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A vitamin D pathway gene–gene interaction affects low-density lipoprotein cholesterol levels
A vitamin D pathway gene-gene interaction affects low-density lipoprotein cholesterol levels Nath´alia Grave, Luciana Tovo-Rodrigues, Jana´ına da Silveira, Diego Luiz Rovaris, Simone Morelo Dal Bosco, Verˆonica Contini, J´ulia Pasqualini Genro PII: DOI: Reference:
S0955-2863(16)30066-3 doi: 10.1016/j.jnutbio.2016.08.002 JNB 7620
To appear in:
The Journal of Nutritional Biochemistry
Received date: Revised date: Accepted date:
9 May 2016 1 August 2016 10 August 2016
Please cite this article as: Grave Nath´alia, Tovo-Rodrigues Luciana, da Silveira Jana´ına, Rovaris Diego Luiz, Bosco Simone Morelo Dal, Contini Verˆonica, Genro J´ ulia Pasqualini, A vitamin D pathway gene-gene interaction affects low-density lipoprotein cholesterol levels, The Journal of Nutritional Biochemistry (2016), doi: 10.1016/j.jnutbio.2016.08.002
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ACCEPTED MANUSCRIPT A VITAMIN D PATHWAY GENE-GENE INTERACTION AFFECTS LOW-
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DENSITY LIPOPROTEIN CHOLESTEROL LEVELS
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Nathália Gravea, Luciana Tovo-Rodriguesb, Janaína da Silveiraa, Diego Luiz Rovarisc, Simone Morelo Dal Boscoa, Verônica Continia,d, Júlia Pasqualini
a
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Genroa,e.
Programa de Pós-Graduação em Biotecnologia, Centro Universitário
UNIVATES, 95.900-000, Lajeado, RS, Brasil. b
Programa de Pós-Graduação em Epidemiologia, Universidade Federal de
c
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Pelotas, 96.020-220, Pelotas, RS, Brasil.
Departamento de Genética, Instituto de Biociências, Universidade Federal
do Rio Grande do Sul, 91.501-970, Porto Alegre, RS, Brasil. Setor de Genética e Biologia Molecular do Museu de Ciências Naturais,
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d
Centro de Ciências Biológicas e da Saúde, Centro Universitário UNIVATES, e
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95.900-000, Lajeado, RS, Brasil. Programa de Pós-Graduação em Biociências, Universidade Federal de
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Ciências da Saúde de Porto Alegre, 90.050-170, Porto Alegre, RS, Brasil.
Corresponding author: Dra. Júlia Pasqualini Genro Departamento de Ciências Básicas da Saúde, Universidade Federal de Ciências da Saúde de Porto Alegre. Endereço: Rua Sarmento Leite 245, sala 403. CEP: 90050-170- Porto Alegre, RS, Brasil. Phone: +55-51-33088373 Email: [email protected]
ACCEPTED MANUSCRIPT Abstract Much evidence suggests an association between vitamin D deficiency and chronic diseases such as obesity and dyslipidemia. Although genetic factors
the
relationship
between
vitamin
D
related-genes
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investigated
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play an important role in the etiology of these diseases, only a few studies have and
anthropometric and lipid profiles. The aim of this study was to investigate the
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association of three vitamin D-related genes with anthropometric and lipid parameters in 542 adult individuals. We analyzed the rs2228570 polymorphism in the vitamin D receptor gene (VDR), rs2134095 in the retinoid X receptor
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gamma gene (RXRG), and rs7041 in the vitamin D-binding protein gene (GC). Polymorphisms were genotyped by TaqMan™ allelic discrimination. Gene-gene interactions were evaluated by the general linear model. The functionality of the polymorphisms was investigated using the following predictors and databases: Sorting
Intolerant
from
Tolerant
(SIFT),
Polymorphism Phenotyping
v2
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(PolyPhen-2), and Human Splicing Finder 3. We identified a significant effect of the interaction between RXRG (rs2134095) and GC (rs7041) on low-density
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lipoprotein cholesterol (LDL-c) levels (P = 0.005). Furthermore, our in silico analysis suggested a functional role for both variants in the regulation of the
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gene products. Our results suggest that the vitamin D related-genes, RXRG and GC, affect LDL-c levels. These findings are in agreement with other studies that
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consistently associate vitamin D and lipid profile. Together our results corroborate the idea that analyzing gene-gene interaction would be helpful to clarify the genetic component of lipid profile. Keywords: Vitamin D receptor; Retinoid X receptor; Vitamin D-binding protein; Polymorphisms; Lipid profile; Gene-gene interaction
ACCEPTED MANUSCRIPT 1. Introduction Many studies have associated vitamin D deficiency with an increased risk of developing chronic diseases [1-4]. Among these diseases, obesity has been
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investigated extensively, mainly because it has reached epidemic proportions
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around the world. Studies investigating the relation between obesity and hypovitaminosis D have increased substantially in recent years [5]. Several
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reports have demonstrated that blood vitamin D levels are decreased in obese individuals [6-8]. Likewise, plasma levels of this vitamin are inversely correlated to an atherogenic lipid profile [9-11].
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Vitamin D, a soluble steroid hormone, can be obtained from food sources as vitamin D3 (cholecalciferol) or converted from 7-dehydrocholesterol in the skin upon ultraviolet B (UVB) radiation exposure. After being absorbed or synthesized, vitamin D3 binds to the vitamin D-binding protein (DBP) and is transported through the bloodstream to the liver, where it is converted to
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25(OH)D (calcidiol), the main circulating form of vitamin D [12, 13]. Calcidiol is biologically inactive, and is converted, mainly in the kidneys, to the biologically
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active form, 1,25(OH)2D (calcitriol), which binds to the vitamin D receptor (VDR) [12, 14]. Then, VDR heterodimerizes with the retinoid X receptor gamma
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(RXRG). This complex binds to DNA at the vitamin D response element (VDRE), controlling the expression of many genes related to skeletal muscle
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and calcium metabolism, and cell proliferation [12, 15]. Although genetic factors are important in the etiology of obesity and its associated phenotypes, the role of the vitamin D pathway gene variants are poorly investigated, and anthropometric measurements and lipid profile analyses have not been carried out. Most previous studies have focused on the vitamin D receptor gene (VDR) and obesity [16-18], although there are some reports of associations between the retinoid X receptor gamma gene (RXRG) and lipid and anthropometric parameters [19]. The vitamin D-binding protein gene (GC), which codes for DBP, has been associated with body-fat percentage [20] and obesity [13]. Several other studies, including two GenomeWide Association Studies (GWAS), have detected associations between GC polymorphisms and circulating levels of vitamin D [13, 21-24].
ACCEPTED MANUSCRIPT Considering the importance of the VDR, RXRG, and DBP proteins in the vitamin D pathway, and the association of this vitamin with obesity phenotypes, in this study, we sought to (1) investigate the association between
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polymorphisms in VDR (rs2228570), RXRG (rs2134095), and GC (rs7041) with
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lipid profile and anthropometric measurements in adult individuals; (2) test the effect of gene-gene interactions on these measurements; and (3) evaluate, in
2.1. Participants
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2. Materials and Methods
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silico, the functionality of these polymorphisms.
The sample was composed of 542 white Brazilian European-descent individuals (median age = 24, minimum = 18, maximum = 66) recruited at
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Centro Universitário UNIVATES, an institution located in Southern Brazil. Therefore, this sample group is representative of an academic community
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(comprised by undergraduate and graduate students, employees, and professors). Since this region is characterized by a German and Italian
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colonization, genetic admixture analyses have shown that the proportion of European ancestry in this location ranges from 90 to 99.9% [25]. It is
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noteworthy that the vitamin D pathway-related genetic variants selected in this study present marked differences depending on the ethnic group [26], which would affect the results if an ethnically nonhomogeneous sample were selected. The exclusion criteria were self-reported nephropathy, coagulation disorder, infectious contagious diseases, adrenal gland disorders, pregnancy, cancer, and mental health disease that would prevent understanding of the informed consent form. The Research Ethics Committee of the Centro Universitário UNIVATES approved this study, and all participants signed an informed consent form. 2.2. Anthropometric measurements Trained professionals collected anthropometric measurements of weight, height, and waist circumference. Weight was measured on a Welmy® R-110
ACCEPTED MANUSCRIPT precision scale on a flat surface. The individuals were instructed to wear shorts and scrubs to minimize excessive clothing weight. Height was measured on barefooted individuals standing against a wall (heels, gluteus, back, and head
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touching the wall) by using a Wiso® stadiometer. Body mass index (BMI) was
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calculated by dividing the individual’s weight (in kilograms) by the square of height (in meters). Waist measurement was performed using a Cescorf® measuring tape, following the World Health Organization protocols [27]. The percentage
was
obtained
by
using
bioelectrical
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body-fat
impedance
(Biodynamics), with the individuals adhering to the following recommendations:
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fasting, no caffeine intake for 24 h prior to the test, no alcohol intake for 48 h prior to the test, empty bladder, no physical activity for 24 h prior to the test, and no menses on the day of the exam. 2.3. Lipid profile
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Blood was collected from the individuals after 12 h of fasting. Total cholesterol (TC), triglycerides, and high-density lipoprotein cholesterol (HDL-c)
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were measured by the enzymatic colorimetric method, according to the Bioclin® commercial kit protocol in an automatic BS-120 system (Mindray®) at the
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institutional clinical analysis laboratory. Measurement reliability was confirmed by commercial normal and pathologic controls. Low-density lipoprotein cholesterol (LDL-c) was estimated using the Friedewald equation (LDL-c = TC –
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HDL-c – [TG/5]) [28].
2.4. DNA extraction and genotyping Genomic DNA was extracted from total blood by using the salting out method adapted from Lahiri & Nurnberger [29]. Extracted DNA was quantified by spectrophotometry in an L-Quant® equipment and stored at −4 ºC. Polymorphisms rs2228570 (VDR), rs2134095 (RXRG), and rs7041 (GC) were genotyped by TaqMan™ allelic discrimination (Applied Biosystems, Foster City, CA), using the polymerase chain reaction (PCR) in a StepOne real time system (Applied Biosystems), according to the manufacturer’s protocols. 2.5. Statistical analysis
ACCEPTED MANUSCRIPT Allele frequencies were calculated, and Hardy-Weinberg equilibrium was tested using Pearson’s chi-squared test. Since all outcomes were continuous variables, analysis of variance (ANOVA) or Kruskal-Wallis test were used
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depending on the data distribution (normal or non-normal). Gene-gene
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interactions were evaluated using the general linear model (two-way ANOVA or two-way analysis of covariance [ANCOVA]). For this analysis, triglycerides were log-transformed to obtain normally distributed values. Effect sizes were
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estimated using eta2, a measure analogous to R2. The partial eta2 is a measure in which the effects of other independent variables and interactions are
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partialled out. All analyses were carried out using the Statistical Package for Social Sciences, version 18.0. An alpha of 0.008 (α Bonferroni = 0.05/6) was used, because
three
independent
polymorphisms
and
two
variable
groups
(anthropometric and lipid measurements) were considered in the study. Age, sex, physical activity, medication use, and daily caloric intake were
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considered possible confounding factors in the association analyses. Tobacco smoking was not included in these analyses since the prevalence of smoking was only 4.1% (Table 1). The inclusion of covariates was performed using a
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statistical definition (association with both the factor and outcome for P ≤ 0.2)
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[30]. This approach was adopted to produce more parsimonious and generalizable models [31]. Age and gender were considered potential confounding variables in models including GC rs7041 and VDR rs2228570,
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respectively. Physical activity was considered a possible confounder in the model evaluating the effects of RXRG rs2134095 and GC rs7041 on body fat. Supplementary Table 1 shows the results of the confounding analyses. Sample size calculations were performed using QUANTO Software, version 1.2.4 [32]. Considering the small effect sizes expected for SNPs in complex traits, the gene only and interaction analyses using the continuous outcomes investigated in this study require sample sizes ranging from 250 (interaction) to 519 (gene only) to achieve a power of at least 80%. Detailed description of the parameters set in these estimations is provided in the Supplementary Material. 2.6. Functional prediction of polymorphisms
ACCEPTED MANUSCRIPT In order to investigate the putative functional effect of the polymorphisms, their impact at the regulatory and protein level were evaluated using the Sorting Intolerant
from
Tolerant
(SIFT)
[33-36],
Polymorphism Phenotyping
v2
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(PolyPhen-2) [37], and Human Splicing Finder 3 [38] programs.
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3. Results
The clinical characteristics and allelic and genotypic frequencies are
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shown in Table 1. Genotypic frequencies of the rs2228570 (VDR), rs2134095 (RXRG), and rs7041 (GC) polymorphisms are in agreement with HardyWeinberg equilibrium.
A comparison of the anthropometric and lipid parameters between the genotypes is shown in Table 2. No significant effect was observed for the
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analyzed measurements for any of the polymorphisms investigated. 3.1 Gene-gene interaction analysis
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A significant interaction was observed between the RXRG and GC polymorphisms when considering LDL-c as the outcome (P = 0.005, eta2 =
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0.029; Table 3). In the presence of the TT genotype of rs7041 (GC), individuals with the CC genotype of rs2134095 (RXRG) showed the highest levels of LDL-
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c. The same was not true for the TT genotype of rs2134095 (RXRG; Figure 1). A similar trend was observed for total cholesterol (P = 0.027, eta2 = 0.021; Table 3).
3.2 Functional prediction of the polymorphisms Because
of
the
gene-gene
interactions
identified
above,
the
polymorphisms rs2134095 (RXRG) and rs7041 (GC) were further explored in terms of a putative alteration in protein functionality. These variants code for synonymous
and
missense
substitutions,
respectively.
None
of
the
polymorphisms resulted in a pathological alteration in the protein structure. The missense aspartic acid to glutamic acid substitution in position 451 (ENST00000504199) and in position 432 (ENST00000273951) of DBP (rs7041) were described as benign (PolyPhen-2) and tolerated (SIFT). On the other
ACCEPTED MANUSCRIPT hand, both polymorphisms seem to have an impact in splicing regulation according to Human Splicing Finder 3. The G allele of the rs7041 polymorphism induces the creation of a
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potential splicing acceptor site (score variation between alleles: 64.79%), and
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the disruption of a potential exonic splicing enhancer when compared to the T allele (Supplementary Figure 1A). Alteration of the C allele for the T allele of rs2134095
leads
to
the
disruption
of
an
exonic
splicing
enhancer
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(Supplementary Figure 1B). Hence, it is reasonable to hypothesize that both variants have an effect on the splicing of the messenger RNA coded by GC and
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RXRG, thus altering the function of the gene product.
4. Discussion
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The aim of this study was to investigate the association between the rs2228570 (VDR), rs2134095 (RXRG), and rs7041 (GC) polymorphisms with the anthropometric and lipid parameters related to obesity. No significant main
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effect of the polymorphisms was observed for the evaluated outcomes. On the other hand, gene-gene interaction analyses indicated a significant interaction
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between the RXRG and GC gene variants and LDL-c levels. Moreover, our in silico analysis suggested a putative functional role for both variants in gene
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product regulation. To the best of our knowledge, this is the first study to analyze this gene-gene interaction considering these parameters. The literature addressing gene variants related to metabolic disease and vitamin D pathway is scarce and shows inconsistent findings. Regarding VDR, two studies did not find any association between gene variants and obesity phenotypes [39, 40]. In contrast, Filus et al. (2008) found associations between VDR polymorphisms and BMI, HDL-c, and fasting insulin [41]. It is important to mention that, although we could not identify any association between the rs2228570 variant and the obesity-related outcomes investigated in this study we cannot reject the hypothesis that this gene might play a role in obesity. Regarding RXRG and GC, we were able to identify a significant effect of the RXRG-GC interaction on LDL-c levels. Although few studies have investigated the association between GC and lipid profile, polymorphisms in this
ACCEPTED MANUSCRIPT gene have been consistently associated with circulating levels of vitamin D [2123, 42], and, in turn, vitamin D levels are linked with lipid profile in adults [43]. Interestingly, RXRG, a key gene in the vitamin D pathway, is located on the
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1q21-q23 chromosomal region, which is closely related to familial combined
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hyperlipidemia (FCHL). FCHL is the most common lipid metabolism atherogenic disorder, and is characterized by several hyperlipidemic phenotypes, where high LDL-c levels are detected [44, 45, 46]. Therefore, this evidence from the
and lipid profile strengthens our findings.
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literature linking vitamin D pathway-related genes, vitamin D circulating levels,
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Several studies have shown a positive correlation between vitamin D levels and HDL-c [47-52] and, although fewer studies have evaluated LDL-c levels, most of them have demonstrated an inverse correlation between vitamin D circulating levels and this lipid fraction [53-57]. Still, it is important to mention that 25(OH)D levels have been inversely correlated to cardiovascular disease
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risk and mortality [58-60]. In a prospective study that included over 40,000 individuals, Anderson et al. (2010) identified an association between low levels
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of 25(OH)D and a variety of risk factors for cardiovascular disease, such as type 2 diabetes mellitus, hypertension, hyperlipidemia, congestive heart failure, and
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stroke [61]. It is possible that the association between cardiovascular disease and vitamin D is mediated by the lipid profile, and genetic variation in Vitamin Drelated genes could play a role in this underlying trait. However, among the
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studies that investigated the outcomes related to coronary disease, not all analyzed the lipid profiles of the participants, and, in general, did not discuss the importance of the vitamin D pathway and its genetic variability in these measurements. Although the biological mechanism responsible for the relationship between vitamin D and cholesterol is unclear, it is important to mention here that both molecules have the same precursor, 7-dehydrocholesterol (7DHC). 7DHC can be converted to pre-vitamin D under the effect of UVB radiation, and it can serve as a substrate for 7-dehydrocholesterol reductase (DHCR7), which catalyzes cholesterol synthesis [62, 63]. Additionally, a cell-culture study using human fibroblasts showed that vitamin D inhibits 3-hydroxy-3-methylglutaryl coenzyme-A reductase (HMG-CoA), a key enzyme in the cholesterol synthesis pathway [64].
ACCEPTED MANUSCRIPT It is also important to highlight that we investigated the potential functionality of the polymorphisms associated with LDL-c, and observed a putative effect of the variants. Substitution of an aspartic acid by a glutamic acid
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in position 432 of DBP, because of rs7041 (GC), was previously hypothesized
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to influence the affinity between 25(OH)D and DBP interaction [65]. Although we cannot rule out the effect on affinity, our results suggest that the interaction between the two molecules could be impaired through alterations in GC splicing
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regulation induced by the polymorphism. Similarly, splicing alterations mediated by rs2134095 (RXRG) could negatively affect the vitamin D response pathway.
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It is well known that chronic metabolic diseases have a multifactorial and complex inheritance. Therefore, it is not surprising that genetic studies have provided conflicting results. Considering that these diseases have a large genetic contribution, and that, so far, the variables identified represent only a small percentage of disease heritability, some authors have discussed where
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the “missing heritability” could be in our genome [66]. In our study, we chose the gene-gene interaction approach, which is considered to account for this
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paradox. Our results indicate that this strategy might be an option for future studies, especially those considering hypothesis-driven gene-gene interactions
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[67].
Our results should be interpreted considering some limitations. First, the circulating levels of 25(OH)D were not measured. Second, our sample size is
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relatively small; however, it allowed us to achieve enough statistical power to perform our analyses. Moreover, these data can be incorporated in future metaanalyses. Third, our sample mainly comprised healthy young adults, and therefore, our study design did not allow the investigation of direct effects on disease. Moreover, because the most of the previous genetic studies have focused on clinical presentation of disease and not on the underlying traits, the evaluation of quantitative outcomes as anthropometric and lipid measures in a sample composed by predominantly healthy individuals can expand our knowledge
of
the
pathophysiologic
pathways
underlying
obesity
and
dyslipidemia. Finally, although our in silico analysis suggests a putative functional effect on the dysregulation of the GC and RXRG splicing mechanisms, further in vitro and in vivo studies should be performed to confirm our hypothesis.
ACCEPTED MANUSCRIPT Our findings demonstrated that the interaction between two vitamin D pathway genes polymorphisms, rs2134095 (RXRG) and rs7041 (GC), has a significant effect on LDL-c levels. These findings are in agreement with other
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studies that consistently associate vitamin D and lipid profile. Together our
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results corroborate the idea that gene-gene interaction would be helpful to
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clarify the genetic component of lipid profile.
Acknowledgements
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The authors would like to thank the Ambulatório de Nutrição from the Centro Universitário UNIVATES, for help with the sample collection, and
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FAPERGS and FUVATES, for financial support.
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[41] Filus A, Trzmiel A, Kuliczkowska-Plaksej J, Tworowska U, Jedrzejuk D, Milewicz A, et al. Relationship between vitamin D receptor BsmI and FokI polymorphisms and anthropometric and biochemical parameters describing metabolic syndrome. Aging Male 2008; 11:134–39. [42] Nissen J, Rasmussen LB, Ravn-Haren G, Andersen EW, Hansen B, Andersen R, et al. Common Variants in CYP2R1 and GC Genes Predict Vitamin D Concentrations in Healthy Danish Children and Adults. PLoS ONE 2014; 9:e89907. [43] Jorde R; Grimnes G. Vitamin D and metabolic health with special reference to the effect of vitamin D on serum lipids. Prog Lipid Res 2011; 50:303-12. [44] Sentinelli F, Minicocci I, Montali A, Nanni L, Romeo S, Incani M, et al. Association of RXR-Gamma Gene Variants with Familial Combined Hyperlipidemia: Genotype and Haplotype Analysis. J Lipids 2013; 2013:517943. [45] Pei W, Baron H, Muller-Myhsok B, Knoblauch H, Al-Yahyaee SA, Hui R, et al. Support for linkage of familial combined hyperlipidemia to chromosome 1q21–q23 in Chinese and German families. Clin Genet 2000; 57:29–34.
ACCEPTED MANUSCRIPT [46] Veerkamp MJ, Graaf J de, Bredie SJ, Hendriks JC, Demacker PN, Stalenhoef AF. Diagnosis of familial combined hyperlipidemia based on lipid phenotype expression in 32 families: results of a 5-year follow-up study. Arterioscl Throm Vas 2002; 22:274–82.
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[47] Maki KC, Rubin MR, Wong LG, McManus JF, Jensen CD, Marshall JW, et al. Serum 25-hydroxyvitamin D is indepen dently associated with high density lipoprotein cholesterol and the metabolic syndrome in men and women. J Clin Lipidol 2009; 3:289–96.
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[48] Kazlauskaite R, Powell LH, Mandapakala C, Cursio JF, Avery EF, Calvin J. Vitamin D is associated with atheroprotective high-density lipoprotein profile in postmenopausal women. J Clin Lipidol 2010; 4:113-19.
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[49] Delvin EE, Lambert M, Levy E, O’Loughlin J, Mark S, Gray-Donald K, et al. Vitamin D status is modestly associated with glycemia and indicators of lipid metabolism in French-Canadian children and adolescents. J Nutr 2010; 140:987–91. [50] Gaddipati VC, Bailey BA, Kuriacose R, Copeland RJ, Manning T, Peiris AN. The relationship of vitamin d status to cardiovascular risk factors and amputation risk in veterans with peripheral arterial disease. J Am Med Dir Assoc 2011; 12:58–61.
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[51] Kayaniyil S, Vieth R, Harris SB, Retnakaran R, Knight JA, Gerstein HC, et al. Association of 25(OH)D and PTH with metabolic syndrome and its traditional and nontraditional components. J Clin Endocrinol Metab 2011; 96:168–75.
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[52] Richart T, Thijs L, Nawrot T, Yu J, Kuznetsova T, Balkestein EJ, et al. The metabolic syndrome and carotid intima-media thickness in relation to the parathyroid hormone to 25-OH-D(3) ratio in a general population. Am J Hypertens 2011; 24:102–9.
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[53] Gagnon C, Lu ZX, Magliano DJ, Dunstan DW, Shaw JE, Zimmet PZ, et al. Low serum 25-hydroxyvitamin D is associated with increased risk of the development of the metabolic syndrome at five years: results from a national, population-based prospective study (The Australian Diabetes, Obesity and Lifestyle Study: AusDiab). J Clin Endocrinol Metab 2012; 97:1953–61. [54] Cutillas-Marco E, Prosper AF, Grant WB, Morales-Suárez-Varela MM. Vitamin D status and hypercholesterolemia in Spanish general population. Dermatoendocrinol 2013; 5:358-62. [55] Zhang MC, Li HX, Liu HM, Lei H, Han L, Gao M, et al. Serum Vitamin D is Llow and Inversely Associated with LDL Cholesterol in the Kazak Ethnic Population: A Cross-Sectional Study. Med Sci Monit 2014; 20:1274-83. [56] Chaudhuri JR, Mridula KR, Anamika A, Boddu DB, Misra PK, Lingaiah A, et al. Deficiency of 25-Hydroxyvitamin D and Dyslipidemia in Indian Subjects. J Lipids 2013; 2013:623420. [57] Karhapää P, Pihlajamäki J, Pörsti I, Kastarinen M, Mustonen J, Niemelä O, et al. Diverse associations of 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D with dyslipidaemias. J Intern Med 2010; 268:604-10. [58] Eaton CB, Young A, Allison MA, Robinson J, Martin LW, Kuller LH, et al. Prospective association of vitamin D concentrations with mortality in
ACCEPTED MANUSCRIPT postmenopausal women: Results from the Women’s Health Initiative (WHI). Am J Clin Nutr 2011; 94:1471–78.
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[59] Grandi NC, Breitling LP, Brenner H. Vitamin D and cardiovascular disease: Systematic review and meta-analysis of prospective studies. Prev. Med 2010; 51:228–33.
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[60] Pilz S, Tomaschitz A, März W, Drechsler C, Ritz E, Zittermann A, et al. Vitamin D, cardiovascular disease and mortality. Clin. Endocrinol 2011; 75:575– 84.
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[61] Anderson JL, May HT, Horne BD, Bair TL, Hall NL, Carlquist JF, et al. Relation of vitamin D deficiency to cardiovascular risk factors, disease status, and incident events in a general healthcare population. Am J Cardiol 2010; 106:963–68.
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[62] Islam MZ, Shamim AA, Akhtaruzzaman M, Karkkainen M, Lamberg-Allardt C. Effect of vitamin D, calcium and multiple micronutrientssupplementation on lipid profile in pre-menopausal Bangladeshi garmentfactory workers with hypovitaminosis D. J Health Popul Nutr 2014; 32:687–95.
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[63] Maki KC, Rubin MR, Wong LG, McManus JF, Jensen CD, Lawless A. Effects of vitamin D supplementation on 25-hydroxyvitamin D, high-density lipoprotein cholesterol, and other cardiovascular disease risk markers in subjects with elevated waist circumference. Int J Food Sci Nutr 2011; 62 :318– 27.
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[64] Gupta AK, Sexton RC, Rudney H. Effect of vitamin D3 derivatives on cholesterol synthesis and HMG-CoA reductase activity in cultured cells. J Lipid Res 1989; 30:379-86.
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[65] Grzegorzewska AE, Ostromecki G, Mostowska A, Sowińska A, Jagodziński PP. Clinical aspects of vitamin D-binding protein gene polymorphisms in hemodialysis patients. Pol Arch Med Wewn 2015; 125:8-17.
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[66] Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature 2009; 461:74753. [67] Rovaris DL, Mota NR, Callegari-Jacques SM, Bau HD. Approaching “phantom heritability” in psychiatry by hypothesis-driven gene–gene interactions. Front Hum Neurosci 2013; 7:210.
ACCEPTED MANUSCRIPT Figure 1. Interaction effect between RXRG (rs2134095) and GC (rs7041) genes on LDL cholesterol levels. Data expressed as mean and standard error.
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rs7041 TT individuals differed from G carriers when they were CC for rs2134095
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(P = 0.039); rs7041 GG individuals differed from T carriers when they were TT for rs2134095 (P = 0.022).
LDL cholesterol mean levels for each genotype combination (rs2134095/rs7041):
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TT/GG = 2.589 (0.089), TT/GT = 2.383 (0.076), TT/TT = 2.224 (0.122), TC/GG = 2.389 (0.090), TC/GT = 2.457 (0.079), TC/TT = 2.563 (0.118), CC/GG = 2.182
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(0.190), CC/GT = 2.327 (0.176), and CC/TT = 2.992 (0.233).
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Anthropometric parameters BMI (kg/m2) Waist circumference (cm) Body fat (%)
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Lipid profile Total cholesterol (mmol/L) HDL cholesterol (mmol/L) LDL cholesterol (mmol/L) Triglycerides (mmol/L)
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Table 1. Clinical characteristics, allelic and genotypic frequencies of the participants. n = 542 Age (years) 24.0 (7.0) Sex (male) 124 (22.9) Lifetime smoking (yes) 22 (4.1) Physical activity (yes) 314 (57.9) Aerobic (yes) 89 (16.4) Muscle-strengthening (yes) 59 (10.9) Combined (yes) 165 (30.4) Medication use (yes) 64 (11.8) Lipid-lowering drugs (yes) 6 (1.1) Antihypertensive drugs (yes) 7 (1.3) Anti-diabetic drugs (yes) 3 (0.6) Thyroid hormone replacement therapy (yes) 14 (2.6) Corticosteroid therapy (yes) 4 (0.7) Psychotropic drugs (yes) 42 (7.7) Daily caloric intake (Kilocalories) 1650.0 (843.6)
24.2 (4.1) 76.0 (10.1) 27.5 (6.9)
4.49 (1.00) 1.56 (0.40) 2.44 (0.81) 0.98 (0.59)
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Gene (polymorphism) VDR (rs2228570) CC 197 (36.3) CT 260 (48.0) TT 85 (15.7) Allele C 654 (60.3) Allele T 430 (39.7) RXRG (rs2134095) TT 252 (46.5) TC 239 (44.1) CC 51 (9.4) Allele T 743 (68.5) Allele C 341 (31.5) GC (rs7041) GG 188 (34.7) GT 247 (45.6) TT 107 (19.7) Allele G 623 (57.5) Allele T 461 (42.5) All values are expressed as mean and (standard deviation) or n and (%), except for age, daily caloric intake, and triglycerides that are expressed as median and (interquartile range). Abbreviations: BMI, body mass index; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol.
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Table 2. Comparison of anthropometric and lipid parameters between the genotypes. VDR gene (rs2228570) RXRG gene (rs2134095) CC CT TT P TT TC CC Anthropometric parameters BMI (kg/m2) 24.5 (4.0) 24.1 (4.2) 24.2 (4.2) 0.600 24.0 (3.8) 24.4 (4.4) 24.6 (4.6) Waist circumference (cm) 77.2 (10.3) 75.4 (10.2) 74.9 (10.0) 0.105 75.2 (9.8) 76.4 (10.4) 77.5 (9.7) Body fat (%) 27.4 (6.8) 27.4 (6.8) 27.8 (7.2) 0.872 27.3 (6.7) 27.6 (6.9) 27.7 (7.6)
GG
0.392 0.237 0.842
24.5 (4.3) 76.5 (9.9) 28.1 (6.8)
24.0 (4.1) 75.7 (10.2) 27.2 (6.8)
24.2 (3.9) 75.7 (10.1) 26.9 (6.9)
0.464 0.698 0.248
4.6 (1.0) 1.6 (0.4) 2.5 (0.8) 1.0 (0.6)
4.5 (1.0) 1.6 (0.4) 2.4 (0.8) 1.0 (0.6)
4.5 (1.0) 1.5 (0.4) 2.6 (0.9) 1.0 (0.7)
0.637 0.460 0.766 0.375
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Lipid profile Total cholesterol (mmol/L) 4.6 (1.0) 4.5 (1.0) 4.4 (0.9) 0.360 4.6 (1.0) 4.5 (1.0) 4.4 (0.8) 0.652 HDL cholesterol (mmol/L) 1.5 (0.4) 1.6 (0.4) 1.6 (0.4) 0.310 1.6 (0.4) 1.5 (0.4) 1.5 (0.4) 0.066 LDL cholesterol (mmol/L) 2.5 (0.8) 2.4 (0.8) 2.3 (0.7) 0.100 2.4 (0.8) 2.5 (0.8) 2.4 (0.8) 0.921 Triglycerides (mmol/L) 1.0 (0.6) 1.0 (0.7) 1.0 (0.6) 0.955 1.0 (0.6) 0.9 (0.6) 1.0 (0.7) 0.136 Data expressed as mean and (standard deviation), except for triglycerides that is expressed as median and (interquartile range). Abbreviations: BMI, body mass index; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol.
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GC gene (rs7041) GT TT
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ACCEPTED MANUSCRIPT Table 3. Gene-gene interaction effects on anthropometric parameters and lipid profile outcomes. VDR*RXRG VDR*GC RXRG*GC
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P interaction P interaction P interaction Anthropometric parameters BMI (kg/m2) 0.696b 0.927d 0.783f b d Waist circumference (cm) 0.856 0.704 0.946f c e Body fat (%) 0.229 0.214 0.755g Lipid profile Total cholesterol (mmol/L) 0.744b 0.657d 0.027h,j HDL cholesterol (mmol/L) 0.317b 0.775b 0.467 0.005i,k LDL cholesterol (mmol/L) 0.726 0.950f Triglycerides (mmol/L)a 0.800b 0.489b 0.472 General Linear Model (two-way ANOVA or ANCOVA). a Log transformed. b Result remains not significant when adjusted for sex; c Result remains not significant when adjusted for sex and physical activity; d Result remains not significant when adjusted for sex and age; e Result remains not significant when adjusted for sex, age, and physical activity; f Result remains not significant when adjusted for age; g Result remains not significant when adjusted for age and physical activity; h Result remains significant when adjusted for age (P = 0.031); i Result remains significant when adjusted for age (P = 0.007). j Partial eta2 = 0.021; k Partial eta2 = 0.029. Abbreviations: BMI, body mass index; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol.
S0955-2863(16)30066-3 doi: 10.1016/j.jnutbio.2016.08.002 JNB 7620
To appear in:
The Journal of Nutritional Biochemistry
Received date: Revised date: Accepted date:
9 May 2016 1 August 2016 10 August 2016
Please cite this article as: Grave Nath´alia, Tovo-Rodrigues Luciana, da Silveira Jana´ına, Rovaris Diego Luiz, Bosco Simone Morelo Dal, Contini Verˆonica, Genro J´ ulia Pasqualini, A vitamin D pathway gene-gene interaction affects low-density lipoprotein cholesterol levels, The Journal of Nutritional Biochemistry (2016), doi: 10.1016/j.jnutbio.2016.08.002
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ACCEPTED MANUSCRIPT A VITAMIN D PATHWAY GENE-GENE INTERACTION AFFECTS LOW-
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DENSITY LIPOPROTEIN CHOLESTEROL LEVELS
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Nathália Gravea, Luciana Tovo-Rodriguesb, Janaína da Silveiraa, Diego Luiz Rovarisc, Simone Morelo Dal Boscoa, Verônica Continia,d, Júlia Pasqualini
a
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Genroa,e.
Programa de Pós-Graduação em Biotecnologia, Centro Universitário
UNIVATES, 95.900-000, Lajeado, RS, Brasil. b
Programa de Pós-Graduação em Epidemiologia, Universidade Federal de
c
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Pelotas, 96.020-220, Pelotas, RS, Brasil.
Departamento de Genética, Instituto de Biociências, Universidade Federal
do Rio Grande do Sul, 91.501-970, Porto Alegre, RS, Brasil. Setor de Genética e Biologia Molecular do Museu de Ciências Naturais,
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d
Centro de Ciências Biológicas e da Saúde, Centro Universitário UNIVATES, e
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95.900-000, Lajeado, RS, Brasil. Programa de Pós-Graduação em Biociências, Universidade Federal de
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Ciências da Saúde de Porto Alegre, 90.050-170, Porto Alegre, RS, Brasil.
Corresponding author: Dra. Júlia Pasqualini Genro Departamento de Ciências Básicas da Saúde, Universidade Federal de Ciências da Saúde de Porto Alegre. Endereço: Rua Sarmento Leite 245, sala 403. CEP: 90050-170- Porto Alegre, RS, Brasil. Phone: +55-51-33088373 Email: [email protected]
ACCEPTED MANUSCRIPT Abstract Much evidence suggests an association between vitamin D deficiency and chronic diseases such as obesity and dyslipidemia. Although genetic factors
the
relationship
between
vitamin
D
related-genes
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investigated
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play an important role in the etiology of these diseases, only a few studies have and
anthropometric and lipid profiles. The aim of this study was to investigate the
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association of three vitamin D-related genes with anthropometric and lipid parameters in 542 adult individuals. We analyzed the rs2228570 polymorphism in the vitamin D receptor gene (VDR), rs2134095 in the retinoid X receptor
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gamma gene (RXRG), and rs7041 in the vitamin D-binding protein gene (GC). Polymorphisms were genotyped by TaqMan™ allelic discrimination. Gene-gene interactions were evaluated by the general linear model. The functionality of the polymorphisms was investigated using the following predictors and databases: Sorting
Intolerant
from
Tolerant
(SIFT),
Polymorphism Phenotyping
v2
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(PolyPhen-2), and Human Splicing Finder 3. We identified a significant effect of the interaction between RXRG (rs2134095) and GC (rs7041) on low-density
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lipoprotein cholesterol (LDL-c) levels (P = 0.005). Furthermore, our in silico analysis suggested a functional role for both variants in the regulation of the
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gene products. Our results suggest that the vitamin D related-genes, RXRG and GC, affect LDL-c levels. These findings are in agreement with other studies that
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consistently associate vitamin D and lipid profile. Together our results corroborate the idea that analyzing gene-gene interaction would be helpful to clarify the genetic component of lipid profile. Keywords: Vitamin D receptor; Retinoid X receptor; Vitamin D-binding protein; Polymorphisms; Lipid profile; Gene-gene interaction
ACCEPTED MANUSCRIPT 1. Introduction Many studies have associated vitamin D deficiency with an increased risk of developing chronic diseases [1-4]. Among these diseases, obesity has been
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investigated extensively, mainly because it has reached epidemic proportions
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around the world. Studies investigating the relation between obesity and hypovitaminosis D have increased substantially in recent years [5]. Several
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reports have demonstrated that blood vitamin D levels are decreased in obese individuals [6-8]. Likewise, plasma levels of this vitamin are inversely correlated to an atherogenic lipid profile [9-11].
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Vitamin D, a soluble steroid hormone, can be obtained from food sources as vitamin D3 (cholecalciferol) or converted from 7-dehydrocholesterol in the skin upon ultraviolet B (UVB) radiation exposure. After being absorbed or synthesized, vitamin D3 binds to the vitamin D-binding protein (DBP) and is transported through the bloodstream to the liver, where it is converted to
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25(OH)D (calcidiol), the main circulating form of vitamin D [12, 13]. Calcidiol is biologically inactive, and is converted, mainly in the kidneys, to the biologically
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active form, 1,25(OH)2D (calcitriol), which binds to the vitamin D receptor (VDR) [12, 14]. Then, VDR heterodimerizes with the retinoid X receptor gamma
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(RXRG). This complex binds to DNA at the vitamin D response element (VDRE), controlling the expression of many genes related to skeletal muscle
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and calcium metabolism, and cell proliferation [12, 15]. Although genetic factors are important in the etiology of obesity and its associated phenotypes, the role of the vitamin D pathway gene variants are poorly investigated, and anthropometric measurements and lipid profile analyses have not been carried out. Most previous studies have focused on the vitamin D receptor gene (VDR) and obesity [16-18], although there are some reports of associations between the retinoid X receptor gamma gene (RXRG) and lipid and anthropometric parameters [19]. The vitamin D-binding protein gene (GC), which codes for DBP, has been associated with body-fat percentage [20] and obesity [13]. Several other studies, including two GenomeWide Association Studies (GWAS), have detected associations between GC polymorphisms and circulating levels of vitamin D [13, 21-24].
ACCEPTED MANUSCRIPT Considering the importance of the VDR, RXRG, and DBP proteins in the vitamin D pathway, and the association of this vitamin with obesity phenotypes, in this study, we sought to (1) investigate the association between
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polymorphisms in VDR (rs2228570), RXRG (rs2134095), and GC (rs7041) with
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lipid profile and anthropometric measurements in adult individuals; (2) test the effect of gene-gene interactions on these measurements; and (3) evaluate, in
2.1. Participants
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2. Materials and Methods
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silico, the functionality of these polymorphisms.
The sample was composed of 542 white Brazilian European-descent individuals (median age = 24, minimum = 18, maximum = 66) recruited at
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Centro Universitário UNIVATES, an institution located in Southern Brazil. Therefore, this sample group is representative of an academic community
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(comprised by undergraduate and graduate students, employees, and professors). Since this region is characterized by a German and Italian
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colonization, genetic admixture analyses have shown that the proportion of European ancestry in this location ranges from 90 to 99.9% [25]. It is
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noteworthy that the vitamin D pathway-related genetic variants selected in this study present marked differences depending on the ethnic group [26], which would affect the results if an ethnically nonhomogeneous sample were selected. The exclusion criteria were self-reported nephropathy, coagulation disorder, infectious contagious diseases, adrenal gland disorders, pregnancy, cancer, and mental health disease that would prevent understanding of the informed consent form. The Research Ethics Committee of the Centro Universitário UNIVATES approved this study, and all participants signed an informed consent form. 2.2. Anthropometric measurements Trained professionals collected anthropometric measurements of weight, height, and waist circumference. Weight was measured on a Welmy® R-110
ACCEPTED MANUSCRIPT precision scale on a flat surface. The individuals were instructed to wear shorts and scrubs to minimize excessive clothing weight. Height was measured on barefooted individuals standing against a wall (heels, gluteus, back, and head
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touching the wall) by using a Wiso® stadiometer. Body mass index (BMI) was
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calculated by dividing the individual’s weight (in kilograms) by the square of height (in meters). Waist measurement was performed using a Cescorf® measuring tape, following the World Health Organization protocols [27]. The percentage
was
obtained
by
using
bioelectrical
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body-fat
impedance
(Biodynamics), with the individuals adhering to the following recommendations:
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fasting, no caffeine intake for 24 h prior to the test, no alcohol intake for 48 h prior to the test, empty bladder, no physical activity for 24 h prior to the test, and no menses on the day of the exam. 2.3. Lipid profile
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Blood was collected from the individuals after 12 h of fasting. Total cholesterol (TC), triglycerides, and high-density lipoprotein cholesterol (HDL-c)
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were measured by the enzymatic colorimetric method, according to the Bioclin® commercial kit protocol in an automatic BS-120 system (Mindray®) at the
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institutional clinical analysis laboratory. Measurement reliability was confirmed by commercial normal and pathologic controls. Low-density lipoprotein cholesterol (LDL-c) was estimated using the Friedewald equation (LDL-c = TC –
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HDL-c – [TG/5]) [28].
2.4. DNA extraction and genotyping Genomic DNA was extracted from total blood by using the salting out method adapted from Lahiri & Nurnberger [29]. Extracted DNA was quantified by spectrophotometry in an L-Quant® equipment and stored at −4 ºC. Polymorphisms rs2228570 (VDR), rs2134095 (RXRG), and rs7041 (GC) were genotyped by TaqMan™ allelic discrimination (Applied Biosystems, Foster City, CA), using the polymerase chain reaction (PCR) in a StepOne real time system (Applied Biosystems), according to the manufacturer’s protocols. 2.5. Statistical analysis
ACCEPTED MANUSCRIPT Allele frequencies were calculated, and Hardy-Weinberg equilibrium was tested using Pearson’s chi-squared test. Since all outcomes were continuous variables, analysis of variance (ANOVA) or Kruskal-Wallis test were used
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depending on the data distribution (normal or non-normal). Gene-gene
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interactions were evaluated using the general linear model (two-way ANOVA or two-way analysis of covariance [ANCOVA]). For this analysis, triglycerides were log-transformed to obtain normally distributed values. Effect sizes were
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estimated using eta2, a measure analogous to R2. The partial eta2 is a measure in which the effects of other independent variables and interactions are
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partialled out. All analyses were carried out using the Statistical Package for Social Sciences, version 18.0. An alpha of 0.008 (α Bonferroni = 0.05/6) was used, because
three
independent
polymorphisms
and
two
variable
groups
(anthropometric and lipid measurements) were considered in the study. Age, sex, physical activity, medication use, and daily caloric intake were
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considered possible confounding factors in the association analyses. Tobacco smoking was not included in these analyses since the prevalence of smoking was only 4.1% (Table 1). The inclusion of covariates was performed using a
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statistical definition (association with both the factor and outcome for P ≤ 0.2)
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[30]. This approach was adopted to produce more parsimonious and generalizable models [31]. Age and gender were considered potential confounding variables in models including GC rs7041 and VDR rs2228570,
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respectively. Physical activity was considered a possible confounder in the model evaluating the effects of RXRG rs2134095 and GC rs7041 on body fat. Supplementary Table 1 shows the results of the confounding analyses. Sample size calculations were performed using QUANTO Software, version 1.2.4 [32]. Considering the small effect sizes expected for SNPs in complex traits, the gene only and interaction analyses using the continuous outcomes investigated in this study require sample sizes ranging from 250 (interaction) to 519 (gene only) to achieve a power of at least 80%. Detailed description of the parameters set in these estimations is provided in the Supplementary Material. 2.6. Functional prediction of polymorphisms
ACCEPTED MANUSCRIPT In order to investigate the putative functional effect of the polymorphisms, their impact at the regulatory and protein level were evaluated using the Sorting Intolerant
from
Tolerant
(SIFT)
[33-36],
Polymorphism Phenotyping
v2
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(PolyPhen-2) [37], and Human Splicing Finder 3 [38] programs.
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3. Results
The clinical characteristics and allelic and genotypic frequencies are
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shown in Table 1. Genotypic frequencies of the rs2228570 (VDR), rs2134095 (RXRG), and rs7041 (GC) polymorphisms are in agreement with HardyWeinberg equilibrium.
A comparison of the anthropometric and lipid parameters between the genotypes is shown in Table 2. No significant effect was observed for the
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analyzed measurements for any of the polymorphisms investigated. 3.1 Gene-gene interaction analysis
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A significant interaction was observed between the RXRG and GC polymorphisms when considering LDL-c as the outcome (P = 0.005, eta2 =
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0.029; Table 3). In the presence of the TT genotype of rs7041 (GC), individuals with the CC genotype of rs2134095 (RXRG) showed the highest levels of LDL-
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c. The same was not true for the TT genotype of rs2134095 (RXRG; Figure 1). A similar trend was observed for total cholesterol (P = 0.027, eta2 = 0.021; Table 3).
3.2 Functional prediction of the polymorphisms Because
of
the
gene-gene
interactions
identified
above,
the
polymorphisms rs2134095 (RXRG) and rs7041 (GC) were further explored in terms of a putative alteration in protein functionality. These variants code for synonymous
and
missense
substitutions,
respectively.
None
of
the
polymorphisms resulted in a pathological alteration in the protein structure. The missense aspartic acid to glutamic acid substitution in position 451 (ENST00000504199) and in position 432 (ENST00000273951) of DBP (rs7041) were described as benign (PolyPhen-2) and tolerated (SIFT). On the other
ACCEPTED MANUSCRIPT hand, both polymorphisms seem to have an impact in splicing regulation according to Human Splicing Finder 3. The G allele of the rs7041 polymorphism induces the creation of a
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potential splicing acceptor site (score variation between alleles: 64.79%), and
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the disruption of a potential exonic splicing enhancer when compared to the T allele (Supplementary Figure 1A). Alteration of the C allele for the T allele of rs2134095
leads
to
the
disruption
of
an
exonic
splicing
enhancer
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(Supplementary Figure 1B). Hence, it is reasonable to hypothesize that both variants have an effect on the splicing of the messenger RNA coded by GC and
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RXRG, thus altering the function of the gene product.
4. Discussion
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The aim of this study was to investigate the association between the rs2228570 (VDR), rs2134095 (RXRG), and rs7041 (GC) polymorphisms with the anthropometric and lipid parameters related to obesity. No significant main
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effect of the polymorphisms was observed for the evaluated outcomes. On the other hand, gene-gene interaction analyses indicated a significant interaction
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between the RXRG and GC gene variants and LDL-c levels. Moreover, our in silico analysis suggested a putative functional role for both variants in gene
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product regulation. To the best of our knowledge, this is the first study to analyze this gene-gene interaction considering these parameters. The literature addressing gene variants related to metabolic disease and vitamin D pathway is scarce and shows inconsistent findings. Regarding VDR, two studies did not find any association between gene variants and obesity phenotypes [39, 40]. In contrast, Filus et al. (2008) found associations between VDR polymorphisms and BMI, HDL-c, and fasting insulin [41]. It is important to mention that, although we could not identify any association between the rs2228570 variant and the obesity-related outcomes investigated in this study we cannot reject the hypothesis that this gene might play a role in obesity. Regarding RXRG and GC, we were able to identify a significant effect of the RXRG-GC interaction on LDL-c levels. Although few studies have investigated the association between GC and lipid profile, polymorphisms in this
ACCEPTED MANUSCRIPT gene have been consistently associated with circulating levels of vitamin D [2123, 42], and, in turn, vitamin D levels are linked with lipid profile in adults [43]. Interestingly, RXRG, a key gene in the vitamin D pathway, is located on the
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1q21-q23 chromosomal region, which is closely related to familial combined
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hyperlipidemia (FCHL). FCHL is the most common lipid metabolism atherogenic disorder, and is characterized by several hyperlipidemic phenotypes, where high LDL-c levels are detected [44, 45, 46]. Therefore, this evidence from the
and lipid profile strengthens our findings.
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literature linking vitamin D pathway-related genes, vitamin D circulating levels,
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Several studies have shown a positive correlation between vitamin D levels and HDL-c [47-52] and, although fewer studies have evaluated LDL-c levels, most of them have demonstrated an inverse correlation between vitamin D circulating levels and this lipid fraction [53-57]. Still, it is important to mention that 25(OH)D levels have been inversely correlated to cardiovascular disease
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risk and mortality [58-60]. In a prospective study that included over 40,000 individuals, Anderson et al. (2010) identified an association between low levels
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of 25(OH)D and a variety of risk factors for cardiovascular disease, such as type 2 diabetes mellitus, hypertension, hyperlipidemia, congestive heart failure, and
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stroke [61]. It is possible that the association between cardiovascular disease and vitamin D is mediated by the lipid profile, and genetic variation in Vitamin Drelated genes could play a role in this underlying trait. However, among the
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studies that investigated the outcomes related to coronary disease, not all analyzed the lipid profiles of the participants, and, in general, did not discuss the importance of the vitamin D pathway and its genetic variability in these measurements. Although the biological mechanism responsible for the relationship between vitamin D and cholesterol is unclear, it is important to mention here that both molecules have the same precursor, 7-dehydrocholesterol (7DHC). 7DHC can be converted to pre-vitamin D under the effect of UVB radiation, and it can serve as a substrate for 7-dehydrocholesterol reductase (DHCR7), which catalyzes cholesterol synthesis [62, 63]. Additionally, a cell-culture study using human fibroblasts showed that vitamin D inhibits 3-hydroxy-3-methylglutaryl coenzyme-A reductase (HMG-CoA), a key enzyme in the cholesterol synthesis pathway [64].
ACCEPTED MANUSCRIPT It is also important to highlight that we investigated the potential functionality of the polymorphisms associated with LDL-c, and observed a putative effect of the variants. Substitution of an aspartic acid by a glutamic acid
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in position 432 of DBP, because of rs7041 (GC), was previously hypothesized
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to influence the affinity between 25(OH)D and DBP interaction [65]. Although we cannot rule out the effect on affinity, our results suggest that the interaction between the two molecules could be impaired through alterations in GC splicing
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regulation induced by the polymorphism. Similarly, splicing alterations mediated by rs2134095 (RXRG) could negatively affect the vitamin D response pathway.
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It is well known that chronic metabolic diseases have a multifactorial and complex inheritance. Therefore, it is not surprising that genetic studies have provided conflicting results. Considering that these diseases have a large genetic contribution, and that, so far, the variables identified represent only a small percentage of disease heritability, some authors have discussed where
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the “missing heritability” could be in our genome [66]. In our study, we chose the gene-gene interaction approach, which is considered to account for this
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paradox. Our results indicate that this strategy might be an option for future studies, especially those considering hypothesis-driven gene-gene interactions
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[67].
Our results should be interpreted considering some limitations. First, the circulating levels of 25(OH)D were not measured. Second, our sample size is
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relatively small; however, it allowed us to achieve enough statistical power to perform our analyses. Moreover, these data can be incorporated in future metaanalyses. Third, our sample mainly comprised healthy young adults, and therefore, our study design did not allow the investigation of direct effects on disease. Moreover, because the most of the previous genetic studies have focused on clinical presentation of disease and not on the underlying traits, the evaluation of quantitative outcomes as anthropometric and lipid measures in a sample composed by predominantly healthy individuals can expand our knowledge
of
the
pathophysiologic
pathways
underlying
obesity
and
dyslipidemia. Finally, although our in silico analysis suggests a putative functional effect on the dysregulation of the GC and RXRG splicing mechanisms, further in vitro and in vivo studies should be performed to confirm our hypothesis.
ACCEPTED MANUSCRIPT Our findings demonstrated that the interaction between two vitamin D pathway genes polymorphisms, rs2134095 (RXRG) and rs7041 (GC), has a significant effect on LDL-c levels. These findings are in agreement with other
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studies that consistently associate vitamin D and lipid profile. Together our
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results corroborate the idea that gene-gene interaction would be helpful to
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clarify the genetic component of lipid profile.
Acknowledgements
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The authors would like to thank the Ambulatório de Nutrição from the Centro Universitário UNIVATES, for help with the sample collection, and
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FAPERGS and FUVATES, for financial support.
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ACCEPTED MANUSCRIPT Figure 1. Interaction effect between RXRG (rs2134095) and GC (rs7041) genes on LDL cholesterol levels. Data expressed as mean and standard error.
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rs7041 TT individuals differed from G carriers when they were CC for rs2134095
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(P = 0.039); rs7041 GG individuals differed from T carriers when they were TT for rs2134095 (P = 0.022).
LDL cholesterol mean levels for each genotype combination (rs2134095/rs7041):
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TT/GG = 2.589 (0.089), TT/GT = 2.383 (0.076), TT/TT = 2.224 (0.122), TC/GG = 2.389 (0.090), TC/GT = 2.457 (0.079), TC/TT = 2.563 (0.118), CC/GG = 2.182
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(0.190), CC/GT = 2.327 (0.176), and CC/TT = 2.992 (0.233).
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Anthropometric parameters BMI (kg/m2) Waist circumference (cm) Body fat (%)
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Lipid profile Total cholesterol (mmol/L) HDL cholesterol (mmol/L) LDL cholesterol (mmol/L) Triglycerides (mmol/L)
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Table 1. Clinical characteristics, allelic and genotypic frequencies of the participants. n = 542 Age (years) 24.0 (7.0) Sex (male) 124 (22.9) Lifetime smoking (yes) 22 (4.1) Physical activity (yes) 314 (57.9) Aerobic (yes) 89 (16.4) Muscle-strengthening (yes) 59 (10.9) Combined (yes) 165 (30.4) Medication use (yes) 64 (11.8) Lipid-lowering drugs (yes) 6 (1.1) Antihypertensive drugs (yes) 7 (1.3) Anti-diabetic drugs (yes) 3 (0.6) Thyroid hormone replacement therapy (yes) 14 (2.6) Corticosteroid therapy (yes) 4 (0.7) Psychotropic drugs (yes) 42 (7.7) Daily caloric intake (Kilocalories) 1650.0 (843.6)
24.2 (4.1) 76.0 (10.1) 27.5 (6.9)
4.49 (1.00) 1.56 (0.40) 2.44 (0.81) 0.98 (0.59)
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Gene (polymorphism) VDR (rs2228570) CC 197 (36.3) CT 260 (48.0) TT 85 (15.7) Allele C 654 (60.3) Allele T 430 (39.7) RXRG (rs2134095) TT 252 (46.5) TC 239 (44.1) CC 51 (9.4) Allele T 743 (68.5) Allele C 341 (31.5) GC (rs7041) GG 188 (34.7) GT 247 (45.6) TT 107 (19.7) Allele G 623 (57.5) Allele T 461 (42.5) All values are expressed as mean and (standard deviation) or n and (%), except for age, daily caloric intake, and triglycerides that are expressed as median and (interquartile range). Abbreviations: BMI, body mass index; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol.
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Table 2. Comparison of anthropometric and lipid parameters between the genotypes. VDR gene (rs2228570) RXRG gene (rs2134095) CC CT TT P TT TC CC Anthropometric parameters BMI (kg/m2) 24.5 (4.0) 24.1 (4.2) 24.2 (4.2) 0.600 24.0 (3.8) 24.4 (4.4) 24.6 (4.6) Waist circumference (cm) 77.2 (10.3) 75.4 (10.2) 74.9 (10.0) 0.105 75.2 (9.8) 76.4 (10.4) 77.5 (9.7) Body fat (%) 27.4 (6.8) 27.4 (6.8) 27.8 (7.2) 0.872 27.3 (6.7) 27.6 (6.9) 27.7 (7.6)
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0.392 0.237 0.842
24.5 (4.3) 76.5 (9.9) 28.1 (6.8)
24.0 (4.1) 75.7 (10.2) 27.2 (6.8)
24.2 (3.9) 75.7 (10.1) 26.9 (6.9)
0.464 0.698 0.248
4.6 (1.0) 1.6 (0.4) 2.5 (0.8) 1.0 (0.6)
4.5 (1.0) 1.6 (0.4) 2.4 (0.8) 1.0 (0.6)
4.5 (1.0) 1.5 (0.4) 2.6 (0.9) 1.0 (0.7)
0.637 0.460 0.766 0.375
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Lipid profile Total cholesterol (mmol/L) 4.6 (1.0) 4.5 (1.0) 4.4 (0.9) 0.360 4.6 (1.0) 4.5 (1.0) 4.4 (0.8) 0.652 HDL cholesterol (mmol/L) 1.5 (0.4) 1.6 (0.4) 1.6 (0.4) 0.310 1.6 (0.4) 1.5 (0.4) 1.5 (0.4) 0.066 LDL cholesterol (mmol/L) 2.5 (0.8) 2.4 (0.8) 2.3 (0.7) 0.100 2.4 (0.8) 2.5 (0.8) 2.4 (0.8) 0.921 Triglycerides (mmol/L) 1.0 (0.6) 1.0 (0.7) 1.0 (0.6) 0.955 1.0 (0.6) 0.9 (0.6) 1.0 (0.7) 0.136 Data expressed as mean and (standard deviation), except for triglycerides that is expressed as median and (interquartile range). Abbreviations: BMI, body mass index; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol.
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P interaction P interaction P interaction Anthropometric parameters BMI (kg/m2) 0.696b 0.927d 0.783f b d Waist circumference (cm) 0.856 0.704 0.946f c e Body fat (%) 0.229 0.214 0.755g Lipid profile Total cholesterol (mmol/L) 0.744b 0.657d 0.027h,j HDL cholesterol (mmol/L) 0.317b 0.775b 0.467 0.005i,k LDL cholesterol (mmol/L) 0.726 0.950f Triglycerides (mmol/L)a 0.800b 0.489b 0.472 General Linear Model (two-way ANOVA or ANCOVA). a Log transformed. b Result remains not significant when adjusted for sex; c Result remains not significant when adjusted for sex and physical activity; d Result remains not significant when adjusted for sex and age; e Result remains not significant when adjusted for sex, age, and physical activity; f Result remains not significant when adjusted for age; g Result remains not significant when adjusted for age and physical activity; h Result remains significant when adjusted for age (P = 0.031); i Result remains significant when adjusted for age (P = 0.007). j Partial eta2 = 0.021; k Partial eta2 = 0.029. Abbreviations: BMI, body mass index; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol.