Introduction

Coronary heart disease (CHD) is one of the main causes of morbidity and death in cardiovascular diseases worldwide, and it is also one of the major diseases that threaten human life and health [1]. According to statistics in 2019, about 8.88 million people died of CHD in the world, and the incidence is still rising. Studies have shown that CHD is a common disease caused by coronary atherosclerosis, and its main feature is lipid deposition in the arterial intima [2, 3]. Specifically, when the body’s lipid metabolism is abnormal, abnormal accumulation of lipid will occur in the arterial intima, which promotes atherosclerotic lesions, causing reduced or blocked blood flow in coronary blood flow, and ultimately leading to CHD [4]. It has been reported that the etiology of CHD is multifactorial, including environmental and genetic factors, as well as their combined effect [5,6,7]. In recent years, a larger number of studies have suggested that genetic factors, especially single nucleotide polymorphisms (SNPs), play a significant role in the mechanism of CHD [8,9,10]. Thus, it is essential to study CHD-related gene polymorphisms.

Cytochrome P450 (P450), an important enzyme of oxidative metabolism, plays a recognized part in the metabolism of endogenous compounds and the metabolic clearance of drugs and other exogenous substances [11]. CYP4V2 is a member of the cytochrome P450 family 4 [11]. CYP4V2 gene, located in the chromosomal region 4q35, contains 11 exons and encodes the protein composed of 525 amino acids [12]. Studies have shown that CYP4V2 is a selective ω-hydroxylase for saturated medium-chain fatty acids [13]. Currently, although CYP4V2 has been proven to be involved in lipid metabolism, its role in the pathogenesis of CHD remains unclear [14].

Therefore, this research aimed to investigate the connection of CYP4V2 rs1398007, rs13146272, rs3736455, rs1053094 and rs56413992 with the risk of CHD in the Chinese Han population, to provide reference value for the diagnosis, prevention and prognosis of CHD. The research flow chart of this study was shown in Fig. 1.

Fig. 1
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Flowchart of a study on the relationship between CYP4V2 polymorphisms and CHD risk

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Material and methods

Participants

On the basis of G*power 3.1.9.7 software, we estimated the sample size of the case group and the control group through an independent sample t-test. The parameters were set as: Tail = 2, Effect size = 0.20, α = 0.05, Power = 0.85, Allocation ratio N2/N1 = 1. Ultimately, this case–control research recruited 487 CHD subjects from the department of cardiology of Central South University Xiangya School of Medicine Affiliated Haikou Hospital and 487 healthy individuals from the medical examination center of the hospital. They are all ethnic Han Chinese. The diagnosis of CHD patients was based on the comprehensive examination of clinical symptoms, medical history, and diagnostic tests, including electrocardiograms and coronary angiography. The inclusion criterion for the CHD group was: single-vessel stenosis > 70% or multi-vessel stenosis > 50% confirmed by coronary angiography. Patients with congenital heart disease, acute myocardial infarction, cardiomyopathy, malignant tumor and chronic inflammatory disease were excluded. The inclusion criterion for the healthy control group was: (1) healthy individuals without cardiovascular diseases confirmed by physical examination; (2) unrelated subjects matched for age and gender with patients with CHD; (3) no family history of cardiovascular and cerebrovascular diseases; (4) no diabetes and hypertension. Besides, basic information about the participants, including age, gender, smoking and alcohol consumption status, complicated with DM and HTN and clinical parameters-total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) was collected from standard questionnaires and medical records. HTN is defined as systolic blood pressure ≥ 140 mmHg and diastolic blood pressure ≥ 90 mmHg, or the patient is receiving antihypertensive medication. DM is defined as fasting venous plasma glucose level ≥ 7.0 mmol/L, random venous plasma glucose level ≥ 11.1 mmol/L, or the patient is being treated with hypoglycemic drugs or insulin. Also, we divided the participants into smokers, non-smokers, drinkers and non-drinkers according to their smoking and drinking habits, respectively. All participants were unrelated Chinese Han population. This research was ratified by the Central South University Xiangya School of Medicine Affiliated Haikou Hospital ethics committee (ZY-IRB-FOM-063), and written informed consent was obtained from each subject.

SNP selection and genotyping

In this study, the following processes were used to select SNPs in CYP4V2 gene: (1) 18,005 loci of CYP4V2 were found in the Ensemble database (https://asia.ensembl.org/index.html); (2) There were 361 variants with minor allele frequency (MAF) greater than 5% in the general population published in the 1000 Genome Project database; (3) Twenty-five variants were obtained according to their functional categories (5'-UTR, 3'-UTR, missense and synonymous); (4) After consulting relevant literature [15,16,17], five variants (rs1398007, rs13146272, rs3736455, rs1053094 and rs56413992) were selected to continue to evaluate the correlation with the risk of CHD.

For each subject, 5 mL of peripheral venous blood sample was collected and stored in EDTA anticoagulant tubes at 4°C for DNA extraction by GoldMag genomic DNA purification kit (GoldMag Co Ltd., Xi’an, China). Primers for PCR amplification were designed using Agena Bioscience Assay Design software, and the information of PCR primers was shown in Supplementary Table 1. SNPs were then genotyped using the Agena MassARRAY iPLEX platform (Agena Bioscience Inc., CA, USA). Finally, Agena Bioscience TYPER 4.0 software was applied to manage and analyze the results of SNP genotyping.

Statistical analyses

SPSS 22.0 was used for statistical analysis in this study. All tests were two-sided, and p < 0.05 was considered statistically significant. Independent sample t-test and Chi-square test were carried out to analyze the sample characteristics of continuous variables and categorical variables, respectively. The chi-square test was used to check whether the genotype distribution of the five loci in the control group was consistent with the HWE. Logistic regression was used to analyze the association between SNPs and CHD risk, and ORs with their corresponding 95% CIs were used to evaluate these association. Stratified analysis was also performed according to age, gender, smoking and drinking status, CHD complicated with DM, and CHD complicated with HTN. In addition, false positive report probability (FPRP) analysis was also performed to verify the reliability of the significant results in this study. Haploview software 4.2 was used to calculate the degree of linkage among these SNPs based on the linkage disequilibrium (LD) map. Multifactor dimensionality reduction (MDR) method was used to analyze SNP-SNP interactions and predict their effect on CHD risk.

Results

Basic clinical characteristics of study participants

The study population consisted of 487 CHD subjects and 487 healthy individuals. The basic information on CHD cases and healthy controls was shown in Table 1. The average age of CHD subjects and healthy individuals were 61.91 ± 10.18 years and 60.82 ± 9.03 years, respectively, which illustrated the age distribution of the two groups matched (p = 0.078). In addition, there was no significant difference in the distribution of gender, smoking and drinking status between the CHD group and the control group (p = 0.733, p = 0.847, p = 0.189). However, there were statistically significant differences in the distribution of TC, TG, HDL-C and LDL-C between the CHD group and the control group.

Table 1 Basic characteristics of the study population
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Basic information and allele frequency distribution of CYP4V2 SNPs

The basic information of SNPs in CYP4V2 were shown in Table 2. The genotype distribution of rs1398007, rs13146272, rs3736455, rs1053094 and rs56413992 in the control group were in accordance with HWE. Among them, rs56413992-T allele in CYP4V2 was associated with a 1.36-fold increased risk of CHD (OR = 1.36, 95% CI = 1.09–1.70, p = 0.007).

Table 2 Basic information and allele frequency distribution of CYP4V2 SNPs
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The overall analysis of the relationship between CYP4V2 SNPs and CHD risk

Adjusting for potential confounders, we performed logistic regression analysis based on co-dominant, dominant, recessive, and additive models, and the results showed that rs56413992 was significantly associated with an increased risk of CHD (Table 3). Specifically, in the heterozygous model, the CT genotype was associated with a 1.40-fold increased risk of CHD compared with the CC genotype (OR = 1.40, 95% CI = 1.06–1.83, p = 0.017). In addition, rs56413992 was also associated with an increased risk of CHD in dominant and log-additive models (dominant model: OR = 1.43, 95% CI = 1.10–1.87, p = 0.007; log-additive model: OR = 1.37, 95% CI = 1.10–1.72, p = 0.006). However, no association of rs1398007, rs13146272, rs3736455 and rs1053094 with CHD risk was observed. The forest diagram of the overall analysis of the relationship between CYP4V2polymorphisms and CHD risk was shown in Fig. 2.

Table 3 Association of CYP4V2 gene polymorphisms with CHD risk in an overall analysis
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Fig. 2
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Forest map of the overall analysis of the relationship between CYP4V2 polymorphisms and CHD risk

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Association of CYP4V2 SNPs with CHD risk stratified analysis by age and gender

Along with a stratified analysis by age (age > 60 or age ≤ 60) and gender, this study investigated the effect of CYP4V2 SNPs on CHD risk. As shown in Table 4, rs56413992 was correlated with an increased risk of CHD in the population aged > 60 (heterozygous model: OR = 1.49, 95% CI = 1.02–2.18, p = 0.042; dominant model: OR = 1.58, 95% CI = 1.09–2.29, p = 0.015; log-additive model: OR = 1.54, 95% CI = 1.12–2.12, p = 0.008). In the Table 5, rs56413992 was linked to a higher risk of CHD in males (dominant model: OR = 1.44, 95% CI = 1.04–2.00, p = 0.027; log-additive model: OR = 1.43, 95% CI = 1.08–1.90, p = 0.013).

Table 4 Association of CYP4V2 gene SNP polymorphisms with CHD risk stratified analysis by age
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Table 5 Association of CYP4V2 gene SNP polymorphisms with CHD risk stratified analysis by gender
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Association of CYP4V2 SNPs with CHD risk stratified analysis by smoking and drinking

The effect of CYP4V2 polymorphisms on CHD risk in subgroups of smoking and drinking was also investigated. As shown in Table 6, the findings suggested that rs56413992 was correlated with a elevated risk of CHD in smokers (dominant model: OR = 1.51, 95% CI = 1.05–2.17, p = 0.025; log-additive model: OR = 1.51, 95% CI = 1.09–2.08, p = 0.012) and drinkers (heterozygous model: OR = 2.26, 95% CI = 1.45–3.51, p < 0.001; dominant model: OR = 2.29, 95% CI = 1.48–3.52, p < 0.001; log-additive model: OR = 2.07, 95% CI = 1.40–3.06, p < 0.001). In addition, the study also suggested that rs1398007 was significantly linked to an increased risk of CHD in drinkers under homozygous (OR = 1.80, 95% CI = 1.17–2.78, p = 0.008), heterozygous (OR = 2.41, 95% CI = 1.11–5.25, p = 0.026), dominant (OR = 1.88, 95% CI = 1.24–2.86, p = 0.003) and log-additive (OR = 1.65, 95% CI = 1.19–2.30, p = 0.003) models, as displayed in the Table 7.

Table 6 Association of CYP4V2 gene SNP polymorphisms with CHD risk stratified analysis by smoking
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Table 7 Association of CYP4V2 gene SNP polymorphisms with CHD risk stratified analysis by drinking
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The effect of CYP4V2 SNPs on the risk of CHD complicated with DM or HTN

In this study, we also selected CHD patients with DM or HTN as the case group, and CHD patients without DM or HTN as the control group, respectively, to explore the association of CYP4V2 SNPs with the risk of CHD complicated with or without DM or HTN. As shown in Table 8, rs1053094 was associated with a reduced risk of CHD complicated with DM compared with CHD patients without DM (homozygous model: OR = 0.63, 95% CI = 0.42–0.96, p = 0.032; heterozygous model: OR = 0.48, 95% CI = 0.24–0.94, p = 0.033; dominant model: OR = 0.60, 95% CI = 0.40–0.89, p = 0.011; log-additive model: OR = 0.67, 95% CI = 0.49–0.91, p = 0.010). At the same time, rs1398007 was related to a decrease risk of CHD complicated with HTN compared with CHD patients without HTN (heterozygous model: OR = 0.57, 95% CI = 0.38–0.84, p = 0.005; dominant model: OR = 0.61, 95% CI = 0.42–0.90, p = 0.012).

Table 8 The effect of CYP4V2 gene polymorphisms on whether CHD was complicated by diabetes mellitus (DM) or hypertension (HTN) risk
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Correlation between CYP4V2 polymorphisms and serum lipid levels

Additionally, we also investigated the relationship between CYP4V2 polymorphisms and serum lipid levels (TC, TG, HDL-C and LDL-C) in CHD patients (Table 9). The results revealed that five candidate SNPs were not correlated with the levels of TC, TG, HDL-C and LDL-C in the CHD patients.

Table 9 Association of CYP4V2 gene polymorphisms with serum lipid levels
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LD and haplotype analysis

We performed the LD analysis among five variants (rs1398007, rs13146272, rs3736455, rs1053094 and rs56413992), as shown in Fig. 3. Rs13146272 and rs3736455 formed 2kb-block 1. Rs1053094 and rs56413992 formed 1kb-block 2. But, no haplotype was found to be associated with CHD risk.

Fig. 3
figure 3

Haplotype block map of CYP4V2 variants. The numbers inside the diamonds indicate the D′for pairwise analyses

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MDR analysis

MDR analysis was used to predict the impact of SNP-SNP interaction on CHD risk. As shown in Table 10, the best model for predicting CHD risk was the five loci model (OR = 1.84, 95% CI = 1.15–2.94, p = 0.010) with cross-validation 10/10 and a balance test accuracy of 0.486. The circle graph (Fig. 4) showed that interaction among five SNPs (rs1398007, rs13146272, rs3736455, rs1053094 and rs56413992) had a strong antagonistic effect on CYP4V2 gene.

Table 10 Multifactor dimensionality reduction (MDR) analysis
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Fig. 4
figure 4

Circle graph of the effect of SNP-SNP interaction on CHD risk. A negative value of the two-locus entropy indicates an antagonistic effect, while a positive value indicates a synergistic effect

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FPRP analysis

In the FPRP analysis, the FPRP values under different prior probabilities were observed, and FPRP values < 0.2 were considered to indicate significant correlation (Supplementary table 2). When the prior probability was 0.25, all statistically significant findings were notable except that rs1398007 TT genotype increased the risk of CHD in drinkers and rs1053094 AA genotype increased the risk of CHD complicated with DM population. When the prior probability was 0.1, the significant association of rs56413992 with an increased risk of CHD in the overall analysis was more noteworthy (T vs C model: FPRP = 0.072, statistical power = 0.805; CT vs CC model: FPRP = 0.152, statistical power = 0.693; CT + TT vs CC model: FPRP = 0.113, statistical power = 0.637; Log-additive model: FPRP = 0.071, statistical power = 0.783).

Discussion

CHD is a complex polygenic genetic disease affected by genetic factors. At present, several gene SNPs have been found to be associated with CHD risk, such as UTS2 (Ser89Asn) [18], CDKN2B-AS1 (rs10738606) [1], GLUT4 (rs5418) [19], CYP11B1 (rs4534, rs6410 and rs5283) [20], CYP24A1 (rs6068816 and rs2296241) [21] and AGT (rs2493132) [22]. Considering that the connection of CYP4V2 polymorphisms with CHD risk has not been reported, five SNPs (rs1398007, rs13146272, rs3736455, rs1053094 and rs56413992) in CYP4V2 gene were eventually genotyped in this study and their association with CHD risk were explored. As a result, this study suggested that CYP4V2 rs56413992 T allele and CT genotype significantly increased the risk of CHD. So CYP4V2-rs56413992 might play a critical part in the pathogenesis of CHD.

CYP4V2 is often referred to as an orphan P450 because its substrate specificity is just beginning to be defined [11]. It has been reported that CYP4V2 gene is involved in the metabolism of lipids (endogenous fatty acids and steroids) by ω-hydroxylation [14]. Interestingly, lipid metabolism also plays a chief part in the pathogenesis of CHD. Recent studies have suggested that atherosclerosis is a key pathological basis of CHD, and one of its main features is lipid deposition caused by abnormal lipid metabolism in the arterial intima [23,24,25]. Therefore, we speculated that CYP4V2 was involved in the pathogenesis of CHD by participating in lipid metabolism, and this study was devoted to investigating the impact of CYP4V2 SNPs on CHD risk.

Multiple studies on CYP4V2 polymorphisms have revealed the relationship between rs13146272 and venous thrombosis [26,27,28,29]. For example, a recent study has found that CYP4V2-rs13146272 is closely linked to the occurrence of venous thromboembolism (VTE) [15]. However, no connection of rs13146272 with CHD risk was observed in our study, which may be related to differences in disease. By contrast, little is known about the SNPs (rs1398007, rs3736455, rs1053094 and rs56413992) in CYP4V2. Therefore, our study is the first to report the association of these SNPs with CHD risk and find that rs56413992, as a risk factor, may play a crucial role in the pathogenesis of CHD.

There are age and gender differences in the occurrence of CHD. According to statistics, the incidence of CHD in people under the age of 40 is lower (0.6%), but with the gradual increase of age, the incidence of CHD will continue to increase [30]. In an age-based stratified analysis, we found that rs56413992 was significantly related to a greater risk of CHD in individuals aged > 60 years, suggesting that rs56413992 was a risk factor for CHD in individuals aged > 60 years. This further confirmed the previous results. In addition, one study has found that CHD is more common in men than women [31, 32]. In our study, we found that rs56413992 significantly increased CHD risk in men, but not in women. Based on this, we speculated that rs56413992 played a great part in the development of CHD in men, which may be closely related to men’s living habits, such as smoking and drinking.

Current researches have shown that smoking and drinking are the main risk factors for CHD. Active smoking and second-hand smoke exposure are estimated to cause more than 30% of CHD mortality, and smokers have twice the risk of fatal event within 10 years compared to non-smokers [33]. Furthermore, numerous epidemiological academic researches have suggested a J-shaped linkage between drinking and CHD morbidity and mortality, that is, non-drinkers and heavy drinkers have a slightly higher risk of CHD compared with light drinkers [34, 35]. This may have a strong relationship with moderate drinking to promote blood circulation, and further experiments are needed to confirm it. Our discovery indicated that rs1398007 increased the risk of CHD in drinkers, and rs56413992 increased CHD risk in smokers and drinkers, suggesting that CYP4V2 rs1398007 and rss56413992 may be a higher risk factor for CHD patients with smoking or alcohol consumption.

In addition, CHD complications HNT and DM are believed to play an integral role in the progression of CHD. Studies have shown that DM is a traditional risk factor for CHD, which exacerbates the progression of atherosclerosis and leads to poor clinical outcomes [36, 37]. Studies have also shown that hypertension accounts for more than 45% of all CHD events worldwide and it is an important risk factor for CHD [38]. In our study, we explored the association of CYP4V2 SNPs with CHD complications (HTN and DM) in the CHD group. The results implied that rs1053094 was related to a lower risk of CHD complicated with DM, and rs1398007 was correlated with a decreased risk of CHD complicated with HTN. However, no significant association of rs1053094 and rs1398007 with CHD risk was observed in the overall analysis. Therefore, this result needs to be validated with a larger sample.

Although our study explored the relationship between CYP4V2 rs56413992 and CHD risk, and suggested that rs56413992 may be a risk factor for CHD, this study also has some limitations. It is well known that there is a strong linkage between abnormal lipid metabolism and an increased risk of CHD. However, the association between blood lipid influencing factors and CHD risk were not explored in our study. In the follow-up study, we will fully consider and incorporate corresponding indicators affecting blood lipids to further study the relationship between CYP4V2 polymorphisms and CHD risk.

Conclusions

This study revealed a potential association between CYP4V2 rs56413992 polymorphisms and the risk and progression of CHD. This study will provide new clues for the early prevention, diagnosis and treatment of CHD.