, C54Y and T400K) SNPs and serum lipid concentrations in 100 Japanese primary hypercholesterolaemic patients. Consistent with both studies (11, 12) we did not find associations between D19H, T400K and C54Y SNPs and plasma lipid concentrations. However, in contrast to Miwa et al. our data showed association between Q604E SNP and VLDL-C and TG concentrations. These differences may be due to small sample size, as well as to differences between both population. Additionally, Santosa et al. (10) examined the association of four SNPs at ABCG5 (Q604E, i7892, i18429 and M216) and four ABCG8 (C54Y, D19H, i14222 and T400K) with plasma lipids concentrations in 35 young women with mildly hypercholesterolemia. They found that C54Y and Q604E SNPs were associated with the response of cholesterol metabolism to weight loss. In the current study, the same SNPs were studied in our population but did not reveal such association between those two SNPs and TC concentrations. In contrast, our analyses showed association between the genotypes ABCG5_Gln604GluC>G SNP and VLDL-C and TG concentrations. However, it is important to consider the differences in the design of these studies, sample size and subject population included.
These novel associations provide strong evidence in support of the important role of ABCG5/G8 transporters in the regulation of plasma lipid kinetics in humans and resemble the importance of these genes in the last steps of the reverse cholesterol transport (RCT) (14). In our study, the presence of the ABCG5_i11836G>A SNP was associated with higher HDL-C. The mechanism underlying the modulation of genetic variants at ABCG5/G8 genes on HDL-C levels is undefined. However, several studies in mice have demonstrated that liver X-receptor-mediated (LXR) activation of the RCT requires ABCG5/G8 (5–7, 30). Therefore, the mechanism through which this particular SNP was associated with HDL-C could be due to up-regulation by LXR that will lead towards an increase of HDL-C concentrations across the activation of the RCT pathway. On the other hand, it has been demonstrated that the effect of ABCG5/G8 genetic variants on HDL-C concentrations is dependent on ABCA1 (ATP-binding cassette transporters A1). These findings may help to explain the associations between ABCG5/G8 polymorphisms and HDL-C (31). Additionally, we found another novel association in which carriers of the minor allele at Gln604GluC>G SNP displayed lower VLDL-C and TG concentrarions. Recently, it has been demonstrated that the activation of LXR induces hypertriglyceridemia in animals (32–34) and that farnesoid X receptor (FXR)-null mice lead to hypertrigliceridemia (35). In this context, the association between ABCG5_Gln604GluC>G with lower VLDL-C and TG could be explained across their up-regulation by FXR or the inactivation of LXR. These interesting mechanisms could be provide novel evidence supporting the hypothesis that ABCG5/G8 expression is regulated by several transcription factors, such as the LXR and FXR, as it has been demonstrated in mice (4, 36).
Another interesting observation from our data was that genetic variation at the ABCG5/G8 genes modulates the effect of cigarette smoking on plasma lipid concentrations in patients with FH. Interestingly, our data revealed that carriers of the minor G, T, A, G alleles at ABCG5/G8 (i7892A>G, i18429C>T, i11836G>A, 5U145T>G) SNPs displayed significantly lower HDL-C, higher TC, higher TG and higher VLDL-C respectively, only in the group of smokers volunteers. It is essential to note that carriers of the minor alleles at the aforementioned polymorphisms are related with serum levels of atherogenic lipoproteins only in presence of cigarette smoking. On the other hand, non-smokers carriers of the minor T and G alleles at ABCG5 (i18429C>T and Gln604GluC>G) SNPs had a protective effect towards significantly lower TG concentrations than homozygous for the major allele. Conversely, this protective effect was not observated in smokers. It is noteworthy that smoking is related with a different effect. In line with these observations, we have previously shown that genetic variation at the ABCG5/G8 genes modulates the effect of cigarette smoking on plasma HDL-C concentrations (14). In contrast to this study, we found other novel interactions between ABCG5/G8 genetic variants and smoking status in which other plasma lipids concentrations were modified, not only HDL-C, but the special characteristics of our population makes difficult any comparation to other non-FH populations. In this regard, it is essential to note that of the five significant interactions, four ABCG5 (i7892A>G, i18429C>T, i11836G>A and Gln604GluC>G) SNPs (table 3) and ABCG8_5U145T>G were related with TG concentrations. Consistent with these findings, the haplotype effect appeared to be modulated by smoking habit. Interestingly, smokers carriers of C-T-A-G-T showed significantly higher plasma TG levels. Among nonsmokers, carriers of C-T-G-G-G showed significantly lower plasma TG levels. The mechanism underlying the observed interactions is unknown. However, it is known that cigarette smoking increases plasma TC and TG levels and decreases HDL-C, and several studies have already demonstrated gene-smoking interactions modifying these effects (13). On the other hand, it has been demonstrated in mice that the interaction between FXR- and LXR-mediated stimuli regulate expression of liver ABCG5/G8 (36). Therefore, from our results we could then hypothesize that smoking decreases the expression of ABCG5/G8 genes, through an inactivation of the LXR/FXR pathway that will lead towards the detriment of RCT in smokers, resulting in changes in plasma lipids concentrations, especially TG and HDL-C. Obviously, we need to be cautious before extrapolating our conclusions to other population and replication of our findings is essential.
In summary, our study demonstrates that ABCG5 and ABCG8 genetic variants modulate plasma lipids concentrations in patients with FH and confirms that the effect is influenced by smoking. Therefore, these results could help to explain the differences in the susceptibility to CHD in FH and support the notion that gene-environmental interactions can affect the clinical phenotype in these patients.
We thank all the patients who participated and the physicians of the lipid clinics throughout Spain. We also thank to the Fundacion Española de Hipercolesterolemia Familiar.
This work was supported by research grants from the Centro Nacional Investigaciones Cardiovasculares (CNIC-08-2008), National Health Institute; CIBER (CBO/6/03), Instituto de Salud Carlos III; CICYT (SAF 01/2466-C05 04 to F P-J, SAF 01/0366 to J L-M, AGL 2004-07907 to J L-M, AGL 2006-01979 to JL-M), the Spanish Ministry of Health (FIS 01/0449, FIS PI041619 to CM); Fundación Cultural “Hospital Reina Sofía-Cajasur”; Consejería de Salud, Servicio Andaluz de Salud (00/212, 00/39, 01/239, 01/243, 02/64, 02/65, 02/78, 03/73, 03/75, 04/237, 04/191, 04/238, 05/396); Consejería de Educación, Plan Andaluz de Investigación, Universidad de Córdoba; Centro Excelencia Investigadora Aceite de Oliva y Salud (CEAS) and by NIH grants HL54776 and DK075030 and contracts 53-K06-5-10 and 58-1950-9-001 from the US Department of Agriculture Research Service.
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