Effect of Interactions Between Endothelial Lipase Gene Polymorphisms and Traditional Cardiovascular Risk Factors on Coronary Heart Disease Susceptibility

Abstract

Background:

Coronary heart disease (CHD) arises from a complex interplay of genetic and environmental factors. This study examines the influence of endothelial lipase gene polymorphisms (rs2000813 and rs3813082) and their interactions with traditional cardiovascular risk factors on CHD susceptibility.

Methods:

This retrospective case–control study enrolled 900 CHD patients and 900 control subjects. We evaluated associations between conventional cardiovascular risk factors and polymorphisms at the rs2000813 and rs3813082 loci in the endothelial lipase gene. Multifactorial analysis was used to assess interactions between traditional risk factors and these polymorphisms. Additionally, we developed a predictive model integrating genetic variants and clinical variables to estimate CHD risk.

Results:

No significant differences were observed in the distribution of rs2000813 genotypes (CC, CT, TT) and alleles (C, T), or rs3813082 genotypes (AA, AC, CC) and alleles (A, C) between CHD and control groups, including among males. However, in females with CHD, the rs2000813CT genotype was significantly more frequent (49.30%) than in controls (37.80%), whereas the CC genotype was less frequent in the CHD group (45.00%) than in controls (55.20%). Multivariate logistic regression identified the rs2000813CT genotype, hypertension, ages ≥60 years, body mass index (BMI) values ≥28 kg/m², total cholesterol (TC) ≥6.2 mmol/L, and apolipoprotein B (ApoB) ≥1.1 g/L as potential risk factors for CHD in women (p < 0.05). Gene–environment interaction analysis revealed that BMI exerted the greatest influence (12.62%). A predictive model incorporating rs2000813 genotypes estimated CHD risk in women with an area under the curve (AUC) of 0.804.

Conclusions:

The rs2000813CT endothelial lipase genotype is potentially associated with an increased CHD risk in females, whereas the CC genotype may confer a protective effect. Integrating endothelial lipase gene variants with traditional cardiovascular risk factors enhances CHD risk prediction in women. Synergistic interaction between endothelial lipase polymorphisms and environmental factors appears to influence CHD occurrence in this population.

Graphical abstract

Keywords

gene-environment interaction / gene polymorphisms / endothelial lipase / coronary heart disease / traditional cardiovascular risk factors

1. Introduction

Coronary heart disease (CHD) encompasses a spectrum of cardiovascular disorders resulting from myocardial ischemia, hypoxia, or necrosis due to coronary artery stenosis or occlusion. It is characterized by high morbidity and mortality rates. According to the World Health Organization’s Global Health Estimates report from December 2020, CHD remains the leading cause of death worldwide, accounting for 16% of all fatalities [1]. In China, the prevalence of CHD is increasing. The China Health Statistics Yearbook 2019 reports that in 2018, the CHD mortality rate was 120.18 per 100,000 in urban populations and 128.24 per 100,000 in rural population [2].

As a chronic condition, CHD is manageable but incurable, requiring long-term diagnosis and treatment. Its etiology primarily stems from a combination of factors, including dyslipidemia, coronary artery endothelial dysfunction, and chronic inflammation [3, 4]. Recent studies have underscored the critical role of genetic variants in CHD and pathophysiology [5, 6]. Despite the implementation of clinical interventions, such as preventive and therapeutic medications, along with guidelines for managing lipid metabolism disorders and chronic inflammatory, the prevalence of CHD remains high [7]. Elucidating the regulatory mechanisms of gene variants in lipid metabolism, inflammatory responses, and vascular endothelial injury is essential to address the underlying causes of CHD and may pave the way for significant advances in its prevention and treatment [8].

Endothelial lipase (EL), encoded by the endothelial lipase gene, is primarily synthesized by vascular endothelial cells. Its expression is upregulated in response to acute inflammatory stimuli, endotoxin exposure, and alterations in vascular wall shear stress. Hirata et al. [9] identified the endothelial lipase gene on the long arm of chromosome 18 (18q21.1), comprising 10 exons and 9 introns. EL plays a pivotal role in lipid metabolism and is implicated in metabolic syndromes, including inflammation and atherosclerosis [10]. This study investigates the effects of endothelial lipase polymorphisms rs2000813 and rs3813082, alongside traditional cardiovascular risk factors and their interactions, on susceptibility to CHD. Furthermore, it aims to develop a risk-prediction model integrating genetic and conventional factors. This model seeks to identify high-risk individuals for early intervention, enhance the prognosis of cardiac events, and provide a theoretical foundation for novel approaches to the diagnosis, treatment, and prevention of CHD.

2. Materials and Methods

2.1 Sample Size Estimation

This study employed a case-control design. The sample size was calculated using the following parameters: an odds ratio (OR) of 1, a significance level (\alpha) of 0.05, a power (1 - \beta) of 0.9, and minor allele frequencies for the rs2000813 T allele of 0.22 (PMAF1) and 0.30 (PMAF2), sourced from the National Center for Biotechnology Information (NCBI) database. Using the two-proportion formula in PASS software (version 21.0.3, Kaysville, UT, USA), we estimated a required sample size of 1260 participants. To account for a 20% attrition rate, an additional 252 participants were included, yielding a total sample size of 1512. This was evenly divided into 756 participants in the coronary heart disease group and 756 in the control group.

2.2 Study Participants

Between January 2019 and December 2021, we enrolled 900 patients diagnosed with coronary artery disease (CAD) who underwent coronary angiography or percutaneous coronary intervention (PCI) at the First Affiliated Hospital of Xinjiang Medical University as the case group. Concurrently, 900 patients who did not meet the diagnostic criteria, as confirmed by coronary angiography or coronary computed tomography angiography (CTA) during the same period, were selected as the control group. Subsequently, 720 participants from each group were randomly allocated in an 8:2 ratio to form the modeling subset, with the remaining 180 participants from each group assigned to the validation subset.

2.3 Case group Inclusion and Exclusion Criteria

Inclusion criteria: (1) Patients aged \ge18 years. (2) Patients hospitalized at the First Affiliated Hospital of Xinjiang Medical University between January 2019 and December 2021 with typical angina symptoms or non-invasive evidence of myocardial ischemia, a prerequisite for admission at this institution. (3) CHD diagnosis confirmed by coronary angiography, demonstrating coronary artery stenosis >50% in diameter.

Exclusion criteria: Patients were excluded if they met any of the following conditions: (1) Incomplete clinical data. (2) Presence of severe heart failure, cardiogenic shock, malignant arrhythmias, tumors, autoimmune diseases, or conditions associated with aortic stenosis. (3) Refusal to participate or noncompliance with the study protocol.

2.4 Control Group Inclusion and Exclusion Criteria

Inclusion criteria: (1) Participants aged \ge18 years. (2) Patients hospitalized at the First Affiliated Hospital of Xinjiang Medical University between January 2019 and December 2021 with typical angina symptoms or noninvasive evidence of myocardial ischemia, a prerequisite for admission at this institution. (3) Absence of CHD confirmed by coronary angiography or CTA.

Exclusion criteria: Patients were excluded if they met any of the following: (1) Incomplete clinical data. (2) Presence of severe heart failure, cardiogenic shock, malignant arrhythmias, tumors, or autoimmune diseases. (3) Refusal to participate or noncompliance with the study protocol.

2.5 Diagnostic Criteria

(1) CHD: Diagnosis required typical angina symptoms and/or non-invasive evidence of myocardial ischemia, coupled with coronary angiography demonstrating \ge50% stenosis in the main trunk, left anterior descending branch (including the diagonal branch), left circumflex artery (including the marginal branch), or right coronary artery (including the posterior descending and posterolateral branch) [11, 12].

(2) Hypertension: Defined as systolic blood pressure \ge140 mmHg and/or diastolic blood pressure of \ge90 mmHg, measured at rest on at least two separate days, or a documented history of hypertension [13].

(3) Type 2 diabetes mellitus: Diagnosed based on random blood glucose \ge11.1 mmol/L (200 mg/dL), fasting glucose levels \ge7.0 mmol/L (126 mg/dL), 2-hour post-load glucose \ge11.1 mmol/L (200 mg/dL) on an oral glucose tolerance test, or a confirmed history of type 2 diabetes mellitus [14].

(4) Smoking calculator: A pack year is defined as twenty cigarettes smoked everyday for one year [15].

(5) Alcohol consumption: Defined as ethanol intake \ge28 g/d for men or \ge14 g/d for women, calculated as ethanol (g) = alcohol volume (mL) \times ethanol percentage (%) \times 0.8 [16].

2.6 General Data Collection

Baseline data, including sex, age, body mass index (BMI), smoking history, alcohol consumption, hypertension, and type 2 diabetes mellitus, were collected from all participants.

2.7 Determination of Biochemical Indicators

After fasting for >12 hours, venous blood was collected from participants in the early morning into anticoagulant tubes. Serum samples were analyzed for the following indices: triglyceride (TG), total cholesterol (TC), high-density protein cholesterol (HDL-C), low-density protein cholesterol (LDL-C), apolipoproteins A and B (ApoA and ApoB), lipoprotein a (Lp(a)), serum uric acid (SUA), serum creatinine (Scr), white blood cell (WBC), neutrophil count (NE), platelet count, creatine kinase-MB (CK-MB), and cardiac troponins I and T (cTnI and cTnT). These measurements were performed by the Laboratory Department of the First Affiliated Hospital of Xinjiang Medical University using an automated biochemical analyzer (Olympus AU1000/2700, SC, USA).

2.8 DNA Extraction and SNPscnTM High Flux

Upon hospital admission, 3 mL of venous blood was collected from the antecubital vein of each participant and anticoagulated with 2% ethylenediaminetetraacetic acid (EDTA) disodium salt. Leukocytes were isolated by centrifugation, and genomic DNA was extracted using the phenol-chloroform method, then stored at –80 °C. The SNPscnTM high-throughput assay was employed to genotype the endothelial lipase gene loci rs2000813 and rs3813082. Based on nucleotide variations, the rs2000813 locus was classified into CC, CT, and TT genotypes, and rs3813082 locus into AA, AC, and CC genotypes (Supplementary Table 1).

2.9 Statistical Methods

Statistical analyses were conducted using SPSS software (version 26.0, IBM Corp., Armonk, NY, USA). Categorical data were reported as counts and percentages (n [%]) and compared using the chi-square (\chi²) test. For continuous data, the Kolmogorov-Smirnov test was employed to assess normality. Normally distributed continuous data were presented as means \pm standard deviations and analyzed with independent-sample t-test for two-group comparisons or one-way analysis of variance (ANOVA) followed by the least significant difference (LSD) t-test for multiple-group comparisons. Non-normally distributed continuous data were expressed as medians with the 25th and 75th percentiles (M [P₂₅, P₇₅]) and evaluated using the Mann-Whitney U test for two groups or the Kruskal-Wallis H test for multiple groups. Spearman correlation, univariate, and multivariate logistic regression analyses were performed to identify risk factors of CHD in women. Gene-environment interactions between the endothelial lipase gene and CHD risk factors in women were assessed using multifactor dimensionality reduction (MDR) software (version 3.0.2, Computational Genetics Laboratory, Hanover, NH, USA). Receiver operating characteristic (ROC) curves were generated with GraphPad Prism (version 8.0.2, GraphPad Software, Inc., San Diego, CA, USA) to evaluate the predictive performance of the model for CHD risk in women. Statistically significance was defined as p 0.05.

3. Results

3.1 Comparison of Baseline Characteristics Between Case and Control Groups

Baseline characteristics (Table 1) revealed no significant differences between the case and control groups in sex distribution, age, alcohol consumption, TG levels, ApoA, SUA, Scr, or other variables. However, the case group exhibited significantly higher prevalence rates of smoking (44.70% vs. 36.60%, p = 0.001), hypertension (48.60% vs. 43.10%, p = 0.023) and diabetes (26.20% vs. 11.90%, p 0.001) compared to the control group. Additionally, the case group had higher BMI, TC, LDL-C, Lp(a), WBC, NE, platelet (PLT), CK-MB, cTnI and cTnT levels, with specific differences noted for HDL-C (1.00 \pm 0.29 vs. 1.08 \pm 0.32, p 0.001), and ApoB (0.87 [0.73, 1.02] vs. 0.84 [0.68, 0.98], p 0.001). Conversely, HDL-C and ApoB levels were significantly lower in the case group.

3.2 Association of Endothelial Lipase Gene Polymorphisms With Coronary Heart Disease Risk

Supplementary Table 2 presents the Hardy-Weinberg equilibrium (HWE) test results. Genotype distributions for both the CHD and control groups conformed to HWE, indicating that the study population represents a Mendelian population in genetic equilibrium. This finding supports the genetic representativeness the sample, a critical factor for the validity of genetic association studies. Additionally, haplotype linkage disequilibrium (LD) analysis of the rs2000813 and rs3813082 loci in the endothelial lipase gene was performed using the SHEsis online platform. The results yielded a D’ value of 0.158 and an r² value of 0.001, suggesting no significant LD between these loci. Thus, rs2000813 and rs3813082 are genetically independent, an important consideration for evaluating their individual and combined effects on CHD susceptibility.

Supplementary Table 3 summarizes the distribution of endothelial lipase gene polymorphisms between the CHD and control groups. No significant differences were observed in the distribution of rs2000813 genotypes (CC, CT, TT) and alleles (C, T), or in rs3813082 genotypes (AA, AC, CC) and alleles (A, C) between the groups (all p > 0.05). These findings suggest that, when considered independently, these polymorphisms do not significantly distinguish the CHD group from the control group in this study.

Supplementary Table 4 details the distribution of endothelial lipase genes polymorphisms in male participants, comparing the CHD and control groups. No significant differences were observed in the distribution of rs2000813 genotypes (CC, CT, TT) and alleles (C, T), or rs3813082 genotypes (AA, AC, CC) and alleles (A, C) between the CHD and the control groups among men (all p > 0.05). This suggests that these polymorphisms are similarly distributed in both groups within the male population. Table 2 presents the distribution of endothelial lipase genes polymorphisms in female participants. In women, the rs2000813 CT genotype was significantly more prevalent in the CHD group than in the control group (49.30% vs. 37.80%, p = 0.040), whereas the CC genotype was less frequent (45.00% vs. 55.20%, p 0.05). No significant differences were found in the distribution of the TT genotype or C and T alleles between the groups. For rs3813082, the distribution of genotypes (AA, AC, CC) and alleles (A and C) showed no significant differences between the CHD and control groups in women (all p > 0.05).

3.3 Analysis of Risk Factors for Coronary Heart Disease in Women

Supplementary Table 5 outlines the classification of blood lipid parameters, SUA, and routine hematological indices. These classifications are based on the 2022 Chinese Clinical Blood Lipid Test Guide [17], the 2019 Basic Diagnosis and Treatment Guide for Gout and Hyperuricemia [18], and the ninth edition of the Diagnostic Standard [19]. These authoritative guidelines provide a standardized framework for assigning values and interpreting the diverse biochemical and hematological data in this study, ensuring consistency and reliability.

Spearman correlation analysis revealed that positive associations between CHD in women and multiple factors, including lifestyle factors (smoking and alcohol consumption), clinical conditions (hypertension and type 2 diabetes mellitus), and biochemical markers (BMI, TC, ApoB, Lp(a), WBC, NE, and PLT). The rs2000813 CT genotype also showed a positive correlation with CHD, whereas the rs2000813 CC genotype was inversely associated with CHD in women. Univariate logistic regression analysis identified hypertension, type 2 diabetes mellitus, advanced age, elevated BMI, and increased levels of TC, ApoB, Lp(a), WBC, NE, and PLT as risk factors for CHD in women, each with distinct ORs. Multivariate logistic regression further confirmed the following as significant predictors: hypertension (OR, 2.305; 95% confidence interval (CI), 1.438–3.696; p 0.001), age \ge60 years (OR, 2.267; 95% CI, 1.392–3.692; p 0.001), BMI \ge28 kg/m² (OR, 8.634; 95% CI, 4.653–16.021; p 0.001), TC \ge6.2 mmol/L (OR, 6.437; 95% CI, 1.074–38.577; p = 0.042), ApoB \ge1.1 g/L (OR, 2.504; 95% CI, 1.138–5.512; p = 0.023), and the rs2000813 CT genotype (OR, 1.614; 95% CI, 1.010–2.579; p = 0.045). These factors, with their respective ORs and 95% CIs, were significant predictors of CHD in women. Detailed statistical results are provided in Table 3.

3.4 MDR-based Analysis of the Association Between Endothelial Lipase Gene-environment Interaction and the Development of Coronary Heart Disease in Women

This study suggests that the rs2000813 CT genotype may increase the risk of CHD in women, while the rs3813082 locus of the endothelial lipase gene may influence lipid metabolism. To explore these genetic contributions further, we analyzed interactions between these loci and environmental factors—including smoking, alcohol consumption, hypertension, type 2 diabetes mellitus, age, BMI, and specific blood markers—using the MDR method. This approach constructed a multilevel interaction model integrating genetic and environmental variables, demonstrating strong predictive performance (Supplementary Table 6).

MDR analysis focused on interactions between the rs2000813 and rs3813082 loci of the endothelial lipase gene. Supplementary Fig. 1A illustrates risk-factor combinations associated with CHD in women. Dark-colored cells denote high-risk combinations, indicating increased CHD susceptibility, whereas light-colored cells represent low-risk combinations, suggesting reduced disease likelihood. White cells indicate no significant association with CHD. Supplementary Fig. 1B, highlights positive interactions between these loci in red, quantifying their individual main effects. Notably, rs2000813 exhibited a stronger influence on CHD risk than rs3813082.

Supplementary Fig. 2 presents the MDR analysis of interactions between the rs2000813 and rs3813082 loci of the endothelial lipase gene and environmental factors in in relation to CHD in women. Bar histograms distinguish the case group (left) from the control group (right), with dark cells indicating high-risk combinations, light cells representing low-risk combinations, and white cells denoting no significant association with CHD. This visualization elucidates the gene-environment interactions influencing CHD in women, offering insights into the complex interplay of genetic and environmental factors.

Supplementary Fig. 3 depicts interaction tree highlighting patterns among risk factors for CHD in women. Traditional cardiovascular risk factors-including neutrophil count, BMI, platelet count, and WBC-cluster on a single main branch, exhibiting a strong negative interaction (shown in dark blue). The introduction of the rs2000813 locus, alongside other risk factors such as alcohol consumption, age, smoking, lipoprotein(a), type 2 diabetes mellitus, hypertension, apolipoprotein B, and total cholesterol, attenuates this negative interaction, transitioning towards a positive interaction (indicated by green and yellow). Ultimately, a weak positive interaction emerges with rs3813082.

In the interaction ring network of the MDR model (Fig. 1), node values represent the information gain from individual attributes (main effects), while values between nodes reflect the information gain from attribute pairs (interaction effects). The main effects of single attributes, ranked in descending order, are as follows: BMI, NE, PLT, WBC, hypertension, age, type 2 diabetes mellitus, TC, ApoB, smoking, alcohol consumption, rs2000813, Lp(a), and rs3813082. Among the interaction effects, the strongest positive interaction occurs between rs2000813 and rs3813082, whereas the most pronounced negative interaction is observed between BMI and NE.

3.5 Construction and Validation of a Predictive Model for Coronary Heart Disease Risk in Women

Logistic regression analysis, conducted using SPSS software (version 26.0), generated prediction probabilities for four models. Model Y1 was defined by the rs2000813 CT genotype, while Model Y2 by the rs2000813 CC genotype. Model Y3 included traditional cardiovascular risk factors (e.g., smoking, hypertension, BMI), while Model Y4 combined all Model Y3 factors with both rs2000813 genotypes. ROC curve analysis revealed that Model Y3 (risk ratio index [RRI] >0.344; sensitivity, 82.5%; specificity, 62.7%) and Model Y4 (RRI >0.515; sensitivity, 68.6%; specificity, 77.2%) outperformed Model Y1 and Y2 in predicting CHD risk in women. Model Y4 demonstrated strong predictive performance, achieving area under the curve (AUC) values of 0.804 (95% CI, 0.766–0.843; p 0.001) and 0.793 (95% CI, 0.715–0.871; p 0.001) in the training and validation sets, respectively. Decision curves for the female patient training set show Model Y4 yields high net benefit in predicting female coronary heart disease risk, demonstrating its good clinical applicability. AUC values and sensitivity/specificity metrics are detailed in Table 4 and Fig. 2.

In this study, 1800 participants were enrolled and divided into a modeling subset (1440 participants) and a validation subset (360 participants) in an 8:2 ratio. These models were reconstructed and validated in female patients. Of these, 590 were women, with 470 (229 CHD, 241 Controls) in the modeling subset and 120 (51 CHD, 69 Controls) in the validation subset. Statistical analysis confirmed no significant differences in the distribution of factors such as smoking status, hypertension prevalence, or rs2000813 genotypes between the two subsets (p > 0.05), as detailed in Table 5. For predicting CHD risk in women, Model Y4 exhibited the highest predictive accuracy, as detailed in Table 4 and Fig. 3.

4. Discussion

CAD is widely recognized for its genetic basis, with cardiovascular diseases (CVD) development driven by interactions between genetic and traditional risk factors. The association between gene polymorphisms and CHD is a prominent focus of global research due to its implications for understanding and managing CAD [20]. Investigating gene-environment interactions is essential not only for addressing the “missing heritability” of complex traits but also for elucidating the biological mechanisms underlying multifactorial diseases. This approach is central to genetics, providing deeper insights into how genetic and environmental factors jointly shape complex health conditions [21]. Genetic variants may not directly cause disease but can influence its onset and progression through subtle interactions with other genes or environmental exposures. This complexity underscores that genetic contributions to disease often manifest significantly only in combination with additional genetic or environmental factors [22]. Studying these interactions is critical for understanding disease etiology and the impact of environmental exposures on health outcomes. Focusing on clinical data and EL gene polymorphisms in CHD patients, this research aims to uncover associations between gene interactions and disease susceptibility. It also seeks to develop a risk prediction model to identify high-risk populations, offering valuable insights for targeted public health strategies and interventions.

Endothelial lipase, encoded by the endothelial lipase gene, is predominantly synthesized by vascular endothelial cells and exhibits robust phospholipase activity with minimal triglyceride lipase activity. This enzyme critically regulates serum HDL-C metabolism, influencing cholesterol transport and cardiovascular health [23]. HDL-C is widely acknowledged for its protective role against atherosclerosis, mediated by anti-inflammatory and antioxidant effects, inhibition of platelet aggregation, and promotion of re-endothelialization. Its association with the incidence and prognosis of CHD underscores its cardiovascular significance [24]. Initially, endothelial lipase was thought to contribute to CHD pathogenesis primarily through HDL-C regulation. Subsequent research, however, demonstrated additional roles, including facilitating macrophage adhesion to the vascular endothelium, promoting platelet aggregation and endothelial cell proliferation, and contributing to atherosclerosis. Notably, suppressing endothelial lipase expression during inflammation may reduce the severity of coronary atherosclerosis [25]. Among the triglyceride lipase family, endothelial lipase is considered the most influential in atherosclerosis, emphasizing its relevance to cardiovascular diseases research [26].

In this study, the rs2000813 CT genotype of the endothelial lipase gene was identified as a potential risk factor for CHD in women, whereas the CC genotype exhibited a protective effect. These findings align with prior study linking endothelial lipase gene single-nucleotide polymorphisms (SNPs) to increased CHD risk, reinforcing the role of genetic factors in this disease [27]. However, Elnaggar et al. [28] suggested that the T allele of the rs2000813 (584C/T) variant may confer protection against CHD, implying a reduced risk for carriers. In contrast, Zhao et al. [29] reported that the CC genotype and C allele may predispose individuals to higher CHD risk, highlighting potential genetic contributions to disease susceptibility. The rs2000813 genotype also appears linked to sex-specific differences in CHD incidence, potentially influenced by variations in sex hormone levels. Mehilli and Presbitero [30] underscored significant disparities in clinical presentation, complications, and cardiovascular risk profiles between men and women with CHD, emphasizing the need to consider sex-specific factors. Women’s susceptibility to endothelial dysfunction and occult CHD may stem from unique factors such as inflammation, mental stress, and autonomic or neuroendocrine dysregulation [31], contributing to sex-specific cardiovascular risks. The increased CHD risk in women with the rs2000813 CT genotype may reflect its specific effects on lipid metabolism and related pathways, though further research is required to elucidate the underlying mechanisms.

In this study, no significant association was observed between rs3813082 polymorphism of the endothelial lipase gene and CHD (p > 0.05). This study did not identify an association between ELrs3813082 gene polymorphism and coronary heart disease. However, it’s important to note that the ELrs3813082 gene’s minimum allele frequency (MAF-C = 0.13) was very small in this study, and the sample size was relatively limited, which may have influenced the results. Larger studies are warranted to clarify the role of rs3813082 in CHD.

This study employed multifactor dimensionality reduction analysis to examine gene-gene and gene-environment interactions in CHD. A positive interaction was identified between the rs2000813 and rs3813082 loci of the endothelial lipase gene. Among individual attributes, the main effects ranked in descending order of influence were BMI, NE, PLT, WBC, hypertension, age, type 2 diabetes mellitus, TC, ApoB, smoking, alcohol consumption, rs2000813, Lp(a), and rs3813082. The strongest interaction occurred between rs2000813 and rs3813082.

Hartiala et al. [32] found that smoking attenuates the protective effect of the ADAMTS7 gene on cardiovascular health, while increased physical activity amplifies the influence of three genetic loci on serum HDL-C levels and diminishes the effect of another locus. Air pollution, meanwhile, elevates cardiovascular disease risk by altering CXCL12 expression, and X chromosome variants significantly regulate sex differences in cardiovascular disease incidence [32]. In this study, significant interactions were identified between the rs2000813 and rs3813082 loci of the endothelial lipase gene and environmental factors-including BMI, NE, PLT, WBC, hypertension, age, type 2 diabetes mellitus, TC, ApoB, smoking, alcohol consumption, and Lp(a)-collectively influencing CHD risk in women. These gene-environment interactions deepen our understanding of CHD pathogenesis and genetic susceptibility, offering critical insights for developing tailored lifestyle recommendations and therapeutic strategies.

Complex interactions between SNPs and traditional cardiovascular risk factors are pivotal in CHD pathogenesis. Integrating these factors with SNPs in CHD-related genes enhances our understanding of disease mechanisms. Notably, severe coronary atherosclerosis may develop asymptomatically for years before angina manifests [33]. Proactively identifying high-risk individuals and managing modifiable risk factors can substantially reduce adverse cardiac events [34]. Developing an effective risk prediction model based on clinical and laboratory data is essential for pinpointing high-risk populations, enabling targeted risk factor management, and optimizing healthcare resource allocation. For instance, Pattarabanjird et al. [35] combined the TID3 rs11574 polymorphism with traditional risk factors, achieving 87.0% accuracy and an AUC of 0.840 in predicting CAD severity, markedly improving prediction efficiency. In this study, we integrated endothelial lipase gene polymorphisms (rs2000813, rs3813082) and traditional CHD risk factors (smoking, alcohol consumption, hypertension, type 2 diabetes mellitus, age \ge60 years, BMI \ge28 kg/m², TC \ge6.2 mmol/L, ApoB \ge1.1 g/L, Lp(a) \ge300 mg/L, WBC >10 \times 10⁹/L, 2.0 \times 10⁹/L \le NE 7.0 \times 10⁹/L, NE \ge7.0 \times 10⁹/L, PLT \ge300 \times 10⁹/L) to predict CHD. This combined model yielded a sensitivity of 68.6%, specificity of 77.2%, and AUC of 0.804, demonstrating superior predictive performance compared to individual factors alone.

This study has several limitations. First, as a single-center study with a relatively small sample size, it may be subject to selection bias, potentially limiting the generalizability of the findings. Second, the prediction model underwent only internal validation, without external validation, necessitating further assessment of its clinical applicability and broader relevance. Consequently, future research should prioritize large-scale, multicenter, prospective studies to validate the predictive utility of endothelial lipase polymorphisms (rs2000813 and rs3813082) combined with traditional cardiovascular risk factors for CHD incidence in women.

5. Conclusions

This study identified the rs2000813 CT genotype of the endothelial lipase gene as a potential risk factor for CHD in women. Furthermore, a synergistic interaction between endothelial lipase gene polymorphisms and environmental factors appears to influence CHD susceptibility in women. When integrated with traditional cardiovascular risk factors, this model exhibits robust predictive performance for CHD in women.

Availability of Data and Materials

Data supporting the findings of this study are available from the corresponding author upon reasonable request within 1 year of publication of this article.

References

[1] Rana JS, Khan SS, Lloyd-Jones DM, Sidney S. Changes in Mortality in Top 10 Causes of Death from 2011 to 2018. Journal of General Internal Medicine. 2021; 36: 2517–2518. https://doi.org/10.1007/s11606-020-06070-z.

[2] Writing committee of the report on cardiovascular health and diseases in China. Report on Cardiovascular Health and Diseases in China 2021: An Updated Summary. Biomedical and Environmental Sciences: BES. 2022; 35: 573–603. https://doi.org/10.3967/bes2022.079.

[3] Agrawal H, Choy HHK, Liu J, Auyoung M, Albert MA. Coronary Artery Disease. Arteriosclerosis, Thrombosis, and Vascular Biology. 2020; 40: e185–e192. https://doi.org/10.1161/ATVBAHA.120.313608.

[4] Zhang B, Li S, Liu H, Wang D, Gao A, Wang Y, et al. Immune Infiltration in Atherosclerosis is Mediated by Cuproptosis-Associated Ferroptosis Genes. Cardiovascular Innovations and Applications. 2023; 7: 978. https://doi.org/10.15212/CVIA.2023.0003.

[5] Walsh R, Jurgens SJ, Erdmann J, Bezzina CR. Genome-wide association studies of cardiovascular disease. Physiological reviews. 2023; 103: 2039–2055. https://doi.org/10.1152/physrev.00024.2022.

[6] Huynh K. Protein interactomes uncover new genetic causes of CHD. Nature Reviews Cardiology. 2022; 19: 284–285. https://doi.org/10.1038/s41569-022-00688-8.

[7] Klarin D, Damrauer SM, Cho K, Sun YV, Teslovich TM, Honerlaw J, et al. Genetics of blood lipids among ∼300,000 multi-ethnic participants of the Million Veteran Program. Nature Genetics. 2018; 50: 1514–1523. https://doi.org/10.1038/s41588-018-0222-9.

[8] Ylä-Herttuala S, Baker AH. Cardiovascular Gene Therapy: Past, Present, and Future. Molecular Therapy: the Journal of the American Society of Gene Therapy. 2017; 25: 1095–1106. https://doi.org/10.1016/j.ymthe.2017.03.027.

[9] Hirata K, Dichek HL, Cioffi JA, Choi SY, Leeper NJ, Quintana L, et al. Cloning of a unique lipase from endothelial cells extends the lipase gene family. The Journal of Biological Chemistry. 1999; 274: 14170–14175. https://doi.org/10.1074/jbc.274.20.14170.

[10] Hong C, Deng R, Wang P, Lu X, Zhao X, Wang X, et al. LIPG: an inflammation and cancer modulator. Cancer Gene Therapy. 2021; 28: 27–32. https://doi.org/10.1038/s41417-020-0188-5.

[11] Taylor A, Yang E. Comparing American and European Guidelines for the Initial Diagnosis of Stable Ischaemic Heart Disease. European Heart Journal. 2020; 41: 811–815. https://doi.org/10.1093/eurheartj/ehaa057.

[12] Rampidis GP, Benetos G, Benz DC, Giannopoulos AA, Buechel RR. A guide for Gensini Score calculation. Atherosclerosis. 2019; 287: 181–183. https://doi.org/10.1016/j.atherosclerosis.2019.05.012.

[13] Joint Committee for Guideline Revision. 2018 Chinese Guidelines for Prevention and Treatment of Hypertension-A report of the Revision Committee of Chinese Guidelines for Prevention and Treatment of Hypertension. Journal of Geriatric Cardiology: JGC. 2019; 16: 182–241. https://doi.org/10.11909/j.issn.1671-5411.2019.03.014.

[14] Jia W, Weng J, Zhu D, Ji L, Lu J, Zhou Z, et al. Standards of medical care for type 2 diabetes in China 2019. Diabetes/metabolism Research and Reviews. 2019; 35: e3158. https://doi.org/10.1002/dmrr.3158.

[15] Masters NJ. Smoking pack years calculator. The British Journal of General Practice: the Journal of the Royal College of General Practitioners. 2020; 70: 230. https://doi.org/10.3399/bjgp20X709553.

[16] Hendriks HFJ. Alcohol and Human Health: What Is the Evidence? Annual Review of Food Science and Technology. 2020; 11: 1–21. https://doi.org/10.1146/annurev-food-032519-051827.

[17] Kim JS. Role of Blood Lipid Levels and Lipid-Lowering Therapy in Stroke Patients with Different Levels of Cerebral Artery Diseases: Reconsidering Recent Stroke Guidelines. Journal of Stroke. 2021; 23: 149–161. https://doi.org/10.5853/jos.2021.01249.

[18] Diagnosis and Management of Gout: Clinical Practice Guidelines From the American College of Physicians. Annals of Internal Medicine. 2017; 166: I–16. https://doi.org/10.7326/P16-9025.

[19] Yue BH. Clinical Hematology Testing. In Wan XH, Lu XF (eds.) Diagnostics (pp. 241–247). 9th edn. People’s Medical Publishing House: Beijing. 2018. (In Chinese)

[20] Ghassibe-Sabbagh M, Platt DE, Youhanna S, Abchee AB, Stewart K, Badro DA, et al. Genetic and environmental influences on total plasma homocysteine and its role in coronary artery disease risk. Atherosclerosis. 2012; 222: 180–186. https://doi.org/10.1016/j.atherosclerosis.2012.02.035.

[21] Kogelman LJA, Kadarmideen HN. Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data. BMC Systems Biology. 2014; 8: S5. https://doi.org/10.1186/1752-0509-8-S2-S5.

[22] Gusareva ES, Van Steen K. Practical aspects of genome-wide association interaction analysis. Human Genetics. 2014; 133: 1343–1358. https://doi.org/10.1007/s00439-014-1480-y.

[23] Robert J, Osto E, von Eckardstein A. The Endothelium Is Both a Target and a Barrier of HDL-C’s Protective Functions. Cells. 2021; 10: 1041. https://doi.org/10.3390/cells10051041.

[24] Trimarco V, Izzo R, Morisco C, Mone P, Virginia Manzi M, Falco A, et al. High HDL-C (High-Density Lipoprotein) Cholesterol Increases Cardiovascular Risk in Hypertensive Patients. Hypertension (Dallas, Tex.: 1979). 2022; 79: 2355–2363. https://doi.org/10.1161/HYPERTENSIONAHA.122.19912.

[25] Das UN. Long-chain polyunsaturated fatty acids, endothelial lipase and atherosclerosis. Prostaglandins, Leukotrienes, and Essential Fatty Acids. 2005; 72: 173–179. https://doi.org/10.1016/j.plefa.2004.10.016.

[26] Kobayashi J. Which is the Best Predictor for the Development of Atherosclerosis Among Circulating Lipoprotein Lipase, Hepatic Lipase, and Endothelial Lipase? Journal of Atherosclerosis and Thrombosis. 2019; 26: 758–759. https://doi.org/10.5551/jat.ED108.

[27] Abudureyimu S, Abulaiti P, Li H, Xing Z, Liu S, Li W, et al. Roles of endothelial lipase gene related single nucleotide polymorphisms in patients with coronary artery disease. Gene. 2021; 788: 145669. https://doi.org/10.1016/j.gene.2021.145669.

[28] Elnaggar IZ, Hussein S, Amin MI, Abdelaziz EA. Association of 584C/T polymorphism in endothelial lipase gene with risk of coronary artery disease. Journal of Cellular Biochemistry. 2019; 120: 14414–14420. https://doi.org/10.1002/jcb.28697.

[29] Zhao H, Zhao R, Hu S, Rong J. Gene polymorphism associated with angiotensinogen (M235T), endothelial lipase (584C/T) and susceptibility to coronary artery disease: a meta-analysis. Bioscience Reports. 2020; 40: BSR20201414. https://doi.org/10.1042/BSR20201414.

[30] Mehilli J, Presbitero P. Coronary artery disease and acute coronary syndrome in women. Heart (British Cardiac Society). 2020; 106: 487–492. https://doi.org/10.1136/heartjnl-2019-315555.

[31] Waheed N, Elias-Smale S, Malas W, Maas AH, Sedlak TL, Tremmel J, et al. Sex differences in non-obstructive coronary artery disease. Cardiovascular Research. 2020; 116: 829–840. https://doi.org/10.1093/cvr/cvaa001.

[32] Hartiala JA, Hilser JR, Biswas S, Lusis AJ, Allayee H. Gene-Environment Interactions for Cardiovascular Disease. Current Atherosclerosis Reports. 2021; 23: 75. https://doi.org/10.1007/s11883-021-00974-9.

[33] Desai MY, Jellis CL, Kotecha R, Johnston DR, Griffin BP. Radiation-Associated Cardiac Disease: A Practical Approach to Diagnosis and Management. JACC. Cardiovascular Imaging. 2018; 11: 1132–1149. https://doi.org/10.1016/j.jcmg.2018.04.028.

[34] Zhou YF, Chen S, Wang G, Chen S, Zhang YB, Chen JX, et al. Effectiveness of a Workplace-Based, Multicomponent Hypertension Management Program in Real-World Practice: A Propensity-Matched Analysis. Hypertension (Dallas, Tex.: 1979). 2022; 79: 230–240. https://doi.org/10.1161/HYPERTENSIONAHA.121.18305.

[35] Pattarabanjird T, Cress C, Nguyen A, Taylor A, Bekiranov S, McNamara C. A Machine Learning Model Utilizing a Novel SNP Shows Enhanced Prediction of Coronary Artery Disease Severity. Genes. 2020; 11: 1446. https://doi.org/10.3390/genes11121446.

Funding

Tianshan Elite Science and Technology Innovation Leading Talents Program of Xinjiang Uyghur Autonomous Region(2022TSYCLJ0023)

National High Level Hospital Clinical Research Funding(2023-GSP-QN-36)

Key Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region(2023D01D13)

Beijing Natural Science Foundation(7222143)

National High Level Hospital Clinical Research Funding(2022-GSP-GG-9)