Correlation of APOE, SLCO1B1 and LPA KIV-2 gene polymorphisms with coronary heart disease in the Teochew population

Abstract

BACKGROUND

Coronary heart disease (CHD) is a prominent cause of mortality and disability worldwide. Like most complex diseases, the risk of CHD in individuals is regulated by the interaction between genetic factors and lifestyle. APOE and SLCO1B1 genetic polymorphisms and LPA KIV-2 copy number variation may influence the development and progression of CHD. Clarifying gene polymorphisms can guide clinical precision and prevention, thereby improving treatment outcomes.

AIM

To investigate the influence of APOE and SLCO1B1 gene polymorphisms, as well as LPA KIV-2 copy number variation on CHD in the Teochew population.

METHODS

A total of 324 patients with CHD and 143 control participants were involved in this study. Single nucleotide polymorphisms rs429358 and rs7412 in the APOE gene, and rs2306283 and rs4149056 in the SLCO1B1 gene were analyzed via high-resolution melting curve analysis. Additionally, PCR was performed to detect KIV-2 copy number variations. Clinical risk factors and potential effects on CHD patients were subsequently assessed.

RESULTS

In the CHD group, the frequencies of APOE allele ε2, ε3, ε4 were 8.02%, 82.97%, and 9.10%, respectively. Compared to the control groups (13.29%, 79.37%, and 7.34%, respectively), the ε2 allele frequency showed a significant difference (8.02% vs 13.29%, P = 0.012). SLCO1B1 allele frequencies in the CHD group were not significantly different from those in the control group (*1a: 26.69% vs 25.52%, *1b: 61.17% vs 65.38%, *5: 0.15% vs 0.35%, *15: 11.83% vs 8.74%). The number of copies of the KIV-2 gene was significantly lower in the CHD group when compared to controls (23.35 ± 8.78 vs 27.21 ± 9.48; P < 0.01). Logistic regression analysis revealed that sex, age, hypertension, diabetes, smoking, the ε2 allele and KIV-2 copy number were factors influencing the presence of CHD.

CONCLUSION

In the Teochew population, the APOE ε2 allele and a higher KIV-2 copy number were associated with a reduced risk of CHD. In contrast, the APOE ε4 allele and SLCO1B1 gene were not associated with CHD.

Key Words: Gene polymorphisms; Coronary heart disease; Teochew population; APOE; SLCO1B1; KIV-2

Core Tip: In this study of 324 patients with coronary heart disease (CHD) and 143 control participants, we analysed polymorphisms in the APOE and SLCO1B1 genes, as well as LPA KIV-2 copy number variation in the Teochew population of southern China. We confirmed that both APOE ε2 allele and higher KIV-2 copy number are associated with a reduced risk of CHD in the Teochew population, whereas neither the APOE ε4 allele nor SLCO1B1 gene showed any association.

INTRODUCTION

Coronary heart disease (CHD) is a prominent cause of mortality and disability worldwide; it arises from the constriction or obstruction of coronary arteries caused by atherosclerosis, ultimately resulting in decreased blood flow and insufficient oxygen supply to the heart muscle[1]. The mortality and morbidity of CHD continue to rise worldwide, particularly in low- and middle-income countries as well as in the elderly population[2]. Like most complex diseases, the risk of CHD in individuals is regulated by the interaction between genetic factors and lifestyle. The impact of genetic polymorphisms on the occurrence and progression of CHD has been well documented; for example, genome-wide association studies have identified more than 300 genetic loci associated with CHD[3]. Owing to differences in gene distribution, the risk of disease may vary across geographic areas and populations. Clarifying gene polymorphisms can guide clinical precision and prevention, thereby improving treatment outcomes.

Apolipoprotein E (ApoE) is a ubiquitous lipid transport protein that binds a variety of lipid species, including cholesterol, phospholipids, and triglycerides (TG), in lipoprotein particles[4]. ApoE plays an important role in lipid metabolism. The APOE gene encodes APOE and contains two single nucleotide polymorphisms (SNPs), rs429358 (388T>C, Cys130Arg) and rs7412 (526C>T, Arg176Cys). These SNPs can generate three haplotypes, namely, ε2 (388T-526T), ε3 (388T-526C) and ε4 (388C-526C), resulting in six genotypes (ε2/ε2, ε2/ε3, ε2/ε4, ε3/ε3, ε3/ε4 and ε4/ε4)[5]. Previous studies suggested that three isoforms, ApoE2 (Cys112/Cys158), ApoE3 (Cys112/Arg158) and ApoE4 (Arg112/Arg158) can lead to changes in lipid and lipoprotein levels. These single amino acid substitutions drastically alter the functionality of ApoE, resulting in isoform-specific variations in its structure[6].

The SLCO1B1 gene encodes OATP1B1, a drug transporter that contributes to the hepatic uptake of statins[7]. Statins are commonly used for the primary and secondary prevention of CHD. SLCO1B1 gene polymorphisms are the main cause of individual differences in statin efficacy and adverse effects. Rs2306283 (388A>G) and rs4149056 (521T>C) are two common SNPs in the SLCO1B1 gene. Among them, the 521T>C SNP induces in a substitution of alanine for valine at amino acid position 174, whereas the 388A>G variant causes an Asn130Asp mutation within the extracellular region of the transporter[8]. These two SNPs can form four haplotypes, including SLCO1B1*1a (388A-521T), SLCO1B1*1b (388G-521T), SLCO1B1*5 (388A-521C), and SLCO1B1*15 (388G-521C).

Lipoprotein(a) [Lp(a)] is a unique liver-derived lipoprotein associated with key processes in atherosclerosis[9]. Lp(a) consists of an LDL-like moiety with one plasminogen-like apolipoprotein(a) covalently bound to apolipoprotein B (apo B). More than 90% of the variance in Lp(a) concentration is determined by genetic variation in the apo (a) gene (LPA). LPA has a highly repetitive structure consisting of 10 highly homologous kringle IV (KIV) domains (subtypes 1 to 10), a kringle V domain and a protease domain. The KIV-2 domain is encoded in a 5.6-kilobase large DNA copy number variation, which can be present up to > 40 × per allele[10].

Current studies in China and other regions have investigated the APOE gene, SLCO1B1 gene, and LPA KIV-2 copy number as candidate genes for CHD, revealing significant interpopulation and geographic variability in allele frequencies. However, findings across different populations and geographic areas have been inconsistent. Chaozhou, located at the junction of Fujian and Guangdong Provinces in China, has a population of 2.57 million. The vast majority of residents in Chaozhou are Teochew people, which is an intriguing Han Chinese population in southern China. The prevalence of CHD among Teochew individuals aged 20 years and older was reported to be 1.41%, with the most common multimorbidity patterns involving cardiovascular diseases, gout, and hyperuricemia[11]. The age-standardized mortality rate in Chaozhou was 602.61 per 100000 population, with CHD ranked second among the leading causes of death[12]. Currently, there are no relevant reports on APOE and SLCO1B1 genetic polymorphisms and LPA KIV-2 copy number variation in this region.

The purpose of this study was to analyse polymorphisms in the APOE and SLCO1B1 genes, as well as LPA KIV-2 copy number variation, in the Teochew population of southern China, providing a theoretical basis for the prevention and treatment of CHD.

MATERIALS AND METHODS

Study subjects

The subjects included 467 patients who underwent coronary angiography due to chest tightness, chest pain, and dyspnoea at Chaozhou Central Hospital from December 2023 to November 2024. Among them, 324 patients were diagnosed with CHD, while 143 patients were in the control group without CHD. The diagnostic criteria for CHD were based on the classification of the American College of Cardiology/American Heart Association. Coronary angiography revealed that at least one main branch of the left main artery, left anterior descending artery, left circumflex artery or right coronary artery was stenotic, with a degree of stenosis exceeding 50%. Patients with malignant neoplasms, severe heart or liver disease, kidney dysfunction, or autoimmune diseases were excluded. This study adhered to the ethical guidelines outlined in the Helsinki Declaration and was approved by the Institutional Review Board of Chaozhou Central Hospital (No. 2023014).

Data collection

The electronic medical records of all patients were thoroughly reviewed. The clinical data collected included demographic information [age, sex, body mass index (BMI)], complications (hypertension and diabetes) and unhealthy lifestyles (smoking and drinking). The laboratory examination indicators included total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apo B, apo A1, ApoE and Lp(a).

Genotyping testing

Peripheral blood samples from all the subjects were collected into tubes with EDTA-K2, stored at 4 °C, and then sent to the molecular laboratory for analysis. DNA was extracted from peripheral venous blood via whole blood DNA extraction kits (Chaozhou Hybribio Co., Ltd.) according to the manufacturer’s instructions. The DNA concentration was determined by measuring the UV absorption at 260 nm with a spectrophotometer (NanoDrop One, Thermo Fisher Scientific Co., Ltd.).

Polymorphisms in the APOE and SLCO1B1 genes were identified via reagent kits (Hybribio Company, Chaozhou, China), and the fluorescence PCR melting curve method was used to determine the corresponding mutation types of the target genes. The target fragments were amplified via the SLAN-96P Real-Time PCR System (Shanghai Hongshi Medical Technology Co., Ltd.). The amplification protocol included an initial denaturation step of 95 °C for 5 minutes, followed by 45 cycles of 95 °C for 15 minutes, 56 °C for 20 seconds, and 72 °C for 20 seconds. This step was followed by a final step of 95 °C for 1 minute and 40 °C for 3 minutes. The PCR products were analysed by monitoring the fluorescence from 40 °C to 80 °C at a temperature transition rate of 0.05 °C per second.

The KIV-2 repeat copy number was detected via a real-time fluorescent quantitative PCR detection kit (Shanghai WeHealth Co., Ltd.). The real-time fluorescent quantitative PCR cycle conditions were as follows: Initial denaturation at 98 °C for 2 minutes, followed by 45 cycles of 98 °C for 10 seconds, 60 °C for 30 seconds, and 68 °C for 30 seconds (with fluorescence collection). For the melting curve analysis, the conditions were set at 95 °C for 5 seconds, 65 °C for 1 minute, and a gradual temperature increase from 65 °C to 95 °C at an increment of 0.05 °C. The KIV-2 repeat copy number was calculated via the following formula: ΔCt_Sample = Ct_{Sample KIV-2} - Ct_{Sample Reference}; ΔCt_Standard = Ct_{Standard KIV-2} - Ct_{Standard Reference}; KIV-2 repeat copy number = 2^-ΔΔCt (ΔΔCt = ΔCt_Sample - ΔCt_Standard).

Statistical analysis

Analysis was performed via the SPSS statistical package version 21.0 (SPSS Inc., Chicago, IL, United States). Hardy-Weinberg equilibrium was assessed via Pearson’s χ² test of the observed genetic frequencies. The measurement data are presented as the mean ± SD, and nonnormally distributed data are presented as the medians (interquartile ranges). The t test and nonparametric tests were used for comparisons between groups. Enumeration data are expressed as a percentage (%) and were compared via the χ² test. The factors influencing CHD incidence were analysed via logistic regression. P < 0.05 was considered statistically significant.

RESULTS

Clinical characteristics

The participants of this study included 467 Teochew individuals aged between 20 and 89 years. Among them, 324 were CHD patients (234 men, 85 women) and 143 were non-CHD patients (78 men, 65 women). As shown in Table 1, the age, male proportion, prevalence of hypertension, prevalence of diabetes, history of smoking, TG, homocysteine, and Lp(a) levels in the CHD group were significantly greater than those in the control group, whereas the levels of HDL-C, and ApoA1 were lower than those in the control group. There was no significant difference BMI, history of alcohol consumption, or TC, LDL-C, ApoB, or ApoE levels between the two groups.

Table 1 Comparison of clinical characteristics between the coronary heart disease and control groups, n (%).

	CHD group (n = 324)	Control group (n = 143)	P value
Clinical data
Male	239 (73.77)	78 (54.55)	< 0.001
Age, years	64.93 ± 11.58	57.27 ± 13.74	< 0.001
BMI, kg/m2	23.21 ± 3.06	23.56 ± 3.89	0.746
Comorbidities
Hypertension	187 (57.72)	43 (30.07)	< 0.001
Diabetes	104 (32.10)	15 (10.49)	< 0.001
History of smoking	114 (35.19)	27 (18.88)	< 0.001
History of drinking	21 (6.48)	5 (3.50)	0.273
Laboratory data
TG, mmol/L	1.40 (0.89)	1.26 (0.72)	0.024
TC, mmol/L	4.69 (1.63)	4.87 (1.40)	0.580
HCY, μmol/L	11.87 (5.93)	9.82 (3.04)	< 0.001
HDL-C, mmol/L	1.07 (0.37)	1.19 (0.40)	< 0.001
LDL-C, mmol/L	2.95 (1.42)	2.87 (1.17)	0.422
ApoA1, g/L	1.05 (0.25)	1.16 (0.30)	< 0.001
ApoB, g/L	0.85 (0.35)	0.83 (0.30)	0.298
Lp(a), mg/L	122.50 (266.13)	100.2 (168.00)	0.007
ApoE, mg/L	38.65 (14.40)	38.95 (15.00)	0.627

BMI: Body mass index; TG: Triglycerides; TC: Total cholesterol; HCY: Homocysteine; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; ApoA1: Apolipoprotein A1; ApoB: Apolipoprotein B; ApoE: Apolipoprotein E; Lp(a): Lipoprotein(a); CHD: Coronary heart disease.

Distribution characteristics of genes

In the CHD group, the frequencies of genotypes ε3/ε3, ε3/ε4, ε2/ε3, ε2/ε2, ε2/ε4, and ε4/ε4 were 69.75%, 15.12%, 11.11%, 1.85%, 1.23%, and 0.93%, respectively. The frequencies of alleles ε3, ε4, and ε2 were 82.97%, 9.10%, and 8.02%, respectively. In the control group, the frequencies of genotypes ε3/ε3, ε2/ε3, ε3/ε4, ε2/ε4, ε2/ε2 and ε4/ε4 were 62.94%, 21.68%, 11.19%, 2.10%, 1.40% and 0.70%, respectively. The frequencies of alleles ε3, ε2 and ε4 were 79.37%, 13.29% and 7.34%, respectively. The results indicate that the control group was in Hardy-Weinberg equilibrium (χ² = 0.243, P = 0.886) for genetic polymorphisms. ε3/ε3 was the most common APOE genotype, and ε3 was also the most frequent allele. Compared with the control group, there was a statistically significant difference in genotype ε2/ε3 among the CHD group (P = 0.003), and the frequency of the allele ε2 was significantly lower in the CHD group than in the control group (P = 0.012). In addition, there was no statistically significant difference in the genotypes of APOE genes (ε2/ε2, ε2/ε4, ε3/ε3, ε3/ε4, and ε4/ε4) or alleles (ε3 and ε4) between the groups (P > 0.05) (Table 2).

Table 2 Genotypes and alleles distribution of APOE gene in the coronary heart disease group and control group, n (%).

Genotype	CHD group (n = 324)	Control group (n = 143)	Total (n = 467)	P value
ε2/ε2	6 (1.85)	2 (1.40)	8 (1.71)	1.000
ε2/ε3	36 (11.11)	31 (21.68)	67 (14.35)	0.003
ε2/ε4	4 (1.23)	3 (2.10)	7 (1.50)	0.442
ε3/ε3	226 (69.75)	90 (62.94)	316 (67.67)	0.147
ε3/ε4	49 (15.12)	16 (11.19)	65 (13.92)	0.258
ε4/ε4	3 (0.93)	1 (0.70)	4 (0.86)	1.000
Allele	CHD group (n = 648)	Control group (n = 286)	Total (n = 934)
ε2	52 (8.02)	38 (13.29)	90 (9.64)	0.012
ε3	537 (82.87)	227 (79.37)	764 (81.80)	0.201
ε4	59 (9.10)	21 (7.34)	80 (8.57)	0.375

CHD: Coronary heart disease.

In the CHD group, the frequencies of genotypes *1b/*1b, *1a/*1b, *1b/*15, *1a/*1a, *1a/*15, *15/*15 and*1a/*5 were 36.72%, 32.10%, 16.98%, 8.02%, 4.94%, 0.93% and 0.31%, respectively. In the control group, the percentages were 42.66%, 31.47%, 13.99%, 7.69%, 3.50%, 0%, and 0.70%, respectively. The *5/*5 and *5/*15 genotypes were not detected in either group. The control group was in Hardy-Weinberg equilibrium (χ² = 4.931, P = 0.085) for this polymorphism. The *1b allele was the most common in both the CHD group and the control group (61.17% and 65.38%), followed by alleles *1a, *15 and *5. There was no statistically significant difference in the frequencies of SLCO1B1 genotypes and alleles between CHD patients and the control group (P > 0.05) (Table 3).

Table 3 Genotypes and alleles distribution of SLCO1B1 gene in the coronary heart disease group and control groups, n (%).

Genotype	CHD group (n = 324)	Control group (n = 143)	Total (n = 467)	P value
*1a/*1a	26 (8.02)	11 (7.69)	37 (7.92)	0.902
*1a/*1b	104 (32.10)	45 (31.47)	149 (31.91)	0.893
*1b/*1b	119 (36.72)	61 (42.66)	180 (38.54)	0.225
*1a/*5	1 (0.31)	1 (0.70)	2 (0.43)	0.519
*1a/*15	16 (4.94)	5 (3.50)	21 (4.50)	0.488
*1b/*15	55 (16.98)	20 (13.99)	75 (16.06)	0.417
*5/*5	0 (0)	0 (0)	0 (0)	NA
*5/*15	0 (0)	0 (0)	0 (0)	NA
*15/*15	3 (0.93)	0 (0)	3 (0.64)	0.556
Allele	CHD group (n = 648)	Control group (n = 286)	Total (n = 934)
*1a	173 (26.69)	73 (25.52)	246 (26.34)	0.640
*1b	397 (61.17)	187 (65.38)	584 (62.53)	0.231
*5	1 (0.15)	1 (0.35)	2 (0.21)	0.520
*15	77 (11.83)	25 (8.74)	102 (10.92)	0.156

CHD: Coronary heart disease.

The LPA KIV-2 copy number variation is shown in Figure 1. In the CHD group, the LPA KIV-2 copy number was 23.35 ± 8.78, whereas in the control group, it was 27.21 ± 9.48. There was a statistically significant difference in copy number between the two groups (P < 0.01), with the copy number of the CHD group being significantly lower than that of the control group. The copy number of KIV-2 is negatively correlated with the concentration of Lp(a), indicating that the concentration of Lp(a) decreases as the KIV-2 copy number increases (CHD: R = -0.527, P < 0.001; Control: R = -0.589, P < 0.001).

Figure 1 The distribution of LPA KIV-2 copy number and the correlation with Lipoprotein(a) concentration. A: The LPA KIV-2 copy number in coronary heart disease group and control group; B: The correlation between LPA KIV-2 copy number and Lipoprotein(a) concentration. CHD: Coronary heart disease.

Blood lipid profiles and gene polymorphisms in CHD

The CHD patients were further classified into three groups according to their genotypes: The E2 group (genotypes ε2/ε2 and ε2/ε3), the E3 group (genotypes ε3/ε3), and the E4 group (genotypes ε3/ε4 and ε4/ε4). The relationships between alleles and blood lipid levels were analysed. Owing to the opposite roles of the ε2 and ε4 alleles in lipid metabolism, four patients with ε2/ε4 alleles were excluded. The results revealed that the levels of TC, LDLC, and ApoB in the E4 group were significantly greater than those in the E3 and E2 groups. Similarly, the TG levels in the E4 group were greater than those in the E3 group (Table 4).

Table 4 Comparison of blood lipid levels among APOE genes.

	E2 (n = 42)	E3 (n = 226)	E4 (n = 52)	P value
TG, mmol/L	1.62 ± 0.89	1.59 ± 1.10	2.09 ± 0.73a	0.022
TC, mmol/L	4.57 ± 1.22	4.72 ± 1.26	5.47 ± 1.70a,b	0.001
HCY, μmol/L	14.24 ± 6.73	13.33 ± 8.24	14.82 ± 8.77	0.442
HDL-C, mmol/L	1.09 ± 0.30	1.11 ± 0.29	1.08 ± 0.25	0.792
LDL-C, mmol/L	2.69 ± 0.98	2.95 ± 1.06	3.54 ± 1.36a,b	< 0.001
ApoA1, g/L	1.05 ± 0.21	1.11 ± 0.62	1.06 ± 0.21	0.730
ApoB, g/L	0.79 ± 0.28	0.86 ± 0.36	1.00 ± 0.33a,b	0.009
LP(a), mg/L	214.55 ± 290.31	275.79 ± 321.48	259.50 ± 331.44	0.517
ApoE, mg/L	46.69 ± 15.84	39.86 ± 14.11	45.43 ± 26.79	0.013

^aP < 0.05 vs E3 group.

^bP < 0.05 vs E2 group.

E2: Ε2/ε2 plus ε2/ε3; E3: Ε3/ε3; E4: Ε3/ε4 plus ε4/ε4. TG: Triglycerides; TC: Total cholesterol; HCY: Homocysteine; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; ApoA1: Apolipoprotein A1; ApoB: Apolipoprotein B; ApoE: Apolipoprotein E; Lp(a): Lipoprotein(a).

According to the SLCO1B1 genotype, we divided the CHD group into three categories: Extensive metabolizer I (*1a/*1a, *1a/*1b, *1b/*1b), intermediate metabolizer II (*1a/*5, *1a/*15, *1b/*15), and poor metabolizer III (*5/*5, *5/*15, *15/*15). Due to the relatively small number of patients in the poor metabolizer III group (3 cases), we excluded them from the comparison. We subsequently analysed the relationships between genotypes and blood lipid levels in columns I and II. The results revealed that the levels of LDL-C (3.08 ± 1.16 vs 2.69 ± 0.91, P < 0.016) and ApoB (0.89 ± 0.37 vs 0.79 ± 0.25, P < 0.045) in group I were significantly greater than those in group II (Table 5).

Table 5 Comparison of blood lipid levels among SLCO1B1 genes.

	Extensive metabolizer I (n = 249)	Intermediate metabolizer II (n = 72)	P value
TG, mmol/L	1.65 ± 1.20	1.66 ± 1.05	0.992
TC, mmol/L	4.88 ± 1.22	4.56 ± 1.08	0.119
HCY, μmol/L	13.73 ± 8.54	13.61 ± 6.54	0.801
HDL-C, mmol/L	1.10 ± 0.29	1.11 ± 0.29	0.778
LDL-C, mmol/L	3.08 ± 1.16	2.69 ± 0.91	0.016
ApoA1, g/L	1.09 ± 0.59	1.09 ± 0.21	0.256
ApoB, g/L	0.89 ± 0.37	0.79 ± 0.25	0.045
LP(a), mg/L	248.62 ± 300.96	323.28 ± 379.60	0.306
ApoE, mg/L	42.03 ± 18.17	39.96 ± 12.88	0.419

Extensive metabolizer I: *1a/*1a,*1a/*1b,*1b/*1b; Intermediate metabolizer II: *1a/*5,*1a/*15,*1b/*15. TG: Triglycerides; TC: Total cholesterol; HCY: Homocysteine; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol; ApoA1: Apolipoprotein A1; ApoB: Apolipoprotein B; ApoE: Apolipoprotein E; Lp(a): Lipoprotein(a).

Logistic regression analysis of risk factors for CHD

Multivariate logistic regression analysis was used to evaluate the incidence of CHD as the dependent variable. Our results revealed that age, sex, hypertension, diabetes, and smoking were independent risk factors for CHD. Compared with APOE E3 (ε3/ε3), APOE E2 (ε2/ε2, ε2/ε3) was significantly associated with CHD (OR 0.395, 95%CI: 0.209-0.745; P = 0.004). Based on the extensive metabolizer phenotype of the SLCO1B1 gene, poor metabolism and intermediate metabolism were not associated with CHD. Additionally, a greater copy number of KIV-2 was identified as a protective factor for CHD (OR 0.949, 95%CI: 0.919-0.979; P < 0.001) (Table 6).

Table 6 Logistic regression analysis of coronary heart disease risk in Teochew populations.

Variables	Unadjusted values		Adjusted values
	OR (95%CI)	P value	OR (95%CI)	P value
Male	2.343 (1.552-3.537)	< 0.001	2.559 (1.395-4.692)	0.002
Age, years	1.050 (1.032-1.067)	< 0.001	1.063 (1.040-1.085)	< 0.001
Hypertension	3.174 (2.086-4.831)	< 0.001	2.358 (1.420-3.917)	0.001
Diabetes	4.034 (2.251-7.230)	< 0.001	3.609 (1.813-7.184)	< 0.001
History of smoking	2.332 (1.448-3.757)	< 0.001	2.042 (1.092-3.818)	0.025
TG, mmol/L	1.581 (1.009-2.447)	0.046	1.526 (0.855-2.725)	0.153
HCY, μmol/L	3.282 (1.824-5.906)	< 0.001	1.888 (0.933-3.819)	0.077
HDL-C, mmol/L	0.363 (0.227-0.581)	< 0.001	0.577 (0.308-1.083)	0.087
ApoA1, g/L	2.320 (1.517-3.548)	< 0.001	1.402 (0.793-2.478)	0.245
LP(a), mg/L	2.358 (1.371-4.056)	0.002	1.577 (0.742-3.350)	0.236
Genotype
E2	0.507 (0.302-0.850)	0.010	0.395 (0.209-0.745)	0.004
E4	1.218 (0.669-2.219)	0.519	1.028 (0.500-2.110)	0.941
SLCO1B1	1.355 (0.824-2.229)	0.231	1.666 (0.896-3.096)	0.107
KIV	0.954 (0.933-0.976)	< 0.001	0.949 (0.919-0.979)	0.001

E2 (ε2/ε2, ε2/ε3), reference genotype: Ε3/ε3; E4 (ε4/ε4, ε3/ε4), reference genotype: Ε3/ε3; SLCO1B1(*1a/*5 plus *1a/*15 plus *1b/*15 plus*15/*15), reference genotype: *1a/*1a plus *1a/*1b plus*1b/*1b. TG: Triglycerides; HCY: Homocysteine; HDL-C: High-density lipoprotein cholesterol; ApoA1: Apolipoprotein A1; Lp(a): Lipoprotein(a).

DISCUSSION

CHD, distinguished as a widespread chronic ailment and a paramount cause of global mortality, represents a critical public health challenge. Over time, extensive studies have highlighted that genetic predispositions play a crucial role in the aetiology of CHD[13]. The exploration of genetic polymorphisms has become a central theme in investigations into hereditary susceptibility and preventive measures for CHD. Early identification and intervention of modifiable risk factors offer the potential to mitigate the immense toll exacted by this disease on individuals and societies alike. In light of this, our study endeavours to elucidate the interplay between CHD and specific genetic markers within the Teochew population-a region with distinctive genetic and environmental characteristics. Focusing on the APOE and SLCO1B1 gene polymorphisms, alongside the copy number variation of KIV-2, we aim to uncover insights essential for developing personalized and precision medicine approaches tailored to CHD management in the Chaoshan area.

The relationships between several cardiovascular disease risk factors and the incidence of CHD, including age, sex, hypertension, diabetes and smoking, were also verified in this study. These indicators were significantly different between the control and CHD groups. However, there are still several clinically recognized risk factors, such as BMI, alcohol consumption and TC levels, which have not been proven to establish a definitive link with CHD. While the distributions of these factors are similar across both groups, subtle discrepancies exist without statistical significance. Such observations may stem from demographic characteristics intrinsic to the Teochew populations, potentially skewing the results owing to regional biases. To eliminate biases caused by sex, age, diabetes, hypertension, and smoking history, we used logistic regression analysis, including these confounding factors as covariates in the regression model, and calculated the adjusted regression coefficients. We found that APOE E2 (ε2/ε2, ε2/ε3) remained significantly associated with CHD, and a higher copy number of KIV-2 was identified as a protective factor against CHD.

APOE plays a pivotal role in lipoprotein synthesis and metabolism and significantly influences susceptibility to cardiovascular diseases. The APOE gene polymorphism has been shown to be related to atherosclerosis[14]. Regional and ethnic differences result in varied frequencies of APOE genotype distributions. In our study, we found that the ε3 allele had the highest distribution frequency in the Chaoshan area, accounting for 81.80%, followed by ε2 (9.64%) and ε4 (8.57%). This distribution pattern is consistent with those observed in numerous cities across China, such as Guangzhou, Chongqing, Meizhou, Fuzhou, etc.[15-18]. Compared with that in the control group, the frequency of the ε2 allele in the CHD group was significantly lower. Although the proportion of ε4 was greater in the CHD group than in the control group, the difference was not statistically significant. APOE ε3/ε3 is the most common genotype, with ε3 being the predominant allele in most populations and considered the wild-type reference subtype[19]. In comparison, individuals carrying the ε4 allele present elevated levels of TG, TC, LDL-C and APOB; this may be attributed to the enhanced lipid and very low-density lipoprotein (VLDL) binding ability of ApoE4 relative to that of ApoE3[20]. Compared with ApoE3, ApoE4 binds better to the surface of VLDL particles and impairs their lipolytic processing in circulation; thus, ApoE4 is associated with a greater pro-atherogenic lipoprotein-cholesterol distribution (a higher VLDL-cholesterol/high-density lipoprotein-cholesterol ratio). Logistic regression analysis revealed that carrying the E2 allele significantly reduces the risk of developing CHD. A study by Xu et al[21] suggested that the APOE ε4 mutation was associated with an increased risk of CHD, whereas the APOE ε2 allele appeared protective against CHD in Caucasians. Conversely, Zhao et al[22] presented a differing perspective, suggesting that the ε2 allele of APOE is a risk factor for premature coronary artery disease, particularly among Asians. This may be attributed to interactions between genetic factors and environmental influences, especially diet, as well as specific lipid metabolism characteristics. Regional differences in the study populations contribute to the variability in research findings.

In our study, the most common SLCO1B1 genotype was *1b/*1b, followed by *1a/*1b*, 1b/*15 *, 1a/*1a*, and 1a/*15. The *1b allele dominated at 62.53%, followed by *1a (26.34%), *15 (10.92%), and *5 (0.21%). The genetic distribution of the Teochew population is consistent with findings from Asian populations[23,24]. Currently, research concerning SLCO1B1 has focused primarily on how genetic polymorphisms affect drug metabolism, drug efficacy, and related side effects. Mutations in SLCO1B1-encoded OATP1B1 lead to a decrease in protein transport capacity, thereby diminishing the liver's ability to take up medications, causing elevated blood concentrations of statins and increasing the likelihood of conditions such as rhabdomyolysis or myopathy. Our study revealed that there was no significant correlation between SLCO1B1 gene polymorphisms and the incidence of CHD. Liu et al[25] suggested that the SNPs rs2306283 and rs4149056 of the SLCO1B1 gene are not associated with CAD susceptibility in the southern Chinese Hakka population. In contrast, Niu et al[26] reported that the SLCO1B1 *1a/*15 genotype reduces the risk of CHD in the Mongolian population, acting as a protective factor against the disease. Variations in SLCO1B1 gene polymorphisms among CHD populations demonstrate ethnic and regional differences. We can develop personalized treatment plans based on the results derived from SLCO1B1 gene polymorphisms. Personalized treatment strategies can be developed to achieve lipid-level reductions while preventing the adverse effects of statin use.

Currently, the precise mechanism by which elevated Lp(a) levels increase the risk of CHD remains incompletely understood, although high Lp(a) seems to provoke inflammation, atherosclerosis, and thrombosis[27]. Lp(a) concentrations are predominantly driven by the LPA gene. We confirmed that the repeat numbers of KIV-2 in the LPA gene were inversely correlated with serum Lp(a) levels, which aligns with Paultre’s conclusion that fewer KIV copies in apo(a) isoforms are associated with higher Lp(a) concentrations, serving as a predictive risk factor for CHD[28]. We also observed that the CHD group presented a significantly lower KIV-2 copy number than did the control group. Logistic regression analysis using CHD risk factors as dependent variables revealed that for every unit increase in copy number, the risk of CHD decreased by 0.949 times, indicating that a higher KIV-2 copy number corresponds to a lower risk. In a study targeting the Danish general population, it was proposed that high Lp(a) levels correlate with a greater risk of all-cause mortality, possibly attributable to low LPA KIV-2 repeat counts[29]. Additionally, some reports suggest that the causal relationship between LPA mutations and CHD remains uncertain[30]. Further research is needed to clarify the pathogenic mechanisms of Lp(a) and lipid levels during different stages of CHD. Dynamic monitoring of lipid profiles in patients with varying degrees of CHD will be more conducive to guiding lipid-lowering treatment and preventing disease progression.

Since this study included both cases and controls, biases may have been introduced. The limited sample size in certain subgroups may compromise the representativeness. There could be interactions and linkage disequilibrium among genes, and in addition to the three genes investigated, many other genes may also play roles in the pathogenesis of CHD.

CONCLUSION

In summary, this study investigated the relationship between APOE, SLCO1B1 gene polymorphisms and LPA KIV-2 copy number variation in the Teochew population with CHD. We confirmed that the APOE ε2 allele and a higher KIV-2 copy number are associated with a reduced risk of CHD. Building on these findings, further research is needed to explore the correlations between genetic polymorphisms and myocardial injury, as well as their prognostic implications, using using multicentre and multiregional samples.