Logic Regression Analysis of Gene Polymorphisms and HDL Levels in a Nationally Representative Sample of Iranian Adolescents: The CASPIAN-III Study

AUTHORS

Mehri Moghadasi 1 , Roya Kelishadi 2 , Hamid Reza Marateb 3 , 4 , Shaghayegh Haghjooy Javanmard 5 , Marjan Mansourian 2 , 5 , 6 , * , Ramin Heshmat 7 , Mohammad Esmaeil Motlagh 8

1 Student Research Center, Biostatistics and Epidemiology Department, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran

2 Pediatrics Department, Child Growth and Development Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

3 Biomedical Engineering Department, Faculty of Engineering, University of Isfahan, Isfahan, Iran

4 Department of Automatic Control, Biomedical Engineering Research Center, Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, Spain

5 Physiology Department, Applied Physiology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

6 Biostatistics and Epidemiology Department, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, Iran

7 Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

8 Pediatrics Department, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

How to Cite: Moghadasi M, Kelishadi R, Marateb H R, Haghjooy Javanmard S, Mansourian M, et al. Logic Regression Analysis of Gene Polymorphisms and HDL Levels in a Nationally Representative Sample of Iranian Adolescents: The CASPIAN-III Study, Int J Endocrinol Metab. 2017 ; 15(3):e14037. doi: 10.5812/ijem.14037.

ARTICLE INFORMATION

International Journal of Endocrinology and Metabolism: 15 (3); e14037
Published Online: July 29, 2017
Article Type: Research Article
Received: November 24, 2016
Accepted: July 1, 2017
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Abstract

Background: To investigate the associations of genetic polymorphism with high-density lipoprotein-cholesterol (HDL-C) levels in Iranian adolescents.

Methods: This multicentre study was conducted on 10 - 18 year-old students from 27 provinces in Iran. Logic regression approach was used to determine the main effects and interactions of polymorphisms related to HDL-C levels.

Results: The rs708272 polymorphism was significantly related to HDL-C levels. Moreover, rs708272 increased HDL-C levels and had a protective effect on HDL-C. The interaction of rs2230808 and rs5880 polymorphisms as well as the interaction of rs320 and rs708272 polymorphisms were associated with lower HDL-C levels. Furthermore, the interaction of rs320 and rs1801177 polymorphisms was associated with lower HDL-C levels.

Conclusions: We found that not only single SNPs, but also interactions of several SNPs affect HDL-C levels. Given the high prevalence of low HDL-C in Middle Eastern populations, further genetic studies are required for detailed analysis.

Keywords

Polymorphism High-Density Lipoprotein Pediatrics Logic Regression

Copyright © 2017, International Journal of Endocrinology and Metabolism. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

1. Background

Non-communicable diseases and notably cardiovascular diseases (CVD) are the primary reason for mortality and the main health burden worldwide (1). Serum lipids and lipoproteins in the pediatric age group are shown to be predictive of future symptomatic CVDs (2). Studies show that lipoproteins, as well as other cardiovascular risk factors, track from childhood to adulthood (3). The assessment of the early onset of CVD risk factors in childhood is important to determine optimal preventive measures, since determining the early development of adult CVDs may help reduce the mortality rate (4, 5). Among lipids, high-density lipoprotein cholesterol (HDL-C) is a protective element against CVD (6). A study presented that having HDL-C 6 to 7 mg/dL higher than average leads to 20% to 27% decrease in the risk of CVD (7).

Low HDL-C levels are quite prevalent in the Middle-Eastern countries (8). Population-based studies in Iran presented a prevalent low HDL-C among children and adolescents (4, 9, 10). Furthermore, its fifth percentile of Iranian pediatric population is lower than that of European and American population (11, 12). Moreover, it is well documented that HDL-C is affected by both genetic contexts and environmental factors, such as demographics, diet, smoking, and weight disorders (13, 14). Although hormonal, environmental, and social factors could specify HDL-C levels, the genetic component accounts up to 76% of the variation in HDL-C levels (15). Single nucleotide polymorphism (SNP) is the most prevalent genetic discrepancy (16). In the evaluation of genotype data, the effect size of associations between one SNPs and a response is usually small. Therefore, it is assumed that not only single SNPs but also interactions of several SNPs is particularly important (17). Most genetic studies on lipid profiles focused on individual SNPs while SNP-SNP interactions are suggested to have a great effect on the structure of complex diseases. As an example, Kelishadi et al. investigated the genetic association with low concentrations of HDL-C in a pediatric population, (18). In this study, single SNPs were taken into accounts and the polymorphism of ApoE gene was not included.

To the best of our knowledge, no analysis has been performed to evaluate an association between the interaction of SNPs and low HDL-C in Middle Eastern adolescents. Therefore, the aim of the present investigation was to determine the influence of some polymorphisms (and their interactions) on HDL-C levels performed for the first time on children and adolescents.

2. Methods

2.1. Study Populations

The subjects in this study were randomly determined between individuals in the CASPIAN-III study, Childhood and Adolescence Surveillance and Prevention of Adult Non-communicable disease study. This survey was performed to determine risky behaviors in Iranian school students (2009 - 2010).

The original study included 5528 10 - 18 year-old students selected from Iranian urban and rural regions (19). A total of 734 frozen blood samples were selected.

Low HDL-C levels were determined as concentrations of < 40 mg/dL. Elevated lipid was considered as total cholesterol higher or equal to 200 mg/dL, low-density lipoprotein cholesterol (LDL-C) higher or equal to 130 mg/dL, and triglycerides higher or equal to 150 mg/dL. High waist circumference was determined as WC higher or equal to 90th percentile of the population studied. Elevated fasting blood sugar was defined as FBS higher or equal to 100 mg/dL. Blood pressure higher or equal to 130/85 mmHg was considered as elevated BP (20).

Normal weight Underweight, overweight, and obesity were determined as a BMI Z-score according to world health organization (WHO) definition (21).

Polymorphisms of LPL rs1801177, LPL rs320, LPL rs328, ABCAI rs2066718, ABCAI rs2230808, CETP rs708272, CETP rs5880, APOC3 rs5128, APOA1 rs2893157, APOA5 rs662799, and ApoE genes related to HDL-C disorder were analyzed (22). Each SNP was represented by the values 0, 1, and 2 based on the pairs of nucleotide. We recoded this variable into 2 binary variables corresponding to a dominant gene (if SNP has at least one variant allele) and a recessive gene (if SNP has 2 variant alleles). Using this method, we generated 2p binary predictors out of p SNPs (23).

2.2. Genetic Studies

2.2.1. DNA Extraction

DNA was extracted from peripheral blood by using the QIAamp DNA Blood Mini kit (Qiagen, Germany). Corbett rotor-gene 6000 instruments (Corbett Research Pty Ltd, Sydney Australia) were used for Real-time PCR and high-resolution melt analysis. Primers were produced by Beacon Designer 7.91 to flank the genomic regions (PREMIER Biosoft International, USA, and TIB MOLBIOL (Germany) were used for synthesizing.

The Standard conditions of amplicons production using type-it HRMkit (Qiagen, Germany) were used (24). The sequence-proven major and minor allele homozygote and heterozygote controls were then included. The HRM analysis was performed, and the samples were clustered (24).

2.3. Statistical Analysis

The continuous variables were shown as mean and standard deviation. However, on the other hand, frequencies and percentages were used for categorical variables. The t-test and χ2 were used to compare the continuous and categorical variables in boys and girls, when appropriate. The association between polymorphisms and other covariates interactions and HDL-C levels was analyzed using logic regression.

Few approaches were proposed for the direct detection of statistical interactions between SNPs in order to enhance the statistical power of the studies. Logic regression was recently proposed as a generalized regression method to identify interactions between binary predictors associated with a response variable. It can detect complicated interactions between predictors that could play important roles in genetics or discover prognostic factors in medical data. This technique was efficiently applied on SNP data (23).

The form of the logic regression model is as below:

Equation 1.

where h(.) is an appropriate link function for the response and the predictors, e.g. linear (h (E [Y|X]) = E [Y]), and logistic regression (h (E [Y|X]) = log(E [Y]/(1 - E [Y]))). X is the covariate matrix, with βi as parameters and the Li as Boolean combinations of the binary predictors such as x2 and x5c or x7, with the operator ‘c’ as complement. Furthermore, the Zj are extra confounders. For each model, a score function was defined reflecting the “quality” of the model under consideration. We endeavored to find Boolean combinations minimizing the scoring function related to the model. Simulated annealing search algorithm was used to search for the best Boolean combinations and estimation of βj (23).

The association between combination of predictors and low HDL-C level was assessed by the randomization test. The best model was first fitted to the data, the response was permuted in random, and the best model was re-fitted. This procedure was repeated several times. If the entire scores of the permuted data were considerably worse than that of the original data, the information existed in the predictors; otherwise, there was no connection between the predictors and the response (23).

To avoid over-fitting in logic regression models, the cross-validation test was applied to determine the optimal model size. The data were split into k equal parts. Then, k times, one partition was left as a test set, and for each possible model size, the best model was selected using k -1 out of k parts, after which the score function on the remaining test set part was computed. For each level of model size, the k score function was added and the model size with the smallest overall score function was selected (23).

Logic regression with logit link function was used to recognize the relationship between the input predators and the outcome. We applied R software (version 3.3.0) for analyzing the data.

3. Results

The average age of the adolescents was 14.66 (2.6) years with the male predominance of 51.6% (sex ratio = 1.068). Table 1 represents the summary of demographic and clinical characteristics of the study subjects by gender.

Table 1. Characteristics of the Participants
VariableTotal (N = 734)Boys (N = 379)Girls (N = 355)P Value
Age, ya14.66 ± 2.6114.56 ± 2.6214.76 ± 2.590.30
BMI, kg/m2a19.14 ± 4.0819.43 ± 4.2018.82 ± 3.930.04c
HDL-C, mg/dLa49.50 ± 21.9348.88 ± 21.2350.16 ± 22.670.43
High FBSb90 (12.26)35 (9.23)55 (15.49)0.01c
High WCb100 (13.62)49 (12.93)51 (14.37)0.57
High TCb43 (5.86)21 (5.54)22 (6.2)0.71
High LDLb58 (7.90)30 (7.92)28 (7.89)0.99
High TGb64 (8.72)28 (7.39)36 (10.14)0.19
Elevated BPb23 (3.13)10 (2.64)13 (3.66)0.43

Abbreviations: BMI, Body Mass Index; BP, Blood Pressure; FBS, Fasting Blood Sugar; HDL-C, High Density Lipoprotein Cholesterol; LDL, Low Density Lipoprotein Cholesterol; TC, Total Cholesterol; TG, Triglycerides; WC, Waist Circumference.

aValues are expressed as mean ± SD.

bValues are expressed as n (%).

cSignificant values.

Genotype and SNP allele frequencies were used to assess genetic association with HDL-C levels (Table 2). None of the SNP distributions represented the deviation from Hardy-Weinberg equilibrium.

Table 2. SNP Genotype and Allele Frequencies in the Study Populationa
PolymorphismGenotype and Allele
LPL rs1801177 genotypesAAAGGG
691 (94.1)43 (5.9)0
LPL rs320 genotypesGGGTTT
259 (35.3)336 (45.8)139 (18.9)
LPL rs328 genotypesCCCGGG
580 (79)139 (18.9)15 (2)
ABCAI rs2066718 genotypesGGGAAA
704 (95.9)30 (4.1)0
ABCAI rs2230808 genotypesAAAGGG
432 (58.9)248 (33.8)54 (7.4)
CETP rs708272 genotypesCCCTTT
268 (36.5)371 (50.5)95 (12.9)
CETP rs5880 genotypesCCCGGG
638 (86.9)92 (12.5)4 (0.5)
APOC3 rs5128 genotypesCCCGGG
612 (83.4)118 (16.1)4 (0.5)
APOA1 rs2893157 genotypesGGGAAA
529 (71.7)193 (26.3)15 (2)
APOA5 rs662799 genotypesCCCTTT
722 (98.4)5 (0.7)7 (1)
ApoE allelese2e3e4
31 (4.2)654 (89.1)49 (6.7)

aABCA1, gene encoding ATP binding cassette transporter A1; APOA1, gene encoding apolipoprotein (apo) A-I; APOA5, gene encoding apo A-V; APOC3 gene encoding apo C-III; CETP, gene encoding cholesteryl ester transfer protein; LPL, gene encoding lipoprotein lipase.

Figure 1 shows the results from the null model randomization test. The score of the NULL, the best scoring, and that of the randomization models were compared. Accordingly, we can conclude that there is an association between the predictors and the low HDL-C.

Figure 1. The Binomial Deviance Score of Randomization Test
The Binomial Deviance Score of Randomization Test

The scores were obtained from the NULL-model randomization procedure.

Figure 2 displays the result of the cross-validation test. Accordingly, the model with 4 trees and 7 leaves was optimal in terms of the test set deviance and the model size.

Figure 2. The Test Scores Obtained by Cross-Validation with Various Numbers of Leaves and Trees in the Logic Regression Models
The Test Scores Obtained by Cross-Validation with Various Numbers of Leaves and Trees in the Logic Regression Models

The numbers in the squares present the number of trees and the number of leaves was indicated by the model size.

Figure 3 displays the ROC curve of fitted model. The Area under the ROC curve was 0.87 (95% CI 0.81, 0.90) indicating the high performance of the proposed method.

Figure 3. Receiver Operating Characteristics (ROC) Curve for the Fitted Logic Model
Receiver Operating Characteristics (ROC) Curve for the Fitted Logic Model

The area under the curve is 0.87 (P < 0.05) showing the Boolean Combinations of polymorphisms identified by the logic regression model, has a predictive value for HDL-C levels.

Table 3 presents the optimal combination rules for low HDL-C based on the logic regression. The model contains 4 logic combinations significantly affecting the low level of the HDL-C:

Table 3. The Results of the Fitted Logic Regression Model with 4 Boolean Combinations of 7 Binary Predictor Variables to Study Interaction Effects of SNPs and Other Risk Factors on HDL-C
Boolean CombinationCoefficientStandard ErrorOdds Ratio95% Confidence IntervalP Value
L1: ((rs2230808 = GG) or (rs5880 ≠ CC))1.2950.2513.65(2.23, 5.98)< 0.0001
L2: ((rs320 = GG) and (rs1801177 ≠ AA))0.7490.2112.12(1.4, 3.2)< 0.0001
L3: ((rs320 ≠ TT) or (rs708272 ≠ TT))0.4360.171.55(1.104, 2.165)0.011
L4: (rs708272 ≠ CC)-2.640.180.071(0.05,0.1)< 0.0001
AIC572.64
AUC0.87

Abbreviations: AIC, Akaike Information Criterion; AUC, Area Under the Curve.

The first combination (L1) contains “((rs2230808 = GG) or (rs5880 ≠ CC))”. This combination indicates that subjects with the GG genotype of ABCA1 rs2230808 or not CC genotype of CETP rs5880 have an odds ratio of 3.65 (95% CI 2.23, 5.98) with low levels of the HDL-C, comparing to the other cases.

The second combination (L2) is explained by the interaction of LPL rs320 and LPL rs1801177 polymorphisms. The estimated odds ratio for this combination was 2.12 (95% CI 1.4, 3.2), inferring that G allele of LPL rs1801177 and GG genotype of LPL rs320 gene is associated with low HDL-C levels.

The third combination (L3) contains “((rs320 ≠ TT) or (rs708272 ≠ TT))”. This form suggests that not TT genotype of LPL rs320 or not TT genotype of CETP rs708272 is associated with higher odds of low HDL-C levels. The odds ratio associated with L3 was 1.55 (95% CI 1.10, 2.17), indicating that, as a group, the subjects who complies with L3, are estimated to have higher odds of low HDL-C levels compared to the other subjects.

The fourth combination (L4) was entirely solely by CETP rs708272 polymorphism. The odds ratio related to this combination was 0.071 (9% CI 0.05, 0.1), inferring that T allele of rs708272 polymorphism is related with high HDL-C levels. The deviance as the model score function was 562.64.

4. Discussion

In this cross-sectional multi-center study, we investigated the effect of 11 genetic variants on the low HDL-C levels phenotypes and identified SNPs that were simultaneously associated with the HDL-C levels of adolescents. Although many studies examined the relationship of single SNP and low HDL-C levels, separately, only few studies analyzed interactions of SNPs that associated with low HDL-C levels.

In the analysis of genotype data, the effect size of associations between single SNPs and a response of interest are usually small. Thus it is assumed that not only single SNPs but also interactions of several SNP are effective. The logic regression is a novel approach for discovering interactions, specifically Boolean combinations of factors that are associated with the response variable (25). There are several methods proposed in the literature to improve the logic regression such as logic feature selection, (26). It is possible to select important interactions first and to design the final model based upon such predictors. Logic feature selection did not improve the quality of the proposed regression model. However, this method was used as an extension to our regression for low HDL-C level prediction (classification), whose results are shown in the supplementary file, Appendix 1.

Previous studies showed that ApoE gene polymorphisms were associated with CVD and affected the lipid profile. The e4 allele was shown as an independent risk factor for Type 2 diabetes mellitus and cardiovascular disease (27). However, in our study, the relationship between the ApoE careers and HDL-C levels in adolescents was not significant. In our study, ApoE carriers were significantly dependent with rs708272 (P value <0.01), rs2230808 (P value < 0.05), and rs320 (P value < 0.001) polymorphisms. Such variables were significant in our model. Thus, discarding the ApoE variable would decrease the model redundancy. Meanwhile, an association with ApoE and HDL-C, levels has been observed in some but not in all studies in the literature (28, 29).

Our findings showed that individuals without CC genotype of CETP rs708272 polymorphism have upper HDL-C levels. In the other words, T allele of rs708272 polymorphism is associated with raised levels of HDL-C. A meta-analysis published in 2003 has systematically shown that CETP rs708272 polymorphism is associated with HDL-C levels (30). The lack of the T allele of rs708272 polymorphism of the CETP gene was related to CAD in the Chinese population only and T allele of the rs708272 was significantly related to higher HDL-C levels in Chinese male (31). The T allele of rs708272 polymorphism was related to elevated plasma HDL-C levels in the Chinese obese population (32). A national study showed that rs708272 polymorphism was protective on dyslipidemia in Iranian children (33). It was shown that the C allele of rs708272 is related to increased CVD and type 2 diabetes mellitus risks (34). In the other study, the relationship between T allele and higher HDL-C levels in Greek children were observed (35). A meta-analysis of 13,677 individuals indicated that the rs708272 polymorphism was highly related to HDL-C concentration and the risk of atherosclerotic CVD, ultimately (36). Furthermore, a significant relation of the T allele of rs708272 polymorphism with high HDL-C levels has been reported for the Framingham (37), Chinese (38), Iranian (39), and Tunisian populations (40).

We observed that an interaction of rs2230808 polymorphism in ABCA1 gene and rs5880 polymorphism in CETP gene is associated with HDL-C levels. Individuals with GG genotype of rs2230808 polymorphism or G allele of rs5880 have lower HDL-C levels comparing to other subjects indicating that both the main effects of this polymorphism and their interactions can affect the HDL-C concentration. The replacement of C by G at amino acid 373 of CETP resulted in the rs5880 polymorphism. The adverse effect of carriers of the rs5880 polymorphism on the HDL-C levels may be explained by increasing plasma CETP concentration (41). The rs5880, CG, and GG genotypes were affiliated with 17.2%, 95.8% lower large HDL-C particle concentrations, and related with 7% and 41% lower HDL-C levels, respectively (42). The G allele of rs5880 was shown to relate to lower HDL-C, while the ischemic CVD risk was unexpectedly related to being decreased 36% in women with the G allele after HDL adjusting (43). A previous study presented that rs5880 polymorphism was negatively associated with lipid profile and resulted in a 4-fold increase in the childhood dyslipidemia risk (33). The multi-ethnic study of Atherosclerosis (MESA) demonstrated that the G allele of rs5880, which is related to higher CETP concentration (19.5%) and activity (9.4%) and lower HDL-C (6.0%), is also related to atherogenic effects (44). Studies of the ABCA1 gene indicated that its both common and rare variants affect levels of HDL-C and the risk of ischemic CVD (45). It was shown that rs2066718 and rs2230808 were related to HDL-C levels (46, 47). Another study in China showed that G allele of rs2230808 was related to decreased HDL-C level (48). The Russian population found that rs2230808 polymorphism did not affect lipids levels in patients with CVD (49). Additionally, in the population of China, no relationship between rs2230808 with lipids levels in patients with type 2 diabetes mellitus was found (50).

Our result showed that an interaction of rs320 polymorphism in LPL gene and rs708272 polymorphism in CETP gene was associated with HDL-C levels. Individuals with TT genotype of rs320 and TT genotype of rs708272 had higher HDL-C levels. The relationship between the rs320, rs1801177, and rs328 polymorphisms with both TG and HDL-C, as well as myocardial infarction was analyzed in AVCD and control subjects. Moreover, it was observed that carriers of the less common allele of the rs320 polymorphism had 0.04 mmol/L higher HDL-C levels and 0.09 mmol/L lower triglycerides levels than non-carriers (51). The rs320 GG genotype and rs320 G allele were observed to be significantly associated with stroke in Indian population. Also, rs320 GG genotype associated significantly with high levels of TG and low levels of HDL. However, this polymorphism did not show any association with LDL-C and VLDL levels (52). It was reported that in Iranian children and adolescents, carriers of T allele of rs320 polymorphism are associated with lower TG and LDL-C and higher TC and HDL-C (24). Other studies showed that in healthy-weight men with coronary heart disease, the rs320 polymorphism alone might impress the HDL-C concentration, in contrast to rs328 alone, which has no influence on any lipid parameters (53).

Our result showed that an interaction of rs320 polymorphism and rs1801177 polymorphism in in LPL gene was associated with HDL-C levels. Individuals without GG genotype of rs320 or AA genotype of rs1801177 had higher HDL-C levels. The G allele of rs1801177 polymorphism is shown to be associated with lower HDL-C levels and higher LDL-C levels in Iranian adolescents (24). It was reported that rs1801177 polymorphism affects serum lipids, and thus increase the CVD risk (54). However, another case–control study showed that the rs1801177 genotype was not significantly correlated with CVD (55).

4.1. Study Limitations and Strengths

Our study was a cross sectional. This is in fact one of the limitations of our work. However, the pediatric age group and the region under the study are 2 advantages of our work in comparison with the state-of-the art.

4.2. Conclusion:

We showed that rs708272 polymorphism in CETP gene has an important effect on the level of HDL-C, independently. Moreover, rs708272 increased HDL-C levels and had a protective effect on HDL-C. The interaction of ABCA1 (rs2230808) as well as CETP (rs5880) and the interaction of LPL (rs320) as well as CETP (rs708272) were associated with lower HDL-C levels. Furthermore, the interaction of LPL (rs320) and LPL (rs1801177) was associated with lower HDL-C levels.

Acknowledgements

References

  • 1.

    Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006; 3(11)[DOI][PubMed]

  • 2.

    Frontini MG, Srinivasan SR, Xu J, Tang R, Bond MG, Berenson GS. Usefulness of childhood non-high density lipoprotein cholesterol levels versus other lipoprotein measures in predicting adult subclinical atherosclerosis: the Bogalusa Heart Study. Pediatrics. 2008; 121(5) : 924 -9 [DOI][PubMed]

  • 3.

    Lauer RM, Clarke WR. Use of cholesterol measurements in childhood for the prediction of adult hypercholesterolemia. The Muscatine Study. JAMA. 1990; 264(23) : 3034 -8 [DOI][PubMed]

  • 4.

    Delavari A, Forouzanfar MH, Alikhani S, Sharifian A, Kelishadi R. First nationwide study of the prevalence of the metabolic syndrome and optimal cutoff points of waist circumference in the Middle East: the national survey of risk factors for noncommunicable diseases of Iran. Diabetes Care. 2009; 32(6) : 1092 -7 [DOI][PubMed]

  • 5.

    Ribas SA, Santana da Silva LC. Anthropometric indices: predictors of dyslipidemia in children and adolescents from north of Brazil. Nutr Hosp. 2012; 27(4) : 1228 -35 [DOI][PubMed]

  • 6.

    Barter PJ, Caulfield M, Eriksson M, Grundy SM, Kastelein JJ, Komajda M, et al. Effects of torcetrapib in patients at high risk for coronary events. N Engl J Med. 2007; 357(21) : 2109 -22 [DOI][PubMed]

  • 7.

    Gotto AJ, Brinton EA. Assessing low levels of high-density lipoprotein cholesterol as a risk factor in coronary heart disease: a working group report and update. J Am Coll Cardiol. 2004; 43(5) : 717 -24 [DOI][PubMed]

  • 8.

    Gehani AA, Al-Hinai AT, Zubaid M, Almahmeed W, Hasani MR, Yusufali AH, et al. Association of risk factors with acute myocardial infarction in Middle Eastern countries: the INTERHEART Middle East study. Eur J Prev Cardiol. 2014; 21(4) : 400 -10 [DOI][PubMed]

  • 9.

    Kelishadi R, Gheiratmand R, Ardalan G, Adeli K, Mehdi Gouya M, Mohammad Razaghi E, et al. Association of anthropometric indices with cardiovascular disease risk factors among children and adolescents: CASPIAN Study. Int J Cardiol. 2007; 117(3) : 340 -8 [DOI][PubMed]

  • 10.

    Kelishadi R, Motlagh ME, Roomizadeh P, Abtahi SH, Qorbani M, Taslimi M, et al. First report on path analysis for cardiometabolic components in a nationally representative sample of pediatric population in the Middle East and North Africa (MENA): the CASPIAN-III Study. Ann Nutr Metab. 2013; 62(3) : 257 -65 [DOI][PubMed]

  • 11.

    Kelishadi R, Ardalan G, Gheiratmand R, Ramezani A. Is family history of premature cardiovascular diseases appropriate for detection of dyslipidemic children in population-based preventive medicine programs? CASPIAN study. Pediatr Cardiol. 2006; 27(6) : 729 -36 [DOI][PubMed]

  • 12.

    Schwandt P, Kelishadi R, Haas GM. Ethnic disparities of the metabolic syndrome in population-based samples of german and Iranian adolescents. Metab Syndr Relat Disord. 2010; 8(2) : 189 -92 [DOI][PubMed]

  • 13.

    Qasim A, Rader DJ. Human genetics of variation in high-density lipoprotein cholesterol. Curr Atheroscler Rep. 2006; 8(3) : 198 -205 [DOI][PubMed]

  • 14.

    Ahmadzadeh A, Azizi F. Genes associated with low serum high-density lipoprotein cholesterol. Arch Iran Med. 2014; 17(6) : 444 -50 [PubMed]

  • 15.

    Snieder H, van Doornen LJ, Boomsma DI. Dissecting the genetic architecture of lipids, lipoproteins, and apolipoproteins: lessons from twin studies. Arterioscler Thromb Vasc Biol. 1999; 19(12) : 2826 -34 [DOI][PubMed]

  • 16.

    Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al. Initial sequencing and analysis of the human genome. Nature. 2001; 409(6822) : 860 -921 [DOI][PubMed]

  • 17.

    Culverhouse R, Suarez BK, Lin J, Reich T. A perspective on epistasis: limits of models displaying no main effect. Am J Hum Genet. 2002; 70(2) : 461 -71 [DOI][PubMed]

  • 18.

    Kelishadi R, Haghjooy Javanmard S, Tajadini MH, Mansourian M, Motlagh ME, Ardalan G, et al. Genetic association with low concentrations of high density lipoprotein-cholesterol in a pediatric population of the Middle East and North Africa: the CASPIAN-III study. Atherosclerosis. 2014; 237(1) : 273 -8 [DOI][PubMed]

  • 19.

    Kelishadi R, Heshmat R, Motlagh ME, Majdzadeh R, Keramatian K, Qorbani M, et al. Methodology and Early Findings of the Third Survey of CASPIAN Study: A National School-based Surveillance of Students' High Risk Behaviors. Int J Prev Med. 2012; 3(6) : 394 -401 [PubMed]

  • 20.

    Kwiterovich PJ. Recognition and management of dyslipidemia in children and adolescents. J Clin Endocrinol Metab. 2008; 93(11) : 4200 -9 [DOI][PubMed]

  • 21.

    Gupta N, Goel K, Shah P, Misra A. Childhood obesity in developing countries: epidemiology, determinants, and prevention. Endocr Rev. 2012; 33(1) : 48 -70 [DOI][PubMed]

  • 22.

    Brunham LR, Hayden MR. Human genetics of HDL: Insight into particle metabolism and function. Prog Lipid Res. 2015; 58 : 14 -25 [DOI][PubMed]

  • 23.

    Ruczinski I, Kooperberg C, LeBlanc M. Logic Regression. J Comput Graph Stat. 2003; 12(3) : 475 -511 [DOI]

  • 24.

    Askari G, Heidari-Beni M, Mansourian M, Esmaeil-Motlagh M, Kelishadi R. Interaction of lipoprotein lipase polymorphisms with body mass index and birth weight to modulate lipid profiles in children and adolescents: the CASPIAN-III Study. Sao Paulo Med J. 2016; 134(2) : 121 -9 [DOI][PubMed]

  • 25.

    Schwender H, Ruczinski I. Logic regression and its extensions. Adv Genet. 2010; 72 : 25 -45 [DOI][PubMed]

  • 26.

    Kuczmarski RJ, Ogden CL, Guo SS, Grummer-Strawn LM, Flegal KM, Mei Z, et al. 2000 CDC Growth Charts for the United States: methods and development. Vital Health Stat 11. 2002; (246) : 1 -190 [PubMed]

  • 27.

    El-Lebedy D, Raslan HM, Mohammed AM. Apolipoprotein E gene polymorphism and risk of type 2 diabetes and cardiovascular disease. Cardiovasc Diabetol. 2016; 15 : 12 [DOI][PubMed]

  • 28.

    Kavey RE, Daniels SR, Lauer RM, Atkins DL, Hayman LL, Taubert K, et al. American Heart Association guidelines for primary prevention of atherosclerotic cardiovascular disease beginning in childhood. Circulation. 2003; 107(11) : 1562 -6 [DOI][PubMed]

  • 29.

    Daniels SR, Greer FR, Committee on N. Lipid screening and cardiovascular health in childhood. Pediatrics. 2008; 122(1) : 198 -208 [DOI][PubMed]

  • 30.

    Boekholdt SM, Thompson JF. Natural genetic variation as a tool in understanding the role of CETP in lipid levels and disease. J Lipid Res. 2003; 44(6) : 1080 -93 [DOI][PubMed]

  • 31.

    Lu Y, Tayebi N, Li H, Saha N, Yang H, Heng CK. Association of CETP Taq1B and -629C > A polymorphisms with coronary artery disease and lipid levels in the multi-ethnic Singaporean population. Lipids Health Dis. 2013; 12 : 85 [DOI][PubMed]

  • 32.

    Ruan X, Ma L, Wang S, Lindpaintner K, Liu X, Wang B, et al. Association of two CETP polymorphisms with HDL levels in the Chinese obese population. Obesity (Silver Spring). 2009; 17(12) : 2196 -201 [DOI][PubMed]

  • 33.

    Heidari-Beni M, Kelishadi R, Mansourian M, Askari G. Interaction of cholesterol ester transfer protein polymorphisms, body mass index, and birth weight with the risk of dyslipidemia in children and adolescents: the CASPIAN-III study. Iran J Basic Med Sci. 2015; 18(11) : 1079 -85 [PubMed]

  • 34.

    Rahimi Z, Nourozi-Rad R, Vaisi-Raygani A, Saidi MR, Rahimi Z, Ahmadi R, et al. Association between cholesteryl ester transfer protein TaqIB variants and risk of coronary artery disease and diabetes mellitus in the population of western Iran. Genet Test Mol Biomarkers. 2011; 15(11) : 813 -9 [DOI][PubMed]

  • 35.

    Smart MC, Dedoussis G, Louizou E, Yannakoulia M, Drenos F, Papoutsakis C, et al. APOE, CETP and LPL genes show strong association with lipid levels in Greek children. Nutr Metab Cardiovasc Dis. 2010; 20(1) : 26 -33 [DOI][PubMed]

  • 36.

    Boekholdt SM, Sacks FM, Jukema JW, Shepherd J, Freeman DJ, McMahon AD, et al. Cholesteryl ester transfer protein TaqIB variant, high-density lipoprotein cholesterol levels, cardiovascular risk, and efficacy of pravastatin treatment: individual patient meta-analysis of 13,677 subjects. Circulation. 2005; 111(3) : 278 -87 [DOI][PubMed]

  • 37.

    Ordovas JM, Cupples LA, Corella D, Otvos JD, Osgood D, Martinez A, et al. Association of cholesteryl ester transfer protein-TaqIB polymorphism with variations in lipoprotein subclasses and coronary heart disease risk: the Framingham study. Arterioscler Thromb Vasc Biol. 2000; 20(5) : 1323 -9 [DOI][PubMed]

  • 38.

    Wu Y, Bai H, Liu R, Liu Y, Liu BW. [Analysis of cholesterol ester transfer protein gene Taq IB and -629 C/A polymorphisms in patients with endogenous hypertriglyceridemia in Chinese population]. Zhonghua Yi Xue Yi Chuan Xue Za Zhi. 2006; 23(6) : 640 -6 [PubMed]

  • 39.

    Kashani Farid MA, Azizi F, Hedayati M, Daneshpour MS, Shamshiri AR, Siassi F. Association between CETP Taq1B and LIPC -514C/T polymorphisms with the serum lipid levels in a group of Tehran's population: a cross sectional study. Lipids Health Dis. 2010; 9 : 96 [DOI][PubMed]

  • 40.

    Rejeb J, Omezzine A, Boumaiza I, Rebhi L, Rejeb NB, Nabli N, et al. Four polymorphisms of cholesteryl ester transfer protein gene and coronary stenosis in a Tunisian population. J Cardiovasc Med (Hagerstown). 2012; 13(9) : 546 -53 [DOI][PubMed]

  • 41.

    Dergunov AD. Prediction of the influences of missense mutations on cholesteryl ester transfer protein structure. Arch Biochem Biophys. 2014; 564 : 67 -73 [DOI][PubMed]

  • 42.

    Tsai MY, Li N, Sharrett AR, Shea S, Jacobs DJ, Tracy R, et al. Associations of genetic variants in ATP-binding cassette A1 and cholesteryl ester transfer protein and differences in lipoprotein subclasses in the multi-ethnic study of atherosclerosis. Clin Chem. 2009; 55(3) : 481 -8 [DOI][PubMed]

  • 43.

    Agerholm-Larsen B, Tybjaerg-Hansen A, Schnohr P, Steffensen R, Nordestgaard BG. Common cholesteryl ester transfer protein mutations, decreased HDL cholesterol, and possible decreased risk of ischemic heart disease: The Copenhagen City Heart Study. Circulation. 2000; 102(18) : 2197 -203 [DOI][PubMed]

  • 44.

    Tsai MY, Johnson C, Kao WH, Sharrett AR, Arends VL, Kronmal R, et al. Cholesteryl ester transfer protein genetic polymorphisms, HDL cholesterol, and subclinical cardiovascular disease in the Multi-Ethnic Study of Atherosclerosis. Atherosclerosis. 2008; 200(2) : 359 -67 [DOI][PubMed]

  • 45.

    Frikke-Schmidt R. Genetic variation in the ABCA1 gene, HDL cholesterol, and risk of ischemic heart disease in the general population. Atherosclerosis. 2010; 208(2) : 305 -16 [DOI][PubMed]

  • 46.

    Frikke-Schmidt R, Nordestgaard BG, Jensen GB, Tybjaerg-Hansen A. Genetic variation in ABC transporter A1 contributes to HDL cholesterol in the general population. J Clin Invest. 2004; 114(9) : 1343 -53 [DOI][PubMed]

  • 47.

    Pajukanta P. Do DNA sequence variants in ABCA1 contribute to HDL cholesterol levels in the general population? J Clin Invest. 2004; 114(9) : 1244 -7 [DOI][PubMed]

  • 48.

    Guo Z, Wu P, Xie D, Wang Q, Liu Y, Cha Z, et al. A new discovered ABCA1 gene polymorphisms and the association of ABCA1 SNPs with coronary artery disease and plasma lipids in Chinese population. J Med Colleges PLA. 2011; 26(4) : 179 -90 [DOI]

  • 49.

    Tupitsina T, Slominsky P, Yufereva Y, Perova N, Aronov D, Limborska S. Linkage analysis by a transmission/disequilibrium test of Russian sibling Pairs with coronary artery disease. Balkan J Med Genet. 2007; 10(2) : 43 -54 [DOI]

  • 50.

    Wang J, Bao YQ, Hu C, Zhang R, Wang CR, Lu JX, et al. Effects of ABCA1 variants on rosiglitazone monotherapy in newly diagnosed type 2 diabetes patients. Acta Pharmacol Sin. 2008; 29(2) : 252 -8 [DOI][PubMed]

  • 51.

    Sagoo GS, Tatt I, Salanti G, Butterworth AS, Sarwar N, van Maarle M, et al. Seven lipoprotein lipase gene polymorphisms, lipid fractions, and coronary disease: a HuGE association review and meta-analysis. Am J Epidemiol. 2008; 168(11) : 1233 -46 [DOI][PubMed]

  • 52.

    Munshi A, Babu MS, Kaul S, Rajeshwar K, Balakrishna N, Jyothy A. Association of LPL gene variant and LDL, HDL, VLDL cholesterol and triglyceride levels with ischemic stroke and its subtypes. J Neurol Sci. 2012; 318(1-2) : 51 -4 [DOI][PubMed]

  • 53.

    Kolovou GD, Kolovou V, Panagiotakos DB, Vasiliadis I, Giannakopoulou V, Kostakou PM, et al. Study of common variants of the apolipoprotein E and lipoprotein lipase genes in patients with coronary heart disease and variable body mass index. Hormones (Athens). 2015; 14(3) : 376 -82 [DOI][PubMed]

  • 54.

    Corsetti JP, Gansevoort RT, Navis G, Sparks CE, Dullaart RP. LPL polymorphism (D9N) predicts cardiovascular disease risk directly and through interaction with CETP polymorphism (TaqIB) in women with high HDL cholesterol and CRP. Atherosclerosis. 2011; 214(2) : 373 -6 [DOI][PubMed]

  • 55.

    Ceja-Espiritu G, Delgado-Enciso I, Ramirez-Flores M, Guzman-Esquivel J, Baltazar-Rodriguez LM. The D9N, N291S, and T495G Polymorphisms of the Lipoprotein Lipase Gene Are Not Associated with Cerebral Infarction. J Stroke Cerebrovasc Dis. 2016; 25(4) : 985 -9 [DOI][PubMed]

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