Influence of genetic combinations on HDL-C levels in a Southern Brazilian population

Abstracts

FUNDAMENTO: Baixos níveis de HDL-c são importantes preditores de doença coronariana, a primeira causa de morte no mundo todo. Muitos fatores afetam os níveis de HDL-c, tais como os polimorfismos de genes que codificam proteínas-chave para a via de transporte reverso de colesterol. OBJETIVO: Investigar a influência de sete polimorfismos dos genes CETP, APOA1, ABCA1 e SCARB1 genes nos níveis de HDL-c em uma população da região sul do Brasil. MÉTODOS: Os polimorfismos foram investigados em uma amostra de 500 indivíduos de descendência europeia, mas os níveis de HDL-c de somente 360 indivíduos foram ajustados para cofatores usando regressão linear múltipla no estudo de associação. A amostra foi dividida em tercis de acordo com os níveis ajustados de HDL-c e frequências de alelos e haplótipos foram comparadas entre o 1º e o 3º tercis dos níveis ajustados de HDL-c. RESULTADOS: Quando as combinações dos alelos de risco foram testadas, a frequência de combinações alélicas em três genes (haplótipo 1 do gene APOA1, variante 2S do gene SCARB1, e alelo B1 do gene CETP) foi significantemente mais alta no tercil inferior dos níveis ajustados de HDL-c (28,3%) do que no tercil superior (14,9%; p=0,008), o que indica que a presença dessas variantes aumentou 2,26 vezes a chance de ter níveis de HDL-C < 39,8 mg/dl. CONCLUSÃO: Espera-se que esses marcadores, quando estudados separadamente, tenham uma pequena influência na característica que está sendo analisada, mas uma influência maior foi detectada quando os marcadores foram estudados em combinação. Em uma população da região sul do Brasil, nossos dados mostraram uma influência significante das combinações das variantes dos genes APOA1, SCARB1 e CETP nos níveis de HDL-c.

Colesterol HDL; população; polimorfismo genético; região sul

FUNDAMENTO: Bajos niveles de HDL-c son importantes predictores de enfermedad coronaria, la primera causa de muerte en todo el mundo. Muchos factores afectan los niveles de HDL-c, tales como los polimorfismos de genes que codifican proteínas-clave para la vía de transporte reverso de colesterol. OBJETIVO: Investigar la influencia de siete polimorfismos de los genes CETP, APOA1, ABCA1 y SCARB1 genes en los niveles de HDL-c en una población de la región sur del Brasil. MÉTODOS: Los polimorfismos fueron investigados en una muestra de 500 individuos de descendencia europea, pero los niveles de HDL-c de solamente 360 individuos fueron ajustados para cofactores usando regresión lineal múltiple en el estudio de asociación. La muestra fue dividida en terciles de acuerdo con los niveles ajustados de HDL-c y frecuencias de alelos y haplotipos fueron comparadas entre el 1º y el 3º terciles de los niveles ajustados de HDL-c. RESULTADOS: Cuando las combinaciones de los alelos de riesgo fueron probadas, la frecuencia de combinaciones alélicas en tres genes (haplotipo 1 del gen APOA1, variante 2S del gen SCARB1, y alelo B1 del gen CETP) fue significativamente más alta en el tercil inferior de los niveles ajustados de HDL-c (28,3%) que en el tercil superior (14,9%; p=0,008), lo que indica que la presencia de esas variantes aumentó 2,26 veces la posibilidad de tener niveles de HDL-C < 39,8 mg/dl. CONCLUSIÓN: Se espera que esos marcadores, cuando sean estudiados separadamente, tengan una pequeña influencia en la característica que está siendo analizada, pero una influencia mayor fue detectada cuando los marcadores fueron estudiados en combinación. En una población de la región sur del Brasil, nuestros datos mostraron una influencia significativa de las combinaciones de las variantes de los genes APOA1, SCARB1 y CETP en los niveles de HDL-c.

Colesterol HDL; población; polimorfismo genético; región sur

ORIGINAL ARTICLE

^IUniversidade Feevale, RS - Brazil

^IICentro Universitário Metodista IPA, RS - Brazil

IIIUniversidade Federal de Ciências da Saúde de Porto Alegre, RS - Brazil

^IVUniversidade Federal do Rio Grande do Sul, Porto Alegre, RS - Brazil

Mailing address

ABSTRACT

BACKGROUND: Low HDL-C levels are important predictors of coronary disease, the first cause of death worldwide. Many factors affect HDL-C levels, such as polymorphisms of genes encoding for key proteins of the reverse cholesterol transport pathway.

OBJECTIVE: To investigate the influence of seven polymorphisms of the CETP, APOA1, ABCA1 and SCARB1 genes on HDL-C levels in a southern Brazilian population.

METHODS: The polymorphisms were investigated in a sample of 500 individuals of European descent, but HDL-C levels from only 360 individuals were adjusted for cofactors using multiple linear regressions in the association study. The sample was divided in tertiles according to adjusted HDL-C levels, and allele and haplotype frequencies were compared between the 1^st and 3^rd tertiles of adjusted HDL-C levels.

RESULTS: When combinations of risk alleles were tested, the frequency of allele combinations in three genes (haplotype 1 of APOA1 gene, variant 2S of SCARB1 gene, and allele B1 of CETP gene) was significantly higher in the lower tertile of adjusted HDL-C (28.3%) than in the upper tertile (14.9%; p=0.008), which indicated that the presence of these variants increased 2.26 times the chances of having HDL-C levels below 39.8 mg/dl.

CONCLUSION: These markers, when studied separately, are expected to have a small influence on the characteristic under analysis, but greater influence was detected when the markers were studied in combination. In a population of southern Brazilians, our data showed a significant influence of variant combinations of APOA1, SCARB1 and CETP genes on HDL-c levels.

Key words: Cholesterol, HDL/genetics; population/genetics; polymorphism, genetic; south region/Brazil.

Introduction

Epidemiologic studies have provided strong evidence that low concentrations of HDL cholesterol (HDL-C) are associated with increased risk of coronary artery disease (CAD)¹. Therefore, the causes of low HDL-C levels have been intensively investigated, and genetic studies have focused on genes encoding for proteins that play important roles in the metabolism of HDL-C or in reverse cholesterol transport (RCT). Proteins in these groups include: apolipoproteins, such as A-I, A-II and E; enzymes, such as CETP, LCAT and LIPC; and membrane receptors, such as ABCA1 and SCARB1. When associated, these candidate genes are substantially polymorphic, and many studies have investigated the association of these polymorphisms with the risk of changes in lipid profiles. However, it remains unclear how these polymorphisms affect lipid profiles and whether this influence is found in different populations; this has not been studied in any South American population to this date. This study investigated the influence of seven polymorphisms of the CETP, APOA1, ABCA1 and SCARB1 genes on HDL-C levels in a southern Brazilian population.

Methods

Subjects

The population sample consisted of 500 individuals of European descent, as previously described². The volunteers included in this sample were enrolled at two clinical centers of the Universidade Federal do Rio Grande do Sul among individuals referred from several city health centers for routine blood tests. A questionnaire was used to collect data on drug intake and lifestyle variables, such as smoking, physical activity, alcohol consumption, oral contraceptive use, menopause, and anthropometric measurements. Smoking status was classified as non-smoker or current smoker; ex-smokers were excluded. All individuals gave their informed consent before inclusion in the study. Exclusion criteria were: pregnancy; secondary hyperlipidemia due to kidney, liver or thyroid disease; and diabetes or fasting blood glucose levels higher than 7 mmol/l³. Women on hormone replacement therapy and individuals taking lipid-lowering medications, beta-blockers or anti-inflammatory drugs were also excluded.

Participants were examined in the morning after a 12-hour fast. Weight was measured in subjects without shoes and with light clothing. Body height was measured without shoes, with heels placed together and the back to the wall. Body mass index (BMI) was calculated as weight/height² (kg/m²). Waist circumference was measured at the smallest horizontal circumference between the 12^th rib and the iliac crest.

Laboratory analyses

Blood samples were collected after a 12-hour fast. Total cholesterol, HDL cholesterol and triglycerides were measured at each Clinical Center using standard enzymatic methods⁴. LDL cholesterol levels were calculated using Friedwald's formula⁵. Glucose levels were also measured to ensure that no individuals with diabetes were included in the study. A salting-out procedure was used to extract DNA from blood samples⁶. DNA was amplified using PCR and oligonucleotide primers under the conditions previously described for CETP⁷, APOA1⁸, ABCA1⁹, and SCARB1¹⁰. The amplification products were subsequently digested with the following restriction enzymes under conditions recommended by the manufacturer: Taq I (CETP - TaqIB), Msp I (APOA1 - g-75a and c+83t), Xag I (ABCA1 - Arg219Lys), Alu I (SCARB1 - Gly2Ser), ApaI (SCARB1 - c780t) and Hae III (SCARB1 - c1050t). Genotypes were determined after electrophoresis on agarose gels containing ethidium bromide, and a 100 bp ladder was used to score the band sizes.

Statistical analyses

Allele frequencies were estimated by gene counting. A c² test for goodness of fit was used to check whether allele frequencies agreed with those expected according to Hardy-Weinberg equilibrium. Maximum-likelihood haplotype frequencies and linkage disequilibrium were estimated using the Arlequin 2000 software package¹¹. SCARB1 and APOA1 gene haplotypes were determined using the method described by Long¹².

Only 360 subjects were analyzed in the association study between SNPs and HDL-C levels because the full sets of data for the other individuals were not available. Multiple linear regressions were performed to adjust HDL-C levels using the backward stepwise method. Covariates entered in the first model were gender, age, waist, smoking, alcohol consumption, BMI, triglyceride levels and menopause, as well as all possible interaction terms. In the final model used for HDL-C adjustment, significant covariates that remained in the model were gender, age, BMI, triglyceride levels, triglyceride levels x age, post-menopausal status and triglyceride levels x menopause for women. Triglyceride levels were log-transformed to remove skewness. The sample was divided according to HDL-C tertiles, and heterogeneity between groups in contingency tables was tested using the c² or the Fisher exact test. Logistic regressions were performed to obtain the odds ratio for each genetic variant or combination of variants. All analyses were made with the Statistical Package for the Social Sciences (SPSS) 11.0 for Windows.

Results

Table 1 shows the allele and genotype frequencies for the CETP, ABCA1 and SCARB1 genes, as well as the haplotype frequencies for the APOA1 gene. The haplotype frequencies of the SCARB1 gene are not shown because they did not influence HDL-C levels in any posterior analysis. All genotype frequencies are in Hardy-Weinberg equilibrium (data not shown). Strong linkage disequilibrium between the two variants of the APOA1 gene was detected (D'= -0.84, c²=10.04, p<0.001), and haplotype 1 (-75g/+83c) was the most common. For the SCARB1 gene, allele 780t was the most frequently found with 1050c, which indicated a significant disequilibrium (D'= - 0.44, c²=5.97, p=0.015). Gly2Ser was not linked to any of the other two variants investigated in this gene.

The prevalence of each haplotype of the APOA1 gene and the allele frequencies of the other variants under study were compared between the 1^st and 3^rd HDL-C tertiles adjusted for non-genetic variables (Table 2), and the clinical characteristics according to these groups are shown in Table 3. Although the difference was not statistically significant, haplotype 1 of the APOA1 gene, the TaqIB*1 allele of the CETP gene, and the 2Ser allele of the SCARB1 gene were more frequent in the 1^st tertile (p=0.18, p=0.16 and p=0.29). When allele and haplotype frequencies were again compared between the first and the fourth HDL-C quartiles, the same trends were found, except for the Gly2Ser SNP of the SCARB1 gene, that had a smaller p value (p=0.14).

Based on these results, risk alleles for low HDL-C levels were selected from the statistical comparisons that yielded values of p lower than 0.25. These variants, labeled "risk variants", are shown in Table 4, which also shows how the carriers of these variants were analyzed later: whether as homozygous, or as heterozygous and homozygous carriers.

The frequency of individuals with genetic risk combinations for low HDL-C levels was compared between the 1^st and 3^rd HDL-C tertiles (Table 5). First, the carriers of combinations of two deleterious variants were evaluated, as shown in Table 4, and then the frequency of carriers of combinations of three deleterious variants was compared. These comparisons showed that being a carrier of risk variants of the APOA1 and CETP genes increased 2.4 times the chance of having HDL-C levels below 39.8 mg/dl (p=0.005). When in addition to this combination the participant also carried the risk allele of the SCARB1 gene, this chance was 2.26 times greater (p=0.008). Although this gene did not seem to change the risk of having low HDL-C levels, this may be a result of the fact that only a small number of carriers had this triple combination. When SCARB1 was analyzed in combinations with CETP and APOA1, borderline results were found (OR=1.6, p=0.08), which was not observed when the CETP and APOA1 genes were evaluated separately (Table 2).

Discussion

Allele frequencies found in this study are mostly similar to those detected in studies with different populations. For the CETP gene, the prevalence of 35.7% for the rare allele is slightly lower than that detected in populations of exclusive European descent, which was about 40%^13-17. In the ABCA1 gene, the values found for the Arg219Lys SNP were within the range of variation found in other populations of European descent: from 25% for Germans to 38.5% for North Americans^9,18-21. Similar results were found for the APOA1 gene when compared with values from other populations of European descent²². Our data on the SCARB1 gene are comparable with those reported in the few studies published to this date, but the 17% allele frequency found in our study for intron 5 SNP and the 29.3% for the exon 8 variant are slightly different from the frequencies found by Richard et al²³ for North Americans (9% and 49%) and by Acton et al¹⁰ for a Spanish population (10.5% and 43.8%). Our analysis of the exon 1 variant showed that the frequency of our rare allele was slightly lower than that found in a Spanish population¹⁰. These differences may reflect differences in the ethnic composition of the populations under comparison.

The pattern of linkage disequilibrium between the variants of the APOA1 gene was similar to those found in the literature²⁴. In our population, no linkage disequilibrium was found between exon 1 SNP of the SCARB1 gene and the other SNPs under study according to findings reported for other populations^10,25,26. However, the pattern of linkage disequilibrium between the intron 5 and exon 8 SNPs differ depending on the population under study: in Spanish¹⁰ and North American²⁵ populations, the intron 5 rare variant was linked to the exon 8 common variant, which was the same that was found in our population. However, in an Austrian sample and in a different population sample in the United States, the two common alleles were linked^23,26.

When evaluated separately, our data did not show any significant influence of the polymorphisms under study on HDL-C levels (Table 4). Three of the variants under study had allele frequencies that were very similar in the comparison between the 1^st and 3^rd tertiles. Two of them are SCARB1 gene polymorphisms, found at intron 5 and exon 8, and the scarce data about them in the literature have not demonstrated their influence on HDL-C levels^10,25-28 nor indicated any associations with sex and use of hormones^23,28,29. Moreover, no influence of the Arg219Lys variant on the ABCA1 gene was found in several studies with different populations^20,21,30.

We selected polymorphisms of the genes CETP, APOA1 and SCARB1 for a combined evaluation. Although some authors detected isolated effects of these genes on HDL-C levels, several other studies found that the influence of these genes is too weak to be detected when they are evaluated separately^13,26,31,32.

These differences in the magnitude of a genetic influence may reflect differences between the genetic composition of each population, as well as environmental differences between them. The evaluation of combined gene effects on HDL-C levels is rare in the literature, which makes comparison of data difficult. The evaluation of two or three risk variants revealed a significant influence of variants of the APOA1, CETP and SCARB1 genes on HDL-C levels. This finding is in agreement with studies of APOA1 and CETP genes in other populations^8,33-37. However, for the Gly2Ser SNP of the SCARB1 gene, the few studies conducted so far associated the presence of the 2Gly allele with a decrease in HDL-C levels. In our population, the combination of the 2Ser allele and variants of APOA1 and CETP genes was more frequent in the participants with lower HDL-C levels (Table 5). Two other authors^25,27 did not detect any significant influence of this allele on HDL-C levels. Therefore, the role of this polymorphism should be further investigated, and we cannot rule out the possibility that it acts as a marker of another variant not yet studied, although it is an exchange of amino acids. The confirmation of this hypothesis would explain the differences between our data and findings reported by Acton et al¹⁰.

The physiologic roles of these three proteins are closely associated: CETP is a key enzyme in HDL metabolism³⁸, whereas SR-BI and apoAI act as receptor and ligand of HDL-C^39,40. Therefore, the interaction detected may be understood when analyzed in association with HDL-C metabolism and absorption path.

This study detected genetic influences on HDL-C levels, but it had some limitations. Major problems were the small sample size for the association study and the fact that data were missing for 140 patients. Therefore, less evident genetic influences, which can only be detected when a larger sample size is used, might not have been detected. Moreover, a prospective study of the influence of allelic combinations should be conducted to confirm the clinical impact of these genes on the lipid profile of our population.

The direction of the genetic influence on HDL-C levels seems to be, in most cases, independent of genetic background or environmental variations, but the magnitude of this effect differs greatly between different populations. Any multifactorial characteristic is influenced by several loci; therefore, analyses of interactions between genes should be conducted, even if their separate effects cannot be detected, because that may be the only way to evaluate their actual genetic role in each population.

Acknowledgements

We thank Maria Perpetua de Oliveira Pinto for technical assistance, and Marcel Arsand and Fabiano Roldão for help in sample collection. Financial support was provided by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil), Programa de Apoio a Nucleos de Excelência (PRONEX, Brazil) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS, Brazil).

Potential Conflict of Interest

No potential conflict of interest relevant to this article was reported.

Sources of Funding

This study was funded by CNPq, CAPES, PRONEX e FAPERGS.

Study Association

This article is part of the thesis of doctoral submitted by Fabiana Michelsen de Andrade, from Universidade Federal do Rio Grande do Sul.

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Influence of genetic combinations on HDL-C levels in a Southern Brazilian population

Fabiana Michelsen de Andrade^I; Marilu Fiegenbaum^II,III; Silvana de Almeida^III; Mara Helena Hutz^IV

Publication Dates

• Publication in this collection
  08 Sept 2010
• Date of issue
  Oct 2010

History

• Reviewed
  03 Mar 2010
• Received
  21 Aug 2009
• Accepted
  12 Apr 2010