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• W2046642124 abstract "Low HDL-cholesterol (HDL-C) is associated with an increased risk for atherosclerosis, and concentrations are modulated by genetic factors and environmental factors such as smoking. Our objective was to assess whether the association of common single-nucleotide polymorphisms (SNPs) at ABCG5/G8 (i18429G>A, i7892T>C, Gln604GluC>G, 5U145A>C, Tyr54CysA>G, Asp19HisG>C, i14222A>G, and Thr400LysC>A) genes with HDL-C differs according to smoking habit. ABCG5/G8 SNPs were genotyped in 845 participants (243 men and 602 women). ABCG5/G8 (i7892T>C, 5U145A>C, Tyr54CysA>G, Thr400LysC>A) SNPs were significantly associated with HDL-C concentrations (P < 0.001–0.013) by which carriers of the minor alleles at the aforementioned polymorphisms and homozygotes for the Thr400 allele displayed lower HDL-C. A significant gene-smoking interaction was found, in which carriers of the minor alleles at ABCG5/G8 (Gln604GluC>G, Asp19HisG>C, i14222A>G) SNPs displayed lower concentrations of HDL-C only if they were smokers (P = 0.001–0.025). Also, for ABCG8_Thr400LysC>A SNP, smokers, but not nonsmokers, homozygous for the Thr400 allele displayed lower HDL-C (P = 0.004). Further analyses supported a significant haplotype global effect on lowering HDL-C (P = 0.002) among smokers. In conclusion, ABCG5/G8 genetic variants modulate HDL-C concentrations, leading to an HDL-C-lowering effect and thereby a potential increased risk for atherosclerosis only in smokers. Low HDL-cholesterol (HDL-C) is associated with an increased risk for atherosclerosis, and concentrations are modulated by genetic factors and environmental factors such as smoking. Our objective was to assess whether the association of common single-nucleotide polymorphisms (SNPs) at ABCG5/G8 (i18429G>A, i7892T>C, Gln604GluC>G, 5U145A>C, Tyr54CysA>G, Asp19HisG>C, i14222A>G, and Thr400LysC>A) genes with HDL-C differs according to smoking habit. ABCG5/G8 SNPs were genotyped in 845 participants (243 men and 602 women). ABCG5/G8 (i7892T>C, 5U145A>C, Tyr54CysA>G, Thr400LysC>A) SNPs were significantly associated with HDL-C concentrations (P < 0.001–0.013) by which carriers of the minor alleles at the aforementioned polymorphisms and homozygotes for the Thr400 allele displayed lower HDL-C. A significant gene-smoking interaction was found, in which carriers of the minor alleles at ABCG5/G8 (Gln604GluC>G, Asp19HisG>C, i14222A>G) SNPs displayed lower concentrations of HDL-C only if they were smokers (P = 0.001–0.025). Also, for ABCG8_Thr400LysC>A SNP, smokers, but not nonsmokers, homozygous for the Thr400 allele displayed lower HDL-C (P = 0.004). Further analyses supported a significant haplotype global effect on lowering HDL-C (P = 0.002) among smokers. In conclusion, ABCG5/G8 genetic variants modulate HDL-C concentrations, leading to an HDL-C-lowering effect and thereby a potential increased risk for atherosclerosis only in smokers. Low concentrations of HDL-cholesterol (HDL-C) have been associated with an increased risk for coronary heart disease (CHD) (1Castelli W.P. Garrison R.J. Wilson P.W. Abbott R.D. Kalousdian S. Kannel W.B. Incidence of coronary heart disease and lipoprotein cholesterol levels. The Framingham Study.J. Am. Med. Assoc. 1986; 256: 2835-2838Crossref PubMed Scopus (2077) Google Scholar). Smoking, another major risk factor for CHD, exerts negative effects on plasma lipids, particularly a decrease of HDL-C (2Campbell S.C. Moffatt R.J. Stamford B.A. Smoking and smoking cessation-The relationship between cardiovascular disease and lipoprotein metabolism: A review.Atherosclerosis. 2008; 201: 225-235Abstract Full Text Full Text PDF PubMed Scopus (231) Google Scholar, 3Craig W.Y. Palomaki G.E. Haddow J.E. Cigarette smoking and serum lipid and lipoprotein concentrations: an analysis of published data.BMJ. 1989; 298: 784-788Crossref PubMed Scopus (686) Google Scholar). One of the most likely mechanisms by which low HDL-C promotes atherosclerosis is through the impairment of cholesterol clearance via the reverse cholesterol transport (RCT) pathway (4Lewis G.F. Rader D.J. New insights into the regulation of HDL metabolism and reverse cholesterol transport.Circ. Res. 2005; 96: 1221-1232Crossref PubMed Scopus (819) Google Scholar). ABCG5 and ABCG8 are cholesterol half-transporters that function together as a heterodimer (5Graf G.A. Yu L. Li W.P. Gerard R. Tuma P.L. Cohen J.C. Hobbs H.H. ABCG5 and ABCG8 are obligate heterodimers for protein trafficking and biliary cholesterol excretion.J. Biol. Chem. 2003; 278: 48275-48282Abstract Full Text Full Text PDF PubMed Scopus (361) Google Scholar). Expression of these transporters mediates the efflux of cholesterol and plant sterols from enterocytes back into the intestinal lumen and their excretion into the bile, thus limiting their accumulation in the body and promoting RCT (5Graf G.A. Yu L. Li W.P. Gerard R. Tuma P.L. Cohen J.C. Hobbs H.H. ABCG5 and ABCG8 are obligate heterodimers for protein trafficking and biliary cholesterol excretion.J. Biol. Chem. 2003; 278: 48275-48282Abstract Full Text Full Text PDF PubMed Scopus (361) Google Scholar, 6Oram J.F. Vaughan A.M. ATP-binding cassette cholesterol transporters and cardiovascular disease.Circ. Res. 2006; 99: 1031-1043Crossref PubMed Scopus (323) Google Scholar). In humans, deleterious mutations in either of these genes cause the genetic disease sitosterolemia (7Hubacek J.A. Berge K.E. Cohen J.C. Hobbs H.H. Mutations in ATP-cassette binding proteins G5 (ABCG5) and G8 (ABCG8) causing sitosterolemia.Hum. Mutat. 2001; 18: 359-360Crossref PubMed Scopus (143) Google Scholar), characterized by highly elevated plasma plant sterols in blood and tissues, with an increased risk for atherosclerosis and CHD that is independent of plasma cholesterol concentrations (8Bjorkhem I. Boberg K. Leitersdorf E. Inborn errors in bile acid biosynthesis and storage of sterols other than cholesterol.in: Scriver C. Beaudet A. Sly W. Valle D. The Metabolic and Molecular Bases of Inherited Disease. Vol. 2. McGraw-Hill, New York2001: 2961-2988Google Scholar). Most of our mechanistic knowledge concerning the role of ABCG5/G8 genes in lipid metabolism comes from animal models. In mice, ABCG5/G8 deficiency has been associated with reduced biliary cholesterol secretion and enhanced sterol absorption (9Yu L. Hammer R.E. Li-Hawkins J. Von Bergmann K. Lutjohann D. Cohen J.C. Hobbs H.H. Disruption of Abcg5 and Abcg8 in mice reveals their crucial role in biliary cholesterol secretion.Proc. Natl. Acad. Sci. USA. 2002; 99: 16237-16242Crossref PubMed Scopus (602) Google Scholar), whereas overexpression of those genes promotes biliary cholesterol secretion, reduces dietary cholesterol absorption, and increases fecal neutral sterol excretion (10Yu L. Li-Hawkins J. Hammer R.E. Berge K.E. Horton J.D. Cohen J.C. Hobbs H.H. Overexpression of ABCG5 and ABCG8 promotes biliary cholesterol secretion and reduces fractional absorption of dietary cholesterol.J. Clin. Invest. 2002; 110: 671-680Crossref PubMed Scopus (602) Google Scholar). Moreover, it has recently been shown that these genes play a key role in the RCT pathway and the prevention of atherosclerosis through their upregulation by liver X receptor (LXR) agonists (11Naik S.U. Wang X. Da Silva J.S. Jaye M. Macphee C.H. Reilly M.P. Billheimer J.T. Rothblat G.H. Rader D.J. Pharmacological activation of liver X receptors promotes reverse cholesterol transport in vivo.Circulation. 2006; 113: 90-97Crossref PubMed Scopus (313) Google Scholar, 12Calpe-Berdiel L. Rotllan N. Fiévet C. Roig R. Blanco-Vaca F. Escola-Gil J.C. Liver X receptor-mediated activation of reverse cholesterol transport from macrophages to feces in vivo requires ATP-binding cassette (ABC) G5/G8.J. Lipid Res. 2008; 49: 1904-1911Abstract Full Text Full Text PDF PubMed Scopus (69) Google Scholar). In humans, previous small studies have investigated the effect of several ABCG5/G8 single-nucleotide polymorphisms (SNPs) on lipids with controversial results (13Hubácek J.A. Berge K.E. Stefková J. Pitha J. Skodová Z. Lánská V. Poledne R. Polymorphisms in ABCG5 and ABCG8 transporters and plasma cholesterol levels.Physiol. Res. 2004; 53: 395-401PubMed Google Scholar, 14Plat J. Bragt M.C. Mensink R.P. Common sequence variations in ABCG8 are related to plant sterol metabolism in healthy volunteers.J. Lipid Res. 2005; 46: 68-75Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar, 15Miwa K. Inazu A. Kobayashi J. Higashikata T. Nohara A. Kawashiri M. Katsuda S. Takata M. Koizumi J. Mabuchi H. ATP-binding cassette transporter G8 M429V polymorphism as a novel genetic marker of higher cholesterol absorption in hypercholesterolaemic Japanese subjects.Clin. Sci. 2005; 109: 183-188Crossref PubMed Scopus (44) Google Scholar, 16Acalovschi M. Ciocan A. Mostean O. Tirziu S. Chiorean E. Keppeler H. Schirin-Sokhan R. Lammert F. Are plasma lipid levels related to ABCG5/ABCG8 polymorphisms? A preliminary study in siblings with gallstones.Eur. J. Intern. Med. 2006; 17: 490-494Abstract Full Text Full Text PDF PubMed Scopus (54) Google Scholar, 17Gylling H. Hallikainen M. Pihlajamäki J. Agren J. Laakso M. Rajaratnam R.A. Rauramaa R. Miettinen T.A. Polymorphisms in the ABCG5 and ABCG8 genes associate with cholesterol absorption and insulin sensitivity.J. Lipid Res. 2004; 45: 1660-1665Abstract Full Text Full Text PDF PubMed Scopus (132) Google Scholar–18Wang Y. Jiang Z.Y. Fei J. Xin L. Cai Q. Jiang Z.H. Zhu Z.G. Han T.Q. Zhang S.D. ATP binding cassette G8 T400K polymorphism may affect the risk of gallstone disease among Chinese males.Clin. Chim. Acta. 2007; 384: 80-85Crossref PubMed Scopus (55) Google Scholar). Some of these studies reported significant associations between ABCG5/G8 SNPs (Gln604Glu, Thr400Lys, and Tyr54Cys) and total cholesterol and LDL-C, including 154 females undergoing weight loss (13Hubácek J.A. Berge K.E. Stefková J. Pitha J. Skodová Z. Lánská V. Poledne R. Polymorphisms in ABCG5 and ABCG8 transporters and plasma cholesterol levels.Physiol. Res. 2004; 53: 395-401PubMed Google Scholar), 112 subjects after consumption of plant stanol esters (14Plat J. Bragt M.C. Mensink R.P. Common sequence variations in ABCG8 are related to plant sterol metabolism in healthy volunteers.J. Lipid Res. 2005; 46: 68-75Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar), and 263 mildly hypercholesterolemic patients (17Gylling H. Hallikainen M. Pihlajamäki J. Agren J. Laakso M. Rajaratnam R.A. Rauramaa R. Miettinen T.A. Polymorphisms in the ABCG5 and ABCG8 genes associate with cholesterol absorption and insulin sensitivity.J. Lipid Res. 2004; 45: 1660-1665Abstract Full Text Full Text PDF PubMed Scopus (132) Google Scholar). However, Miwa et al. (15Miwa K. Inazu A. Kobayashi J. Higashikata T. Nohara A. Kawashiri M. Katsuda S. Takata M. Koizumi J. Mabuchi H. ATP-binding cassette transporter G8 M429V polymorphism as a novel genetic marker of higher cholesterol absorption in hypercholesterolaemic Japanese subjects.Clin. Sci. 2005; 109: 183-188Crossref PubMed Scopus (44) Google Scholar) reported no significant associations with lipids in 100 Japanese patients with hypercholesterolemia. Only two studies in patients with gallstone disease reported significant associations with HDL-C (16Acalovschi M. Ciocan A. Mostean O. Tirziu S. Chiorean E. Keppeler H. Schirin-Sokhan R. Lammert F. Are plasma lipid levels related to ABCG5/ABCG8 polymorphisms? A preliminary study in siblings with gallstones.Eur. J. Intern. Med. 2006; 17: 490-494Abstract Full Text Full Text PDF PubMed Scopus (54) Google Scholar) and triglyceride (TG) concentrations (16Acalovschi M. Ciocan A. Mostean O. Tirziu S. Chiorean E. Keppeler H. Schirin-Sokhan R. Lammert F. Are plasma lipid levels related to ABCG5/ABCG8 polymorphisms? A preliminary study in siblings with gallstones.Eur. J. Intern. Med. 2006; 17: 490-494Abstract Full Text Full Text PDF PubMed Scopus (54) Google Scholar, 18Wang Y. Jiang Z.Y. Fei J. Xin L. Cai Q. Jiang Z.H. Zhu Z.G. Han T.Q. Zhang S.D. ATP binding cassette G8 T400K polymorphism may affect the risk of gallstone disease among Chinese males.Clin. Chim. Acta. 2007; 384: 80-85Crossref PubMed Scopus (55) Google Scholar), but not with total cholesterol and LDL-C, for ABCG5 Gln604Glu and ABCG8 Thr400Lys SNPs, respectively. Previously, our group investigated the aforementioned polymorphisms in different populations in relation to LDL-C concentrations, without assessment of potential associations with HDL-C concentrations (19Santosa S. Demonty I. Lichtenstein A.H. Ordovas J.M. Jones P.J. Single nucleotide polymorphisms in ABCG5 and ABCG8 are associated with changes in cholesterol metabolism during weight loss.J. Lipid Res. 2007; 48: 2607-2613Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar, 20Weggemans R.M. Zock P.L. Tai E.S. Ordovas J.M. Molhuizen H.O. Katan M.B. ATP binding cassette G5 C1950G polymorphism may affect blood cholesterol concentrations in humans.Clin. Genet. 2002; 62: 226-229Crossref PubMed Scopus (55) Google Scholar). Therefore, the effect of ABCG5/G8 SNPs on lipids remains to be elucidated. Among the behavioral factors affecting lipoprotein concentrations, smoking has been consistently reported to decrease HDL-C concentrations. To date, no large population studies have reported interactions between common polymorphisms in ABCG5/G8 genes, lipid concentrations, and smoking. Given the impact of cigarette smoking on HDL-C concentrations and the relevant role of ABCG5/G8 genes in the RCT pathway, the aim of the present study was to assess whether the association between ABCG5/G8 polymorphisms and lipids, particularly with HDL-C concentrations, differs depending on smoking habit. The initially estimated sample size for the Boston Puerto Rican Health Study was ∼1,000 participants who were self-identified as Puerto Ricans living in the greater Boston metropolitan area. Adult Puerto Ricans who live on the US mainland have been identified as a vulnerable group at increased risk for age-related chronic diseases. Health disparities affecting a high percentage of this population include diabetes, hypertension, and prior CHD as main risk factors for the development of atherosclerosis. Participants were recruited from the Greater Boston area and surrounding areas, primarily using year 2000 census data to identify high-density blocks containing Hispanics from the target age range. Randomly selected census blocks with 10 or more Hispanics aged 45 years and older were enumerated door to door. Blocks were visited at least three times and up to six times, on different days of the week, weekends, and at varying times of day in an attempt to reach those who were not at home during initial enumeration. Households with at least one eligible adult were identified, and one participant per qualified household was invited to participate. Complete demographic, biochemical, and genotype data were available in 845 participants (243 men and 602 women, age 58 ± 7 years). Participants aged 45–75 years were recruited from the Boston Center for Population Health and Health Disparities to participate in the Boston Puerto Rican Health Study, a longitudinal cohort study on stress, nutrition, health, and aging (http://hnrcwww.hnrc.tufts.edu/departnebts/labs/prchd/). The design of the study was approved by the Institutional Review Board, and all participants provided informed consent. The detailed design and methodology of the study have been described previously (21Lai C.Q. Tucker K.L. Parnell L.D. Adiconis X. García-Bailo B. Griffith J. Meydani M. Ordovás J.M. PPARGC1A variation associated with DNA damage, diabetes, and cardiovascular diseases: the Boston Puerto Rican Health Study.Diabetes. 2008; 57: 809-816Crossref PubMed Scopus (62) Google Scholar). Information on sociodemographics, health status and history, and behavior was collected by home interview administered by bilingual interviewers. CHD was defined as a positive response to the question “Have you ever been told by a physician that you had a heart attack or angina.” Anthropometric and blood pressure (BP) measurements were collected using standard methods. Weight was measured with a beam balance and height with a fixed stadiometer. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. BP was measured in duplicate at three points during the interview with an oscillometric device (Dinamap Pro Series 100, GE Medical Systems) while participants were seated and had rested for at least 5 min. Reported systolic and diastolic BP values were the mean of the last two measurement points. Smoking and alcohol intake were determined by questionnaire and defined for this analysis as current versus never or past smoking and alcohol use. Physical activity was estimated as a score based on the Paffenbarger questionnaire of the Harvard Alumni Activity Survey (22Lee I.M. Paffenbarger Jr., R.S. Physical activity and stroke incidence: the Harvard Alumni Health Study.Stroke. 1998; 29: 2049-2054Crossref PubMed Scopus (247) Google Scholar). The physical activity score was constructed by weighting time spent in various activities by their respective energy costs. We used a weighted 24 h score of typical daily activity, based on hours spent doing heavy, moderate, light, or sedentary activity as well as sleeping, that was categorized as follows: 0–29, sedentary; 30–39, light activity; 40–49, moderate activity; and >50, heavy activity. Using American Diabetes Association criteria, participants were classified as having diabetes if fasting plasma glucose concentration was ≥126 mg/dl or if use of insulin or diabetes medication was reported. Blood samples were drawn after an overnight fast. Plasma samples were stored and analyzed together. Total cholesterol was measured using a cholesterol esterase-cholesterol oxidase reaction on an Olympus AU400e autoanalyzer (Olympus America, Inc., Melville, NY). HDL-C was measured with the same reaction after precipitation of non-HDL cholesterol with magnesium-dextran and before plasma samples were frozen. LDL-C was measured by use of a homogeneous direct method (LDL Direct Liquid Select Cholesterol Reagent; Equal Diagnostics). TGs were measured by a glycerol-blanked enzymatic method on the Olympus AU400e centrifugal analyzer (Olympus America, Inc.). DNA was extracted from blood samples and purified using commercial Puregene reagents (Gentra Systems) following the manufacturer’s instructions. Three ABCG5 SNPs (i18429G>A, rs4148189; i7892T>C, rs4131229; and Gln604GluC>G, rs6720173) and five ABCG8 SNPs (5U145A>C, rs3806471; Tyr54CysA>G, rs4148211; Asp19HisG>C, rs11887534; i14222A>G, rs6709904; and Thr400LysC>A, rs4148217) 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′ untranslated region (5′-UTR) of ABCG8 but also lies approximately 216 bp upstream of the ABCG5 mRNA start, this SNP sequence was analyzed by MAPPER (23Marinescu V.D. Kohane I.S. Riva A. MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes.BMC Bioinformatics. 2005; 6: 79Crossref PubMed Scopus (178) Google Scholar), 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 poly-pyrimidine tract near splice acceptors. Assessing LD structure at the ABCG5/G8 loci facilitated the selection of tag SNPs representing different LD blocks. In our experience, genotyping more SNPs across such a relatively small genetic region (∼60 kbp) is not likely to add value to the phenotype-genotype association analysis. 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 (24Livak K.J. Allelic discrimination using fluorogenic probes and the 5′ nuclease assay.Genet. Anal. 1999; 14: 143-149Crossref PubMed Scopus (1204) Google Scholar). The description of ABCG5/G8 SNPs, probes, and sequences, as well as ABI assay-on-demand ID, is presented in supplementary Table I. SPSS software (version 15.0) was used for statistical analyses. A logarithmic transformation was applied to measures of plasma TG to normalize the distribution of the data. Data were presented as means ± SD for continuous variables and as frequencies or percentages for categorical variables. Differences in mean values were assessed by ANOVA and unpaired t-tests. Categorical variables were compared by using the Pearson χ2 test or Fisher’s exact test. Potential confounding factors were age, sex, BMI, physical activity, smoking habit (current vs. never and past smokers), alcohol consumption (current vs. never and past drinkers), medications (treatment for hypertension, diabetes, hyperlipidemia, and use of hormone therapy by women), and prior CHD. All analyses were further adjusted by population admixture estimated using the program STRUCTURE 2.2 (see below). Potential interactions between ABCG5/G8 polymorphisms and smoking in determining lipid values (as continuous variables) were tested using the ANOVA test. Two-sided P values <0.05 were considered statistically significant. The pairwise LD between SNPs was estimated as correlation coefficient (R) using the HelixTree software package (Golden Helix). For haplotype analysis, we estimated haplotype frequencies using the expectation-maximization algorithm (25Excoffier L. Slatkin M. Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population.Mol. Biol. Evol. 1995; 12: 921-927PubMed Google Scholar). The major goals of haplotype analysis were to explore the interaction among variants and to increase the power to detect associations between genotypes and phenotypes. In this regard, we selected SNPs on the basis of significant individual associations with the phenotypes to ensure reasonable statistical power. To determine the association between haplotypes and phenotypes, we used haplotype trend regression analysis implemented in HelixTree (26HelixTree Manual. Ver. 5.3.0.at http://goldenhelix.com/HelixTreeManual/compositehaplotypemethodchm.html#x133–75400023.7Date accessed: January 8, 2009Google Scholar). The regression coefficient (β) determines the effect of the haplotype on the phenotype in which inferred haplotypes are considered as predictors, and the aforementioned confounding factors as covariates. Analyses were adjusted for potential confounders and population admixture (see below). P values were further adjusted for multiple tests by a permutation test involving all possible shufflings of all estimated haplotypes versus all phenotypes under a null hypothesis. The permutation P value gave the probability that the significant P value was not observed simply by chance in this study. Population admixture was estimated based on the genotypes of 100 ancestral informative markers (AIMs) using two programs: STRUCTURE 2.2 (27Falush D. Stephens M. Pritchard J.K. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies.Genetics. 2003; 164: 1567-1587Crossref PubMed Google Scholar) and IAE3CI (28Tsai H.J. Choudhry S. Naqvi M. Rodriguez-Cintron W. Burchard E.G. Ziv E. Comparison of three methods to estimate genetic ancestry and control for stratification in genetic association studies among admixed populations.Hum. Genet. 2005; 118: 424-433Crossref PubMed Scopus (90) Google Scholar), with reference to three ancestral populations: European settlers, native Taíno Indians, and West Africans (29Choudhry S. Taub M. Mei R. Rodriguez-Santana J. Rodriguez-Cintron W. Shriver M.D. Ziv E. Risch N.J. Burchard E.G. Genome-wide screen for asthma in Puerto Ricans: evidence for association with 5q23 region.Hum. Genet. 2008; 123: 455-468Crossref PubMed Scopus (65) Google Scholar). The existence of genetic subgroups or substructure in a population may lead to spurious associations. To estimate individual ancestry, several panels of AIMs have been developed for Hispanic populations. For the Puerto Rican population, a panel of 100 AIMs was found to be necessary to properly estimate ancestral proportions by using a combination of simulated and applied data (28Tsai H.J. Choudhry S. Naqvi M. Rodriguez-Cintron W. Burchard E.G. Ziv E. Comparison of three methods to estimate genetic ancestry and control for stratification in genetic association studies among admixed populations.Hum. Genet. 2005; 118: 424-433Crossref PubMed Scopus (90) Google Scholar). Using the estimated admixture of each subject as a covariate, we adjusted for population admixture in all statistical analyses. Characteristics of the participants and genotype frequencies by smoking status are shown in Table 1. Smokers were younger and had lower BMI than nonsmokers. As expected, smokers displayed lower HDL-C and higher TG concentrations than nonsmokers. Smokers were more likely to drink alcohol and less likely to receive treatment for diabetes, hypertension, hyperlipidemia or, for women, hormone replacement therapy, than were nonsmokers. No significant differences in other variables examined were observed. Analysis of these characteristics did not differ significantly by sex (see supplementary Tables II and III). Given the higher prevalence of men who reported smoking compared with women (33% vs. 21%, P < 0.001), all performed statistical analyses were adjusted by sex.TABLE 1Demographic, biochemical, and genotypic characteristics of participants by smoking statusNonsmokers (n = 640)Smokers (n = 205)PAge, years58 ± 7.356 ± 6.9<0.001Body mass index, kg/m232.6 ± 6.529.6 ± 6.3<0.001Systolic blood pressure, mmHg136 ± 18.0135 ± 20.80.723Diastolic blood pressure, mmHg81 ± 10.682 ± 11.30.100Total cholesterol, mg/dl184 ± 42.2183 ± 42.50.452LDL cholesterol, mg/dl108 ± 34.9105 ± 34.70.219HDL cholesterol, mg/dl46 ± 12.443 ± 13.70.026Log (triglyceride, mg/dl)2.14 ± 0.222.19 ± 0.250.009Cigarettes/day011 ± 9<0.001Current drinkers, n (%)208 (33)110 (54)<0.001On diabetes treatment, n (%)248 (39)61 (30)0.020On hypertension treatment, n (%)349 (55)89 (43)0.006On lipid-lowering treatment, n (%)147 (23)30 (15)0.010On hormone treatment, n (%)292 (46)65 (32)<0.001Prior coronary heart disease, n (%)136 (21)32 (16)0.087ABCG5/G8 polymorphisms, n (%) ABCG5_i18429G>AGG353 (55)106 (52)0.421GA+AA287 (45)99 (48) ABCG5_i7892T>CTT302 (47)94 (46)0.748TC+CC338 (53)111 (54) ABCG5_Gln604GluC>GCC374 (58)108 (53)0.168CG+GG266 (42)97 (47) ABCG8_5U145A>CAA300 (47)99 (48)0.748AC+CC340 (53)106 (52) ABCG8_Tyr54CysA>GAA322 (50)100 (49)0.748AG+GG318 (50)105 (51) ABCG8_Asp19HisG>CGG559 (87)178 (87)0.904CG+CC81 (13)27 (13) ABCG8_i14222A>GAA445 (70)134 (65)0.263AG+GG195 (31)71 (35) ABCG8_Thr400LysC>ACC388 (61)126 (62)0.870CA+AA252 (39)79 (39)All values are mean ± SD. Open table in a new tab All values are mean ± SD. For all ABCG5/G8 polymorphisms, there was no departure from Hardy-Weinberg equilibrium (P > 0.05). The pairwise LD in correlation coefficients of all eight SNPs is presented in supplementary Table IV. Given that all pairwise LDs were <0.80, all eight SNPs were retained for further analysis. Because of low genotype frequencies of individuals homozygous for the minor alleles, and because the analysis did not suggest a recessive mode of action, we analyzed all SNPs using two genotype categories. Considering the homogeneity of the effect observed by sex for all variables examined, men and women were pooled together for subsequent analyses. We examined associations between ABCG5/G8 SNPs and lipids (Table 2). For the ABCG5_i7892T>C SNP, C allele carriers had lower HDL-C than TT participants (P = 0.013). Lower HDL-C concentrations were also observed in carriers of the minor alleles at ABCG8 (5U145A>C and Tyr54CysA>G) SNPs (P < 0.001 for both) and homozygotes for the major allele at ABCG8_Thr400LysC>A SNP (P = 0.012). For ABCG8 (Asp19HisG>C and 14222A>G) SNPs, carriers of the minor alleles displayed lower LDL-C concentrations than did those homozygous for the major alleles (P = 0.016 and P = 0.046, respectively). No other significant associations were found between these SNPs and other lipid variables.TABLE 2Associations between ABCG5/G8 SNPs and fasting lipid profilesABCG5_i18429G>AGG (n = 459)GA+AA (n = 386)P Total cholesterol183.1 ± 43.6184.2 ± 40.80.971 LDL cholesterol106.6 ± 35.7107.2 ± 33.90.807 HDL cholesterol45.0 ± 12.845.0 ± 12.50.936 Log triglycerides2.15 ± 0.232.14 ± 0.230.605ABCG5_i7892T>CTT (n = 396)TC+CC (n = 449)P Total cholesterol183.1 ± 43.4184.1 ± 41.30.734 LDL cholesterol105.8 ± 35.9107.8 ± 34.00.391 HDL cholesterol46.1 ± 13.744.0 ± 11.70.013 Log triglycerides2.14 ± 0.242.15 ± 0.220.541ABCG5_Gln604GluC>GCC (n = 482)CG+GG (n = 363)P Total cholesterol183.1 ± 43.7184.2 ± 40.50.698 LDL cholesterol105.8 ± 34.6108.3 ± 35.20.304 HDL cholesterol45.0 ± 12.645.0 ± 12.80.966 Log triglycerides2.15 ± 0.242.15 ± 0.220.970ABCG8_5U145A>CAA (n = 399)AC+CC (n = 446)P Total cholesterol185.2 ± 43.0182.2 ± 41.70.299 LDL cholesterol107.5 ± 35.9106.4 ± 34.00.631 HDL cholesterol46.6 ± 13.843.6 ± 11.4<0.001 Log triglycerides2.14 ± 0.242.15 ± 0.230.392ABCG8_Tyr54CysA>GAA (n = 422)AG+GG (n = 423)P Total cholesterol184.0 ± 41.8183.2 ± 42.80.773 LDL cholesterol106.4 ± 35.3107.4 ± 34.50.661 HDL cholesterol46.6 ± 13.643.4 ± 11.4<0.001 Log triglycerides2.14 ± 0.242.16 ± 0.220.248ABCG8_Asp19HisG>CGG (n = 737)CG+CC (n = 108)P Total cholesterol184.8 ± 42.2175.6 ± 42.10.029 LDL cholesterol108.0 ± 35.399.5 ± 30.80.016 HDL cholesterol45" @default.
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