Next Article in Journal
Emerging Roles of TWIK-1 Heterodimerization in the Brain
Next Article in Special Issue
Non-Native Conformational Isomers of the Catalytic Domain of PCSK9 Induce an Immune Response, Reduce Lipids and Increase LDL Receptor Levels
Previous Article in Journal
Proteomic Analysis Reveals the Dynamic Role of Silicon in Alleviation of Hyperhydricity in Carnation Grown In Vitro
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 19
Issue 1
10.3390/ijms19010049
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Relationship between Lipid Phenotypes, Overweight, Lipid Lowering Drug Response and KIF6 and HMG-CoA Genotypes in a Subset of the Brisighella Heart Study Population

by
Sabrina Angelini
1,*,
Martina Rosticci
2,†,
Gianmichele Massimo
1,2,†,
Muriel Musti
3,
Gloria Ravegnini
1,
Nicola Consolini
1,
Giulia Sammarini
1,
Sergio D’Addato
2,
Elisabetta Rizzoli
2,
Dauren Botbayev
1,4,
Claudio Borghi
2,
Giorgio Cantelli-Forti
5,
Arrigo F. Cicero
2,‡ and
Patrizia Hrelia
1,‡
1
Department of Pharmacy and Biotechnology, via Irnerio 48, University of Bologna, 40126 Bologna, Italy
2
Department of Medical and Surgical, University of Bologna, 40126 Bologna, Italy
3
Department of Public Health, Epidemiological Service, Local Health Authority of Bologna, 40126 Bologna, Italy
4
Department of Biotechnology, Faculty of Biology and Biotechnology, Кazakh National University Named after al-Farabi, 050040 Almaty, Kazakhstan
5
Department for Life Quality Studies, Corso d’Augusto 237, University of Bologna, 47921 Rimini, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
These two authors jointly directed this work.
Int. J. Mol. Sci. 2018, 19(1), 49; https://doi.org/10.3390/ijms19010049
Submission received: 4 December 2017 / Revised: 19 December 2017 / Accepted: 20 December 2017 / Published: 24 December 2017
(This article belongs to the Special Issue Cholesterol and Lipoprotein Metabolism)
Browse Figures
Versions Notes

Abstract

:
The existence of genetic traits might explain the susceptibility to develop hypercholesterolemia and the inter-individual differences in statin response. This study was performed to evaluate whether individuals’ polymorphisms in HMG-CoA and KIF6 genes are independently associated with hypercholesterolemia, other lipid-associated traits, and statin response in unselected individuals enrolled in the Brisighella heart study (Survey 2012). A total of 1622 individuals, of which 183 under statin medication, were genotyped for a total of five polymorphisms (KIF6 rs20455, rs9471077, rs9462535; HMG-CoA rs3761740, rs3846662). The relationships between the five loci and clinical characteristics were analyzed. The principal basic parameters calculated on 12 h fasting blood included total cholesterol (TC), High Density Lipoprotein Cholesterol (HDL-C), Low-Density Lipoprotein Cholesterol (LDL-C), and triglycerides (TG). Hypercholesterolemia was defined as a TC >200 mg/dL or use of lipid-lowering medication. 965 individuals were characterized by hypercholesterolemia; these subjects were significantly older (p < 0.001), with body mass index (BMI) and waist circumference significantly higher (p < 0.001) compared to the others. HMG-CoA rs3846662 GG genotype was significantly over-represented in the hypercholesterolemic group (p = 0.030). HMG-CoA rs3846662 genotype was associated with the level of TC and LDL-C. Furthermore, in the same subset of untreated subjects, we observed a significant correlation between the KIF6 rs20455 and HDL-C. KIF6 variants were associated with a significantly lower (rs20455) or higher (rs9471077 and rs9462535) risk of obesity, in males only. No association between responsiveness to statins and the polymorphisms under investigation were observed. Our results showed associations between HMG-CoA rs3846662 and KIF6 rs20455 and lipid phenotypes, which may have an influence on dyslipidemia-related events. Moreover, this represents the first study implicating KIF6 variants with obesity in men, and point to the possible involvement of this genetic locus in the known gender-related differences in coronary artery disease.

1. Introduction

Cardiovascular diseases represent the leading causes of mortality and morbidity worldwide and contribute remarkably to the increase of health care expenditures [1]. Based on these considerations, addressing cardiovascular diseases requires concrete actions and, in particular, a strategy, both globally and nationally, which includes prevention and reduction of risk factors, and adequate surveillance and monitoring [2]. Epidemiological studies have established the benefit of reducing low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG) in diminishing cardiovascular disease events [2,3,4,5,6]. In light of these studies, the use of lipid-lowering drugs, mainly statins, may represent a primary prevention strategy of excellence. The significance of this approach is supported by a recent work by McConnachie et al. showing that a five-year primary prevention treatment with statins leads to long term benefits, reducing coronary-causes of death, and diminishing first hospital admission for cardiovascular events [7]. Statins are the first choice LDL-lowering drugs with a recognized universal efficacy in preventing vascular events. The recognition of statin efficacy is highlighted by a recent suggestion to expand the use of statins even in individuals with a low-risk profile [6,7]. Despite the enthusiasm, we have to pay attention to some uncertainty related to the non-achievement of the desired LDL-C targets in about 50% of the high-risk treated population, and the occurrence of cardiovascular events even in patients who achieve the LDL-C levels [8,9,10]. These two aspects suggest the existence of genetic traits that might explain the susceptibility to develop hypercholesterolemia and, secondly, the inter-individual response under statin regimen.
Several genome-wide studies have highlighted the involvement of numerous genes in lipid metabolism, in particular controlling serum High Density Lipoprotein Cholesterol (HDL-C), LDL-C and TG [11,12]. These loci include common variants, which have been consistently associated with lipid levels in candidate-gene studies reported over the past decade. Currently, the most studied variants are the G to A base change affecting the splicing site of exon 13 in the 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase (rs3846662) and the arginine to tryptophan substitution at codon 719 in the Kinesin-like protein 6 gene (KIF6, rs20455). HMG-CoA reductase is the rate-limiting enzyme in cholesterol biosynthesis and the LDL-C-lowering effect of statins are mediated through its inhibition [13]. KIF6 belongs to a superfamily of motor proteins (kinesins) that act upon microtubules as intracellular transport of cargo, including membrane organelles, protein complexes and mRNAs within cells [14]. KIF6 is not directly involved in lipid metabolism; however, several studies reported the association between KIF6 gene polymorphisms, coronary heart disease and events reduction from statin therapy [15,16,17,18,19,20,21]. All these considerations prompted us to perform a pharmacogenetic study to determine whether individuals’ polymorphisms in HMG-CoA reductase and KIF6 genes are independently associated with hypercholesterolemia, other lipid-associated traits and statin response in a sample of unselected individuals enrolled in the Brisighella Heart Study (BHeS). Given the complexity of the investigated traits, we selected other polymorphisms, in addition to the two previously described, already associated with serum lipids levels and/or cardiovascular events [19,22]. In the present study, we also explored association between the investigated polymorphisms and obesity. This last association is driven by the fact that obesity is among the cardiovascular risk traits, together with lipid levels. Moreover, obesity is a well-established contributor to the development of dyslipidemia [23].

2. Results

2.1. Study Population: Main Characteristics and Genotype Distribution

The cohort we studied included 1622 subjects participating to the 2012 BHeS survey; main demographic, clinical and laboratory parameters are reported in Table 1. A total of 965 individuals were characterized by hypercholesterolemia; the hypercholesterolemic group included individuals with TC >200 mg/dL (n. 782) or under statin therapy (n. 183). Hypercholesterolemic subjects were significantly older (p < 0.001), with body mass index (BMI) and waist circumference significantly higher (p < 0.001) compared to the non-hypercholesterolemic individuals. With regards to lipidic parameters, all of them (LDL-C, HDL-C, TG, apoB and apoA1) were significantly higher in subjects with hypercholesterolemia, compared to non-hypercholesterolemic individuals (p < 0.001, for all); even though there was a relatively wide standard deviation in both groups (Table 1).
Deviation from the HW equilibrium was observed for a single polymorphism (KIF6, variant rs9462535) in the group of subjects with no hypercholesterolemia; no departure from the HW equilibrium was observed for any other polymorphism, either in the overall population or in the hypercholesterolemic, or not, groups (Table 2). Moreover, there was no significant difference in age, gender, BMI, waist circumference and clinical variables among genotype groups within the same group of study subjects. In the overall population, the minor allele frequency (MAF) is consistent with the one reported for a population from Toscana, Italy, and available on the 1000 Genome Project (http://www.1000genomes.org; Table S1).

2.2. KIF6 and HMG-CoA Genotypes and Lipid Phenotypes -TC, LDL-C, HDL-C, TG Levels

Genotype distribution revealed a significantly different distribution of the HMG-CoA rs3846662 between hypercholesterolemic and individuals with normal TC value (Table 3). In particular, the GG genotype was significantly over-represented in the hypercholesterolemic group (23.6% vs. 18.7%, p = 0.030) with an OR 1.40 (95% CI 1.03–1.88, following adjustment for age, gender and BMI). None of the other analyzed polymorphisms showed association with hypercholesterolemia. Notably, excluding the subjects under lipid-lowering therapy (n. 183), the HMG-CoA rs3846662 genotype was also significantly associated with the level of TC and LDL-C (Figure 1 and Figure 2, respectively). In both cases the homozygous variant genotype (GG) was significantly associated with higher mean levels of TC and LDL-C compared to the AG and AA genotypes (TC: 212.9 ± 40.3 vs. 204.3 ± 38.8 and 202.6 ± 38.2, p = 0.003; LDL-C: 144.8 ± 35.2 vs. 136.5 ± 34.7 and 134.1 ± 33.0, p < 0.001). Furthermore, in the same subset of untreated individuals we observed a significant correlation between the KIF6 rs20455 and HDL-C. In particular, carriers of the GG genotype showed a significantly higher level of HDL-C compared to the AG and AA genotypes (47.9 ± 11.9 vs. 46.2 ± 10.7 vs. 45.4 ± 10.4, Figure 3, p = 0.037). Interestingly, the wild-type (AA) genotype was also associated with higher level of LDL-C compared to the other genotypes (140.4 ± 36.1 vs. 135.7 ± 33.8 vs. 136.2 ± 31.9); however, the difference was only marginal (p = 0.085). None of the investigated polymorphisms were associated with TG levels.

2.3. KIF6 and HMG-CoA as Susceptibility Locus for Obesity Assessed as BMI and Waist Circumference

BMI was categorized using standard World Health Organization for healthy weight, overweight (≥25 and <30 kg/m2) and obese (≥30 kg/m2). Considering obesity as a well-established contributor to dyslipidemia development and cardiovascular risk traits, BMI was dichotomized in obese subjects (BMI ≥ 30.0 kg/m2) or not. Considering the overall population, stratified according to KIF6 and HMG-CoA genotypes, none of the analyzed polymorphisms were associated with BMI (Table 4). We also considered the association with waist circumference that better assess body fat distribution compared to BMI. Waist circumference was first categorized according to the standard clinical guidelines for men (<94, 94–100, ≥100 cm) and women (<71, 71–90, ≥90 cm), corresponding to the BMI standard clinical categories for healthy, overweight and obese. The analysis was performed separately for men and women, dichotomizing the population in healthy weight (waist circumference <90 cm in women and <100 cm in men) or not. The analysis highlighted important gene-gender interactions. In particular, KIF6 polymorphisms were associated with a lower (rs20455) or higher (rs9471077 and rs9462535) risk of obesity, in males only (Table 5). In particular, the rs2455 AG and GG genotypes were associated with lower risk of obesity (OR 0.61; 95% CI 0.43–0.86, p = 0.004, and OR 0.58; 95% CI 0.35–0.95, p = 0.029, respectively). On the other hand, rs9471077 -AA- and rs9462535 -CC- conferred risk for obesity (OR 1.72; 95% CI 1.06–2.79, p = 0.027, and OR 1.72; 95% CI 1.06–2.80, p = 0.027, respectively) following adjustment for age. No association or trend to was observed in females (Table 6).

2.4. KIF6 and HMG-CoA Genotypes and Statin Response

Among the studied subjects, approximately 60% (n. 965) suffered from hypercholesterolemia. Amongst them, only 19.0% (n. 183) were under statin therapy. To assess differential responsiveness to statins, we tested the association between the polymorphisms under investigation and LDL-C levels in treated individuals, considering as responsive the individuals with LDL-C <130 mg/dL. Neither the presence of KIF6 or HMG-CoA polymorphisms was associated with treatment response (Table 7).

3. Discussion

Serum lipids are important determinants of cardiovascular events, strongly related to morbidity [11,24,25,26,27,28]. Therefore, screening for increased circulating lipid levels and early treatment with statins are a key strategy for cardiovascular event prevention in the clinical practice. Besides this, several studies have suggested that lipid phenotypes are linked to genetic factors. In view of this, many efforts have been made to identify potential lipid-associated loci that could contribute to increase the armamentarium of available preventive strategies. The identification of a genetic feature, associated with the lipid phenotype, has a pivotal role in stepping up targeted medical controls and allows prompt intervention. The lipid phenotype is certainly recognized as a complex trait, involving multiple genes, and with a significant gene-environment interaction [27]. In the attempt to overcome the genotype-phenotype gap in complex diseases, candidate gene/pathway approaches have traditionally been used. The success of these approaches strongly depends upon the correctness of the initial choice, which may lead to miss (i.e., unselected genes) significant results. In this context, genome-wide association studies (GWAS) represent a powerful tool. Starting from 2008, we witnessed an explosive rise in the number of GWAS identifying common genetic variants associated with LDL-C [11,12,28,29,30,31,32,33]. Among the discovered hits, three studies [12,32,33] reported the significant association of a variant in the HMG-CoA gene (rs3846662) and LDL-C level. The result was further replicated in gene-candidate studies [34,35,36]. Stimulated by all these studies, we replicated the finding on the HMG-CoA polymorphisms rs3846662, in an independent and homogeneous Italian population. Overall, all the studies agree on the association of the HMG-CoA-rs3846662-minor G allele with higher levels of LDL-C; in our study, in particular, the association was more evident in the homozygous GG genotype group. The polymorphism is a common variant located in intron 13 of the HMG-CoA, affecting alternative splicing of the exon. Indeed, an in vitro study by Burkhardt and colleagues highlighted its functional role in modulating alternative splicing of HMG-CoA mRNA in human lymphoblastoid cells homozygous for the HMG-CoA minor allele [37]. In particular, the variant allele is associated with a significantly lower mRNA level of the alternatively spliced (Δ13 exon) HMG-CoA. The authors speculated the polymorphism rs3846662 is located in a binding motif for a splice auxiliary protein, and allele status determines a change in the binding affinity of this protein. Consequently, a diminished HMG-CoA activity would promote a lower cellular cholesterol synthesis and, subsequently, an increase of cholesterol uptake from plasma through the LDL-receptor pathway to maintain intracellular cholesterol homeostasis [37,38]. The finding that the splice variant could not restore the enzyme activity when expressed in HMG-CoA deficient cells corroborates the functional role of the HMG-CoA polymorphism (rs3846662). Despite its pharmacological interest, the exact molecular mechanism governing this process is largely unknown. Most probably, the presence of the A-major allele in homozygous increases the proportion of HMG-CoA mRNA lacking exon 13 [37]. This alternative spliced variant is characterized by the lack of a portion of the catalytic domain (encoded by exon 13), and therefore may potentially affect stability of the enzyme [37,39,40]. Most recently, it has been recognized the existence of specific splicing factors influencing stability, translation and structure of mRNAs, contributing to the observed phenotypic trait [41]. In this context, the identification of HNRNPA1, a mRNA splicing regulator modulating the expression of HMG-CoA in an allele-related manner, and altering the RNA stability [40], takes on a great importance in pharmacology. Specifically, HNRNPA1 promotes HMG-CoA exon 13 skipping, more predominantly in the presence of the A-major allele, which also directly contributes to this exon-skipping phenomenon.
Besides the HMG-CoA polymorphism, we also observed a correlation between KIF6 rs20455 and HDL level; in particular, homozygous carriers of the G-minor allele showed higher serum HDL-C. The rs20455 variant is a non-synonymous polymorphism, leading to the replacement of a non-polar Trp with a basic Arg at codon 719, widely screened in relation to cardiovascular events, serum lipids and statin treatment outcome in various populations, with contrasting results [20,21,42,43,44,45,46,47]. To the best of our knowledge, our study is the first reporting an association with the KIF6 719Arg variant and HDL-C, while no apparent difference or opposite results were observed in previous studies [20,21,42,43,44,45,46,47]. Interestingly, KIF6 polymorphisms (rs20455, rs9471077 and 9462535) were associated with risk of obesity, calculated as waist circumference, but in males only. Currently, obesity represents one of the major challenges and a public health problem worldwide. Adiposity, in particular abdominal obesity, is recognized as the key contributor to diabetes and other chronic diseases, and as a risk factor for hypercholesterolemia and further cardiovascular risk traits [27,28,29]. Several studies have suggested that obesity is linked to genetic and environmental factors, and identifying genetic traits, including polymorphisms, may point to dysregulated genomic pathways. Recently, GWAS studies have identified several loci associated with obesity, mostly defined according to BMI values; however, none of them reported any association with KIF6 variants [48,49,50,51,52]. In our study, we found no correlation between any of the analyzed genetic variants and BMI, while we identified an interesting gene-gender interaction assessing obesity through waist circumference. Appropriate measures to prevent traits associated with cardiovascular events (as diabetes, dyslipidemia, hypertension, etc.) are mostly taken according to BMI anthropometric measures, aimed at identifying obese individuals. However, in the last decade waist circumference has emerged as a more accurate predictor of the metabolic risk of obesity compared to BMI [53,54,55,56,57]. Based on these considerations, we analyzed men and women separately, as sex-specific cutoffs for waist circumference should be used to identify increased risk associated with abdominal fat. Our results showed that KIF6 variants might confer risk of obesity in men only. This finding is interesting as the importance of gender in cardiovascular diseases has been widely described [58,59], and KIF6 variants might potentially explain the gender-related differences in atherosclerosis development. KIF6 encodes an intracellular protein involved in cellular cargo transportation, also expressed in coronary endothelial cells [60], intuitively pointing to a possible role in cardiovascular function. Diverse variants within this gene may differently alter intracellular transport in endothelial cells in men and women, predisposing to coronary endothelial dysfunction and coronary heart disease. Recently, a study by Yoshino and co-workers reported a significant association between KIF6 rs20456 and coronary epicardial endothelial dysfunction in men only [61], while, in contrast, a previous study showed association between KIF6 rs20455 and increased risk of coronary heart disease and myocardial infarction in Han Chinese women [43]. Overall, to establish with certainty if our gender-specific finding reflects true-causal association, rather than a chance result, further studies deepening the underlying mechanism are required. Indeed, the homogeneous population and uniformity of life-style, minimizing population bias, points to a possible genetic locus for obesity in men. However, the association between KIF6 polymorphisms and obesity needs confirmation or refutation in additional studies.
With regard to lipid-lowering therapy with statins, we did not find any association between HMG-CoA or KIF6 genotypes and achievement of <130 mg/dL LDL-C level. The data on KIF6 are in disagreement with the literature, as several studies have shown that KIF6 719Arg allele carriers might have a greater lipid-lowering response from statin therapy compared to non-carriers [15,16,17,44,62,63,64]. Furthermore, some (but not all) KIF6 719Arg carriers undergoing statin therapy have shown a greater reduction of coronary heart disease compared to non-carriers [17,18]. Although these findings have been debated, a KIF6 (rs20455) genetic test is commercially available to physicians and could be routinely included in the clinical practice. A recent study has highlighted that provision of KIF6-testing results and supporting information directly to patients was associated with significantly higher adherence and persistence to statin therapy over six months of follow-up [46]. In the era of personalized medicine, this study suggests that pharmacogenetic testing approaches may represent a promising tool in the promotion of healthy patient behaviors, which is then translated into clinical utility (i.e., higher statin adherence and persistence). A different reason might be why we have not observed any association between KIF6 genotypes and response to statin therapy. In particular, important limitations are: (i) sample size; (ii) statin responsiveness definition; (iii) kind of administered statin. With regard to the sample size, only 183 subjects out of 965 hypercholesterolemics were under statin therapy, greatly reducing the statistical power. However, this distribution is representative of treated subjects in the whole BHeS group where the decision to treat hypercholesterolemia is left to local General Physicians. Moreover, some cases of hypercholesterolemia were firstly diagnosed at the time of this population survey. Then, statin treatment is not considered as a first line for hypercholesterolemia in subjects at low global cardiovascular disease risk [65]. Regarding responsiveness, we used achievement of LDL-C <130 mg/dL as a measure of response, while a more appropriate analysis would be to consider percentage reduction from baseline. However, the study started in 1972, and participants were evaluated every 4 years, so that a reliable baseline level is impossible to establish. An alternative analysis would be considering percentage reduction from the previous BheS survey. However, this approach is not properly correct, as in a four-year interval, it is difficult to establish the adherence to statin therapy and, therefore, the lipid levels observed. Alternatively, we could measure participants’ lipid levels 6 months prior to the BheS follow-up; however, this option was not scheduled, therefore we cannot calculate percentage reduction. Lastly, concerning the issue of the kind of administered statins, to date, benefit of statin therapy for KIF6 carriers has been demonstrated for atorvastatin and pravastatin treatment only. In our study, it is difficult to draw a pharmacogenetic conclusion, as subjects under lipid-lowering treatment are heterogeneous in terms of the type of statin and dosage.
In conclusion, our results showed important associations between HMG-CoA rs3846662 and KIF6 rs20455 and lipid phenotypes, which may have an influence on dyslipidemia-related events. Next to this, to the best of our knowledge, this represents the first study implicating KIF6 variants with obesity in men only, and point to the possible involvement of this genetic locus in the known gender-related differences in coronary artery disease risk.

4. Methods

4.1. Study Population

The BHeS is a prospective population-based epidemiological investigation, started in 1972, involving 2939 unselected individuals, free of cardiovascular disease at enrollment, all resident in the rural town of Brisighella, located in northern Italy [66,67,68]. Brisighella was selected as the typical example of a rural community in the process of industrialization, with a very low rate of immigration and/or emigration, that basically guarantees a good degree of genetic homogeneity, and above all with uniformity of lifestyle habits. Participants were clinically evaluated at baseline and every 4 years following enrollment, when extensive clinical and laboratory data were obtained. The BHeS protocol and its sub-studies have been approved by the Ethical Board of the University of Bologna (protocol n BHS72—follow-up, 17 April 2012). Study participation was on a voluntary basis; a formal consent was obtained and signed by all participants before inclusion for study participation and anonymous data publication in accordance with national legislation. Study participation could be withdrawn by any subject, at any time during the study, according to the Helsinki Declaration and later Amendments.

4.2. Phenotyping

This population sample, participating in the 2012 survey, included 1622 individuals, of whom 183 were under statin treatment. Main demographic, clinical, lipid and lipoprotein characteristics of the studied cohort are summarized in Table 1. The basic parameters were evaluated with standardized methods by trained lab technicians at the S. Orsola-Malphighi Hosptial, Bologna, in our non-accredited research lab on 12 h fasting blood included TC, HDL-C, total triglycerides (TG), apoB and apoA1. LDL-C was estimated with Friedewald formula. When TG values were higher than 400 mg/dL, the LDL-C values was measured by beta-quantification.
Hypercholesterolemia was defined as a TC >200 mg/dL, or use of lipid lowering medications. We selected the value of 200 mg/dL based on the definition given by the National Center for Disease Prevention and Health Promotion. The criteria to start statin therapies were those suggested by the more recent European Atherosclerosis Society at the time the treatment began [69]. All the individuals included in the study were in primary prevention for cardiovascular disease. Subjects with high probability to be affected by heterozygous Familial Hypercholesterolemia (hFH) have been identified by the application of the Dutch Lipid Score and then a priori excluded from the analysis [70].

4.3. Genetic Analysis

Genomic DNA was isolated from EDTA-anticoagulated whole blood using the QIAamp DNA Blood kit (Qiagen, Hilden, Germany) as recommended by manufacturer. For genotype analysis, DNA samples were diluted and stored at −20 °C as 10 ng/μL aliquots. For DNA analysis, a total of 5 polymorphisms, 2 in the HMG-CoA reductase gene and 3 in the KIF6 gene, were genotyped (Table S1). Genotyping was performed by RT-PCR using the 5′-nuclease allelic discrimination assay (TaqMan®, Applied Biosystems, Foster City, CA, USA), according to the manufacturer instructions. Negative controls were included in each reaction as quality control. Moreover, 50% of randomly selected samples were genotyped twice, and the results were 100% concordant. All the analyses were performed in our non-accredited research laboratory at the Department of Pharmacy and Biotechnology, University of Bologna.

4.4. Statistical Analysis

Continuous variables are presented as mean ± standard deviation (SD), while categorical variables as absolute frequency. The association between cholesterol levels, response to statins, obesity, calculated as BMI or fat waist circumference, and the various genotypes were tested with Pearson’s chi-square test or analyses of variance as appropriate. The distribution of genotypes was tested for Hardy-Weinberg (HW) equilibrium. Multivariate logistic regression analyses were performed to evaluate the association between: genotypes and hypercholesterolemia and statin response (adjusted for age, sex, BMI or waist circumference), genotypes and obesity (adjusted for age and sex), genotypes and fat waist circumference (model by sex, adjusted for age). Results were presented as odd ratios (OR) and 95% confidence interval (95% CI). The level of significance was set at p < 0.05; Statistical analysis was conducted using Stata Intercooled version 12.0 [71].

Supplementary Materials

Supplementary materials can be found at www.mdpi.com/1422-0067/19/1/49/s1.

Acknowledgments

This work was supported by a grant from the Fondazione del Monte di Bologna e Ravenna. Gloria Ravegnini is supported by a MSD Italia fellowship granted by and on behalf of Merck Sharp & Dohme Corporation.

Author Contributions

Sabrina Angelini, Arrigo F. Cicero, and Patrizia Hrelia conceived and designed the experiments; Martina Rosticci, Gianmichele Massimo, Gloria Ravegnini, Nicola Consolini, Giulia Sammarini, Elisabetta Rizzoli, and Dauren Botbayev; performed the experiments; Sabrina Angelini, Muriel Musti, and Gloria Ravegnini analyzed the data; Sergio D’Addato, Claudio Borghi, Giorgio Cantelli-Forti contributed reagents/materials/analysis tools; Sabrina Angelini wrote the paper. All authors approved the final version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. World Health Organization in Collaboration with World Heart Federation and World Stroke Organization. Global Atlas on Cardiovascular Disease Prevention and Control. France, 2011. Available online: http://whqlibdoc.who.int/publications/2011/9789241564373_eng.pdf (accessed on 4 September 2017).
  2. Expert panel on Detection, evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel. JAMA 2001, 285, 2486–2497. [Google Scholar]
  3. Sacks, F.M.; Expert Group on HDL Cholesterol. The role of high-density lipoprotein (HDL) cholesterol in the prevention and treatment of coronary heart disease: Expert group recommendations. Am. J. Cardiol. 2002, 90, 139–143. [Google Scholar] [CrossRef]
  4. CTT Collaboration; Baigent, C.; Blackwell, L.; Emberson, J.; Holland, L.E.; Reith, C.; Bhala, N.; Peto, R.; Barnes, E.H.; Keech, A.; et al. Efficacy and safety of more intensive lowering of LDL cholesterol: A meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet 2010, 376, 1670–1681. [Google Scholar]
  5. Carey, V.J.; Bishop, L.; Laranjo, N.; Haeshfield, B.J.; Kwiat, C.; Sacks, F.M. Contribution of high plasma triglycerides and low high-density lipoprotein cholesterol to residual risk of coronary heart disease after establishment of low-density lipoprotein cholesterol control. Am. J. Cardiol. 2010, 106, 757–763. [Google Scholar] [CrossRef] [PubMed]
  6. CTT Collaboration; Mihaylova, B.; Emberson, J.; Blackwell, L.; Keech, A.; Simes, J.; Barnes, E.H.; Voysey, M.; Gray, A.; Collins, R.; et al. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: Meta-analysis of individual data from 27 randomised trials. Lancet 2012, 380, 581–590. [Google Scholar]
  7. McConnachie, A.; Walker, A.; Robertson, M.; Marchbank, L.; Peacock, J.; Packard, C.J.; Cobbe, S.M.; Ford, I. Long-Term impact on healthcare resource utilization of statin treatment, and its cost effectiveness in the primary prevention of cardiovascular disease: A record linkage study. Eur. Heart J. 2014, 35, 290–298. [Google Scholar] [CrossRef] [PubMed]
  8. Santos, R.D.; Waters, D.D.; Tarasenko, L.; Messig, M.; Jukema, J.W.; Ferrieres, J.; Foody, J.M. A comparison of non-HDL and LDL cholesterol goal attainment in a large, multinational patient population: The Lipid Treatment Assessment Project 2. Atherosclerosis 2012, 224, 150–153. [Google Scholar] [CrossRef] [PubMed]
  9. Ewang-Emukowhate, M.; Wierzbicki, A.S. Lipid-lowering agents. J. Cardiovasc. Pharmacol. Ther. 2013, 18, 401–411. [Google Scholar] [CrossRef] [PubMed]
  10. Arsenault, B.J.; Boekholdt, S.M.; Hovingh, K.; Hyde, C.L.; DeMicco, D.A.; Chatterjee, A.; Barter, P.; Deedwania, P.; Waters, D.D.; LaRosa, J.C.; et al. The 719 Arg variant of KIF6 and cardiovascular outcomes in statin-treated, stable coronary patients of the treating to new targets and incremental decrease in end points through aggressive lipid-lowering prospective studies. Circ. Cardiovasc. Genet. 2012, 5, 51–57. [Google Scholar] [CrossRef] [PubMed]
  11. Aulchenko, Y.S.; Ripatti, S.; Lindqvist, I.; Boomsma, D.; Heid, I.M.; Pramstaller, P.P.; Penninx, B.W.; Janssens, A.C.; Wilson, J.F.; Spector, T.; et al. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat. Genet. 2009, 41, 47–55. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Kathiresan, S.; Willer, C.J.; Peloso, G.M.; Demissie, S.; Musunuro, K.; Schadt, E.E.; Kaplan, L.; Bennet, D.; Li, Y.; Tanaka, T.; et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 2009, 41, 56–65. [Google Scholar] [CrossRef] [PubMed]
  13. Law, M.R.; Wald, N.J.; Rudnicka, A.R. Quantifying effect of statins on low density lipoprotein cholesterol, ischaemic heart disease, and stroke: Systematic review and meta-analysis. BMJ 2003, 326, 1423. [Google Scholar] [CrossRef] [PubMed]
  14. Miki, H.; Setou, M.; Kaneshiro, K.; Hirokawa, N. All kinesin superfamily protein, KIF, genes in mouse and human. Proc. Natl. Acad. Sci. USA 2001, 98, 7004–7011. [Google Scholar] [CrossRef] [PubMed]
  15. Iakoubova, O.A.; Tong, C.H.; Rowland, C.M.; Kirchgessner, T.G.; Young, B.A.; Arellano, A.R.; Shiffman, D.; Sabatine, M.S.; Campo, H.; Packard, C.J.; et al. Association of the Trp719Arg polymorphism in kinesin-like protein 6 with myocardial infarction and coronary heart disease in 2 prospective trials: The CARE and WOSCOPS trials. J. Am. Coll. Cardiol. 2008, 51, 435–443. [Google Scholar] [CrossRef] [PubMed]
  16. Shiffman, D.; Chasman, D.I.; Zee, R.Y.; Iakoubova, O.A.; Louie, J.Z.; Devlin, J.J.; Ridker, P.M. A kinesin family member 6 variant is associated with coronary heart disease in the Women’s Health Study. J. Am. Coll. Cardiol. 2008, 51, 444–448. [Google Scholar] [CrossRef] [PubMed]
  17. Li, Y.; Iakoubova, O.; Shiffman, D.; Devlin, J.J.; Forrester, J.S.; Superko, H.R. KIF6 polymorphism as a predictor of risk of coronary events and of clinical event reduction by statin therapy. Am. J. Cardiol. 2010, 106, 994–998. [Google Scholar] [CrossRef] [PubMed]
  18. Ference, B.A.; Yoo, W.; Flack, J.M.; Clarke, M. A common KIF6 polymorphism increases vulnerability to low-density lipoprotein cholesterol: Two meta-analyses and a meta-regression analysis. PLoS ONE 2011, 6, e28834. [Google Scholar] [CrossRef] [PubMed]
  19. Li, Y.; Sabatine, M.S.; Tong, C.H.; Ford, I.; Kirchgessner, T.G.; Packard, C.J.; Robertson, M.; Rowland, C.M.; Bare, L.A.; Shepherd, J.; et al. Genetic variants in the KIF6 region and coronary event reduction from statin therapy. Hum. Genet. 2011, 129, 17–23. [Google Scholar] [CrossRef] [PubMed]
  20. Wu, G.; Li, G.-B.; Dai, B. Association of KIF6 variant with lipid level and angiographic coronary artery disease events risk in the Han Chinese population. Molecules 2012, 17, 11269–11280. [Google Scholar] [CrossRef] [PubMed]
  21. Ancheta, I.B.; Battie, C.A.; Richard, C.V.; Ancheta, C.V.; Borja-Hart, N.; Volgman, A.S.; Conley, Y. The Association between KIF6 Single Nucleotide Polymorphism rs20455 and Serum Lipids in Filipino-American Women. Nurs. Res. Pract. 2014, 2014, 328954. [Google Scholar] [CrossRef] [PubMed]
  22. Akadam-Teker, B.; Kurnaz, O.; Coskunpinar, E.; Daglar-Aday, A.; Kucukhuseyin, O.; Cakmak, H.A.; Teker, E.; Bugra, Z.; Ozturk, O.; Yilmaz-Aydogan, H. The effects of age and gender on the relationship between HMGCR promoter-911 SNP (rs3761740) and serum lipids in patients with coronary heart disease. Gene 2013, 528, 93–98. [Google Scholar] [CrossRef] [PubMed]
  23. Klop, B.; Elte, J.W.; Cabezas, M.C. Dyslipidemia in obesity: Mechanisms and potential targets. Nutrients 2013, 5, 1218–1240. [Google Scholar] [CrossRef] [PubMed]
  24. Ingelsson, E.; Schaefer, E.J.; Contois, J.H.; McNamara, J.R.; Sullivan, L.; Keyes, M.J.; Pencina, M.J.; Schoonmaker, C.; Wilson, P.W.; D’Agostino, R.B.; et al. Clinical utility of different lipid measures for prediction of coronary heart disease in man and women. JAMA 2007, 298, 776–785. [Google Scholar] [CrossRef] [PubMed]
  25. Akao, H.; Polisecki, E.; Kajinami, K.; Trompet, S.; Robertson, M.; Ford, I.; Jukema, J.W.; de Craen, A.J.; Westendorp, R.G.; Shepherd, J.; et al. KIF6, LPA, TAS2R50, and VAMP8 genetic variation, low density lipoprotein cholesterol lowering response to pravastatin, and heart disease risk reduction in the elderly. Atherosclerosis 2012, 220, 456–462. [Google Scholar] [CrossRef] [PubMed]
  26. Mozaffarian, D.; kabagambe, E.K.; Johnson, C.O.; Lemaitre, R.N.; Manichaikul, A.; Sun, Q.; Foy, M.; Wang, L.; Wiener, H.; Irvin, M.R.; et al. Genetic loci associated with circulating phospholipid trans fatty acids: A meta-analysis of genome-wide association studies from the CHARGE Consortium. Am. J. Clin. Nutr. 2015, 101, 398–406. [Google Scholar] [CrossRef] [PubMed]
  27. Heller, D.A.; de Faire, U.; Pedersen, N.L.; Dahlen, G.; McClearn, G.E. Genetic and environmental influences on serum lipid levels in twins. N. Engl. J. Med. 1993, 328, 1150–1156. [Google Scholar] [CrossRef] [PubMed]
  28. Chasman, D.I.; Pare, G.; Zee, R.Y.L.; Parker, A.N.; Cook, N.R.; Buring, J.E.; Kwiatkowski, D.J.; Rose, L.M.; Smith, J.D.; Williams, P.T.; et al. Genetic loci associated with plasma concentration of low-density lipoprotein cholesterol, high-density cholesterol, triglycerides, apolipoprotein A1, and apolipoprotein B among 6382 white women in genome-wide analysis with replication. Circ. Cardiovasc. Genet. 2008, 1, 21–30. [Google Scholar] [CrossRef] [PubMed]
  29. Sandhu, M.S.; Waterworth, D.M.; Debenham, S.L.; Wheeler, E.; Papadakis, K.; Zhao, J.H.; Song, K.; Yuan, X.; Johnson, T.; Ashford, S.; et al. LDL-cholesterol concentrations: A genome-wide association study. Lancet 2008, 371, 483–491. [Google Scholar] [CrossRef]
  30. Wallace, C.; Newhouse, S.J.; Braund, P.; Zhang, F.; Tobin, M.; Falchi, M.; Ahmadi, K.; Dobson, R.J.; Marçano, A.C.B.; Hajat, C.; et al. Genome-wide association studies identifies genes for biomarkers of cardiovascular disease: Serum urate and dyslipidemia. Am. J. Hum. Genet. 2008, 82, 139–149. [Google Scholar] [CrossRef] [PubMed]
  31. Willer, C.J.; Sanna, S.; Jackson, A.U.; Scuteri, A.; Bonnycastle, L.L.; Clarke, R.; Heath, S.C.; Timpson, N.J.; Najjar, S.S.; Stringham, H.M.; et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet. 2008, 40, 161–169. [Google Scholar] [CrossRef] [PubMed]
  32. Lowe, J.K.; Maller, J.B.; Pe’er, I.; Neale, B.M.; Salit, J.; Kenny, E.E.; Shea, J.L.; Burkhardt, R.; Smith, J.G.; Ji, W.; et al. Genome-wide association studies in an isolated founder population from the Pacific Island of Kosrae. PLoS Genet. 2009, 5, e1000365. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Hiura, Y.; Shen, C.S.; Kokubo, Y.; Okamura, T.; Morisaki, T.; Tomoike, H.; Yoshida, T.; Sakamato, H.; Goto, Y.; Nonogi, H.; et al. Identification of genetic markers associated with high-density lipoprotein-cholesterol by genome-wide screening in a Japanese population. Circ. J. 2009, 73, 1119–1126. [Google Scholar] [CrossRef] [PubMed]
  34. Hiura, Y.; Tabara, Y.; Kokubo, Y.; Okamura, T.; Goto, Y.; Nonogi, H.; Miki, T.; Tomoike, H.; Iwai, N. Association of the functional variant in the 3-hydroxy-3-methylglutarylcoenzyme a reductase gene with low-density lipoprotein cholesterol in Japanese. Circ. J. 2010, 74, 518–522. [Google Scholar] [CrossRef] [PubMed]
  35. Chung, J.Y.; Cho, S.K.; Oh, E.S.; Lee, D.H.; Lim, L.A.; Jang, S.B.; Lee, Y.J.; Park, K.; Park, M.S. Effect of HMGCR variant alleles on low-density lipoprotein cholesterol-lowering response to atorvastatin in healthy Korean subjects. J. Clin. Pharmacol. 2012, 52, 339–346. [Google Scholar] [CrossRef] [PubMed]
  36. Leduc, V.; Bourque, L.; Poirier, J.; Dufour, R. Role of rs3846662 and HMCCR alternative splicing in statin efficacy and baseline lipid levels in familial hypercholesterolemia. Pharmacogenom. Genom. 2016, 26, 1–11. [Google Scholar] [CrossRef] [PubMed]
  37. Burkhardt, R.; Kenny, E.E.; Lowe, J.K.; Birkeland, A.; Josowitz, R.; Noel, M.; Salit, J.; Maller, J.B.; Pe’er, I.; Daly, M.J.; et al. Common SNPs in HMGCR in micronesians and whites associated with LDL-cholesterol levels affect alternative splicing of exon13. Arterioscler. Thromb. Vasc. Biol. 2008, 28, 2078–2084. [Google Scholar] [CrossRef] [PubMed]
  38. Yu, C.-Y.; Theusch, E.; Lo, K.; Mangravite, L.M.; Naidoo, D.; Kutilova, M.; Medina, M.W. HNRNPA1 regulates HMGCR alternative splicing and modulates cellular cholesterol metabolism. Hum. Mol. Genet. 2014, 23, 319–332. [Google Scholar] [CrossRef] [PubMed]
  39. Cheng, H.H.; Xu, L.; Kumagai, H.; Simoni, R.D. Oligomerization state influences the degradation rate of 3-hydroxy-3-methylglutaryl-CoA reductase. J. Biol. Chem. 1999, 274, 17171–17178. [Google Scholar] [CrossRef] [PubMed]
  40. Istvan, E.S.; Palnitkar, M.; Buchanan, S.K.; Deisenhofer, J. Crystal structure of the catalytic portion of human HMG-CoA reductase: Insights into regulation of activity and catalysis. Embo J. 2000, 19, 819–830. [Google Scholar] [CrossRef] [PubMed]
  41. Manning, K.S.; Coooper, T.A. The roles of RNA processing in translating genotype to phenotype. Nat. Rev. Mol. Cell Biol. 2017, 18, 102–114. [Google Scholar] [CrossRef] [PubMed]
  42. Assimes, T.L.; Hólm, H.; Kathiresan, S.; Reilly, M.P.; Thorleifsson, G.; Voight, B.F.; Erdmann, J.; Willenborg, C.; Vaidya, D.; Xie, C.; et al. Lack of association between the Trp719Arg polymorphism in kinesin-like Protein-6 and coronary artery disease in 19 case-control studies. J. Am. Coll. Cardiol. 2010, 56, 1553–1563. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Peng, P.; Lian, J.; Huang, R.; Xu, L.; Huang, Y.; Ba, Y.; Yang, X.; Huang, X.; Dong, C.; Zhang, L.; et al. Meta-analyses of KIF6 Trp719Arg in coronary heart disease and statin therapeutic effect. PLoS ONE 2012, 7, e50126. [Google Scholar] [CrossRef] [PubMed]
  44. Sabbagh, M.; Malek-Ahmadi, M.; Levenson, I.; Sparks, D.L. KIF6 719Arg allele is associated with statin effects on cholesterol levels in amnestic mild cognitive impairment and Alzheimer’s disease patients. J. Alzheimers Dis. 2013, 33, 111–116. [Google Scholar] [PubMed]
  45. Wu, G.; Li, G.B.; Dai, B.; Zhang, D.Q. Novel KIF6 polymorphism increases susceptibility to type 2 diabetes mellitus and coronary heart disease in Han Chinese men. J. Diabetes Res. 2014, 2014, 871439. [Google Scholar] [CrossRef] [PubMed]
  46. Charland, S.L.; Agatep, B.C.; Herrera, V.; Schrader, B.; Frueh, F.W.; Ryvkin, M.; Shabbeer, J.; Devlin, J.J.; Superko, H.R.; Stanek, E.J. Providing patients with pharmacogenetic test results affects adherence to statin therapy: Results of the Additional KIF6 Risk Offers Better Adherence to Statins (AKROBATS) trial. Pharmacogenom. J. 2014, 14, 272–280. [Google Scholar] [CrossRef] [PubMed]
  47. Vishnuprabu, D.; Geetha, S.; Bhaskar, L.V.K.S.; Mahapatra, N.R.; Munirajan, A.K. Genotyping and meta-analysis of KIF6 Trp719Arg polymorphism in South Indian Coronary Artery Disease patients: A case–control study. Meta Gene 2015, 5, 129–134. [Google Scholar] [CrossRef] [PubMed]
  48. Alshahid, M.; Wakil, S.M.; Al-Najai, M.; Muiya, N.P.; Elhawari, S.; Gueco, D.; Andres, E.; Hagos, S.; Mazhar, N.; Meyer, B.F.; et al. New susceptibility locus for obesity and dyslipidemia on chromosome 3q22.3. Hum. Genom. 2013, 7, 15. [Google Scholar] [CrossRef] [PubMed]
  49. He, H.; Sun, D.; Zeng, Y.; Wang, R.; Zhu, W.; Cao, S.; Bray, G.A.; Chen, W.; Shen, H.; Sacks, F.M.; et al. A systems genetics approach identified GPD1L and its molecular mechanism for obesity in human adipose tissue. Sci. Rep. 2017, 7, 1799. [Google Scholar] [CrossRef] [PubMed]
  50. Graff, M.; Scott, R.A.; Justice, A.E.; Young, K.L.; Feitosa, M.F.; Barata, L.; Winkler, T.W.; Chu, A.Y.; Mahajan, A.; Hadley, D.; et al. Genome-wide physical activity interactions in adiposity—A meta-analysis of 200,452 adults. PLoS Genet. 2017, 13, e1006528. [Google Scholar] [CrossRef] [PubMed]
  51. Justice, A.E.; Winkler, T.W.; Feitosa, M.F.; Graff, M.; Fisher, V.A.; Young, K.; Barata, L.; Deng, X.; Czajkowski, J.; Hadley, D.; et al. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat. Commun. 2017, 8, 14977. [Google Scholar] [CrossRef] [PubMed]
  52. Sabo, A.; Mishra, P.; Dugan-Perez, S.; Voruganti, V.S.; Kent, J.W., Jr.; Kalra, D.; Cole, S.A.; Comuzzie, A.G.; Musny, D.M.; Gibbs, R.A.; et al. Exome sequencing reveals novel genetic loci influencing obesity-related traits in Hispanic children. Obesity 2017, 25, 1270–1276. [Google Scholar] [CrossRef] [PubMed]
  53. Wang, Y.; Jacobs, E.; Patel, A.; Rodríguez, C.; McCullough, M.; Thun, M.; Calle, E. A prospective study of waist circumference and body mass index in relation to colorectal cancer incidence. Cancer Causes Control 2008, 19, 783–792. [Google Scholar] [CrossRef] [PubMed]
  54. The Decoda Study Group; Nyamdorj, R. BMI compared with central obesity indicators in relation to diabetes and hypertension in asians. Obesity 2008, 16, 1622–1635. [Google Scholar]
  55. Zeller, M.; Steg, P.G.; Ravisy, J.; Lorgis, L.; Laurent, Y.; Sicard, P.; Janin-Manificant, L.; Beer, J.C.; Makki, H.; Lagrost, A.C.; et al. Relation between body mass index, waist circumference, and death after acute myocardial infarction. Circulation 2008, 118, 482–490. [Google Scholar] [CrossRef] [PubMed]
  56. Kodama, S.; Horikawa, C.; Fujihara, K.; Heianza, Y.; Hirasawa, R.; Yachi, Y.; Sugawara, A.; Tanaka, S.; Shimano, H.; Iida, K.T.; et al. Comparisons of the strength of associations with future type 2 diabetes risk among anthropometric obesity indicators, including waist-to-height ratio: A meta-analysis. Am. J. Epidemiol. 2012, 176, 959–969. [Google Scholar] [CrossRef] [PubMed]
  57. Carmienke, S.; Freitag, M.H.; Pischon, T.; Schlattmann, P.; Fankhaenel, T.; Goebel, H.; Gensichen, J. General and abdominal obesity parameters and their combination in relation to mortality: A systematic review and meta-regression analysis. Eur. J. Clin. Nutr. 2013, 67, 573–585. [Google Scholar] [CrossRef] [PubMed]
  58. O’Donnell, C.J.; Nabel, E.G. Genomics of cardiovascular disease. N. Engl. J. Med. 2011, 365, 2098–2109. [Google Scholar] [CrossRef] [PubMed]
  59. Spence, J.D.; Pilote, L. Importance of sex and gender in atherosclerosis and cardiovascular disease. Atherosclerosis 2015, 241, 208–210. [Google Scholar] [CrossRef] [PubMed]
  60. Povel, C.M.; Boer, J.M.; Onland-Moret, N.C.; Dolle, M.E.; Feskens, E.J.; van der Schouw, Y.T. Single nucleotide polymorphisms (SNPs) involved in insulin resistance, weight regulation, lipid metabolism and inflammation in relation to metabolic syndrome: An epidemiological study. Cardiovasc. Diabetol. 2012, 11, 133. [Google Scholar] [CrossRef] [PubMed]
  61. Yoshino, S.; Cilluffo, R.; Prasad, M.; Best, P.J.M.; Atkinson, E.J.; Aoki, T.; Cunningham, J.M.; de Andrade, M.; Lerman, L.O.; Lerman, A. Sex-specific genetic variants are associated with coronary endothelial dysfunction. J. Am. Heart Assoc. 2016, 5, e002544. [Google Scholar] [CrossRef] [PubMed]
  62. Iakoubova, O.A.; Sabatine, M.S.; Rowland, C.M.; Tong, C.H.; Catanese, J.J.; Ranade, K.; Simonsen, K.L.; Kirchgessner, T.G.; Cannon, C.P.; Devlin, J.J.; et al. Polymorphism in KIF6 gene and benefit from statins after acute coronary syndromes: Results from the PROVE IT-TIMI 22 study. J. Am. Coll. Cardiol. 2008, 51, 449–455. [Google Scholar] [CrossRef] [PubMed]
  63. Shiffman, D.; O’Meara, E.S.; Bare, L.A.; Rowland, C.M.; Louie, J.Z.; Arellano, A.R.; Lumley, T.; Rice, K.; Iakoubova, O.; Luke, M.M.; et al. Association of gene variants with incident myocardial infarction in the Cardiovascular Health Study. Arterioscler. Thromb. Vasc. Biol. 2008, 28, 173–179. [Google Scholar] [CrossRef] [PubMed]
  64. Iakoubova, O.A.; Robertson, M.; Tong, C.H.; Rowland, C.M.; Catanese, J.J.; Blauw, G.J.; Jukema, J.W.; Murphy, M.B.; Devlin, J.J.; Ford, I.; et al. KIF6 Trp719Arg polymorphism and the effect of statin therapy in elderly patients: Results from the PROSPER study. Eur. J. Cardiovasc. Prev. Rehabil. 2010, 17, 455–461. [Google Scholar] [CrossRef] [PubMed]
  65. Catapano, A.L.; Graham, I.; de Backer, G.; Wiklund, O.; Chapman, M.J.; Drexel, H.; Hoes, A.W.; Jennings, C.S.; Landmesser, U.; Pedersen, T.R.; et al. 2016 ESC/EAS guidelines for the management of dyslipidaemias: The task force for the management of dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS) developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Atherosclerosis 2016, 253, 281–344. [Google Scholar] [PubMed]
  66. Descovich, G.C.; Cavallo, G.; Mannino, G.; Lenzi, S. The Brisighella survey: A study of dietary habits. Minerva Med. 1983, 74, 547–555. [Google Scholar] [PubMed]
  67. Cicero, A.F.; Rosticci, M.; Morbini, M.; Parini, A.; Grandi, E.; D’Addato, S.; Borghi, C.; Brisighella Heart Study Group. Serum LDL cholesterol and new onset of arterial hypertension: An 8-year follow-up. Eur. J. Clin. Investig. 2014, 44, 926–932. [Google Scholar] [CrossRef] [PubMed]
  68. Fogacci, F.; Cicero, A.F.G.; D’Addato, S.; Giovannini, M.; Borghi, C.; Brisighella Heart Study Group. Effect of spontaneous changes in dietary components and lipoprotein (a) levels: Data from the Brisighella Heart Study. Atherosclerosis 2017, 262, 202–204. [Google Scholar] [CrossRef] [PubMed]
  69. Perk, J.; De Backer, G.; Gohlke, H.; Graham, I.; Reiner, Z.; Verschuren, W.M.; Albus, C.; Benlian, P.; Boysen, G.; Cifkova, R.; et al. European guidelines on cardiovascular disease prevention in clinical practice (version 2012): The fifth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts). Int. J. Behav. Med. 2012, 19, 403–488. [Google Scholar] [PubMed]
  70. Brett, T.; Watts, G.F.; Arnold-Reed, D.E.; Bell, D.A.; Garton-Smith, J.; Vickery, A.W.; Ryan, J.D.; Pang, J. Challenges in the care of familial hypercholesterolemia: A community care perspective. Expert Rev. Cardiovasc. Ther. 2015, 13, 1091–1100. [Google Scholar] [CrossRef] [PubMed]
  71. Stata Corporation. Stata Statistical Software, Release 11, Stata Corporation: College Station, TX, USA, 2011.
Ijms 19 00049 g001 550
Figure 1. Association of the functional variant in HMG-CoA gene (rs3846662) with TC level in a subset of the Brisighella Heart Study population. Black lines represent TC mean value.
Figure 1. Association of the functional variant in HMG-CoA gene (rs3846662) with TC level in a subset of the Brisighella Heart Study population. Black lines represent TC mean value.
Ijms 19 00049 g001
Ijms 19 00049 g002 550
Figure 2. Association of the functional variant in HMG-CoA gene (rs3846662) with LDL-C level in a subset of the Brisighella Heart Study population. Black lines represent LDL-C mean value.
Figure 2. Association of the functional variant in HMG-CoA gene (rs3846662) with LDL-C level in a subset of the Brisighella Heart Study population. Black lines represent LDL-C mean value.
Ijms 19 00049 g002
Ijms 19 00049 g003 550
Figure 3. Association of the variant in KIF6 gene (rs20455) with HDL-C level in a subset of the Brisighella heart Study population. Black lines represent HDL-C mean value.
Figure 3. Association of the variant in KIF6 gene (rs20455) with HDL-C level in a subset of the Brisighella heart Study population. Black lines represent HDL-C mean value.
Ijms 19 00049 g003
Table
Table 1. Main demographic, clinical and laboratory characteristic of the Brisighella population—the hypercholesterolemic group included individuals with total cholesterol >200 mg/dL and subjects under therapy with statins.
Table 1. Main demographic, clinical and laboratory characteristic of the Brisighella population—the hypercholesterolemic group included individuals with total cholesterol >200 mg/dL and subjects under therapy with statins.
CharacteristicsAll (n. 1622)Hypercholesterolemia *
Yes (n. 965; 59.5%)No (n. 657; 40.5%)
Age, years
mean ± SD 58.6 ± 16.546.5 ± 17.3
Range 19–9719–90
Gender n (%)
Male786 (48.5%)456 (47.2%)330 (50.2%)
Female836 (51.5%)509 (52.8%)327 (49.8%)
Smoking Status †
Never smoker903 (55.7%)531 (55.0)372 (56.6%)
Former smoker403 (24.8%)262 (27.1%)141 (21.5%)
Current smoker277 (17.1%)150 (15.5%)127 (19.3%)
BMI
mean ± SD26.5 ± 4.527.1 ± 4.425.8 ± 4.6
Range16.5–47.516.5–46.117.1–47.5
Waist Circumference, cm
mean ± SD90.8 ± 12.992.8 ± 12.287.9 ± 13.3
Range59–14061–14059–134
Total Cholesterol, mg/dL
mean ± SD204.0 ± 38.8225 ± 33.4172.2 ± 19.6
Range96–354111–35496–199
LDL, mg/dL
mean ± SD135.6 ± 34.6152.8 ± 32.1110.3 ± 18.9
Range35.6–264.239.6–264.235.6–149.4
HDL, mg/dL
mean ± SD46.0 ± 10.747.3 ±11.044.1 ± 10.0
Range20–11725–9620–117
Triglycerides, mg/dL
mean ± SD112.8 ± 67.7127.8 ± 69.490.8 ± 58.4
Range11–80911–54611–809
APO B, mg/dL
mean ± SD86.3 ± 25.395.2 ± 24.773.8 ± 20.4
Range0.7–1890.7–1890.8–150
APO A1, mg/dL
mean ± SD142.2 ± 29.0145.4 ± 29.1137.8 ± 28.3
Range72–26374–26372–261
* Hypercholesterolemia: The group YES included individuals with total cholesterol >200 mg/dL and individual under therapy with statins; † smoking status is not available for 39 subjects.
Table
Table 2. KIF6 and HMG-CoA genotypes distribution in the studied population *.
Table 2. KIF6 and HMG-CoA genotypes distribution in the studied population *.
GenotypeAll (n. 1622)Hypercholesterolemia
Yes (n. 965; 59.5%)No (n. 657; 40.5%)
KIF6nnn
rs20455
AA640 (39.6%)402 (41.7%)238 (36.4%)
AG773 (47.8%)444 (46.1%)329 (50.3%)
GG205 (12.6%)118 (12.2%)87 (13.3%)
Variant allele frequency (q)q = 0.366q = 0.353q = 0.385
Hardy-Weinberg p valuep = 0.228p = 0.787p = 0.108
rs9471077
GG223 (13.8%)129 (13.4%)94 (14.3%)
GA799 (49.3%)460 (47.7%)339 (51.8%)
AA597 (36.9%)375 (38.9%)222 (33.9%)
Variant allele frequency (q)q = 0.615q = 0.628q = 0.598
Hardy-Weinberg p valuep = 0.086p = 0.518p = 0.051
rs9462535
AA220 (13.6%)129 (13.4%)91 (13.9%)
AC798 (49.3%)457 (47.4%)341 (52.1%)
CC601 (37.1%)378 (39.2%)223 (34.0%)
Variant allele frequency (q)q = 0.618q = 0.629q = 0.601
Hardy-Weinberg p valuep = 0.079p = 0.621p = 0.029
HMG-CoA
rs3761740
CC1325 (81.8%)788 (81.7%)537 (82.0%)
CA281 (17.4%)170 (17.6%)111 (16.9%)
AA13 (0.8%)6 (0.7%)7 (1.1%)
Variant allele frequency (q)q = 0.095q = 0.094q = 0.095
Hardy-Weinberg p valuep = 0.653p = 0.329p = 0.639
rs3846662
AA491 (30.3%)281 (29.1%)210 (32.1%)
AG778 (48.1%)456 (47.3%)322 (49.2%)
GG350 (21.6%)228 (23.6%)122 (18.7%)
Variant allele frequency (q)q = 0.457q = 0.473q = 0.433
Hardy-Weinberg p valuep = 0.204p = 0.106p = 0.942
* More than 99% of the participants were successfully genotyped for all the variants.
Table
Table 3. Influence of KIF6 and HMG-CoA genotypes on hypercholesterolemia (TC >200 mg/dL) †.
Table 3. Influence of KIF6 and HMG-CoA genotypes on hypercholesterolemia (TC >200 mg/dL) †.
GenotypeHypercholesterolemia
Yes (n. 965; 59.5%)No (n. 657; 40.5%)OR (95% CI)p Value *
n (%)n (%)
KIF6
rs20455
AA402 (41.7%)238 (36.4%)1.0
AG444 (46.1%)329 (50.3%)0.84 (0.67–1.05)0.127
GG118 (12.2%)87 (13.3%)0.83 (0.59–1.17)0.294
rs9471077
GG129 (%)94 (%)1.0
GA460 (%)339 (%)1.08 (0.78–1.49)0.636
AA375 (%)222 (%)1.29 (0.93–1.80)0.133
rs9462535
AA129 (13.4%)91 (13.9%)1.0
AC457 (47.4%)341 (52.1%)1.02 (0.7–1.41)0.776
CC378 (39.2%)223 (34.0%)1.23 (0.88–1.71)0.231
HMG-CoA
rs3761740
CC788 (81.7%)537 (82.0%)1.0
CA170 (17.6%)111 (16.9%)1.04 (0.79–1.38)0.776
AA6 (0.70%)7 (1.10%)0.56 (0.18–1.77)0.327
rs3846662
AA281 (29.1%)210 (32.1%)1.0
AG456 (47.3%)322 (49.2%)0.97 (0.76–1.2)0.832
GG228 (23.6%)122 (18.7%)1.40 (1.03–1.88)0.030
* Age, Gender, and BMI adjusted; † More than 99% of the participants were successfully genotyped for all the variants.
Table
Table 4. Influence of KIF6 and HMG-CoA genotypes † on obesity, calculated according to BMI.
Table 4. Influence of KIF6 and HMG-CoA genotypes † on obesity, calculated according to BMI.
GenotypeObese *
Yes (n = 328; 30.0%)No (n = 1294; 70.0%)OR (95% CI)p Value ‡
KIF6n (%)
rs20455
AA144 (43.9%)496 (38.5%)1.0
AG148 (45.1%)625 (48.4%)0.83 (0.64–1.08)0.174
GG36 (11.0%)169 (13.1%)0.75 (0.50–1.13)0.170
rs9471077
GG44 (13.4%)179 (13.9%)1.0
GA155 (47.3%)644 (49.9%)1.01 (0.70–1.48)0.642
AA129 (39.3%)468 (36.3%)1.14 (0.77–1.68)0.510
rs9462535
AA42 (12.8%)178 (13.8%)1.0
AC155 (47.3%)643 (49.8%)1.06 (0.72–1.55)0.777
CC131 (39.9%)470 (36.4%)1.19 (0.80–1.76)0.387
HMG-CoA
rs3761740
CC270 (82.3%)1055 (81.7%)1.0
CA55 (16.8%)226 (17.5%)0.94 (0.68–1.30)0.704
AA3 (0.9%)10 (0.8%)1.17 (0.32–4.34)0.809
rs3846662
AA106 (32.3%)385 (29.8%)1.0
AG154 (47.0%)624 (48.4%)0.84 (0.63–1.12)0.231
GG68 (20.7%)282 (21.8%)0.84 (0.60–1.19)0.338
* Obese: BMI ≥30 kg/m2; ‡ Age and gender adjusted; † More than 99% of the participants were successfully genotyped for all the variants.
Table
Table 5. Influence of KIF6 and HMG-CoA genotypes † on obesity, calculated according to waist circumference in males.
Table 5. Influence of KIF6 and HMG-CoA genotypes † on obesity, calculated according to waist circumference in males.
GenotypeObese *
Yes (n. 234; 30.0%)No (n. 546; 70.0%)OR (95% CI)p Value ‡
KIF6n (%)
rs20455
AA113 (48.3%)195 (35.9%)1.0
AG92 (39.3%)264 (48.5%)0.61 (0.43–0.86)0.004
GG29 (12.4)85 (15.6%)0.58 (0.35–0.95)0.029
rs9471077
GG32 (13.7%)88 (16.2%)1.0
GA97 (41.4%)276 (50.7%)1.03 (0.64–1.66)0.897
AA105 (44.9%)180 (33.1%)1.72 (1.06–2.79)0.027
rs9462535
AA31 (13.2%)86 (15.8%)1.0
AC95 (40.6%)274 (50.4%)1.02 (0.63–1.66)0.920
CC108 (46.2%)184 (33.8%)1.72 (1.06–2.80)0.027
HMG-CoA
rs3761740
CC183 (78.2%)431 (79.2%)1.0
CA48 (20.5%)106 (19.5%)1.00 (0.68–1.48)0.985
AA3 (1.3%)7 (1.3%)0.98 (0.25–3.90)0.975
rs3846662
AA73 (31.2%)161 (29.6%)1.0
AG114 (48.7%)254 (46.7%)0.90 (0.62–1.29)0.553
GG47 (20.1%)129 (23.7%)0.74 (0.48–1.16)0.193
* Obese: waist circumference ≥100 cm; ‡ Age adjusted; † More than 99% of the participants were successfully genotyped for all the variants.
Table
Table 6. Influence of KIF6 and HMG-CoA genotypes † on obesity, calculated according to waist circumference in females.
Table 6. Influence of KIF6 and HMG-CoA genotypes † on obesity, calculated according to waist circumference in females.
GenotypeObese *
Yes (n. 350; 42.6%)No (n. 472; 57.4%)OR (95% CI)p Value ‡
KIF6n (%)
rs20455
AA140 (40.1%)186 (39.5%)1.0
AG172 (49.3%)234 (49.7%)1.05 (0.75–1.46)0.791
GG37 (10.6)51 (10.8%)1.14 (0.67–1.93)0.630
rs9471077
GG46 (13.2%)55 (11.7%)1.0
GA174 (49.9%)240 (50.9%)0.89 (0.55–1.44)0.630
AA129 (36.9%)177 (37.5%)0.81 (0.49–1.34)0.415
rs9462535
AA45 (12.9%)56 (11.9%)1.0
AC175 (50.1%)242 (51.2%)0.93 (0.57–1.50)0.755
CC129 (37.0%)174 (36.9%)0.85 (0.51–1.40)0.526
HMG-CoA
rs3761740
CC292 (83.7%)401 (85.0%)1.0
CA56 (16.0%)70 (14.8%)1.27 (0.82–1.95)0.280
AA1 (0.3%)1 (0.2%)1.28 (0.07–0.90)0.869
rs3846662
AA114 (32.6%)136 (28.9%)1.0
AG168 (48.0%)236 (50.1%)0.73 (0.51–1.04)0.083
GG68 (19.4%)99 (21.0%)0.76 (0.49–1.18)0.217
* Obese: waist circumference ≥90 cm; ‡ Age adjusted; † More than 99% of the participants were successfully genotyped for all the variants.
Table
Table 7. Influence of KIF6 and HMG-CoA genotypes † on hypercholesterolemia treatment response.
Table 7. Influence of KIF6 and HMG-CoA genotypes † on hypercholesterolemia treatment response.
GenotypeHypercholesterolemia Treatment Response
No (n. 122; 66.7%)Yes (n. 61; 33.3%)OR (95% CI)p Value *
KIF6n (%)
rs20455
AA50 (41.3)31 (50.8)1.0
AG53 (43.8)24 (39.4)0.69 (0.35–1.36)0.287
GG18 (14.9)6 (9.8)0.54 (0.19–1.54)0.250
rs9471077
GG23 (19.0)6 (9.8)1.0
GA53 (43.8)25 (41.0)1.70 (0.61–4.77)0.310
AA45 (37.2)30 (49.2)2.41 (0.87–6.69)0.082
rs9462535
AA23 (19.0)6 (9.8)1.0
AC52 (43.0)26 (42.6)1.81 (0.65–5.06)0.258
CC46 (38.0)29 (47.6)2.34 (0.84–6.53)0.103
HMG-CoA
rs3761740
CC96 (79.4)47 (77.1)1.0
CA24 (19.8)13 (21.3)1.17 (0.54–2.54)0.690
AA1 (0.8)1 (1.6)1.76 (0.10–29.7)0.695
rs3846662
AA35 (28.7)19 (31.2)1.0
AG66 (54.1)31 (50.8)0.90 (0.44–1.83)0.769
GG21 (17.2)11 (18.0)0.91 (0.36–2.31)0.840
Responder NO: LDL ≥ 130 mg/dL; YES < 130 mg/dL. * Age, Gender, and waist circumference adjusted; † More than 99% of the participants were successfully genotyped for all the variants.

Share and Cite

MDPI and ACS Style

Angelini, S.; Rosticci, M.; Massimo, G.; Musti, M.; Ravegnini, G.; Consolini, N.; Sammarini, G.; D’Addato, S.; Rizzoli, E.; Botbayev, D.; et al. Relationship between Lipid Phenotypes, Overweight, Lipid Lowering Drug Response and KIF6 and HMG-CoA Genotypes in a Subset of the Brisighella Heart Study Population. Int. J. Mol. Sci. 2018, 19, 49. https://doi.org/10.3390/ijms19010049

AMA Style

Angelini S, Rosticci M, Massimo G, Musti M, Ravegnini G, Consolini N, Sammarini G, D’Addato S, Rizzoli E, Botbayev D, et al. Relationship between Lipid Phenotypes, Overweight, Lipid Lowering Drug Response and KIF6 and HMG-CoA Genotypes in a Subset of the Brisighella Heart Study Population. International Journal of Molecular Sciences. 2018; 19(1):49. https://doi.org/10.3390/ijms19010049

Chicago/Turabian Style

Angelini, Sabrina, Martina Rosticci, Gianmichele Massimo, Muriel Musti, Gloria Ravegnini, Nicola Consolini, Giulia Sammarini, Sergio D’Addato, Elisabetta Rizzoli, Dauren Botbayev, and et al. 2018. "Relationship between Lipid Phenotypes, Overweight, Lipid Lowering Drug Response and KIF6 and HMG-CoA Genotypes in a Subset of the Brisighella Heart Study Population" International Journal of Molecular Sciences 19, no. 1: 49. https://doi.org/10.3390/ijms19010049

APA Style

Angelini, S., Rosticci, M., Massimo, G., Musti, M., Ravegnini, G., Consolini, N., Sammarini, G., D’Addato, S., Rizzoli, E., Botbayev, D., Borghi, C., Cantelli-Forti, G., Cicero, A. F., & Hrelia, P. (2018). Relationship between Lipid Phenotypes, Overweight, Lipid Lowering Drug Response and KIF6 and HMG-CoA Genotypes in a Subset of the Brisighella Heart Study Population. International Journal of Molecular Sciences, 19(1), 49. https://doi.org/10.3390/ijms19010049

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop