Iranian Journal of Medical Sciences

Document Type : Original Article(s)

Authors

1 Research Center for Food Hygiene and Safety, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

2 Department of Nutrition, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

3 Cardiovascular Research Center, Non-Communicable Diseases Research Institute, Afshar Hospital, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

4 Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

5 Department of Medical Genetics, Faculty of Medicine, Shahid Sadoughi University of Medical Sciences, BP634, F8916978477 Yazd, Iran

6 Mother and Newborn Health Research Center, Shahid Sadoughi Hospital, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

7 Division of Genetics, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Biotechnology, University of Isfahan, Isfahan, Iran

8 Dr. Mazaheri’s Medical Genetics Lab, Yazd, Iran

Abstract

Background: Several studies assessed the relationship between the cholesterol ester transfer protein (CETP) Taq1B gene polymorphism (rs708272) with risk factors of cardiovascular diseases (CVDs). However, their findings were inconsistent. The present study investigated the relationship between CVD risk factors and the Taq1B variant in patients undergoing coronary angiography. 
Methods: This cross-sectional study was conducted on 476 patients aged 30-76 years old of both sexes from 2020-2021, in Yazd (Iran). The Taq1B polymorphism genotypes were evaluated using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) on DNA extracted from whole blood. Standard protocols were used to measure cardio-metabolic markers. To determine the association between CVDs risk factors and the rs708272 variant, binary logistic regression was used in crude and adjusted models.
Results: Taq1B polymorphism genotype frequencies were 10.7% for B1B1, 72.3% for B1B2, and 17% for B2B2. There was no significant association between abnormal levels of CVDs risk factors and different genotypes of the Taq1B variant, Gensini score (P=0.64), Syntax score (P=0.79), systolic blood pressure (P=0.55), diastolic blood pressure (P=0.58), and waist circumference (P=0.79). There was no significant association between genotypes of the rs708272 variant and any abnormal serum lipid levels. After adjusting for confounders, the results remained non-significant.
Conclusion: There was no significant association between CVDs risk factors and CETP rs708272 polymorphism. The relationship between CETP gene variants and CVD occurrences varied across groups, implying that more research in different regions is required. 
A preprint version of this manuscript is available at https://www.researchsquare.com/article/rs-2575215/v1 with doi: 10.21203/rs.3.rs-2575215/v1.

Keywords

What’s Known

The studies that were conducted in different populations in Iran lacked consistency. Furthermore, previous research did not investigate the relationship between the Taq1B variant and all of the metabolic syndrome components. Moreover, the observational studies on this issue in Iran are quite scarce, with small sample sizes that do not completely account for confounders.

What’s New

In line with previous studies, the present study found no relationship between the Taq1B variant and plasma lipid levels or other cardiovascular disease risk factors in patients undergoing angiography in Yazd (Iran). This finding might be specific to this region.

Introduction

Cardiovascular diseases (CVDs) are a group of disorders that affect the heart and blood vessels, and are the leading cause of death worldwide, accounting for 22.2 million deaths per year by 2030. 1 Age, sex, family history, smoking, physical inactivity, unhealthy diet, impaired glucose and lipid levels, high blood pressure, and waist circumference (WC), and metabolic syndrome (MetS) are all CVD risk factors. 2 , 3 Previous studies found a correlation between abnormal blood lipid levels such as elevated triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), decreased high-density lipoprotein cholesterol (HDL-C), abdominal obesity, and the risk of CVD. 4 , 5 Evidence suggests that lifestyle modifications such as a healthy diet, increasing physical activity, quitting smoking, and losing weight could improve and control risk factors of CVDs. 6

Genetic variants, such as single nucleotide polymorphisms (SNPs), were also shown through lipid metabolism and could have an association with CVDs. 7 The cholesterol ester transfer protein (CETP) gene encodes the CETP, which transfers cholesteryl esters from HDL to iatrogenic lipoproteins in exchange for TG. 8 A silent mutation base in the 277th nucleotide of the first intron of the CETP gene might change the Guanine base (G) to Adenine base (A), resulting in the rs708272 (Taq1B) variation. G called as B1 allele (frequent allele) with a restriction site for Taq1 endonuclease enzyme and A called as B2 allele (less common allele) without Taq1 restriction site. 9 Previous review studies suggested that the Taq1B2 variant of the CETP gene may reduce the severity of blood vessel stenosis by increasing HDL-C levels and decreasing plasma CETP. 7 , 10 However, the reported association between CETP gene variants and lipid levels was contradictory in different populations. 11 - 13 According to the findings of an epidemiological and clinical investigation, MetS was associated with an increased risk of CVDs. 14 However, the evidence on the association of MetS components and Taq1B polymorphism with CVDs was limited and inconsistent. 15 , 16

Since the evidence on the association between CVD risk factors and Taq1B polymorphism was inconsistent, it seemed necessary to investigate how genetic variations in different populations can lead to different findings. Therefore, since there were few studies in different regions of Iran, this study aimed to assess the relationship between the CETPTaq1B polymorphism and CVDs risk factors, such as WC, lipid profiles, fasting blood glucose (FBS), systolic blood pressure (SBP), diastolic blood pressure (DBP), and severity of stenosis coronary arteries across Taq1B genotypes in patients undergoing coronary angiography.

Materials and Methods

Study Design and Participants

The present cross-sectional study assessed 476 patients who were referred to Afshar Hospital (Yazd, Iran) for angiography between September 2020 and October 2021. The participant’s age ranged from 35 to 75. All participants provided written informed consent before participating in the study. Subjects were excluded if they had a history of cancer, chronic heart failure (CHF), a history of myocardial infarction (MI), percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), chronic kidney disease stage III or higher, liver disease or were receiving medication for liver disease, certain perceptual or psychological disorders, immune system failure, an acquired immunodeficiency syndrome (AIDS), or extreme obesity (body mass index [BMI] more than 40). Pregnant and lactating women, as well as those with oral intake restrictions were also excluded. This study was approved by the Ethics Committee of Shahid Sadoughi University of Medical Sciences, Yazd, Iran, (IR.SSU.SPH.REC.1400.079).

DNA Extraction and Genotyping

Genome Deoxyribonucleic acid (DNA) was extracted from white blood cells in 100 µL of peripheral whole blood using the Kit protocol (SIMBIOLAB, IRAN). The extracted DNA was stored at -20 °C until analysis. The CETP-Taq1B variant was amplified using the polymerase chain reaction (PCR) method, with a 20 µL volume solution consisting of 2 µL genomic DAN, 6 µL water, 10 µL Master Mix (Amplicon, Denmark), and 1 µL of each primer 5’-ACTAGCCCAGAGAGAGGAGTG-3’ and 5’-CAGCCGCACACTAACCCTA-3’ synthesized by SinaClon, Iran. Amplification was applied with one denaturation cycle for 5 min denaturation cycle at 95 °C, followed by 40 cycles for 30 sec at 95 °C, annealing at 66 °C for 30 sec, and primary extension at 72 °C for 30 sec. The final extension was one cycle at 72 °C in 5 min. The PCR products were electrophoresed on 2% agarose gel (Sina Clon Co., Iran) and then digested by endonuclease enzyme of the Taq1 (Fermentase, Lithuania) after incubating at 37 °C for 24 h in a 30 µL (10 µL PCR products, 2 µL buffer, 0.5 µL Taq1 enzymes, and 17.5 µL water). Digested fragments of the 708272-CETP were electrophoresed using 2% agarose gel with a voltage of 100 V for one hour. Finally, using ultraviolet transilluminationl, genotypes were identified on the gel based on fragment length.

The patient’s weight and height were measured based on the standard protocols utilizing Omron BF-511 portable digital scales (with an accuracy of 100 gr) and a tape measure (with an accuracy of 0.1 cm). The WC was measured with a non-stretch tape in the middle of the iliac crown and the lowest rib in the standing position with an accuracy of 0.1 cm. Based on the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III), abdominal obesity was defined as WC>102 for men and >88 cm for women. 17 , 18 BMI was calculated by dividing weight (Kg) by squared height (m2). All these variables were measured by nutrition-trained students.

Physical Activity Assessment

Daily physical activity was evaluated using the International Physical Activity Questionnaire (IPAQ). 19 Activity levels were measured in metabolic equivalent (MET) hours per week and categorized as sedentary, moderate, or active based on a list of regular daily activities throughout the previous week.

Laboratory Measurements

After overnight fasting, we collected 4 mL of venous blood from each participant. After being placed in tubes containing ethylenediaminetetraacetic acid (EDTA), the samples were centrifuged for 5 min at 5000 rpm. According to a standard laboratory protocol, biomarkers such as TG, HDL-C, LDL-C, total cholesterol (TC), and FBS were measured using Pars Azmun kits (Tehran, Iran). Abnormal levels of biochemical markers were defined as follows TG≥150 mg/dL, FBS≥110 mg/ dL; HDL-C<40 mg/dL for men, and HDL-C<50 mg/dL for women; TC≥200 mg/dL and LDL-C≥130 mg/dL. 17 , 18

Calculation Syntax and Gensini Scores

The severity of coronary artery stenosis in participants undergoing angiography was determined using two scoring criteria: Syntax and Gensini. According to Gensini, the percentage of lumen obstruction in atherosclerotic lesions was 1 point for ≤25% obstruction, 2 points for 26-50% obstruction, 4 points for 51-75% obstruction, 8 points for 76-90% obstruction, 16 points for 91-99% obstruction, and 32 points for full obstruction. The scores were multiplied by coefficients ranging from1 to 5, depending on the number of coronary arteries and obstructed sections. A coefficient value of 5 was used for the main left coronary artery, 2.5 for the anterior descending and proximal of the left coronary artery, 1.5 for the mid-segment of the left anterior descending coronary artery, 1 for the proximal right section, and other segments received a coefficient of 0.5. The total Gensini score (GS) was calculated by adding the stenosis scores and coefficients for each duct. 20 , 21 GS≥23 was considered as intermediate-high-risk severity of coronary artery stenosis and <23 as low-risk. 22

A web-based computer program (http://www.syntaxscore.com) was used to calculate the SYNTAX score (SS).The questions on the list concerning SS were related to functional and anatomical parameters of the obstruction ≥50%, and stenosis of arteries with a diameter of ≥1.5 mm. 22 , 23 The SYNTAX score (SS) was calculated as the sum of all obstruction scores. The SS lower than 22 was considered to be of low severity of coronary artery stenosis, while SS≥22 was of moderate-severe severity. 22 The coronary angiographies were analyzed by experienced cardiologists, who were anonymized to the patients’ identity except for age and sex.

Assessment of Other Variables

Trained interviewers utilized a general questionnaire to collect other socioeconomic information including, age, sex, smoking status, job, educational levels, menstrual status, and drug use history. Prior to angiography, BP was measured by hospital-experienced nurses according to standard protocol. The patients with BP≥140 and ≥90 mmHg were classified as having systolic and diastolic BP, respectively. 17 , 18

Statistical Analysis

Data were analyzed using SPSS 24.0 (IBM Corporation, Chicago, IL). To assess CVD risk factors across Taq1B genotypes, one-way ANOVA analysis for continuous variables and the Chi square test for categorical variables were used. Odds Binary logistic regressions in crude and multivariable-adjusted models were used to evaluate the odds ratio (OR) and 95% confidence intervals (CIs) of CVD risk factors across genotypes. The univariate logistic test was used to identify confounders for adjustment, which were then entered into the multivariate models. According to the Hosmer-Lemeshow principle, variables with a P≤0.2 were considered confounders. Age, sex, BMI, physical activity, smoking status, used medications, job, education level, economic status, menopausal status, family size, and marital status were all included as confounders. Pearson’s Chi square test was used for assessing the Hardy-Weinberg equilibrium (HWE). All analyses were conducted using a statistical package for the social sciences (SPSS) software version 24 (IBM Corporation, USA). P<0.05 was considered statistically significant, except for HWE, where P value>0.05 was used.

Results

General Characteristics of Study Participants

The basic characteristics of participants for each genotype of the CETP-Taq1B polymorphism is shown in table 1. The participant’s mean age was 56.90±9.31 years. The frequency of the CETP-Taq1B genotypes was (10.7%), (72.3%), and (17%) for B1B1, B1B2, and B2B2, respectively. There was no statistically significant difference in age (P=0.73), BMI (P=0.52), weight (P=0.77), height (P=0.88), WC (P=0.61), physical activity (P=0.73), sex (P=0.93) across the CETP-Taq1B genotypes. The genotypes were all within the HWE (P>0.999). The electrophoresis results of the digested products showed the following fragments: homozygous B1B1 with two bands of 175 and 345 base pairs (bp), heterozygous B1B2 with three bands of 520, 175, and 345 bp, and homozygous B2B2 with one band of 520 bp (figure 1).

Variables CETP-rs708272
Total genotypes B1B1 B1B2 B2B2 P value
(n=476) n=51 (10.7%) n=344 (72.3%) n=81 (17%)
Age (year, mean±SD) 56.90±9.31 57.84±10.03 56.74±9.07 56.99±9.91 0.73
BMI (Kg/m2, mean±SD) 27.42±4.28 27.30±3.99 27.31±4.37 27.93±4.08 0.52
Weight (Kg, mean±SD) 74.02±12.92 74.07±14.99 73.79±12.78 74.99±12.13 0.77
Height (cm, mean±SD) 164.41±9.89 164.24±11.29 164.54±9.85 163.95±9.16 0.88
WC (cm, mean±SD) 99.62±10.95 100.64±11.29 99.29±11.13 100.30±10.01 0.61
Physical activity, n (%) Sedentary 158 (33.2) 17 (33.3) 116 (33.7) 25 (30.9) 0.73
Moderate 160 (33.6) 20 (39.2) 112 (32.6) 28 (34.6)
Active 158 (33.2) 14 (27.5) 116 (33.7) 28 (34.6)
Sex, n (%) Male 301 (63.2) 33 (64.7) 218 (63.4) 50 (61.7) 0.93
Female 175 (36.8) 18 (35.3) 126 (36.6) 31 (38.3)
Smoking status, n (%) Non-smoker 307 (64.5) 32 (62.7) 218 (63.4) 57 (70.4) 0.66
Former smoker 17 (3.6) 2 (3.9) 14 (4.1) 1 (1.2)
Current smoker 152 (31.9) 17 (33.3) 112 (32.6) 23 (28.4)
Medicine consumption; yes, n (%) Anti-hypertensives 208 (43.7) 23 (45.1) 151 (43.9) 34 (42) 0.93
Anti-hyperlipidemic 172 (36.1) 18 (35.3) 121 (35.2) 33 (40.7) 0.63
Anti-diabetic 156 (32.8) 16 (31.4) 114 (33.1) 26 (32.1) 0.95
BMI: Body mass index; WC: Waist circumference; One-way ANOVA was used for continuous variables, and Chi square test was used for categorical variables. P<0.05 was considered significant.
Table 1.General characteristics of study participants according to the CETP-Taq1B genotypes

Figure 1. This figure shows the digested fragments of the 708272-CETP on 2% agarose gel electrophoresis. The ladder marker (lane 1) was 50 bp, the homozygous B1B1 genotype (lane 6) had two bands of 175 bp and 345 bp. The heterozygous B1B2 genotype (lanes 3 and 5) had three bands of 175 bp, 345 bp, and 520 bp. The homozygous B2B2 genotype (lanes 2 and 4) had one band of 520 bp.

Comparison of CVD Risk Factors Across Genotypes of the CETP-Taq1B

The CVD risk factors according to genotypes of the CETP-Taq1B are presented in table 2. As shown, no significant difference was found between FBS (P=0.56), TC (P=0.99), LDL-C (P=0.59), TG (P=0.22), HDL-C (P=0.26), WC (P=0.61), SBP (P=0.72), DBP (P=0.88), and scores of severity of coronary artery stenosis (GS=0.59) and (SS=0.83) in different Taq1B genotypes.

Variable Tertiles of CETP-Taq1B genotypes
Total genotypes B1B1 B1B2 B2B2 P value
(n=476) (n=51) (n=344) (n=81)
Mean±SD Mean±SD Mean±SD Mean±SD
WC (cm) 99.62±10.95 100.64±11.29 99.29±11.13 100.30±10.01 0.61
TC (mg/dL) 199.16±110.05 198.44±132.16 199.43±107.48 198.53±106.73 0.99
LDL (mg/dL) 98.03±41.86 92.22±38.42 98.67±40.51 99.07±48.95 0.59
HDL (mg/dL) 48.59±12.02 46.55±10.88 48.53±12.14 50.14±12.15 0.26
TG (mg/dL) 155.27±85.66 168.63±102.70 150.81±75.50 164.96±109.23 0.22
FBS (mg/dL) 134.24±63.62 129.93±59.64 133.27±61.80 140.79±72.89 0.56
SBP (mgHg) 128.53±13.91 127.61±11.84 128.85±14.30 127.72±13.53 0.72
DBP (mgHg) 79.01±11.16 78.48±8.80 79.17±10.16 78.68±15.88 0.88
Gensini score 34.62±42.12 32.69±46.58 35.82±40.98 30.61±44.19 0.59
Syntax score 10.65±12.96 9.93±14.88 10.88±12.39 10.14±14.13 0.83
TC: Total cholesterol; HDL-C: High-density lipoprotein cholesterol; FBS: Fasting blood sugar; LDL-C: Low-density lipoprotein cholesterol; TG; Triglyceride; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; One-way ANOVA was used. P<0.05 was considered significant.
Table 2.Measures of CVDs risk factors across CETP-Taq1B genotypes

Association of CVD Risk Factors with Genotypes of CETP-Taq1B

Table 3 shows the odds ratios (95% CIs) of CVD risk factors across CETP-Taq1B genotypes. The following OR (95% CIs) of abnormal biochemical profiles were not associated with genotypes: high serum LDL (OR: 1.12; 95% CI: 0.53-2.35, P=0.75), TG (OR: 0.97; 95% CI: 0.46-2.03, P=0.94), TC (OR: 1.29; 95% CI: 0.58-2.86, P=0.52), FBS (OR: 1.38; 95% CI: 0.67-2.84, P=0.37), and low HDL (OR: 0.58; 95% CI: 0.26-1.27, P=0.17). There was no significant relationship between stenosis scores of coronary arteries across genotypes, GS (OR: 0.84; 95% CI: 0.40-1.74, P=0.64), and SS (OR: 0.88; 95% CI: 0.34-2.28, P=0.79). There was no relationship between other variables, including SBP (OR: 0.79; 95% CI: 0.37-1.68, P=0.55), DBP (OR: 0.76; 95% CI: 0.29-2.0, P=0.58), and WC (OR: 0.90; 95% CI: 0.43-1.89, P=0.79). After adjusting confounders, the associations remained non-significant in both models 1 and 2 for all outcomes.

Variable B1B1 B1B2 B2B2 P value* P trend
(n=51) (n=344) (n=81)
High WC
Crude 1.00 0.91 (0.49-1.69) 0.90 (0.43-1.89) 0.79 0.82
Model 1 1.00 0.67 (0.25-1.83) 0.44 (0.13-1.50) 0.19 0.18
Model 2 1.00 0.69 (0.24-2.01) 0.45 (0.13-1.61) 0.22 0.20
High serum HDL
Crude 1.00 0.86 (0.46-1.62) 0.58 (0.26-1.27) 0.17 0.14
Model 1 1.00 0.86 (0.44-1.65) 0.54 (0.24-1.21) 0.13 0.10
Model 2 1.00 0.93 (0.47-1.85) 0.61 (0.26-1.42) 0.25 0.21
High serum LDL
Crude 1.00 1.41 (0.75-2.65) 1.12 (0.53-2.35) 0.75 0.96
Model 1 1.00 1.39 (0.74-2.61) 1.00 (0.47-2.12) 0.99 0.78
Model 2 1.00 1.27 (0.66-2.44) 0.78 (0.40-1.89) 0.73 0.53
Low serum TC
Crude 1.00 1.58 (0.80-3.12) 1.29 (0.58-2.86) 0.52 0.73
Model 1 1.00 1.61 (0.80-3.24) 0.99 (0.43-2.30) 0.99 0.72
Model 2 1.00 1.47 (0.70-3.10) 0.72 (0.29-1.82) 0.50 0.34
High serum TG
Crude 1.00 0.92 (0.49-1.71) 0.97 (0.46-2.03) 0.94 0.99
Model 1 1.00 0.95 (0.49-1.81) 0.78 (0.35-1.71) 0.54 0.51
Model 2 1.00 0.82 (0.40-1.65) 0.75 (0.32-1.75) 0.50 0.52
High serum FBS
Crude 1.00 1.01 (0.55-1.84) 1.38 (0.67-2.84) 0.37 0.29
Model 1 1.00 1.14 (0.61-2.11) 1.43 (0.66-3.02) 0.34 0.32
Model 2 1.00 1.13 (0.59-2.17) 1.35 (0.61-2.96) 0.44 0.43
High SBP
Crude 1.00 0.95 (0.50-1.79) 0.79 (0.37-1.68) 0.55 0.50
Model 1 1.00 0.97 (0.51-1.85) 0.81 (0.38-1.74) 0.59 0.54
Model 2 1.00 1.07 (0.55-2.07) 0.91 (0.42-1.99) 0.82 0.73
High DBP
Crude 1.00 1.03 (0.47-2.24) 0.76 (0.29-2.0) 0.58 0.52
Model 1 1.00 1.03 (0.47-2.25) 0.76 (0.29-2.01) 0.58 0.52
Model 2 1.00 1.09 (0.48-2.47) 0.82 (0.30-2.25) 0.71 0.63
High Syntax score
Crude 1.00 1.09 (0.50-2.37) 0.88 (0.34-2.28) 0.79 0.90
Model 1 1.00 1.17 (0.52-2.60) 0.98 (0.36-2.62) 0.97 0.90
Model 2 1.00 1.25 (0.55-2.84) 1.03 (0.38-2.81) 0.94 0.97
High Gensini score
Crude 1.00 1.46 (0.79-2.66) 0.84 (0.40-1.74) 0.64 0.48
Model 1 1.00 1.53 (0.79-2.94) 0.85 (0.38-1.90) 0.70 0.49
Model 2 1.00 1.40 (0.71-2.73) 0.86 (0.38-1.96) 0.73 0.58
HDL-C: High-density lipoprotein cholesterol; FBS: Fasting blood sugar, LDL-C: Low-density lipoprotein cholesterol; TG: Triglyceride; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; *P value: Genotype B2B2 compared to genotype B1B1. For high WC: Confounders adjusted in model 1: age, sex, BMI, and physical activity; Confounders adjusted in model 2: Additionally, job, menopausal status, used medications, smoking status, education level, marital status. For high serum HDL: Confounders adjusted in model 1: sex, physical activity; Confounders adjusted in model 2: Additionally, job, menopausal status, used medications, smoking status, family size. For high serum LDL: Confounders adjusted in model 1: age; Confounders adjusted in model 2: Additionally, used medications, smoking status, education level. For high serum TC: Confounders adjusted in model 1: age, sex, BMI; Confounders adjusted in model 2: Additionally, used medications, economic status, smoking status, marital status, education level, family size. For high serum TG: Confounders adjusted in model 1: age, sex, BMI, physical activity; Confounders adjusted in model 2: Additionally, job, menopausal status, used medication. For high serum FBS: Confounders adjusted in model 1: age, BMI; Confounders adjusted in model 2: Additionally, job, used medications, economic status. For high SBP: Confounders adjusted in model 1: sex, physical activity; Confounders adjusted in model 2: Additionally, smoking status, menopausal status, education level, job, family size, marital status, and used medications. For high DBP: Confounders adjusted in model 1: sex; Confounders adjusted in model 2: Additionally, smoking status, menopausal status, family size. For high Syntax Score: Confounders adjusted in model 1: sex, age; Confounders adjusted in model 2: Additionally, family size, job, education level, menopausal status, smoking status, used medications. For a high Gensini Score: Confounders adjusted in model 1: sex, age, BMI; Confounders adjusted in model 2: Additionally, menopausal status, smoking status, economic status, and job.
Table 3.Multi-variable adjusted OR for CVDs risk factors across CETP-Taq1B genotypes (n=476)

Discussion

The Taq1B variant was not associated with any CVD risk factors. As previously stated, CVDs were multifactorial, and genetic variants, in addition to environmental factors, can play an independent role in their incidence. Some studies, but not all, concurred that the Taq1B2 variant can reduce the risk of CVD events by lowering CETP concentration and adjusting plasma lipid levels. 7 , 10 In the present study, the frequency of the B2 allele was more than B1 allele (53% vs. 47%), which was similar to certain Asian studies, 11 , 24 but contrary to others. 12 , 13 , 25 , 26 We found no difference in plasma TG, or LDL-C levels between different genotypes of the Taq1B variant; which was consistent with the findings of previous research in Iran. 13 , 26 , 27 However, in contrast to our findings, they found that B2B2 genotype carriers had higher HDL-C levels than B1B1 genotype carriers. Besides, the present study found no correlation with HDL-C, which was similar to Rahimi and others. 13 According to an Egyptian study, subjects with the B2 allele had higher HDL-C levels and lower TG, LDL-C, and TC levels. 28 Similarly, studies in other populations supported the important role of the B2 allele in lipid metabolism and the severity of risk coronary stenosis. 25 , 29 , 30 They suggested that Taq1B might cause coronary arteries stenosis through abnormalities in HDL-C. However, the results of other studies were inconsistent. 11 , 13 , 31 Raina and others found no association between rs708272 variant and coronary stenosis in Jammu community, 11 with a similar allelic frequency to ours. Allelic heterogeneity in different populations, study design type, subjects’ health status, ethnicities and races, sample sizes, genotyping methods and environmental factors, and other CETP gene SNPs may explain the disparity in findings.

In this study, no association between rs708272 genotypes and high blood pressure and fasting glucose was found. Hou and others conducted a case-control study in China and found no relationship. 16 Moreover, Corella and colleagues found no association between Taq1B polymorphism and systolic blood pressure. 32 Although they reported a small association, it was significant with diastolic blood pressure. The findings of a study in Thai population indicated that CETP-Taq1B might not be a genetic risk factor for MetS traits. 15 According to Porchay-Balderelli and others, there was no association between Taq1B genotypes and blood pressure. 33 El-Lebedy and colleagues conducted a study in an Egyptian population and reported that carriers of the B1 allele increased the risk for DM2 up to 1.75-fold. 34 In a Spanish population, Lopez and colleagues found that GG genotype carriers had higher blood sugar. 35 Similar to the findings of a study by Corella and others, the present study found no relationship between the TaqIB variant and serum FBS. 32 Different results were reported in different populations, which could be attributed to ethnic group differences in variation prevalence.

Perez-Robles and others demonstrated that Mexican women with abdominal obesity, who carry the Taq1B2 allele, might have impaired lipid metabolism due to environmental factors. 36 Another study suggested that B2B2 carriers with a BMI≥27 might have lower HDL-C levels. 37 The present study found no relation between the Taq1B variant and abdominal obesity. The location of the Taq1B polymorphism at the CETP locus, as well as its proximity to other CETP SNPs, may render it more vulnerable to various factors. 37 , 38 However, as previously stated, the evidence supporting a relationship between rs708272-CETP and MetS components was contradictory. Hence, further studies on other CETP gene variants are required to clarify the observed relationships.

One of the major strengths of this study was that several confounders were measured and adjusted in the analysis. Nonetheless, the present study had some limitations. Since in this study we did not measure plasma CETP levels, which have a significant role in the metabolism of lipoproteins, the interpretations was rather challenging. Measuring HDL3 39 as a marker related to plasma CETP levels was essential. It is recommended to analyze all SNPs of the CETP gene associated with CVD risk factors and to their interactions with each other and other variables.

Conclusion

There was no association between CVD risk factors and Taq1B genotypes. Given that the findings of the present study was inconclusive in other populations, more extensive research are required to account for all possible relevant factors.

Acknowledgment

The authors would like to thank the personnel at the Department of Nutrition, School of Public Health and Genetics, and the Department of Cardiology of Afshar Hospital, Shahid Sadoughi University of Medical Sciences (Yazd, Iran) for their support and assistance with this study. This research was funded by Shahid Sadoughi University of Medical Sciences.

Authors’ Contribution

A.SA, AN, A.AV, Z.D, M.T, M.M, V.A, S.BR, M.MN, MY, SS.KH, F.M, and SM.SH substantially contributed to the conception, design, acquisition, and analysis of the work. All authors involved in drafting the work or reviewing the manuscript, and final approval of the version to be published, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Conflict of Interest:

None declared.

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