CETP Taq1B polymorphism and its association with cardiovascular risk factors in an Iranian population under angiography, A Cross-Sectional Study

doi:10.21203/rs.3.rs-2575215/v1

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CETP Taq1B polymorphism and its association with cardiovascular risk factors in an Iranian population under angiography, A Cross-Sectional Study

https://doi.org/10.21203/rs.3.rs-2575215/v1

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Background: Several studies have assessed the association of the cholesterol ester transfer protein (CETP) (rs708272) TaqIB gene polymorphism with risk factors of CVDs; however, their results are inconsistent. The current study investigated the relationship between CVD risk factors and the Taq1B variant in a population from Iran that was undergoing coronary angiography.

Methods: This cross-sectional study was conducted on 476 subjects aged (30-76) years of both sexes. Genotypes for Taq1B polymorphism were determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) using extracted DNA from whole blood. Cardio-metabolic markers were measured by standard protocols. To determine the association between CVDs risk factors and the rs708272 variant, binary logistic regression was used in crude and adjusted models.

Results: Genotype frequencies of the Taq1B polymorphism were 10.7% for B1B1, 72.3% for B1B2, and 17% for B2B2. No significant association was observed 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), waist circumference (p = 0.79). None of the abnormal serum levels were related to genotypes of the rs708272 variant. Results remained not significant after adjusting for confounders.

Conclusion: Risk factors for CVDs were not associated with CETP rs708272 polymorphism in our population. Various findings reported in different populations that it suggested more studies in various regions in connection with CETP gene variants which are linked to CVD events.

CETP Taq1B polymorphism

profile lipid

cardiovascular risk factors

angiography

Cardiovascular diseases (CVDs) are a set of diseases of the heart and arteries which cause an increasing number of deaths worldwide, with 22.2 million deaths per year by 2030 (1). In general, CVD risk factors comprise age, sex, family history (2), smoking (3), adherence to an unhealthy diet (4), physical inactivity (5), insulin resistance (IR) (6), impaired glucose and lipid levels (7), elevated blood pressure, and waist circumference (WC) (6, 8, 9). Some CVD risk factors are components of metabolic syndrome (MetS) (10). Reports are in agreement with a correlation between abdominal obesity and CVD outcomes because of increasing IR and the production of bad inflammatory agents (11, 12). Abnormal blood lipids levels with elevated triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and decreased high-density lipoprotein cholesterol (HDL-C) (13, 14) have been associated with the incidence of CVD events (15, 16), as they may cause stenosis of the arteries (17). Evidence suggests that lifestyle modifications consisting of a healthy diet, increased physical activity, no smoking, and weight reduction can improve and control risk factors of CVDs (18–21).

Genetic variants including single nucleotide polymorphisms (SNPs) have also been linked to CVD risk factors (22–26). The CETP gene is on location 16q21, which encodes the cholesteryl ester transfer protein (CETP). According to the evidence, CETP identified an inverse transfer of cholesteryl esters from HDL to iatrogenic lipoproteins in exchange with TG (27, 28). A silent mutation base in the 277th nucleotide of the 1st intron at the CETP gene from G to A led to the formation of the rs708272 (Taq1B) variant. Cutting enzyme Taq1 can produce the two alleles of B1 and B2 from Taq1B (29, 30). Contrary to some previous studies (31, 32), recent findings about the correlation of the rs708272 with some CVDs risk factors are controversial (33, 34). Some but not all (35, 36) have stated that carrier's allele B2 of Taq1B or subjects with the A/A genotype have higher HDL-C levels; that are inversed in those with the B1 allele (37–40).

Various studies have claimed the Taq1B variant by a reduction in TG and an increase in HDL-c values may have a protective role against risk factors of CVD (41–43), those suggested B2B2 genotype of rs708272 possibly play a major role in lower concentration and function of CETP. However, other studies have not shown such relations or have reported conflicting results (36, 44–46). Furthermore, current studies thought that location, ethnicity, and related data to social, cultural, and lifestyle issues in a population under study are factors very important that can be correlated with genetic variants and risk factors for CVD including dyslipidemia, diabetes, abdominal obesity, and blood pressure (high/low), arteries stenosis (47–49). There are contradictions in studies that have not been confirmed and are still in the challenge together, some thought that the B2 allele of CETP may be affected by the aforementioned various factors that may be caused the rs708272 variant to alter metabolic outcomes (45, 50–54).

Therefore, this study aimed in assessing the relationship between the CETP Taq1B polymorphism and risk factors of CVDs, such as WC, lipid profiles, fasting blood glucose (FBS), systolic blood pressure (SBP), diastolic blood pressure (DBP), and scores of artery stenosis across genotypes of the CETP-Taq1B on Iranian angiographic subjects.

Study design and participants

The current cross-sectional study assessed 476 patients undergoing angiography who were referred to Afshar Hospital in Yazd, Iran, from September 2020 to October 2021. Participants ranged in age from 30 to 76 and were of both sexes. Out of all participants provided written consent to be included in the study. Subjects were excluded if they had: a history of cancer, chronic heart failure (CHF), history of myocardial infarction (MI), percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), chronic kidney disease stage 3 or higher, liver disease or was receiving medication for liver disease, certain perceptual or psychological disorders, immune system failure, an acquired immunodeficiency syndrome (AIDS), or extreme obesity (BMI > 40). Pregnant and lactating women and those with restrictions on oral intake were also excluded. The present study was approved by the Ethics Committee of Shahid Sadoughi University (SSU) of Medical Sciences Yazd, Iran (IR.SSU.SPH.REC.1400.079).

Dna Extraction And Genotyping

Genome DNA was extracted from white blood cells in 100 µL peripheral whole blood according to the Kit described protocol (SIMBIOLAB, IRAN). Extracted DNA was kept at --20°C until analysis. The CETP-TaqIB variant was amplified by the polymerase chain reaction (PCR) method, its solution had a 20 µL volume 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’ that synthesized by SinaClon, Iran. Amplification was applied with 1 cycle denaturation in 5 minutes at 95°C, followed by 40 cycles in 30 seconds at 95°C, annealing at 66°C for 30 seconds, and primary extension at 72°C for 30 seconds. The final extension was for 1 cycle at 72°C in 5 minutes. The PCR products were electrophoresed on 2% agarose gel (SinaClon, Iran) and then digested after incubating at 37°C overnight by endonuclease of the Taq1 (Fermentase, Lithuania), which contained 30 µL (10 µL PCR product, 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 for 1 hour. Finally, three genotypes were identified on the gel by ultraviolet transillumination based on fragment length, which was homozygous B1B1 containing 2 bands with 175 and 345 base pair (bp) and heterozygous B1B2 with 3 bands: 520 and 175, and 345 (bp), as well as homozygous B2B2 with 1 band: 520 (bp) Fig. 1. Genotyping was confirmed by sequencing the PCR products.

Anthropometric Assessment

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). WC was measured with an accuracy of 0.5 cm using a non-stretch tape at the middle of the iliac crown and lowest rib in the standing position. Based on Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III), WC > 102 and > 88 cm were considered as abdominal obesity for men and women, respectively (55). Body mass index (BMI) was calculated through rate weight (kg) to the square of height (m²). All those were measured by nutrition-trained students.

Physical Activity Assessment

Daily physical activity was evaluated using the International Physical Activity Questionnaire (IPAQ) (56). Activity levels were considered as metabolic equivalent (MET) hours per week and categorized as sedentary, moderate, or intense based on a list of usual activities per day, over the previous week.

Laboratory Measurements

Venous blood samples (4 ml) were drawn from participants after overnight fasting. The samples were poured into tubes containing EDTA and then centrifuged at 5000 rpm for 5 min. Biomarkers such as TG, HDL-C, LDL-C, TC, and FBS were measured according to a standard laboratory protocol using Pars Azmun kits (Tehran, Iran). Hypertriglyceridemia was considered as TG ≥ 150 mg/dl, FBS ≥ 110 mg/ dl; HDL-C < 40 mg dl for men, and HDL-C < 50 mg/ dl for women and TC > 200 mg/dl (55). LDL-C > 100 mg/dl were considered abnormal levels of serum cholesterol (57).

Calculation Syntax And Gensini Scores

The severity of coronary artery stenosis in participants undergoing angiography was determined based on two scoring criteria: Syntax and Gensini. Atherosclerotic lesions scored according to Gensini, were the percentage of lumen obstruction: 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. Scores were multiplied by coefficients 1 to 5 depending on the coronary arteries and obstructed sections. A coefficient 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 0.5 for other segments. Total Gensini scores were calculated from the sum of scores for stenosis and coefficients for each duct (58, 59). GS ≥ 20 were considered as intermediate - high-risk CAD and < 20 low-risk (60).

According to an online calculator version 2.0, the SYNTAX score was calculated (http://syntaxscore.org/calculator/syntaxscore/frameset.htm). List questions SYNTAX score were regarding functional and anatomical parameters of the obstruction ≥ 50% stenosis of arteries with a diameter of ≥ 1.5 mm. Finally, the SYNTAX score was calculated to sum of all obstruction scores. (Scores < 23 were considered low intensity and ≥ 23 moderate to severe (61, 62). The coronary angiograms were explained by experienced cardiologists who were blinded for some variables except for age and sex.

Assessment Of Other Variables

Demographic data including age, gender, smoking status, jobs, educational levels, menstrual status and intake history of drugs, and other related information to socioeconomic was collected by trained interviewers using a general questionnaire. Blood pressure (BP) was measured before angiography by hospital-experienced nurses according to standard protocol. Subjects with BP ≥ 130 and ≥ 85 mm HG are categorized into systolic and diastolic BP, respectively (55).

Statistical analysis

One-way ANOVA analysis for continuous variables and the chi-square test for categorical variables were used throughout the TaqIB genotypes to compare risk factors of CVDs. Odds ratios with a 95% confidence interval (OR [95%CIs]) of CVD risk factors across genotypes were estimated by binary logistic regressions in crude and multivariable-adjusted models. Hardy–Weinberg equilibrium (HWE) was assessed using the goodness-of-fit test. A p-value < 0.05 was considered significant. All analyses were conducted using SPSS software version 24 (IBM Corporation, USA).

General characteristics of study participants

The basic characteristics of participants according to every genotype of the CETP-TaqIB polymorphism are shown in Table 1. Overall, the mean standard deviation (SD) age of the participants was 56.90 (9.31) years. The genotype frequency (percentage) of the CETP TaqIB was (10.7%), (72.3%), and (17%) for B1B1, B1B2, and B2B2, respectively. None of the variables showed any significant difference across genotypes of the CETP TaqIB) p > 0.05). Genotypes were not within Hardy–Weinberg equilibrium (p-value < 0.05).

Table 1

General characteristics of study participants according to the CETP TaqIB genotypes
Variables	CETP (rs708272)
Variables	Total genotypes (N = 476)	B1B1 (N = 51 = 10.7%)	B1B2 (N = 344 = 72.3%)	B2B2 (N = 81 = 17%)	P value^*
Age, years	56.90 ± 9.31	57.84 ± 10.03	56.74 ± 9.07	56.99 ± 9.91	0.73
BMI, kg/m²	27.42 ± 4.28	27.30 ± 3.99	27.31 ± 4.37	27.93 ± 4.08	0.52
Weight	74.02 ± 12.92	74.07 ± 14.99	73.79 ± 12.78	74.99 ± 12.13	0.77
Height	164.41 ± 9.89	164.24 ± 11.29	164.54 ± 9.85	163.95 ± 9.16	0.88
WC, cm	99.62 ± 10.95	100.64 ± 11.29	99.29 ± 11.13	100.30 ± 10.01	0.61
Physical activity, n (%)					0.73
Sedentary	155(33.1)	17(34)	113(33.5)	25(30.9)
Moderate	157(33.5)	20(40)	111(32.9)	26(32.1)
Active	156(33.3)	13(26)	113(33.5)	30(37)
Sex, n (%)					0.93
Male	301(63.2)	33(64.7)	218(63.4)	50(61.7)
Female	175(36.8)	18(35.3)	126(36.6)	31(38.3)
Smoking status, n (%)					0.66
Non smoker	307(64.5)	32(62.7)	218(63.4)	57(70.4)
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; values are means ± standard deviation (SDs), n (%): numbers (percentage). ^*Obtained from One- way ANOVA for continuous variables and chi – square test for categorical. P-Values < 0.05 was considered significant

Comparison of CVD Risk factors across genotypes of the CETP-TaqIB

The CVD risk factors according to genotypes of the CETP-TaqIB are presented in Table 2. As shown, no significant difference was found among lipid profiles such as TC, LDL-C, TG, and HDL-C, and FBS levels as well as stenosis scores across genotypes (p > 0.05). Other variables including WC, SBP, and DBP also showed no significant difference among genotypes.

Table 2

Measures of CVDs risk factors across CETP TaqIB genotypes
Variable	Total genotypes (N = 476)	Tertiles of CETP TaqIB genotypes			P value^*
Variable	Total genotypes (N = 476)	B1B1 (N = 51)	B1B2 (N = 344)	B2B2 (N = 81)	P value^*
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, mg Hg	128.53 ± 13.91	127.61 ± 11.84	128.85 ± 14.30	127.72 ± 13.53	0.72
DBP, mg Hg	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

* Obtained from One- way ANOVA, P-Values < 0.05 was considered significant, All variables data are shown based on mean (SD), 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.

Association of CVD risk factors with genotypes of CETP TaqIB

OR (95%CIs) of CVD risk factors across genotypes of CETP-TaqIB are provided in Table 3. Odds ratios of abnormal biochemical profiles were not associated with genotypes as follows: 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). No significant relationship was found between stenosis scores of coronary arteries across genotypes, Gensini scores (OR: 0.84; 95% CI: 0.40–1.74, p = 0.64), and syntax scores (OR: 0.88; 95% CI: 0.34–2.28, p = 0.79). There were 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). All of these associations remained not significant after adjusting for age, gender, BMI, and physical activity in model I. Additional adjustments were made in model II for smoking status; consumed medications including anti-hypertensive, anti-hyperlipidemia, and anti-diabetic medications; job status; education level; economic status; menstrual status; family size, and marital status. However, findings remained not significant for all mentioned variables (data not shown).

Table 3

Multi-variable adjusted OR(95%CI) for CVDs risk factors across CETP- TaqIB genotypes (n = 476)
Variable	B1B1	B1B2	B2B2	P value*	P trend
High WC
Crude	1.00	0.91 (0.49–1.69)	0.90 (0.43–1.89)	0.79	0.82
Model1	1.00	0.67 (0.25–1.83)	0.44 (0.13–1.50)	0.19	0.18
Model2	1.00	0.60 (0.20–1.81)	0.52 (0.14–1.98)	0.34	0.37
High serum HDL
Crude	1.00	0.86 (0.46–1.62)	0.58 (0.26–1.27)	0.17	0.14
Model1	1.00	0.83 (0.42–1.62)	0.57 (0.25–1.31)	0.19	0.17,
Model2	1.00	1.15 (0.55–2.43)	0.70 (0.27–1.76)	0.45	0.39
High serum LDL
Crude	1.00	1.41 (0.75–2.65)	1.12 (0.53–2.35)	0.75	0.96
Model1	1.00	1.47 (0.77–2.80)	0.92 (0.42–2.0)	0.83	0.61
Model2	1.00	1.35 (0.67–2.72)	0.69 (0.29–1.62)	0.39	0.26
Low serum TC
Crude	1.00	1.58 (0.80–3.12)	1.29 (0.58–2.86)	0.52	0.73
Model1	1.00	1.58 (0.78–3.19)	0.95 (0.41–2.24)	0.92	0.67
Model2	1.00	1.49 (0.70–3.19)	0.70 (0.27–1.81)	0.47	0.33
High serum TG
Crude	1.00	0.92 (0.49–1.71)	0.97 (0.46–2.03)	0.94	0.99
Model1	1.00	0.95 (0.49–1.81)	0.78 (0.35–1.71)	0.54	0.51
Model2	1.00	0.80 (0.38–1.64)	0.55 (0.22–1.36)	0.19	0.19
High serum FBS
Crude	1.00	1.01 (0.55–1.84)	1.38 (0.67–2.84)	0.37	0.29
Model1	1.00	1.12 (0.60–2.09)	1.38 (0.65–2.92)	0.40	0.37
Model2	1.00	1.07 (0.55–2.10)	1.16 (0.52–2.60)	0.71	0.71
High SBP
Crude	1.00	0.95 (0.50–1.79)	0.79 (0.37–1.68)	0.55	0.50
Model1	1.00	1.04 (0.54–1.99)	0.79 (0.36–1.73)	0.56	0.48
Model2	1.00	1.20 (0.60–2.42)	0.84 (0.36–1.94)	0.68	0.57
High DBP
Crude	1.00	1.03 (0.47–2.24)	0.76 (0.29–2.0)	0.58	0.52
Model1	1.00	1.02 (0.46–2.25)	0.64 (0.23–1.77)	0.39	0.35
Model2	1.00	1.13 (0.48–2.65)	0.63 (0.20–1.94)	0.42	0.39
High Syntax sore
Crude	1.00	1.07 (0.49–2.32)	0.88 (0.34–2.28)	0.79	0.73
Model1	1.00	1.20 (0.53–2.70)	1.11 (0.41–2.99)	0.83	0.87
Model2	1.00	1.34 (0.58–3.12)	1.28 (0.46–3.59)	0.63	0.66
High Gensini score
Crude	1.00	1.46 (0.79–2.66)	0.84 (0.40–1.74)	0.64	0.41
Model1	1.00	1.66 (0.85–3.21)	0.93 (0.41–2.09)	0.86	0.63
Model2	1.00	1.71 (0.85–3.45)	1.01 (0.43–2.38)	0.97	0.83

Model1: Adjusted for age, gender, BMI, physical activity Model2: Additionally, adjustment for smoking status, consumed medications of anti-hypertensive , anti-hyperlipidemia, anti-diabetic, job, education level, economic status. menstrual status, family size, marital status HDL-C; high-density lipoprotein cholesterol, FBS; basting blood sugar, LDL-C; low-density lipoprotein cholesterol, TG; triglyceride, SBP; systolic blood pressure, DBP; diastolic blood pressure,*P-value: Genotype B2B2 compered to genotype B1B1<

As stated, CVDs are multifactorial, and in addition to environmental factors, genetic variants also can play an independent role in their incidence. Most studies but not all (36, 44) are agreed with together that carriers B2 allele have a lower CETP concentration and higher HDL-c levels with a reduced risk of CVD events compared with the B1 allele (41, 42). The frequency of the B2 allele in our study was more than B1 (53% vs. 47%), which is in line with Bhanushali et al. study, however, there was no difference in HDL-c levels between various genotypes of Taq1B. Studies recently conducted by Jyotdeep et al. and Perez-Robles et al. were consistent with us (33, 63). Some Iranian studies also have shown different findings in dissimilar populations (28, 45). Gaojun et al. showed that CETP rs708272 polymorphism was not associated with lipid levels in females, even when they controlled confounder factors (64). Another study in Turkey, pointed out that the Taq1B variant protects subjects against coronary disease risk by increasing HDL-C levels (65). A study on a multi-ethnic population, reported only chinese men who carrying the B2 allele had higher values of HDL-c (53). Several causes can be attributed to the differences in the mentioned conflicting findings, including various ethnicities, cultures, designs, sample sizes, specific sexes and other environmental factors.

In this study, no association was observed between genotypes of the rs708272 polymorphism and high or low blood pressure, as others have also reported (45, 66). However, one study showed the rs708272 polymorphism to be correlated to varied systolic and diastolic blood pressure through raised CETP values (50). Dalia et al. showed that carriers of the B1 allele (B1B1 and/or B1B2) increase up to 1.75-fold the risk for DM2 (67). Lopez et al. studied on a Spanish population and indicated that those with the GG ( B1B1) genotype had higher blood sugar (51). Results in various populations have been reported differently; as in the current study, we found no association between FBS and the TaqIB variant.

Abnormal changes in plasma lipid profiles can be associated with coronary arteries stenosis (52), but in this regard, we observed no relationship in this study, which is in line with numerous studies (44, 52, 53, 68, 69). Increased HDL-C levels and decreased CETP were suggested as protective factors against adverse changes in coronary arteries in particular in carriers of the B2 allele (38), however, it is still conflicting in studies (36, 44). The mechanisms are not exactly known. Studies with larger sample sizes in different genetic variations are needed.

A case-control study indicated that subjects with the AA genotype compared with the rs708272-GG carriers had less abdominal obesity and hypertension (70). Another investigation claimed that HDL-C levels may be reduced by an increasing in BMI ≥ 27 in the B2B2 carriers (49). Perez-Robles reported Mexican women with abdominal obesity which carry the B2 allele via effect of environmental factors on rs708272 variant possibly vary lipid metabolism (63). However, Some studies are conflicting for the existence of a relation between rs708272 CETP and MetS (71, 72). In the current study, no relation was found between abdominal obesity and TaqIB variants. Based on the evidence of the position of the Taq1B polymorphism at the locus and its relation to other SNPs of CETP and this minor allele this SNP may be affected more than the major allele through various factors furthermore it required more studies in CETP variants to interpretate better relationships (49, 54).

Out of the major strengths of this study was, variations in the participants may made findings generalizable to Iranians. Many confounders were measured and adjusted in the analysis. The present study had several limitations. According to current evidence, the TaqIB variant plays an important role in CETP activity and concentration, but it was not measured in this study, making interpretation to some extent difficult. Out of all variants at the CETP gene, wildly the TaqIB SNP was studied, while it seems all CETP polymorphisms and their interaction with each other should be examined. Assessment of HDL3 (73) as a marker related to CETP was necessary to facilitate discussions which we did not measure. Alcohol is documented (74) as an effective item of risk allele for raising HDL-C levels, but it was not assessed in the current investigation.

No correlation was observed among risk factors of CVDs across TaqIB genotypes in the studied Iranian population. Considering that current evidence are also inconsistent, wider studies must be carried out until all relevant parameters be considered in studying the association of the TaqIB variant and risk factors cardiometabolic in multiethnic regions.

BMI, body mass index; WC, waist circumference; 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; CETP, cholesterol ester transfer protein; CVD, Cardiovascular diseases; MI, myocardial infarction, MetS, metabolic syndrome; PCR-RFLP, Polymerase chain reaction-restriction fragment length polymorphism; IR, insulin resistance; HWE, Hardy–Weinberg equilibrium; MET, metabolic equivalent; IPAQ, International Physical Activity Questionnaire; SD, standard deviation; OR, Odds ratios; CI, confidence interval.

Ethics approval and consent to participate

The ethical committee of Shahid Sadoughi University of Medical Sciences in Yazd approved the written informed consent (code number: 1400.079). The written informed consent was signed by all participants before the beginning study. All experiments were performed in accordance with relevant guidelines and regulations. The present study obtained ethical approval from the Ethics Committee of Shahid Sadoughi University (SSU) of Medical Sciences Yazd, Iran (IR.SSU.SPH.REC.1400.079).

Consent for publication

Not applicable.

Availability of data and materials

The data and materials of the current study are available from the corresponding author on reasonable request.

Competing interests

The authors have declared no competing interests.

Funding

Shahid Sadoughi University of Medical Sciences (SSUMS) supported this study.

Author's contributions

A.SA designed the study. A.AV, Z.D, M.T, M.M, V.A, S.BR conducted the study; A.AV: wrote the manuscript and F.M involved in the analysis. M.MN, M.Y advised for genetic and SS.KH, SM.SH gives scientific recommendations. AN: critically revised the manuscript; AN, A.SA: supervised the study. The final version of the manuscript was approved by all authors.

Acknowledgments

We acknowledge the contribution of the participants and co-researchers.

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CETP Taq1B polymorphism and its association with cardiovascular risk factors in an Iranian population under angiography, A Cross-Sectional Study

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Abstract

Figures

Introduction

Materials And Methods

Study design and participants

Dna Extraction And Genotyping

Anthropometric Assessment

Physical Activity Assessment

Laboratory Measurements

Calculation Syntax And Gensini Scores

Assessment Of Other Variables

Statistical analysis

Results

General characteristics of study participants

Comparison of CVD Risk factors across genotypes of the CETP-TaqIB

Association of CVD risk factors with genotypes of CETP TaqIB

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

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