Association of ABCA1 and LIPG Polymorphisms with Coronary Heart Disease in a Chinese Han Population: A Case-Control Study with Sex-Stratified Analysis

Abstract

Objective

This study aims to investigate the association between the single nucleotide polymorphisms (SNPs) rs1800977 and rs2066714 in the ATP-binding cassette transporter A1 (ABCA1) gene, and rs2000813 and rs4939883 in the endothelial lipase (LIPG) gene, with coronary heart disease (CHD) in the Chinese Han population.

Methods

A case-control study was conducted, including 266 CHD patients and 112 controls. Genotyping of the SNPs was performed using PCR and single base extension methods. Allele and genotype frequencies were compared, and sex-stratified analyses were conducted.

Results

No significant differences were observed in allele or genotype frequencies for ABCA1 rs1800977, ABCA1 rs2066714, and LIPG rs2000813 between CHD patients and controls in the overall population or sex-stratified analyses. In the overall population, the allele frequency comparisons showed no significant associations for ABCA1 rs1800977 (P = 0.776), ABCA1 rs2066714 (P = 0.609), LIPG rs2000813 (P = 0.824), or LIPG rs4939883 (P = 0.837). In females, the T allele frequency of LIPG rs4939883 was higher in CHD patients than in controls, and the T allele showed a nominal unadjusted association with increased CHD risk (crude OR = 1.735, 95% CI: 1.032–2.915, P = 0.038).

Conclusion

ABCA1 rs1800977, ABCA1 rs2066714, and LIPG rs2000813 were not significantly associated with CHD susceptibility in this Chinese Han population. LIPG rs4939883 showed a potential sex-specific association with CHD, with the T allele showing a nominal unadjusted association with increased CHD risk in females. This finding should be interpreted cautiously and requires validation in larger independent cohorts.

Introduction

Coronary heart disease (CHD) remains one of the leading causes of mortality and morbidity worldwide.^1–3 In China, the incidence has increased significantly in recent years, largely due to rapid economic development and lifestyle changes.^4,5 While environmental factors such as hypertension, hyperlipidemia, smoking, and poor dietary habits contribute to CHD, genetic factors also play a critical role in disease susceptibility.^6–10

The ATP-binding cassette transporter A1 (ABCA1) gene and endothelial lipase (LIPG) gene are important regulators of lipid metabolism and atherosclerosis.^11,12 The ABCA1 gene encodes a transmembrane protein that facilitates reverse cholesterol transport (RCT), which reduces cholesterol deposition in arterial walls.³ The LIPG gene encodes endothelial lipase, which is primarily involved in the catabolism of HDL, thus regulating plasma HDL-C levels.^13,14 Lower HDL-C levels are strongly associated with increased CHD risk.¹⁵ Polymorphisms in these genes, including rs1800977 and rs2066714 in ABCA1, and rs2000813 and rs4939883 in LIPG, have been linked to lipid metabolism and CHD risk, although findings have been inconsistent across populations.^16–21

Notably, SNP frequencies for rs1800977 (MAF = 0.3738), rs2066714 (MAF = 0.1352), rs2000813 (MAF = 0.3022), and rs4939883 (MAF = 0.17) are available in the 1000 Genomes Project, highlighting their varying prevalence across different populations. These discrepancies underscore the importance of considering population-specific genetic variations when investigating genetic risk factors for CHD. Additionally, sex differences in the development and progression of CHD are well established, with premenopausal women experiencing lower CHD risk compared to men, likely due to the protective effects of estrogen.^22–24

However, the potential sex-specific effects of ABCA1 and LIPG gene polymorphisms on CHD risk remain insufficiently characterized, particularly in the Chinese Han population. Given the important roles of ABCA1 and LIPG in lipid metabolism and the known sex-related differences in CHD susceptibility, this study aimed to investigate the associations of ABCA1 rs1800977, ABCA1 rs2066714, LIPG rs2000813, and LIPG rs4939883 with CHD risk in a Chinese Han population, with particular attention to sex-stratified analyses. We hypothesized that selected polymorphisms in ABCA1 and LIPG may be associated with CHD susceptibility and that these associations may differ between males and females.

Materials and Methods

Study Population

This case-control study recruited Han Chinese individuals at Qingdao Municipal Hospital between September 2016 and October 2017. Eligible participants were classified into the CHD group or control group according to predefined clinical and imaging criteria. A sample size calculation was performed prior to the study, assuming an odds ratio (OR) of 1.5 for the association between the single nucleotide polymorphisms (SNPs) and CHD risk, with a power of 80% and a significance level of 0.05. Based on this calculation, at least 250 cases and 100 controls were required.

Patients were included in the CHD group if they met one of the following criteria: (1) a history of coronary stent implantation or coronary artery bypass grafting; or (2) coronary angiography showing stenosis of ≥50% in at least one major coronary artery, including the left main, left anterior descending, or right coronary artery.

Control subjects were recruited from individuals who underwent coronary computed tomography angiography (CTA) or coronary angiography during the same period because of suspected CHD-related symptoms or clinical indications, but were finally confirmed to have normal coronary arteries and had no history of CHD.

Exclusion criteria included: (1) recent or current documented medication exposure before blood sampling that could substantially affect metabolic parameters, such as lipid-lowering drugs, glucose-lowering agents, or antihypertensive drugs; (2) active inflammatory diseases, significant valvular heart disease, renal or hepatic dysfunction, myocarditis, or malignancies; and (3) pregnancy or lactation. Significant valvular heart disease was defined as moderate-to-severe valvular stenosis or regurgitation confirmed by echocardiography. Renal dysfunction was defined as a documented history of chronic kidney disease or evidently abnormal renal function. Hepatic dysfunction was defined as a documented history of chronic liver disease or evidently abnormal liver function.

Written informed consent was obtained from all participants before enrollment and blood sample collection between September 2016 and October 2017. For the present genetic association analysis and manuscript submission, the use of the previously collected clinical data and biological samples was reviewed and approved as a supplementary ethics review by the Ethics Committee of Qingdao Municipal Hospital, University of Health and Rehabilitation Sciences (Approval No. 2026-KY-044-050; date of review: March 16, 2026). This supplementary approval was for the present analysis and manuscript submission rather than for new participant recruitment. No new participants were recruited in 2026. The study was conducted in accordance with the Declaration of Helsinki.

To minimize potential selection bias, cases and controls were recruited from the same hospital during the same period. Laboratory personnel conducting genotyping were blinded to participants’ case or control status to reduce information bias. No matching was performed between cases and controls.

Variables

The primary outcome variable was the occurrence of CHD, as defined above. Exposure variables were the polymorphisms of the ABCA1 gene at rs1800977 and rs2066714, and the LIPG gene at rs2000813 and rs4939883. Baseline clinical and metabolic variables included age, sex, body mass index (BMI), history of hypertension, diabetes, smoking status, alcohol consumption, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and fasting blood glucose.

Sample Collection and Biochemical Measurements

Demographic and clinical information, including age, sex, BMI, history of hypertension, diabetes, smoking status, and alcohol consumption, was collected using standardized questionnaires and medical records. All participants had 5 mL of fasting venous blood collected in the morning. Blood samples were drawn into tubes containing ethylenediaminetetraacetic acid (EDTA) as an anticoagulant and stored at −20 °C until analysis.

Serum biochemical parameters were measured using an automated biochemical analyzer. The parameters measured included total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and fasting blood glucose levels.

DNA Extraction and Genotyping

Genomic DNA was extracted from peripheral blood leukocytes using a commercial DNA extraction kit (BioTeKe Corporation, Beijing, China) according to the manufacturer’s instructions. The purity and concentration of the extracted DNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA), and DNA integrity was verified by 1.25% agarose gel electrophoresis.

Genotyping was performed for four SNPs: rs1800977 and rs2066714 in the ABCA1 gene, and rs2000813 and rs4939883 in the LIPG gene. The MassARRAY iPLEX platform (Agena Bioscience, USA), which combines polymerase chain reaction (PCR) and single-base extension methods, was used for genotyping. Primers for PCR amplification and single-base extension were designed using AssayDesigner 3.1 software based on sequences from the NCBI dbSNP database.

PCR amplification was performed in a total reaction volume of 5 μL, containing 20 ng of genomic DNA, 0.5 μM of each primer, and PCR Master Mix (Thermo Fisher Scientific). The PCR conditions were as follows: initial denaturation at 94 °C for 5 minutes; followed by 45 cycles of denaturation at 94 °C for 20 seconds, annealing at 56 °C for 30 seconds, and extension at 72 °C for 1 minute; with a final extension at 72 °C for 3 minutes. PCR products were treated with shrimp alkaline phosphatase to remove unincorporated nucleotides. Single-base extension reactions were then performed using the iPLEX Gold Reagent Kit (Agena Bioscience) according to the manufacturer’s protocol.

The extension products were desalted and dispensed onto a 384-well SpectroCHIP bioarray (Agena Bioscience). Data acquisition was conducted using the MassARRAY Analyzer 4 platform. Genotype calling and data analysis were performed using MassARRAY TYPER 4.0 software.

Statistical Analysis

Participants with missing genotype data or incomplete clinical information would have been excluded from the analysis. In the final analytic cohort, all included participants had complete data for the variables analyzed. Statistical analyses were performed using SPSS version 22.0 (IBM Corporation, USA). Continuous variables were expressed as mean ± standard deviation (SD) and compared between two groups using independent-samples t-tests. Categorical variables were presented as frequencies and percentages and compared using the chi-square test or Fisher’s exact test, as appropriate.

Hardy-Weinberg equilibrium (HWE) for each SNP in the control group was assessed using the SHEsis online software platform (http://analysis.bio-x.cn). All SNPs conformed to HWE (P > 0.05), indicating that the genotype distributions were representative of the population.

Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated from allele and genotype frequency comparisons to estimate the associations between SNPs and CHD risk. Dominant and recessive genetic models were also evaluated. The dominant model compared individuals carrying at least one variant allele with wild-type homozygotes, while the recessive model compared variant homozygotes with the combined group of heterozygotes and wild-type homozygotes.

Because of the exploratory nature of this study and the limited sample size, particularly in sex-stratified analyses, multivariable-adjusted genetic association models were not presented. Therefore, the reported ORs should be interpreted as crude/unadjusted estimates, and residual confounding by age, sex, hypertension, diabetes, smoking, alcohol consumption, BMI, and lipid-related parameters cannot be excluded.

Considering the potential influence of sex on CHD risk and genetic associations, stratified analyses were performed separately for males and females. Given the exploratory nature of this study and the relatively limited sample size in sex-stratified analyses, nominal P values were reported, and the results were interpreted cautiously. A two-sided P value <0.05 was considered statistically significant for exploratory analyses.

Haplotype and Gene-Gene Interaction Analyses

Although haplotype and gene-gene interaction analyses are valuable for exploring complex genetic relationships, these analyses were not performed in our study. The main reason for this decision is the sample size, which is relatively small for performing such analyses. These approaches generally require larger sample sizes to ensure sufficient statistical power and minimize the risk of false-positive findings. Additionally, the primary focus of this study was to investigate the individual effects of specific SNPs in the ABCA1 and LIPG genes. Future studies with larger sample sizes may further explore haplotype structures and gene-gene interactions to provide more comprehensive insights into genetic susceptibility to coronary heart disease.

Results

Baseline Characteristics

A total of 378 participants were included in the final analysis, including 266 CHD patients and 112 control subjects. The CHD group consisted of 173 males and 93 females, with an average age of 66.6 ± 11.2 years. The control group included 47 males and 65 females, with an average age of 62.3 ± 11.9 years. The baseline demographic and clinical characteristics of the study participants are shown in Table 1.

Table 1.

Comparison of Clinical and Metabolic Indicators Between the CHD Group and the Control Group

Variables	Control Group (n=112)	CHD Group (n=266)	P value
Age (years)	62.3±11.9	66.6±11.2	<0.01**
Male/Female (n)	47/65	173/93	<0.01**
BMI (kg/m2)	25.75±3.25	25.27±3.16	0.34
Smoking (%)	20 (17.86)	114 (42.86)	<0.01**
Drinking (%)	6 (5.36)	89 (33.46)	<0.01**
Hypertension (%)	25 (22.30)	166 (62.41)	<0.01**
Diabetes (%)	7 (6.25)	43 (16.17)	<0.01**
TC (mmol/L)	4.49±1.07	4.63±1.33	0.49
TG (mmol/L)	1.54±0.87	1.58±0.95	0.76
HDL-C (mmol/L)	1.52±0.85	1.09±0.61	<0.01**
LDL-C (mmol/L)	2.36±0.95	2.83±1.14	<0.01**
Fasting Glucose (mmol/L)	5.87±2.05	6.18±2.50	0.21

Notes:**P < 0.01.

Abbreviations: BMI, body mass index; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

As presented in Table 1, the CHD group had a significantly higher proportion of males compared with the control group (65.0% vs 42.0%, P < 0.01). CHD patients were older than controls (66.6 ± 11.2 vs 62.3 ± 11.9 years, P < 0.01). The prevalence of smoking and alcohol consumption was significantly higher in the CHD group (42.86% vs 17.86% for smoking; 33.46% vs 5.36% for alcohol consumption; both P < 0.01). The CHD group also had a higher prevalence of hypertension (62.41% vs 22.30%, P < 0.01) and diabetes (16.17% vs 6.25%, P < 0.01).

In terms of metabolic parameters, the CHD group exhibited higher levels of low-density lipoprotein cholesterol (LDL-C) (2.83 ± 1.14 vs 2.36 ± 0.95 mmol/L, P < 0.01) and lower levels of high-density lipoprotein cholesterol (HDL-C) (1.09 ± 0.61 vs 1.52 ± 0.85 mmol/L, P < 0.01) compared with the control group. There were no significant differences between the two groups in body mass index (BMI), total cholesterol (TC), triglycerides (TG), or fasting glucose levels (P > 0.05).

Allele Frequency Distribution

Selected allele frequency results of the ABCA1 and LIPG polymorphisms are summarized in Table 2. In the overall population, no significant differences in allele frequencies were observed between CHD patients and controls for ABCA1 rs1800977 (P = 0.776), ABCA1 rs2066714 (P = 0.609), LIPG rs2000813 (P = 0.824), or LIPG rs4939883 (P = 0.837).

Table 2.

Selected Allele Frequency Distribution of the Study Population [n (%)]

Locus	Group	Allele	CHD Group n (%)	Control Group n (%)	OR Value	95% CI	P value
rs1800977	Total	G	376(70.68)	156(69.64)	Reference
		A	156(29.32)	68(30.36)	0.952	0.677–1.338	0.776
rs4939883	Female	C	126(67.74)	102(78.46)	Reference
		T	60(32.26)	28(21.54)	1.735	1.032–2.915	0.038*

Notes: Crude ORs were calculated for the minor or variant allele versus the reference allele. For rs4939883 in females, the crude OR was calculated for the T allele versus the C allele. Sex-stratified results are shown for loci with significant or potentially relevant findings. *P < 0.05.

Abbreviations: CHD, coronary heart disease; OR, odds ratio; CI, confidence interval.

In sex-stratified analyses, LIPG rs4939883 showed a nominal association with CHD in females. The T allele frequency was higher in female CHD patients than in female controls, and the T allele showed a nominal unadjusted association with increased CHD risk (crude OR = 1.735, 95% CI: 1.032–2.915, P = 0.038). No significant allele-level association was observed in males.

Genotype Frequency Distribution

Selected genotype distributions relevant to the main findings are shown in Table 3. No significant genotype-level association was observed for the examined loci in the overall population or in sex-stratified analyses.

Table 3.

Selected Genotype Frequency Distribution Between the CHD Group and the Control Group [n (%)]

Locus	Group	Genotype	CHD Group (n=266,F=93)	Control Group (n=112,F=65)	P value
rs1800977	Total	GG	132 (49.62)	59 (52.68)	0.166
		GA	112 (42.11)	38 (33.93)
		AA	22 (8.27)	15 (13.39)
rs4939883	Female	CC	43 (46.24)	39 (60.00)	0.095
		CT	40 (43.01)	24 (36.92)
		TT	10 (10.75)	2 (3.08)

Notes: Selected genotype distributions are shown for the overall population and sex-stratified analyses relevant to the main findings. P values were calculated for comparisons of genotype distributions between CHD patients and controls.

Abbreviation: CHD, coronary heart disease.

Genotype Frequency Distribution and Hardy-Weinberg Equilibrium

To assess whether the genotype frequencies at the studied loci were in Hardy-Weinberg equilibrium (HWE), we conducted HWE tests for each SNP. The results of the HWE tests for all four SNPs are summarized as follows: rs1800977 (A/G): The HWE test showed no significant deviation from equilibrium, with a P-value of 0.973. rs2000813 (T/C): The HWE test indicated no significant deviation from equilibrium (P = 0.988). rs2066714 (C/T): The genotype distribution for this SNP was also in Hardy-Weinberg equilibrium (P = 0.843). rs4939883 (T/C): The test for HWE revealed no significant deviation from equilibrium (P = 0.968).

All SNPs analyzed were found to be in Hardy-Weinberg equilibrium, suggesting that the genotyping did not introduce any systematic bias into the study, and the data are suitable for further genetic analysis.

Genotype Frequency Distribution Under Dominant and Recessive Models

Under the dominant and recessive models, no statistically significant associations were observed for the examined polymorphisms in the overall population or in sex-stratified analyses (Table 4 and Table 5). In females, LIPG rs4939883 showed a nonsignificant unadjusted trend toward increased CHD risk under both the dominant model (CT+TT vs CC: crude OR = 1.744, 95% CI: 0.918–3.315, P = 0.090) and the recessive model (TT vs CC+CT: crude OR = 3.795, 95% CI: 0.803–17.936, P = 0.092).

Table 4.

Genotype Frequency Comparison Between the CHD Group and the Control Group (Dominant Model) [n (%)]

Locus	Group	Genotype	CHD Group n(%)	Control Group n(%)	OR Value	95% CI	P value
rs1800977	Total	GG	132(49.62)	59(52.68)	Reference	–	–
		AA+GA	134(50.38)	53(47.32)	1.130	0.726–1.758	0.588
rs2066714	Total	CC	134(50.38)	52(46.43)	Reference	–	–
		TT+TC	132(49.62)	60(53.57)	0.854	0.549–1.329	0.483
rs2000813	Total	CC	150(56.39)	59(52.68)	Reference	–	–
		TT+TC	116(43.61)	53(47.32)	0.861	0.553–1.341	0.508
rs4939883	Total	CC	177(66.54)	73(65.18)	Reference	–	–
		TT+TC	89(33.46)	39(34.82)	0.941	0.591–1.498	0.798
	Female	CC	43(46.24)	39(60.00)	Reference	–	–
		TT+TC	50(53.76)	26(40.00)	1.744	0.918–3.315	0.090

Notes: Crude ORs were calculated for the variant-containing genotypes versus the reference genotype under the dominant model. Sex-stratified results are shown for selected loci relevant to the main findings. CHD group: n=266, males=173, females=93; control group: n=112, males=47, females=65.

Abbreviations: CHD, coronary heart disease; OR, odds ratio; CI, confidence interval.

Table 5.

Genotype Frequency Comparison Between the CHD Group and the Control Group (Recessive Model) [n (%)]

Locus	Group	Genotype	CHD Group n (%)	Control Group n (%)	OR Value	95% CI	P value
rs1800977	Total	GG+GA	244(91.73)	97(86.61)	Reference	–	–
		AA	22(8.27)	15(13.39)	0.583	0.290–1.171	0.129
rs2066714	Total	CC+CT	251(94.36)	106(94.64)	Reference	–	–
		TT	15(5.64)	6(5.36)	1.056	0.399–2.795	0.913
rs2000813	Total	CC+CT	246(92.48)	106(94.64)	Reference	–	–
		TT	20(7.52)	6(5.36)	1.436	0.561–3.678	0.450
rs4939883	Total	CC+CT	247(92.86)	107(95.54)	Reference	–	–
		TT	19(7.14)	5(4.46)	1.646	0.599–4.524	0.334
	Female	CC+CT	83(89.25)	63(96.92)	Reference	–	–
		TT	10(10.75)	2(3.08)	3.795	0.803–17.936	0.092

Notes: Crude ORs were calculated for variant homozygotes versus the combined group of heterozygotes and wild-type homozygotes under the recessive model. Sex-stratified results are shown for selected loci relevant to the main findings.

Abbreviations: CHD, coronary heart disease; OR, odds ratio; CI, confidence interval.

Missing Data

Participants with missing genotype data or incomplete clinical information would have been excluded from the analysis. In the final analytic cohort, all included participants had complete data for the variables analyzed; therefore, no additional participants were excluded due to missing data.

Discussion

This study explored the relationship between polymorphisms at the ABCA1 rs1800977 and rs2066714 loci and the LIPG rs2000813 and rs4939883 loci with the risk of coronary heart disease (CHD) in the Chinese Han population. The results indicate that the ABCA1 rs1800977, rs2066714, and LIPG rs2000813 loci are not significantly associated with CHD susceptibility in either the overall population or in the sex-stratified analyses. However, a potential sex-specific association was observed for LIPG rs4939883 in females. Specifically, the T allele frequency was higher in female CHD patients than in female controls, suggesting that the rs4939883 T allele may be associated with increased CHD susceptibility in females.

Our findings are consistent with several studies that reported no significant association between the ABCA1 rs1800977 and rs2066714 loci and CHD risk. However, other research has suggested that these loci may influence lipid levels and atherosclerosis, with findings varying based on ethnic groups, sample size, and study methodologies.²⁵ These discrepancies may also be due to gene-environment interactions, which could vary among different populations and environmental settings.

Regarding the LIPG rs2000813 locus, our results align with previous studies that found no significant association with CHD risk.²⁶ In contrast, the LIPG rs4939883 locus has been less extensively studied in relation to CHD. In the present study, we observed that the T allele frequency was higher in female CHD patients than in female controls, suggesting a potential association between the rs4939883 T allele and increased CHD susceptibility in females. Previous studies have suggested that LIPG variants may be related to lipid metabolism, including HDL-C-related phenotypes, supporting the biological plausibility of this locus.²⁷ However, because multivariable-adjusted genetic association models were not presented, we cannot determine whether the observed association is independent of HDL-C levels.

The observed result of the present study was that the T allele frequency of LIPG rs4939883 was higher in female CHD patients than in female controls, suggesting a potential sex-specific association between rs4939883 and CHD susceptibility. The possible biological mechanisms underlying this association remain speculative, as LIPG expression, endothelial lipase activity, HDL-C catabolism, and estrogen-related pathways were not directly examined in this study. One possible explanation is that rs4939883, or variants in linkage disequilibrium with this locus, may influence LIPG expression or endothelial lipase activity, thereby affecting HDL-C metabolism. However, this hypothesis was not tested experimentally in the present study and requires validation in future functional studies. Another possible explanation is that sex hormone-related factors may modify the relationship between LIPG variants and CHD risk in women. Nevertheless, menopausal status and estrogen levels were not collected in this study; therefore, the potential involvement of estrogen-related pathways remains speculative.^28–31

Strengths and Limitations

The primary strength of this study is that it investigated the associations of selected ABCA1 and LIPG polymorphisms with CHD susceptibility in a Chinese Han population and included sex-stratified analyses. In particular, we observed a potential association between the LIPG rs4939883 T allele and increased CHD risk in females. This finding may provide preliminary evidence for sex-specific genetic susceptibility to CHD, although it should be interpreted cautiously and requires validation in larger independent cohorts. In addition, the case-control design and the use of coronary CTA or coronary angiography to define CHD and control status helped improve the clinical classification of study participants.

However, there are several limitations to this study that should be acknowledged. First, the relatively small sample size of female participants may have reduced the statistical power, increasing the possibility of type II errors or false-negative results. This limitation suggests that the findings should be interpreted with caution and validated in larger cohorts. Second, the lack of detailed environmental and lifestyle data, such as dietary habits and physical activity levels, prevents a comprehensive evaluation of gene-environment interactions that could influence CHD risk. Third, we did not perform functional assays to experimentally validate the impact of the rs4939883 polymorphism on LIPG gene expression and endothelial lipase activity. Without this functional validation, the biological mechanisms by which this SNP may contribute to CHD remain speculative. Lastly, the absence of hormone level measurements, particularly estrogen, limits our ability to directly assess the interplay between sex hormones and genetic polymorphisms in modulating CHD risk among females.

Additionally, although this study provides valuable insights into the genetic mechanisms of CHD in a Han Chinese female population, the findings should be interpreted with caution due to the single-center design and ethnically homogenous sample. Genetic variations and environmental factors differ across populations; therefore, the potential ethnic-specific implications of this association warrant further investigation in diverse ethnic groups. Future studies should aim to replicate these findings in multiethnic cohorts to enhance the generalizability of these results.

Another important limitation is that the reported ORs were crude/unadjusted estimates derived from allele and genotype frequency comparisons. Multivariable-adjusted genetic association models were not presented because of the exploratory nature of this study and the limited sample size, particularly in sex-stratified analyses. Therefore, the observed association between LIPG rs4939883 and CHD risk in females cannot be considered independent of age, sex, hypertension, diabetes, smoking, alcohol consumption, BMI, HDL-C, LDL-C, or other cardiometabolic factors. Therefore, the possibility of residual confounding cannot be excluded, and we cannot determine whether the association between LIPG rs4939883 and CHD risk in females is independent of HDL-C levels. In addition, genotype-specific associations with lipid parameters or blood pressure levels were not further evaluated, and no additional post hoc sensitivity analysis excluding cardiovascular comorbidities was performed after establishment of the final analytic cohort. Menopausal status and estrogen levels were also not collected, which limits our ability to assess whether hormonal status modifies the observed sex-specific association. These issues may limit the robustness and mechanistic interpretation of the findings and should be addressed in future studies with larger sample sizes, individual-level phenotype data, and more comprehensive adjustment for cardiometabolic and hormonal factors.

Generalisability

Given that our study focused on a single-center Han Chinese population, the generalizability of our findings may be limited. Future studies involving multiethnic cohorts are necessary to assess the broader applicability of the results and examine the role of ethnic-specific genetic factors in modulating CHD risk.

Future Directions

To address these limitations, future research should focus on several key areas. Expanding the sample size through large-scale, multicenter studies with a higher number of female participants is crucial for validating our findings and enhancing statistical power. Such studies would provide more robust data to confirm the sex-specific association observed. Conducting functional studies is also essential; investigating how the rs4939883 polymorphism affects LIPG gene expression and endothelial lipase activity using molecular and cellular biology techniques would elucidate the underlying biological mechanisms. Integrating ML models with genetic and clinical data could further refine risk prediction and uncover complex patterns, as demonstrated in studies predicting coronary artery disease using ECG features and forecasting short-term mortality in pulmonary embolism patients with deep learning.^32,33 Collecting comprehensive environmental and lifestyle information will enable researchers to analyze gene-environment interactions more effectively, shedding light on how these factors collectively influence CHD risk. Measuring estrogen and other sex hormone levels in participants could further clarify the role of hormonal regulation in the relationship between genetic polymorphisms and CHD, potentially informing future research on sex-specific CHD risk stratification. Moreover, multiethnic studies are needed to replicate these findings and examine the role of ethnic-specific genetic factors in modulating CHD risk. Although polygenic risk scores have been proposed for CHD risk prediction, the present findings are not sufficient to support the immediate inclusion of LIPG rs4939883 in such tools. Before clinical application, this variant should be validated in larger independent cohorts, and its incremental predictive value beyond established clinical risk factors and existing genetic risk scores should be evaluated.

Conclusion

In conclusion, ABCA1 rs1800977, ABCA1 rs2066714, and LIPG rs2000813 were not significantly associated with CHD susceptibility in this Chinese Han population. LIPG rs4939883 showed a potential sex-specific association with CHD among females, with the T allele showing a nominal unadjusted association with increased CHD risk. However, given the single-center case-control design, limited female sample size, absence of menopausal status and hormone-level information, and lack of multivariable-adjusted genetic association models, this finding should be interpreted cautiously. Further large-scale, multicenter studies with independent replication cohorts and more comprehensive clinical, metabolic, and hormonal data are needed to validate this association.

Funding Statement

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Abbreviations

CHD, Coronary Heart Disease; ABCA1, ATP-Binding Cassette Transporter A1; SNP, Single Nucleotide Polymorphism; HWE, Hardy-Weinberg Equilibrium; TC, Total Cholesterol; TG, Triglycerides; OR, Odds Ratio; CI, Confidence Interval.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. Jin Ni served as the primary corresponding author and took the leading role in study supervision, manuscript coordination, and communication with the journal.

Disclosure

The authors declare no competing interests.