Impact of the ApoE Genotype on Coronary Artery Disease and the Incidence of Myocardial Infarction: A Clinical Observational Study

Impact of the ApoE Genotype on Coronary Artery Disease and the Incidence of Myocardial Infarction: A Clinical Observational Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of the ApoE Genotype on Coronary Artery Disease and the Incidence of Myocardial Infarction: A Clinical Observational Study Meng-li Li, Xun Yang, Ning-Jun Zhu, Zhen Wang, Zheng Huang, Ting-ting Fan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7094595/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: This study aimed to investigate the relationships between the expression of apolipoprotein E (APOE) and gene polymorphisms of solute carrier organic anion transporter family member 1B1 (SLCO1B1) with the blood lipid profile and coronary artery disease severity in Han Chinese individuals living in eastern China. Methods: This study enrolled 91 patients hospitalized at the Second Affiliated Hospital of Anhui Medical University from June 2024 to December 2024. The serum lipid profiles, including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and lipoprotein(a) [LP(a)], were measured via the Olympus AU5400 system. The genotypes of the APOE and SLCO1B1 genes were determined by Sanger sequencing. The participantswere stratified into three groups on the basis of their Gensini scores. Differences in blood lipid levels and APOE/SLCO1B1 genotype distributions among these groups were statistically analyzed. The Gensini score model and myocardial infarction risk model were subsequently constructed via APOE genotyping, LDL-C levels, and other differential biomarkers identified from the analysis. Results: Patients with the ApoE ε4 genotype had higher LDL-C and Lp(a) levels and higher Gensini scores (F=11.591, P<0.001), indicating more severe coronary artery lesions than the other groups did. Multiple linear regression analysis revealed both LDL-C levels and ApoE genotypes as independent predictors of the severity of coronary artery lesions, whereas SLCO1B1 genotype had a minor effect on lipid levels and coronary artery lesion severity. Notably, this study specifically analyzed the impact of ApoE polymorphisms on the incidence of myocardial infarction and reported a statistically significant difference in the incidence of myocardial infarction among different ApoE genotypes (χ²=6.49, P=0.039). The prediction model showed excellent predictive performance (area under the curve (AUC): 0.793 in the Gensini score prediction model and AUC: 0.855 in the myocardial infarction prediction model). Conclusion: APOE genotype is associated with the concentrations of LDL-C and Lp(a) as well as the severity of coronary artery lesions and the occurrence of myocardial infarction. APOE Gensini scores Lipoprotein(a) [LP(a)] Low-density lipoprotein cholesterol (LDL-C) Myocardial infarction Figures Figure 1 Figure 2 1. Introduction 1.1 Background Coronary artery disease (CAD) is the leading cause of cardiovascular disease mortality worldwide, particularly in East China, where the incidence of coronary heart disease remains high due to factors such as regional dietary habits and genetic characteristics, and there is a widespread presence of patients with complex coronary artery lesions [ 1 ] . This disease not only severely impacts the quality of life and family economic income of patients but also adds an economic burden to social healthcare [ 2 ] . Despite mature treatment methods for CHD, such as pharmacological therapy and interventional surgical treatment, several limitations remain, including insufficient individualized treatment, high incidence, and high mortality. Therefore, it is particularly important to conduct in-depth research on the factors influencing CAD [ 3 ] . 1.2 Rationale and knowledge gap The APOE gene is a candidate gene for susceptibility to hyperlipidemia and atherosclerotic vascular diseases. The APOE gene has three major alleles: ɛ3 (388T-526C), ɛ2 (388T-526T), and ɛ4 (388C-526C). These three alleles combine to form six possible genotypes. The SLCO1B1 gene encodes organic anion transporting polypeptide 1B1 (OATP1B1), which plays an important role in the metabolism of statins and is associated with genetic polymorphisms [ 4 – 6 ] . Previous studies have shown that APOE genotypes play a key role in lipid metabolism [ 7 , 8 ] , whereas the SLCO1B1 gene is closely related to drug metabolism and lipid regulation [ 9 ] . Polymorphisms in these two genes may have a significant impact on cardiovascular health [ 10 , 11 ] . It is well known that LDL-C, a lipid indicator, is significantly associated with coronary artery disease [ 1 ] . Over the past few decades, we have focused more on LDL-C levels, but recent studies have shown that, regardless of the LDL-C level, elevated plasma Lp-(a) levels can independently increase cardiovascular events, particularly the incidence of CAD [ 12 , 13 ] . However, there is currently no consensus on the relationship between APOE gene polymorphisms and Lp(a) or coronary artery lesions. Therefore, the underlying association between APOE gene polymorphisms and CHD remains a focus of current clinical research [ 14 ] . An in-depth analysis of the roles of the APOE and SLCO1B1 genes will provide new perspectives for understanding the genetic risk of coronary artery disease [ 11 , 15 ] . 1.3 Objective This study employed a method that combined genetic analysis with clinical data from a population in East China to assess lipid levels and the degree of coronary artery lesions. The advantage of this method lies in its ability to integrate clinical and demographic characteristics with genetic information, providing a more comprehensive disease risk assessment. The specific aim of this study was to reveal the relationships between gene polymorphisms of APOE and SLCO1B1 and lipid profiles, including Lp(a) and LDL-C, as well as coronary artery lesions, thereby providing a theoretical basis for early prevention and personalized therapeutic approaches for coronary artery disease. By analyzing the genetic polymorphisms within this specific population, this study aimed to better understand the role of genetic factors in cardiovascular diseases. 2. Methods 2.1 Study subjects This study prospectively enrolled 91 consecutive patients hospitalized at the Second Affiliated Hospital of Anhui Medical University from September 2024 to December 2024. The inclusion criteria were as follows: (1) patients willing to undergo genetic testing; (2) patients presenting with chest pain or discomfort, an ECG showing ischemic changes, and a clinical assessment indicating that coronary artery disease cannot be excluded, confirmed by a reduction of more than 50% in the diameter of at least one major coronary artery, as determined by coronary angiography; and (3) residents of Anhui, confirmed by household registration, with no biological relation to other subjects. The exclusion criteria were as follows: (1) severe liver and kidney dysfunction (eGFR 3 times the upper limit of normal); (2) history of cardiomyopathy, valvular heart disease, malignant tumors, tuberculosis, or other major diseases affecting inflammatory factors and lipid metabolism; (3) biological relationship with other subjects up to the third degree, verified through the household registration system; (4) incomplete medical records or blood samples; and (5) nonprovision of informed consent. Ethical Statement: This study adhered to the ethical standards of the updated version of the Declaration of Helsinki (1964) and was approved by the Human Ethics Committee of the Second Affiliated Hospital of Anhui Medical University (Approval No: YX2024--115). All patients signed informed consent forms. 2.2 Data collection Standardized case report forms were used to collect data on the following characteristics: 2.2.1 Anthropometric indicators: age, sex, and body mass index (BMI) (measured by trained nurses via calibrated instruments). 2.2.2 Cardiovascular risk factors: smoking, alcohol consumption, hypertension (≥140/90 mmHg or receiving antihypertensive treatment), diabetes (glycated hemoglobin (HbA1c) ≥ 6.5% or receiving hypoglycemic treatment), stroke, and atrial fibrillation. 2.2. 3 Laboratory parameters: Approximately 3 ml of blood was collected from each subject. The serum was promptly separated and analyzed; samples that could not be tested immediately were stored at -80°C until analysis. Lipid levels, including those of five lipid components (TC, TG, LDL-C, HDL-C, and LP(a)), troponin, and HbA1c, were measured via the Olympus AU5400 system (Olympus Corporation, Tokyo, Japan) according to the manufacturer's instructions. 2.2.4 Coronary angiography characteristics: affected vessels (left main, left anterior descending and its branches, circumflex and its branches, right coronary and its branches) and degree of stenosis (quantitative coronary angiography analysis). The modified Gensini scoring system was used to quantify the severity of coronary lesions. The scoring included the following: - Stenosis weights: - 100% stenosis: 32 points - 90–99% stenosis: 16 points - 75–89% stenosis: 8 points - 50–74% stenosis: 4 points - 26–49% stenosis: 2 points - 1–25% stenosis: 1 point - Anatomical coefficients (multiplicative factors applied to the stenosis weights): - Left main artery ×5 - Proximal left anterior descending artery ×2.5 - Mid-left anterior descending artery ×1.5 - Distal left anterior descending artery ×1 - Diagonal branch D1 ×1 - Diagonal branch D2 ×0.5 - Proximal circumflex artery ×2.5 - Mid circumflex artery ×1.5 - Distal circumflex artery ×1 - Each segment of the right coronary artery ×1 The total Gensini score for each patient was calculated by summing the weighted scores of all affected vessels. Patients were divided into three groups according to the total Gensini score: the low score group (GSlow), with scores ≤31 points; the medium score group (GS mid), with scores ranging from 32–61 points; and the high score group (GS high), with scores ≥62 points. These thresholds were determined on the basis of pretrial receiver operating characteristic (ROC) analysis. 2.3 ApoE gene polymorphism detection 2.3.1 DNA extraction: Gene chip technology (purchased from Wuhan Haijili Biotechnology Technology Co., Ltd.) combined with the PCR in vitro amplification method was used to detect human ApoE gene polymorphisms in selected patients; 2.3.2 Peripheral blood cell DNA extraction: Two milliliters of venous blood was collected from patients and anticoagulated, and DNA was extracted via a genomic extraction kit (purchased from Sun Yat-sen University Da'an Gene Co., Ltd.), following the instructions, and the quality and concentration of the extracted DNA were measured via a UV spectrophotometer (purchased from Thermo Fisher Scientific, USA). 2.3.3 PCR-RELP: Eight microlitres of PCR amplification product was added to 2 μL of 10× buffer and 1 μL of specific restriction endonuclease (purchased from Dalian Baosheng Bioengineering Co., Ltd.), 9 μL of deionized water was added, and the mixture was reacted at 37°C for 12–14 hours. 2.3.4 PCR product electrophoresis detection 2.3.5 Gene sequencing: The fragile sites of the gene, namely, APOE (112T>C and 158C>C), were selected. Slco1b1 (388A>G, 521T>C) was subjected to large-scale amplification, electrophoresis, and enzyme digestion to determine the genotype. All of the above procedures were performed by professional personnel in our hospital's central laboratory. The results indicated that the ApoE gene was divided into three phenotypic groups: the ɛ2 phenotypic group (ɛ2/ɛ2, ɛ2/ɛ3), the ɛ3 phenotypic group (ɛ2/ɛ4, ɛ3/ɛ3), and the ɛ4 phenotypic group (ɛ3/ɛ4, ɛ4/ɛ4). The SLCO1B1 gene was divided into three groups: the normal OATP1B1 functional group: S-nor (1a/1a, 1a/1b, 1b/1b); the intermediate OATP1B1 functional group: S-mid (1a/5, 1a/15, 1b/15); and the low OATP1B1 functional group: S-low (5/5, 5/15, 15/15). 2.4 Statistical analysis Statistical analyses were performed via SPSS 25.0. The quantitative data were tested for normality and are expressed as the means ± standard deviations. One-way ANOVA was used for comparisons between groups. Qualitative data are presented as frequencies and percentages, and chi-square tests were conducted to assess differences between groups. The genotype distributions of APOE and SLCO1B1 were tested for Hardy‒Weinberg equilibrium. Linear regression analysis was performed to examine the relationships between APOE and SLCO1B1 gene polymorphisms and the severity of CHD, which was treated as a continuous variable. Multiple logistic regression analysis was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the incidence of myocardial infarction, adjusting for various risk factors. All risk factors were included as covariates in the regression models. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive performance of the models for the severity of coronary lesions and the occurrence of myocardial infarction. P values less than 0.05 were considered significant. TABLE 1: Comparison of clinical characteristics among the three groups on the basis of the Gensini score. GSlow （n=26,28.57%） GSmid （n=39,42.86%） GShigh （n=26,28.57%） F/χ 2 P Age, years 62.8±11.3 61.1±12.2 63.3±12.5 0.285 0.753 BMI，kg/m2 23.90±3.20 24.40±2.68 26.12±4.85 2.861 0.063 Gender，n（%） 0.787 0.675 Male 16（61.5） 28（71.8） 17（65.4） Female 10（38.5） 11（28.2） 9（34.6） smoking, n(%) 8（30.8） 14（35.9） 11（42.3） 0.753 0.686 alcoholism, n(%) 18（69.2） 25（64.1） 18（69.2） 0.265 0.876 Hypertension, n(%) 12（46.2） 23（59.0） 19（73.1） 3.909 0.142 Diabetes, n(%) 4（15.4） 13（33.3） 8（30.8） 2.727 0.257 Stroke，n（%） 3（11.5） 6（15.4） 5（19.2） 0.637 0.711 Atrial fibrillation,n（%） 11（42.3） 13（33.3） 10（38.5） 1.835 0.382 TG, mmol/L 1.81±1.81 1.69±0.86 1.75±1.08 0.057 0.944 CHO, mmol/L 4.28±1.21 4.58±1.17 4.52±1.05 0.563 0.572 HDL-C, mmol/L 1.24±0.31 1.14±0.29 1.16±0.29 1.103 0.337 LP（a）,mmol/l 136.77±94.70 177.72±132.39 267.00±236.71 4.484 0.014 LDL-C, mmol/L 2.40±0.92 2.55±0.92 3.04±0.88 3.609 0.031 HbA1C（%） 5.92±0.77 6.31±1.46 6.18±1.14 0.872 0.441 Hs-cTNI,pg/ml 12.92±31.16 1277.09±4277.69 1394.35±345.86 1.352 0.264 LA,mm 33.57±4.39 35.87±4.75 34.58±3.91 2.165 0.121 LVEDD,mm 45.12±4.92 46.69±5.46 47.04±5.03 1.049 0.355 Note: Values for measurement data are expressed as the means ± SDs. Values for count data are expressed as n (%). Abbreviations: HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; TG, triglyceride; TC, total cholesterol; BMI: body mass index; LP(a): lipoprotein (a); HbA1C: glycosylated hemoglobin; Hs-cTNI: high-sensitivity cardiac troponin I; LA: left atrial diameter; LVEDD: left ventricular end-diastolic diameter. 3. Results 3.1 Comparison of clinical data A total of 91 patients were included in this study, with 26 (28.57%) patients in the GSlow group, 39 (42.86%) in the GSmid group, and 26 (28.57%) in the GShigh group. The baseline data revealed statistically significant differences among the three cohorts (p < 0.05) in LDL-C, Lp(a), and HDL-C concentrations: patients with higher LDL-C and LP(a) and lower HDL-C presented higher Gensini scores. Specifically, the LDL-C concentrations were 2.40 ± 0.92 mmol/L, 2.55 ± 0.92 mmol/L, and 3.04 ± 0.88 mmol/L; the Lp(a) concentrations were 136.77 ± 94.70 mmol/L, 177.72 ± 132.39 mmol/L, and 267.00 ± 236.71 mmol/L; and the HDL-C concentrations were 1.24 ± 0.31 mmol/L, 1.14 ± 0.29 mmol/L, and 1.16 ± 0.29 mmol/L in cohorts 1, 2, and 3, respectively. Baseline characteristics, such as age, sex, body mass index (BMI), and risk factors for smoking, alcohol consumption, hypertension, diabetes, stroke, and atrial fibrillation, along with pertinent biochemical and echocardiographic parameters, were not significantly different across the three groups, confirming their comparability at baseline. (Table 1, see the last page) 3. 2 Phenotypes and frequency distributions of the APOE and SLCO1B1 genes The APOE genotypes identified include five variants: ɛ2/ɛ3, ɛ3/ɛ3, ɛ2/ɛ4, ɛ3/ɛ4, and ɛ4/ɛ4. The number of cases and frequencies of the alleles ɛ3, ɛ4, and ɛ2 were 56 (61.54%), 18 (19.78%), and 17 (18.68%), respectively. A total of six SLCO1B1 genotypes were identified, with frequencies of 1a/1a (11%), 1a/1b (35.2%), 1b/1b (41.8%), 1a/15 (3.3%), 1b/15 (7.7%), and 15/15 (1.1%). On the basis of SLCO1B1 metabolic capacity, the cases were divided into three groups, S-normal, S-intermediate, and S-low, with 79 (86.81%), 11 (12.08%), and 1 (1.1%) cases, respectively. The distribution of APOE genotypes was consistent with the Hardy‒Weinberg equilibrium (chi-square [χ2] = 5.159, p = 0.397), as was the distribution of SLCO1B1 genotypes (chi-square [χ2]= 3.113, p = 0.577). With respect to APOE, the ɛ3/ɛ3 genotype was the most common, followed by the ɛ2/ɛ3, ɛ3/ɛ4, ɛ2/ɛ4, and ɛ4/ɛ4 genotypes, whereas the most prevalent SLCO1B1 genotype was the 1b/1b genotype, followed by the 1a/1b, 1a/1a, 1b/15, 1a/15, and 15/15 genotypes (Table 2). There was a statistically significant difference in the ɛ2/ɛ3/ɛ4 genotypes among the GSlow, GSmid, and GShigh groups (chi-square [χ2] = 20.751, p < 0.001). However, there was no statistically significant difference in SLCO1B1 phenotypes among the GSlow, GSmid, and GShigh groups (chi-square [χ2] = 9.961, p = 0.444) (Table 2). TABLE 2: Distribution of ApoE/SLCO1B1 genotypes and allele frequencies Genotypes ɛ2/ɛ2 ɛ2/ɛ3 ɛ3/ɛ3 ɛ3/ɛ4 ɛ2/ɛ4 ɛ4/ɛ4 n(%) 0(0) 17(18.68) 53(58.24) 17(18.68) 3(3.3) 1(1.1) Genotypes 1a/1a 1a/1b 1b/1b 1a/15 1b/15 15/15 n(%) 10(9.9) 32(35.2) 38(41.8) 3(3.3) 7(8.7) 1(1.1) Alleles ɛ2 ɛ3 ɛ4 S-nor S-mid S-low n(%) 17(18.68) 56(61.54) 18(19.78) 80(87.9) 11（11） 1（1.1） GSlow(n,%) 9(52.9) 15(26.8) 2(11.1) 19(24.1) 7(63.6) 0(0) GSmid(n,%) 7(41.2) 28(50) 4(22.2) 36(45.6) 3(27.3) 0(0) GShigh(n,%) 1(5.9) 13(23.2) 12(66.7) 24(30.3) 1(9.1) 1(100) P values 0.000(χ2 = 20.751) 0.444(χ2 = 9.961) Note: GSlow: Gensini score ≤31 points; GSmid: Gensini score 32–61 points; GShigh: Gensini score > points; S-nor: normal OATP1B1 functional group, including genotypes 1a/1a, 1a/1b, and 1b/1b; S-mid: intermediate OATP1B1 functional group, including genotypes 1a/5, 1a/15, and 1b/15; S-low: low OATP1B1 functional group, including genotypes 5/5, 5/15, and 15/15. 3.3 Comparison of lipid indicators among APOE and SLCO1B1 gene phenotypic groups We analyzed the relationships between APOE alleles (ɛ2, ɛ3, and ɛ4) and serum lipid levels and found that LDL-C was significantly elevated in ɛ4 carriers (3.15 ± 1.10 mmol/L), intermediate in ɛ3 carriers (2.65 ± 0.85 mmol/L), and lowest in ɛ2 carriers (2.10 ± 0.12 mmol/L); p = 0.003. A similar pattern was consistently observed for LP(a) levels, with ɛ4 carriers showing significantly higher LP(a) levels (293.89 ± 209.79 mmol/L), ɛ3 carriers showing intermediate levels (181.30 ± 157.42 mmol/L), and ɛ2 carriers showing the lowest levels (116.82 ± 88.05 mmol/L), p = 0.005. There were no statistically significant differences in total cholesterol, high-density lipoprotein cholesterol (HDL-C), or triglyceride levels among the APOE alleles (p > 0.05); similarly, no statistically significant differences were observed in total cholesterol, low-density lipoprotein cholesterol (LDL-C), HDL-C, or triglyceride levels among the different phenotype groups classified by SLCO1B1 gene variants. (Table 3). TABLE 3 : Comparison of lipid indicators among APOE and SLCO1B1 gene phenotypic groups. ɛ2 ɛ3 ɛ4 F P TG, mmol/L 1.52±1.05 1.73±0.99 1.97±1.98 0.569 0.568 CHO, mmol/L 4.09±0.82 4.59±1.18 4.49±1.26 1.222 0.300 HDL-C, mmol/L 1.27±0.34 1.14±0.28 1.19±0.29 1.292 0.280 LP（a）,mmol/l 116.82±88.05 181.30±157.42 293.89±209.79 5.696 0.005 LDL-C, mmol/L 2.10±0.12 2.65±0.85 3.15±1.10 6.079 0.003 S-normal S-mid S-slow F P TG, mmol/L 1.78±1.32 1.45±0.57 1.89±0.00 0.337 0.715 CHO, mmol/L 4.45±1.12 4.59±1.40 5.17±0.00 0.254 0.777 HDL-C, mmol/L 1.17±0.30 1.19±0.29 1.28±0.00 0.1 0.905 LP（a）,mmol/l 201.65±176.33 122.91±61.22 147.00±0.00 1.105 0.336 LDL-C, mmol/L 2.64±0.92 2.63±1.12 3.35±0.00 0.282 0.755 Note: Values for measurement data are expressed as the means ± SDs. Abbreviations: HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; TG, triglyceride; TC: total cholesterol; BMI: body mass index; LP(a): lipoprotein (a); 3.4 Comparison of modified Gensini scores and the proportion of patients with myocardial infarction among the APOE and SLCO1B1 genes There were statistically significant differences in the Gensini scores among the ApoE gene phenotype ɛ2, ɛ3, and ɛ4 groups (F=11.591, P<0.001). Patients with the ɛ4 gene phenotype had the highest Gensini score (73.83±36.01), those with the ɛ3 gene phenotype had a median Gensini score (46.73±25.14), and those with the ɛ2 gene phenotype had the lowest Gensini score (31.35±20.16). The proportion of patients presenting with myocardial infarction showed a similar trend among the three groups: 11.76% vs 23.79% vs 38.89%, P=0.039. With respect to the SLCO1B1 genotype grouping, there was a trend toward higher Gensini scores in one group (51.54±29.89 vs 30.18±22.8 vs 75.00), but this difference did not reach statistical significance (P=0.056). There was no significant difference in the incidence of myocardial infarction among the three genotype groups (χ2=1.108, P=0.575). See Table 4 and Figure 1. TABLE 4 : Comparison of Gensini scores and the proportion of AMI among the APOE and SLCO1B1 genes ɛ2 ɛ3 ɛ4 F/χ 2 P Gensini Score 31.35±20.16 46.73±25.14 73.83±36.01 11.591 ＜0.001 AMI(n,%) 2(11.76) 15(23.79) 7(34.6) 6.49 0.039 S-normal S-mid S-slow F/χ 2 P Gensini Score 51.54±29.89 30.18±22.85 75 2.983 0.056 AMI(n,%) 24(30.38) 2(18.18) 0(0) 1.108 0.575 Note: AMI: Acute myocardial infarction; S-nor: normal OATP1B1 functional group, including genotypes 1a/1a, 1a/1b, and 1b/1b; S-mid: intermediate OATP1B1 functional group, including genotypes 1a/5, 1a/15, and 1b/15; S-low: low OATP1B1 functional group, including genotypes 5/5, 5/15, and 15/15. 3.5 Univariate and multivariate linear regression analysis of independent predictors of the modified Gensini score for coronary artery lesions A linear regression analysis was conducted to identify independent predictors of the modified Gensini score for coronary artery lesions. The results revealed that LDL-C levels, LP(a) levels, and APOE genotype were significantly associated with the score. After adjustment, only LDL-C level (regression coefficient 9.063, 95% confidence interval 2.612–15.541, p = 0.006) and APOE genotype (regression coefficient 14.265, 95% confidence interval 4.692–23.900, p = 0.004) remained statistically significant predictors of the modified Gensini score, as shown in Table 5. TABLE 5 : Univariate and multivariate linear regression analysis of independent predictors of the modified Gensini score for coronary artery lesions. Unadjusted values adjusted values p Value OR 95% CI p Value OR 95% CI LP(a) 0.002 0.057 0.022-0.093 0.172 0.024 -0.011-0.060 LDL-C 0.000 12.426 6.229-18.622 0.006 9.063 2.612-15.514 HDL-C 0.541 -6.499 -27.55-14.553 0.177 -13.011 -31.993-5.972 APOE allele 0.000 21.343 12.332-30.355 0.004 14.265 4.692-23.900 SLCO1B1 allele 0.145 -12.011 -28.246-4.224 0.101 -11.791 -25.945-2.363 Note: Values for measurement data are expressed as the means ± SDs. Abbreviations: HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; TG, triglyceride; TC: total cholesterol; BMI: body mass index; LP(a): lipoprotein (a); 3.6 ROC curve model for the Gensini score and incidence of myocardial infarction To assess the predictive performance of the model, ROC curve analysis was performed, which evaluated the model's specificity and sensitivity. The area under the curve (AUC) was 0.793 (>0.5), and the 95% confidence interval was 0.696–0.890. indicating acceptable discrimination for predicting the modified Gensini score on the basis of LDL-C and APOE. ROC curve analysis revealed good predictive performance, with an area under the curve (AUC) of 0.855 (>0.5) and a 95% confidence interval of 0.774–0.936; these findings demonstrate the clinical significance of the model in predicting myocardial infarction on the basis of LDL-C level, Lp(a) level, and APOE. See Figure 2. 4. Discussion 4.1 Key findings In this study, patients with the APOE gene phenotype E4 presented the highest levels of LDL-C and LP (a). The Gensini score was used as the outcome measure. In the group with a medium Gensini score (GSmid), the proportion of the ε3 allele was the highest (50%). The group with a high Gensini score (GShigh) had the highest proportion of the ε4 allele (66.7%) and the lowest proportion of the ε2 allele (5.9%). Conversely, the group with a low Gensini score (GSlow) presented the highest proportion of the ε2 allele (52.9%) and the lowest proportion of the ε4 allele (11.1%). The distribution of alleles among the three groups was statistically significant (p < 0.05). The Gensini score was highest in patients with the ɛ4 genotype (73.83 ± 36.01), intermediate in those with the ɛ3 genotype (46.73 ± 25.14), and lowest in patients with the ɛ2 genotype (31.35 ± 20.16). These findings indicate that the severity of coronary artery lesions varies according to APOE genotype. Moreover, APOE can be used as an independent risk factor for the Gensini score to predict the extent of coronary artery lesions, which is consistent with the findings of previous studies. There was no significant difference among the three groups when SLCO1B1 genotype distributions were compared with Gensini scores (p = 0.056). Furthermore, subgroup analysis revealed a statistically significant difference in the distribution of the three APOE genotypes among patients with myocardial infarction (p < 0.05). Multivariate analysis revealed that APOE gene polymorphisms and LDL-C levels were independent risk factors for the severity of coronary artery lesions and the occurrence of myocardial infarction. The prediction model showed excellent predictive performance (area under the curve (AUC): 0.793 for the Gensini score prediction model and 0.855 for the myocardial infarction prediction model). 4.2 Strengths and limitations In addition to a detailed and realistic analysis of the relationships between APOE and SLCO1B1 gene polymorphisms and the severity of coronary artery lesions, our study was particularly important because we innovatively analyzed the associations between these gene polymorphisms, LP(a) levels, and myocardial infarction. In addition, we constructed a clinical risk scoring model based on specific indicators, such as APOE genotype information and LDL-C, and the model results suggested high prediction performance. However, the study has limitations due to its small sample size and lack of long-term follow-up data. Future research directions include expanding the sample size and conducting multicenter collaborations. Moreover, it remains necessary to verify the relationships among gene polymorphisms, blood lipid levels, and coronary artery lesions in combination with basic experiments. 4.3 Comparison with similar research Coronary heart disease is a common chronic disease worldwide. In recent years, the incidence of coronary heart disease and myocardial infarction has gradually increased, becoming one of the main causes of death [16] . In fact, there is no consensus on the correlation between APOE gene polymorphisms and the severity of coronary artery lesions. Bennett et al. reported an approximately linear relationship between APOE genotype, LDL level, and CAD risk [17] . It has been suggested that E2 carriers have a 20% lower risk of CAD than do individuals with the E3 genotype, whereas E4 carriers have a slightly higher risk of CAD [18, 19] . Other scholars have suggested that APOE gene polymorphisms are not related to the severity of coronary artery lesions or to the occurrence of myocardial infarction [20] . The pathogenesis of CHD is complex and involves interactions among genes, genetic factors, and environmental factors, which may explain the differing conclusions of these studies. In our study, the Gensini score was highest in patients with the ɛ4 genotype (73.83 ± 36.01), intermediate in those with the ɛ3 genotype (46.73 ± 25.14), and lowest in patients with the ɛ2 genotype (31.35 ± 20.16). These findings indicate that the severity of coronary artery lesions varies according to APOE genotype. Moreover, APOE can be used as an independent risk factor for the Gensini score to predict the extent of coronary artery lesions, which is consistent with the findings of previous studies. There was no significant difference among the three groups when SLCO1B1 genotype distributions were compared with Gensini scores (p = 0.056). Ghassibe-Sabbagh M et al. reported that the rs4149056 variant in SLCO1B1 was positively associated with hyperhomocysteinemia and might increase the risk of CHD [21] ; however, their study was limited by a small sample size and lacked basic experimental or animal evidence. Similarly, FF Hao et al. reported no correlation between SLCO1B1 c.388A>G and c.521T>C gene polymorphisms and the incidence of CHD in the Yunnan Bai population [22] , which aligns with the findings of the present study. This study is the first systematic investigation of the associations between the occurrence of coronary artery lesions and myocardial infarction and the polymorphisms of the APOE and SLCO1B1 genes in the human population in East China. It also innovatively analyzes the correlation between Lp(a) and the ApoE gene. We are aware of a number of studies suggesting that ApoE gene polymorphisms modulate LDL-C-mediated atherosclerosis [23] ; however, few studies have focused on the impact of Lp(a), which is a new research hotspot in blood lipids. Lp(a) is a specialized lipoprotein that is strongly associated with cardiovascular disease and calcified aortic stenosis. Pathophysiological, epidemiological, and genetic studies have shown that elevated plasma Lp(a) levels, regardless of the LDL-C level, independently increase the incidence of cardiovascular events [24, 25] . Our study confirmed that Lp(a) was affected by ApoE gene polymorphisms and that the Lp(a) levels of ε4 carriers were significantly greater (293.89 ± 209.79 nmol/L) than those of ε3 carriers, who presented intermediate Lp(a) levels (181.30 ± 157.42 nmol/L), and those of ε2 carriers, who presented the lowest Lp(a) levels (116.82 ± 88.05 nmol/L), p=0.005. Gilliland TC et al. suggested that ApoE gene polymorphisms were the second most important gene for LP(a) levels [25] , which further confirmed the validity of our findings. 4.4 Explanations of findings The ε3 allele has been defined as a neutral gene that maintains the normal physiological activities of the body, whereas ε2 and ε4 are considered variant alleles. Among these alleles, ε2 tends to be a protective allele that can reduce the risk of CHD, whereas ε4 is regarded as the main pathogenic allele associated with CHD [26] . Many studies have shown that coronary artery lesions result from the interaction of various internal genetic factors and external environmental factors. Atherosclerosis is the fundamental pathological change, and dyslipidemia, as the primary risk factor for atherosclerosis, plays a crucial role in the occurrence, development, and prognosis of coronary artery lesions [27, 28] . The ApoE gene has been shown to regulate lipid metabolism in the body through multiple pathways; it significantly influences blood lipid levels and is a susceptibility gene for hyperlipidemia and atherosclerotic vascular diseases (including coronary heart disease, cerebral infarction, and peripheral vascular disease) [12, 29, 30] . Consequently, it has become a research hotspot in the clinical management and prognosis evaluation of CHD. The human ApoE gene is located on chromosome 19 (19q13.2) and exhibits significant genetic polymorphisms; its expression varies among different regions and ethnic groups [31] . Two nonsynonymous single nucleotide polymorphisms, rs429358 (388T>C) and rs7412 (526C>T), together form three alleles—ε2, ε3, and ε4. These polymorphisms cause differences in amino acid sequence and protein conformation, resulting in varying affinities of the three ApoE isoforms for their receptors, which ultimately lead to different ApoE alleles playing distinct roles in lipid metabolism and cardiovascular diseases [32] . APOE allele variants are known to explain up to 7% of interindividual differences in LDL-C and total cholesterol levels, and dyslipidemia can be partially attributed to genetic factors [33] . In our study, we found that patients with the APOE ε4 genotype had increased serum LDL-C levels, suggesting that the APOE gene is an important factor in the exacerbation of coronary artery disease, as it affects serum lipid levels. Furthermore, APOE may increase the risk of coronary artery disease through its interaction with antioxidants and the immune system [34] . 4.5 Implications and actions needed By revealing potential genetic risk factors, these findings provide a theoretical foundation for the development of personalized treatment and prevention strategies for cardiovascular diseases. Research needs to focus on basic studies on the local expression and function of APOE in blood vessel wall cells (endothelial cells, smooth muscle cells, and macrophages). Additionally, how genotypes affect the function and activity of these cells during the process of atherosclerosis should be investigated. From a clinical perspective, advanced imaging techniques such as coronary CT, IVUS, and OCT are being used to conduct thorough investigations of the differences in morphology, composition, and functional characteristics of coronary plaques among patients with different genotypes. The ultimate goal is to explore new intervention strategies targeting the APOE pathway, such as APOE mimetic peptides and/or proteins, gene therapy, and other therapeutic approaches. 5. Conclusion In summary, this study revealed a correlation between APOE and SLCO1B1 gene polymorphisms and variations in blood lipid levels, coronary artery lesions, and myocardial infarction, highlighting the important role of genetic factors in the development of cardiovascular diseases. These findings provide a theoretical basis for the development of individualized treatment and prevention strategies for cardiovascular diseases by revealing the underlying genetic risk factors. By linking APOE gene polymorphisms to aberrant serum lipid and inflammation profiles, we found that individuals carrying the ε4 allele exhibit dysregulated lipid metabolism and abnormal inflammatory markers. This dysregulation increases the risk of cardiovascular disease (CVD) and acute myocardial infarction (AMI) [35, 36] . Therefore, early detection and timely diagnosis are essential for implementing therapeutic, dietary, and lifestyle interventions to reduce risk and prevent or delay diseases associated with lipids and inflammation. Declarations Supplementary Appendix APOE: apolipoprotein E; SLCO1B1: solute carrier organic anion transporter family member 1B1; HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; TG, triglycerides; TC, total cholesterol; BMI: body mass index; LP(a): lipoprotein (a); HbA1C: glycosylated hemoglobin; Hs-cTNI: high-sensitivity cardiac troponin I; LA: left atrial diameter; LVEDD: left ventricular end-diastolic diameter. AMI: Acute myocardial infarction; CAD: coronary artery disease; OATP1B1: organic anion transporting polypeptide 1B1; HbA1c: glycated hemoglobin; CI: confidence interval; OR: odds ratio; CVD: cardiovascular disease; AMI: acute myocardial infarction; Ethics approval and consent to participate: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved . This study adhered to the ethical standards of the updated version of the Declaration of Helsinki (1964) and was approved by the Human Ethics Committee of the Second Affiliated Hospital of Anhui Medical University (Approval No: YX2024--115). All patients signed informed consent forms. Consent for publication Not applicable Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.It is noted that the nature of the data for this study are clinical data. Competing interests he authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding: This research was funded by the Anhui Provincial Scientific Research Preparation Plan Project. Title: METTL3/IGF2BP2 regulate the stability of Atp6v0d2 mRNA in atherosclerotic macrophage polarization; ID: 2022AH050770 Authors' contributions MLL designed the research study and wrote the original drafts; XY and JNZ provided help and advice on conceptual execution. ZW and ZH collected the data and performed the statistical analysis. ZJZ wrote and revised the manuscript and participated in supervision. All the authors contributed to editorial changes in the manuscript. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved its submission. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work. Acknowledgments None. References Chamaria S, Ueyama H, Yasumura K, et al. Coronary plaque vulnerability in statin-treated patients with elevated LDL-C and hs-CRP: optical coherence tomography study. The International Journal of Cardiovascular Imaging 2022;38(5):1157–1167. Farina FM, Serio S, Hall IF, et al. The epigenetic enzyme DOT1L orchestrates vascular smooth muscle cell–monocyte crosstalk and protects against atherosclerosis via the NF-κB pathway. European Heart Journal 2022;43(43):4562–4576. Xu D, Xie L, Cheng C, Xue F, Sun C. Triglyceride-rich lipoproteins and cardiovascular diseases. Frontiers in Endocrinology 2024;15. Maggo SD, Kennedy MA, Clark DW. Clinical implications of pharmacogenetic variation on the effects of statins. Drug Saf 2011;34(1):1–19. Jofre Monseny L, Minihane AM, Rimbach G. Impact of apoE genotype on oxidative stress, inflammation and disease risk. Molecular Nutrition & Food Research 2008;52(1):131–145. Postmus I, Trompet S, Deshmukh HA, et al. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins. Nature Communications 2014;5(1). Scholz M, Horn K, Pott J, et al. Genome-wide meta-analysis of phytosterols reveals five novel loci and a detrimental effect on coronary atherosclerosis. Nature Communications 2022;13(1). Xu M, Lv C, Wang H, et al. Peanut skin extract ameliorates high-fat diet-induced atherosclerosis by regulating lipid metabolism, inflammation reaction and gut microbiota in ApoE(-/-) mice. Food Res Int 2022;154:111014. Niemi M, Pasanen MK, Neuvonen PJ. Organic anion transporting polypeptide 1B1: a genetically polymorphic transporter of major importance for hepatic drug uptake. Pharmacol Rev 2011;63(1):157–81. Wang S, Wang L, Li H, et al. Correlation analysis of plasma lipid profiles and the prognosis of head and neck squamous cell carcinoma. Oral Diseases 2024;30(2):329–341. Zhang J, Gong Y, Peng J, et al. Therapeutic evaluation of rosuvastatin on lipids and endothelial cell functionalities in coronary artery lesions coinciding with hyperlipidemia. American journal of translational research 2023;15(5):3152–3161. Wang Y, Yang S, Zhang S, Lu X, Ma W. Apolipoprotein E Gene Polymorphism Effects on Lipid Metabolism and Risk of Cerebral Infarction in Northwest Han Chinese Population. Pharmgenomics Pers Med 2023;16:303–312. Al Hageh C, Chacar S, Ghassibe-Sabbagh M, et al. Elevated Lp(a) Levels Correlate with Severe and Multiple Coronary Artery Stenotic Lesions. Vascular health and risk management 2023;19:31–41. Karjalainen JP, Mononen N, Hutri-Kahonen N, et al. New evidence from plasma ceramides links apoE polymorphism to greater risk of coronary artery disease in Finnish adults. J Lipid Res 2019;60(9):1622–1629. Zhang ZH, Yue Sun LC, Gu HY, Jiang DC, Yi ZM. Associations BetweenSLCO1B1,APOE and CYP2C9 and Lipid-Lowering Efficacy and Pharmacokinetics of Fluvastatin: A Meta-Analysis. Pharmacogenomics 2023;24(8):475–484. Benjamin EJ, Blaha MJ, Chiuve SE, et al. Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association. Circulation (New York, N.Y.) 2017;135(10):e146-e603. Bennet AM, Di Angelantonio E, Ye Z, et al. Association of Apolipoprotein E Genotypes With Lipid Levels and Coronary Risk. JAMA: the journal of the American Medical Association 2007;298(11):1300–1311. Ozuynuk AS, Erkan AF, Dogan N, et al. Examining the effects of the CLU and APOE polymorphisms' combination on coronary artery disease complexed with type 2 diabetes mellitus. Journal of diabetes and its complications 2022;36(1):108078–108078. Yousuf FA, Iqbal MP. Review: Apolipoprotein E (Apo E) gene polymorphism and coronary heart disease in Asian populations. Pakistan journal of pharmaceutical sciences 2015;28(4):1439. Larifla L, Armand C, Bangou J, et al. Association of APOE gene polymorphism with lipid profile and coronary artery disease in Afro-Caribbeans. PLOS ONE 2017;12(7):e0181620. Al HC, Alefishat E, Ghassibe-Sabbagh M, et al. Homocysteine levels, H-Hypertension, and the MTHFR C677T genotypes: A complex interaction. Heliyon 2023;9(6):e16444. Liu Q, Wu H, Yu Z, Huang Q, Zhong Z. APOE gene varepsilon4 allele (388C-526C) effects on serum lipids and risk of coronary artery disease in southern Chinese Hakka population. J Clin Lab Anal 2021;35(9):e23925. Bea AM, Larrea-Sebal A, Marco-Benedi V, et al. Contribution ofAPOE Genetic Variants to Dyslipidemia. Arteriosclerosis, Thrombosis, and Vascular Biology 2023;43(6):1066–1077. Volgman AS, Koschinsky ML, Mehta A, Rosenson RS. Genetics and Pathophysiological Mechanisms of Lipoprotein(a)-Associated Cardiovascular Risk. Journal of the American Heart Association 2024;13(12). Gilliland TC, Liu Y, Mohebi R, et al. Lipoprotein(a), Oxidized Phospholipids, and Coronary Artery Disease Severity and Outcomes. J Am Coll Cardiol 2023;81(18):1780–1792. Long Y, Zhao X, Liu C, et al. A Case–Control Study of the Association of the Polymorphisms of MTHFR and APOE with Risk Factors and the Severity of Coronary Artery Disease. Cardiology 2019;142(3):149–157. Maddox TM, Stanislawski MA, Grunwald GK, et al. Nonobstructive Coronary Artery Disease and Risk of Myocardial Infarction. JAMA 2014;312(17):1754. Elliott J, Bodinier B, Bond TA, et al. Predictive Accuracy of a Polygenic Risk Score–Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease. JAMA 2020;323(7):636. Long L, Sun Q. Analysis of APOE and SLCO1B1 Gene Polymorphism and Correlation with Dyslipidemia in China. Clin Lab 2022;68(11). Alagarsamy J, Jaeschke A, Hui DY. Apolipoprotein E in Cardiometabolic and Neurological Health and Diseases. International Journal of Molecular Sciences 2022;23(17):9892. Niu R, Guo L, Dong X, et al. Analysis of the Difference inSLCO1B1 andAPOE Gene Polymorphisms Between Mongolian and Han Populations. Pharmacogenomics 2022;23(14):783–790. Oria RB, de Almeida JZ, Moreira CN, Guerrant RL, Figueiredo JR. Apolipoprotein E Effects on Mammalian Ovarian Steroidogenesis and Human Fertility. Trends Endocrinol Metab 2020;31(11):872–883. Eichner JE DSPG, Aje D. Apolipoprotein E polymorphism and cardiovascular disease: a HuGE review. Am J Epidemiol:2002;155(6):487–95.doi: 10.1093/aje/155.6.487 . Yu ZW BZRQ. Oxi-inflamm-aging and its association with the polymorphism of ApoE genes. Sheng Li Xue Bao:2013;65(3):338–46. LICASTRO F, CHIAPELLI M, CALDARERA CM, CARUSO C, LIO D, CORDER EH. Acute Myocardial Infarction and Proinflammatory Gene Variants. Annals of the New York Academy of Sciences 2007;1119(1):227–242. Ranjith N, Pegoraro RJ, Rom L. Lipid Profiles and Associated Gene Polymorphisms in Young Asian Indian Patients With Acute Myocardial Infarction and the Metabolic Syndrome. Metabolic Syndrome and Related Disorders 2009;7(6):571–578. Additional Declarations No competing interests reported. Supplementary Files rawdata.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 22 Oct, 2025 Reviews received at journal 21 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers invited by journal 11 Aug, 2025 Editor assigned by journal 01 Aug, 2025 Submission checks completed at journal 01 Aug, 2025 First submitted to journal 10 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7094595","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501282398,"identity":"0ebb7912-f600-45f5-b7d8-a203d9d6d051","order_by":0,"name":"Meng-li Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meng-li","middleName":"","lastName":"Li","suffix":""},{"id":501282399,"identity":"90e5abb6-b6e8-4247-b970-ffa496e41d05","order_by":1,"name":"Xun Yang","email":"","orcid":"","institution":"First Affiliated Hospital of Anhui Medical 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15:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7094595/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7094595/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89455011,"identity":"661414d7-33f1-475f-b013-91c92377640e","added_by":"auto","created_at":"2025-08-20 06:49:31","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":156721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox plot of the effects of APOE andSLCO1B1 on the Gensini score and incidence of AMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: APOE gene expression grouping-box plot\u003c/p\u003e\n\u003cp\u003eB: Distribution of the incidence of acute myocardial infarction and the APOE gene expression group-box plot.\u003c/p\u003e\n\u003cp\u003eC: Distribution between the Gensini score and SLCO1B1 gene expression group-box plots.\u003c/p\u003e\n\u003cp\u003eD: Distribution between the incidence of acute myocardial infarction and SLCO1B1 gene expression in a group-box plot.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7094595/v1/c699efa6dbe7b7357c63f4ca.jpeg"},{"id":89453546,"identity":"7dba6113-304b-4bc1-9502-eccd218d2ecf","added_by":"auto","created_at":"2025-08-20 06:41:31","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70381,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe predictive efficacy of the nomogram in predicting AMI and the Gensini score was validated through receiver operating characteristic (ROC) curve analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: A prediction model for the severity of coronary artery lesions based on the Gensini score: AUC=0.793, P=0.000, 95% CI=0.696--0.890)\u003c/p\u003e\n\u003cp\u003eB: A prediction model for acute myocardial infarction (AMI): AUC=0.855, P=0.000, 95% CI=0.744--0.917)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7094595/v1/244f5126ee10a2655ae07d4f.jpeg"},{"id":89456052,"identity":"87f0c730-d39a-4f61-99f6-2ef88c2e0348","added_by":"auto","created_at":"2025-08-20 06:57:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1848263,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7094595/v1/4b26a71f-4347-4121-b15d-2479aff0ff24.pdf"},{"id":89453556,"identity":"be9c86ea-e7ac-4583-ae0b-1319bad21c8a","added_by":"auto","created_at":"2025-08-20 06:41:31","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14648691,"visible":true,"origin":"","legend":"","description":"","filename":"rawdata.zip","url":"https://assets-eu.researchsquare.com/files/rs-7094595/v1/593d99ce763fe22e38c4b4a0.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of the ApoE Genotype on Coronary Artery Disease and the Incidence of Myocardial Infarction: A Clinical Observational Study","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Background\u003c/h2\u003e\u003cp\u003eCoronary artery disease (CAD) is the leading cause of cardiovascular disease mortality worldwide, particularly in East China, where the incidence of coronary heart disease remains high due to factors such as regional dietary habits and genetic characteristics, and there is a widespread presence of patients with complex coronary artery lesions\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. This disease not only severely impacts the quality of life and family economic income of patients but also adds an economic burden to social healthcare\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Despite mature treatment methods for CHD, such as pharmacological therapy and interventional surgical treatment, several limitations remain, including insufficient individualized treatment, high incidence, and high mortality. Therefore, it is particularly important to conduct in-depth research on the factors influencing CAD\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Rationale and knowledge gap\u003c/h2\u003e\u003cp\u003eThe APOE gene is a candidate gene for susceptibility to hyperlipidemia and atherosclerotic vascular diseases. The APOE gene has three major alleles: ɛ3 (388T-526C), ɛ2 (388T-526T), and ɛ4 (388C-526C). These three alleles combine to form six possible genotypes. The SLCO1B1 gene encodes organic anion transporting polypeptide 1B1 (OATP1B1), which plays an important role in the metabolism of statins and is associated with genetic polymorphisms\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Previous studies have shown that APOE genotypes play a key role in lipid metabolism \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, whereas the SLCO1B1 gene is closely related to drug metabolism and lipid regulation \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Polymorphisms in these two genes may have a significant impact on cardiovascular health \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. It is well known that LDL-C, a lipid indicator, is significantly associated with coronary artery disease \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Over the past few decades, we have focused more on LDL-C levels, but recent studies have shown that, regardless of the LDL-C level, elevated plasma Lp-(a) levels can independently increase cardiovascular events, particularly the incidence of CAD\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. However, there is currently no consensus on the relationship between APOE gene polymorphisms and Lp(a) or coronary artery lesions. Therefore, the underlying association between APOE gene polymorphisms and CHD remains a focus of current clinical research \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. An in-depth analysis of the roles of the APOE and SLCO1B1 genes will provide new perspectives for understanding the genetic risk of coronary artery disease\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Objective\u003c/h2\u003e\u003cp\u003eThis study employed a method that combined genetic analysis with clinical data from a population in East China to assess lipid levels and the degree of coronary artery lesions. The advantage of this method lies in its ability to integrate clinical and demographic characteristics with genetic information, providing a more comprehensive disease risk assessment. The specific aim of this study was to reveal the relationships between gene polymorphisms of APOE and SLCO1B1 and lipid profiles, including Lp(a) and LDL-C, as well as coronary artery lesions, thereby providing a theoretical basis for early prevention and personalized therapeutic approaches for coronary artery disease. By analyzing the genetic polymorphisms within this specific population, this study aimed to better understand the role of genetic factors in cardiovascular diseases.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStudy\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esubjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study prospectively enrolled\u0026nbsp;91 consecutive patients hospitalized at the Second Affiliated Hospital of Anhui Medical University from September 2024 to December 2024. The inclusion criteria were as follows: (1) patients willing to undergo genetic testing; (2) patients presenting with chest pain or discomfort, an ECG showing ischemic changes, and a clinical assessment indicating that coronary artery disease cannot be excluded, confirmed by a reduction of more than 50% in the diameter of at least one major coronary artery, as determined by coronary angiography; and (3) residents of Anhui, confirmed by household registration, with no biological relation to other subjects. The exclusion criteria were as follows: (1) severe liver and kidney dysfunction (eGFR \u0026lt; 30 mL/min/1.73 m² or ALT/AST \u0026gt; 3 times the upper limit of normal); (2) history of cardiomyopathy, valvular heart disease, malignant tumors, tuberculosis, or other major diseases affecting inflammatory factors and lipid metabolism; (3) biological relationship with other subjects up to the third degree, verified through the household registration system; (4) incomplete medical records or blood samples; and (5) nonprovision of informed consent.\u003c/p\u003e\n\u003cp\u003eEthical Statement: This study adhered to the ethical standards\u0026nbsp;of the updated version of the Declaration of Helsinki (1964) and was approved by the Human Ethics Committee of the Second Affiliated Hospital of Anhui Medical University (Approval No: YX2024--115). All patients signed informed consent forms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecollection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStandardized case report forms were used to collect\u0026nbsp;data on the following characteristics:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1\u0026nbsp;\u003c/strong\u003eAnthropometric indicators: age, sex,\u0026nbsp;and body mass index\u0026nbsp;(BMI) (measured by trained nurses\u0026nbsp;via calibrated instruments).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2\u003c/strong\u003e Cardiovascular risk factors: smoking, alcohol consumption, hypertension (≥140/90 mmHg or receiving antihypertensive treatment), diabetes (glycated hemoglobin\u0026nbsp;(HbA1c) ≥ 6.5% or receiving hypoglycemic treatment), stroke, and atrial fibrillation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003eLaboratory parameters: Approximately 3 ml of blood was collected from each subject.\u0026nbsp;The serum was promptly separated and analyzed; samples that could not be tested immediately were stored at -80°C until analysis. Lipid levels, including those of five lipid components (TC, TG, LDL-C, HDL-C, and LP(a)), troponin, and HbA1c, were measured via the Olympus AU5400 system (Olympus Corporation, Tokyo, Japan) according to the manufacturer's instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.4\u0026nbsp;\u003c/strong\u003eCoronary angiography characteristics: affected vessels (left main, left anterior descending and its branches, circumflex and its branches, right coronary and its branches) and degree of stenosis (quantitative coronary angiography analysis).\u003c/p\u003e\n\u003cp\u003eThe modified Gensini scoring system was used to quantify the severity of coronary lesions. The scoring included\u0026nbsp;the following:\u003c/p\u003e\n\u003cp\u003e- Stenosis weights:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - 100% stenosis: 32 points\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - 90–99% stenosis: 16 points\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - 75–89% stenosis: 8 points\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - 50–74% stenosis: 4 points\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - 26–49% stenosis: 2 points\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - 1–25% stenosis: 1 point\u003c/p\u003e\n\u003cp\u003e- Anatomical coefficients (multiplicative factors applied to the stenosis weights):\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - Left main artery ×5\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - Proximal left anterior descending artery ×2.5\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - Mid-left anterior descending artery ×1.5\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - Distal left anterior descending artery ×1\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - Diagonal branch D1 ×1\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - Diagonal branch D2 ×0.5\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - Proximal circumflex artery ×2.5\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - Mid circumflex artery ×1.5\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - Distal circumflex artery ×1\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; - Each segment of the right coronary artery ×1\u003c/p\u003e\n\u003cp\u003eThe total Gensini score for each patient was calculated by summing the weighted scores of all affected vessels.\u0026nbsp;Patients were divided into three groups according to the total Gensini score: the low score group (GSlow), with scores ≤31 points;\u0026nbsp;the medium score group (GS mid), with scores ranging from 32–61 points; and the high score group (GS high), with scores ≥62 points. These thresholds were determined on the basis of pretrial receiver operating characteristic (ROC) analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u003c/strong\u003e\u003cstrong\u003eApoE\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003egene polymorphism detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1\u0026nbsp;\u003c/strong\u003eDNA extraction:\u0026nbsp;Gene chip technology (purchased from Wuhan Haijili Biotechnology\u0026nbsp;Technology Co., Ltd.) combined with the PCR in vitro amplification method\u0026nbsp;was used to detect human ApoE gene polymorphisms in selected patients;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.2\u003c/strong\u003ePeripheral blood cell DNA extraction:\u0026nbsp;Two milliliters of venous blood was collected from patients and anticoagulated, and DNA was extracted via a genomic extraction kit (purchased from Sun Yat-sen University Da'an Gene Co., Ltd.), following the instructions, and the quality and concentration of the extracted DNA were measured via a UV spectrophotometer (purchased from Thermo Fisher Scientific, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.3\u0026nbsp;\u003c/strong\u003ePCR-RELP:\u0026nbsp;Eight microlitres of PCR amplification product was added to 2 μL of 10× buffer and 1 μL of specific restriction endonuclease (purchased from Dalian Baosheng Bioengineering Co., Ltd.), 9 μL of deionized water was added, and the mixture was reacted at 37°C for 12–14 hours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.4\u003c/strong\u003e PCR product electrophoresis detection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.5\u003c/strong\u003e Gene sequencing:\u0026nbsp;The fragile sites of the gene, namely, APOE (112T\u0026gt;C and 158C\u0026gt;C), were selected. Slco1b1 (388A\u0026gt;G, 521T\u0026gt;C) was subjected to large-scale amplification, electrophoresis, and enzyme digestion to determine the genotype. All of the above procedures were performed by professional personnel in our hospital's central laboratory. The results indicated that the ApoE gene was divided into three phenotypic groups: the\u0026nbsp;ɛ2\u0026nbsp;phenotypic group (ɛ2/ɛ2,\u0026nbsp;ɛ2/ɛ3),\u0026nbsp;the\u0026nbsp;ɛ3\u0026nbsp;phenotypic group (ɛ2/ɛ4,\u0026nbsp;ɛ3/ɛ3), and\u0026nbsp;the\u0026nbsp;ɛ4\u0026nbsp;phenotypic group (ɛ3/ɛ4,\u0026nbsp;ɛ4/ɛ4). The SLCO1B1 gene was divided into three groups:\u0026nbsp;the normal OATP1B1 functional group: S-nor (1a/1a, 1a/1b, 1b/1b); the intermediate OATP1B1 functional group: S-mid (1a/5, 1a/15, 1b/15); and the low OATP1B1 functional group: S-low (5/5, 5/15, 15/15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatistical\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed via SPSS 25.0. The quantitative data were tested for normality and are expressed as the means ± standard deviations. One-way ANOVA was used for comparisons between groups. Qualitative data are presented as frequencies and percentages, and chi-square tests were conducted to assess differences between groups. The genotype distributions of APOE and SLCO1B1 were tested for Hardy‒Weinberg equilibrium. Linear regression analysis was performed to examine the relationships between APOE and SLCO1B1 gene polymorphisms and the severity of CHD, which was treated as a continuous variable. Multiple logistic regression analysis was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the incidence of myocardial infarction, adjusting for various risk factors. All risk factors were included as covariates in the regression models. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive performance of the models for the severity of coronary lesions and the occurrence of myocardial infarction. P values less than 0.05 were considered significant.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTABLE 1: Comparison of clinical characteristics among\u0026nbsp;the three groups on\u0026nbsp;the basis of the Gensini score.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"563\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eGSlow\u003c/p\u003e\n \u003cp\u003e（n=26,28.57%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGSmid\u003c/p\u003e\n \u003cp\u003e（n=39,42.86%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eGShigh\u003c/p\u003e\n \u003cp\u003e（n=26,28.57%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eF/\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"88\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e62.8\u0026plusmn;11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e61.1\u0026plusmn;12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e63.3\u0026plusmn;12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eBMI，kg/m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e23.90\u0026plusmn;3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e24.40\u0026plusmn;2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e26.12\u0026plusmn;4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eGender，n（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e16（61.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e28（71.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e17（65.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e10（38.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e11（28.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e9（34.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003esmoking, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e8（30.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e14（35.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e11（42.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003ealcoholism, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e18（69.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e25（64.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e18（69.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eHypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e12（46.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e23（59.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e19（73.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e3.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eDiabetes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e4（15.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e13（33.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e8（30.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eStroke，n（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e3（11.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6（15.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e5（19.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAtrial fibrillation,n（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e11（42.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e13（33.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e10（38.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"68\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eTG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.81\u0026plusmn;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.69\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.75\u0026plusmn;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eCHO, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e4.28\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4.58\u0026plusmn;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e4.52\u0026plusmn;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.24\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.14\u0026plusmn;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.16\u0026plusmn;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eLP（a）,mmol/l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e136.77\u0026plusmn;94.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e177.72\u0026plusmn;132.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e267.00\u0026plusmn;236.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e4.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"51\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e2.40\u0026plusmn;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2.55\u0026plusmn;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e3.04\u0026plusmn;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e3.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"62\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eHbA1C（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e5.92\u0026plusmn;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6.31\u0026plusmn;1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e6.18\u0026plusmn;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eHs-cTNI,pg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e12.92\u0026plusmn;31.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1277.09\u0026plusmn;4277.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1394.35\u0026plusmn;345.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"51\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eLA,mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e33.57\u0026plusmn;4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e35.87\u0026plusmn;4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e34.58\u0026plusmn;3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eLVEDD,mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e45.12\u0026plusmn;4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e46.69\u0026plusmn;5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e47.04\u0026plusmn;5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Values for measurement data are expressed as\u0026nbsp;the means \u0026plusmn; SDs. Values for count data are expressed as n (%).\u003c/p\u003e\n\u003cp\u003eAbbreviations: HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; TG, triglyceride; TC, total cholesterol; BMI: body mass index; LP(a): lipoprotein (a); HbA1C: glycosylated hemoglobin; Hs-cTNI: high-sensitivity cardiac troponin I; LA: left atrial diameter; LVEDD: left ventricular end-diastolic diameter.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Comparison of clinical data\u003c/p\u003e\n\u003cp\u003eA total of 91 patients were included in this study, with 26\u0026nbsp;(28.57%) patients in the GSlow group, 39 (42.86%) in the GSmid group, and 26\u0026nbsp;(28.57%)\u0026nbsp;in the GShigh group.\u0026nbsp;The baseline data revealed statistically significant differences among the three cohorts (p \u0026lt; 0.05) in LDL-C, Lp(a), and HDL-C concentrations:\u0026nbsp;patients with higher LDL-C and LP(a) and lower HDL-C\u0026nbsp;presented\u0026nbsp;higher\u0026nbsp;Gensini\u0026nbsp;scores.\u0026nbsp;Specifically,\u0026nbsp;the LDL-C concentrations were 2.40 \u0026plusmn; 0.92 mmol/L, 2.55 \u0026plusmn; 0.92 mmol/L, and 3.04 \u0026plusmn; 0.88 mmol/L; the Lp(a) concentrations were 136.77 \u0026plusmn; 94.70 mmol/L, 177.72 \u0026plusmn; 132.39 mmol/L, and 267.00 \u0026plusmn; 236.71 mmol/L; and the HDL-C concentrations were 1.24 \u0026plusmn; 0.31 mmol/L, 1.14 \u0026plusmn; 0.29 mmol/L, and 1.16 \u0026plusmn; 0.29 mmol/L in cohorts 1, 2, and 3, respectively.\u0026nbsp;Baseline characteristics, such as age, sex, body mass index (BMI), and risk factors\u0026nbsp;for smoking, alcohol consumption, hypertension, diabetes, stroke, and atrial fibrillation, along with pertinent biochemical and echocardiographic parameters, were not significantly different across the three groups, confirming their comparability at baseline. (Table 1,\u0026nbsp;see the last page)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e\u003cstrong\u003e2 Phenotypes\u0026nbsp;and frequency\u0026nbsp;distributions\u0026nbsp;of\u0026nbsp;the APOE and SLCO1B1 genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe APOE genotypes identified include five variants: ɛ2/ɛ3, ɛ3/ɛ3, ɛ2/ɛ4, ɛ3/ɛ4, and ɛ4/ɛ4. The number of cases and frequencies of the alleles ɛ3, ɛ4, and ɛ2 were 56 (61.54%), 18 (19.78%), and 17 (18.68%), respectively. A total of six SLCO1B1 genotypes were identified, with frequencies of 1a/1a (11%), 1a/1b (35.2%), 1b/1b (41.8%), 1a/15 (3.3%), 1b/15 (7.7%), and 15/15 (1.1%).\u0026nbsp;On the basis of SLCO1B1 metabolic capacity, the cases were divided into three groups, S-normal, S-intermediate, and S-low, with 79 (86.81%), 11 (12.08%), and 1 (1.1%) cases, respectively. The distribution of APOE genotypes was consistent with the Hardy‒Weinberg equilibrium (chi-square [\u0026chi;2] = 5.159, p = 0.397), as was the distribution of SLCO1B1 genotypes (chi-square [\u0026chi;2]= 3.113, p = 0.577). With respect to APOE, the ɛ3/ɛ3 genotype was the most common, followed by the ɛ2/ɛ3, ɛ3/ɛ4, ɛ2/ɛ4, and ɛ4/ɛ4 genotypes, whereas the most prevalent SLCO1B1 genotype was the 1b/1b genotype, followed by the 1a/1b, 1a/1a, 1b/15, 1a/15, and 15/15 genotypes (Table 2). There was a statistically significant difference in the ɛ2/ɛ3/ɛ4 genotypes among the GSlow, GSmid, and GShigh groups (chi-square [\u0026chi;2] = 20.751, p \u0026lt; 0.001). However, there was no statistically significant difference in SLCO1B1 phenotypes among the GSlow, GSmid, and GShigh groups (chi-square [\u0026chi;2] = 9.961, p = 0.444) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2: Distribution of ApoE/SLCO1B1 genotypes and allele frequencies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003eGenotypes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003eɛ2/ɛ2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003eɛ2/ɛ3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003eɛ3/ɛ3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u0026nbsp;\u003cbr\u003eɛ3/ɛ4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u0026nbsp;\u003cbr\u003eɛ2/ɛ4\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003eɛ4/ɛ4\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003en(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e0(0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e17(18.68)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e53(58.24)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e17(18.68)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e3(3.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e1(1.1)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003eGenotypes\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e1a/1a\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e1a/1b\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e1b/1b\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e1a/15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e1b/15\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e15/15\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003en(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e10(9.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e32(35.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e38(41.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e3(3.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e7(8.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e1(1.1)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003eAlleles\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003eɛ2\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003eɛ3\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\u0026nbsp;\u003cbr\u003eɛ4\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u0026nbsp;\u003cbr\u003eS-nor\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u0026nbsp;\u003cbr\u003eS-mid\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cbr\u003eS-low\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18px;\"\u003en(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e17(18.68)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e56(61.54)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e18(19.78)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e80(87.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e11（11）\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e1（1.1）\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003eGSlow(n,%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e9(52.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e15(26.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e2(11.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e19(24.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e7(63.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e0(0)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003eGSmid(n,%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e7(41.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e28(50)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e4(22.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e36(45.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e3(27.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e0(0)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003eGShigh(n,%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e1(5.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e13(23.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e12(66.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e24(30.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e1(9.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e1(100)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003eP values\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 39px;\"\u003e0.000(\u0026chi;2 = 20.751)\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 42px;\"\u003e0.444(\u0026chi;2 = 9.961)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:\u0026nbsp;GSlow:\u0026nbsp;Gensini score\u0026nbsp;\u0026le;31 points;\u0026nbsp;GSmid:\u0026nbsp;Gensini score\u0026nbsp;32\u0026ndash;61 points; GShigh:\u0026nbsp;Gensini score \u0026gt;\u0026nbsp;points; S-nor:\u0026nbsp;normal OATP1B1 functional group,\u0026nbsp;including genotypes 1a/1a, 1a/1b, and 1b/1b; S-mid:\u0026nbsp;intermediate OATP1B1 functional group,\u0026nbsp;including genotypes 1a/5, 1a/15,\u0026nbsp;and 1b/15;\u0026nbsp;S-low:\u0026nbsp;low OATP1B1 functional group,\u0026nbsp;including genotypes 5/5, 5/15,\u0026nbsp;and 15/15.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eComparison of lipid indicators among APOE and SLCO1B1 gene phenotypic groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the relationships between APOE alleles (ɛ2, ɛ3, and ɛ4) and serum lipid levels and found that LDL-C was significantly elevated in ɛ4 carriers (3.15 \u0026plusmn; 1.10 mmol/L), intermediate in ɛ3 carriers (2.65 \u0026plusmn; 0.85 mmol/L), and lowest in ɛ2 carriers (2.10 \u0026plusmn; 0.12 mmol/L); p = 0.003. A similar pattern was consistently observed for LP(a) levels, with ɛ4 carriers showing significantly higher LP(a) levels (293.89 \u0026plusmn; 209.79 mmol/L), ɛ3 carriers showing intermediate levels (181.30 \u0026plusmn; 157.42 mmol/L), and ɛ2 carriers showing the lowest levels (116.82 \u0026plusmn; 88.05 mmol/L), p = 0.005. There were no statistically significant differences in total cholesterol, high-density lipoprotein cholesterol (HDL-C), or triglyceride levels among the APOE alleles (p \u0026gt; 0.05); similarly, no statistically significant differences were observed in total cholesterol, low-density lipoprotein cholesterol (LDL-C), HDL-C, or triglyceride levels among the different phenotype groups classified by SLCO1B1 gene variants. (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e: Comparison of lipid indicators among APOE and SLCO1B1 gene phenotypic groups.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cstrong\u003eɛ2\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cstrong\u003eɛ3\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cstrong\u003eɛ4\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003eTG, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e1.52\u0026plusmn;1.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e1.73\u0026plusmn;0.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e1.97\u0026plusmn;1.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e0.569\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e0.568\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003eCHO, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e4.09\u0026plusmn;0.82\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e4.59\u0026plusmn;1.18\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e4.49\u0026plusmn;1.26\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e1.222\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e0.300\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003eHDL-C, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e1.27\u0026plusmn;0.34\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e1.14\u0026plusmn;0.28\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e1.19\u0026plusmn;0.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e1.292\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e0.280\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003eLP（a）,mmol/l\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e116.82\u0026plusmn;88.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e181.30\u0026plusmn;157.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e293.89\u0026plusmn;209.79\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e5.696\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003eLDL-C, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e2.10\u0026plusmn;0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e2.65\u0026plusmn;0.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e3.15\u0026plusmn;1.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e6.079\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e0.003\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cstrong\u003eS-normal\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cstrong\u003eS-mid\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cstrong\u003eS-slow\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003eTG, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e1.78\u0026plusmn;1.32\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e1.45\u0026plusmn;0.57\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e1.89\u0026plusmn;0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 9px;\"\u003e0.337\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 8px;\"\u003e0.715\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003eCHO, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e4.45\u0026plusmn;1.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e4.59\u0026plusmn;1.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e5.17\u0026plusmn;0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 9px;\"\u003e0.254\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 8px;\"\u003e0.777\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 22px;\"\u003eHDL-C, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e1.17\u0026plusmn;0.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e1.19\u0026plusmn;0.29\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e1.28\u0026plusmn;0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 9px;\"\u003e0.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 8px;\"\u003e0.905\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003eLP（a）,mmol/l\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e201.65\u0026plusmn;176.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e122.91\u0026plusmn;61.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e147.00\u0026plusmn;0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e1.105\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e0.336\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003eLDL-C, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e2.64\u0026plusmn;0.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e2.63\u0026plusmn;1.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e3.35\u0026plusmn;0.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e0.282\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e0.755\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Values for measurement data\u0026nbsp;are expressed as the means \u0026plusmn; SDs.\u003c/p\u003e\n\u003cp\u003eAbbreviations: HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; TG, triglyceride; TC: total cholesterol; BMI: body mass index; LP(a): lipoprotein (a);\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eComparison of modified Gensini scores and the proportion of patients with myocardial infarction among\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe APOE and SLCO1B1\u0026nbsp;genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere\u0026nbsp;were statistically significant differences in the Gensini scores among the ApoE gene phenotype ɛ2, ɛ3, and ɛ4 groups (F=11.591, P\u0026lt;0.001). Patients with the ɛ4 gene phenotype had the highest Gensini score (73.83\u0026plusmn;36.01), those with the ɛ3 gene phenotype had a median Gensini score (46.73\u0026plusmn;25.14), and those with the ɛ2 gene phenotype had the lowest Gensini score (31.35\u0026plusmn;20.16). The proportion of patients presenting with myocardial infarction showed a similar trend among the three groups: 11.76% vs 23.79% vs 38.89%, P=0.039. With respect to the SLCO1B1 genotype grouping, there was a trend toward higher Gensini scores in one group (51.54\u0026plusmn;29.89 vs 30.18\u0026plusmn;22.8 vs 75.00), but this difference did not reach statistical significance (P=0.056). There was no significant difference in the incidence of myocardial infarction among the three genotype groups (\u0026chi;2=1.108, P=0.575). See Table 4 and Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e: Comparison of Gensini scores and the proportion of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAMI\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;among\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe APOE and SLCO1B1\u0026nbsp;genes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"537\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003eɛ2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003eɛ3\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003eɛ4\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003eF/\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003eP\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003eGensini Score\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e31.35\u0026plusmn;20.16\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e46.73\u0026plusmn;25.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e73.83\u0026plusmn;36.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e11.591\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e＜0.001\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003eAMI(n,%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e2(11.76)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e15(23.79)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e7(34.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e6.49\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e0.039\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003eS-normal\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003eS-mid\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003eS-slow\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003eF/\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003eP\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003eGensini Score\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e51.54\u0026plusmn;29.89\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e30.18\u0026plusmn;22.85\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e75\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e2.983\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e0.056\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 92px;\"\u003eAMI(n,%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e24(30.38)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e2(18.18)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e0(0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e1.108\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e0.575\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: AMI: Acute myocardial infarction; S-nor: normal OATP1B1 functional group, including genotypes 1a/1a, 1a/1b, and 1b/1b; S-mid: intermediate OATP1B1 functional group, including genotypes 1a/5, 1a/15, and 1b/15; S-low: low OATP1B1 functional group, including genotypes 5/5, 5/15, and 15/15.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eUnivariate and multivariate linear regression analysis of independent predictors of the modified Gensini score for coronary artery lesions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA linear regression analysis was conducted to identify independent predictors of the modified Gensini score for coronary artery lesions. The results revealed that LDL-C levels, LP(a) levels, and APOE genotype were significantly associated with the score. After adjustment, only LDL-C level (regression coefficient 9.063, 95% confidence interval 2.612\u0026ndash;15.541, p = 0.006) and APOE genotype (regression coefficient 14.265, 95% confidence interval 4.692\u0026ndash;23.900, p = 0.004) remained statistically significant predictors of the modified Gensini score, as shown in Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e: Univariate and multivariate linear regression analysis of independent predictors of the modified Gensini score for coronary artery lesions.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 38px;\"\u003e\u003cstrong\u003eUnadjusted values\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 42px;\"\u003e\u003cstrong\u003eadjusted\u003c/strong\u003e\u0026nbsp; \u003cstrong\u003evalues\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003ep Value\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003eOR\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003ep Value\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003eOR\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e95% CI\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003eLP(a)\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e0.057\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e0.022-0.093\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e0.172\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e0.024\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e-0.011-0.060\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003eLDL-C\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e0.000\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e12.426\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e6.229-18.622\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e0.006\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e9.063\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e2.612-15.514\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003eHDL-C\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e0.541\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e-6.499\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e-27.55-14.553\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e0.177\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e-13.011\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e-31.993-5.972\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003eAPOE allele\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e0.000\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e21.343\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e12.332-30.355\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e0.004\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e14.265\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e4.692-23.900\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003eSLCO1B1 allele\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e0.145\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e-12.011\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e-28.246-4.224\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e0.101\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e-11.791\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e-25.945-2.363\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Values for measurement data\u0026nbsp;are expressed as the means \u0026plusmn; SDs.\u003c/p\u003e\n\u003cp\u003eAbbreviations: HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; TG, triglyceride; TC: total cholesterol; BMI: body mass index; LP(a): lipoprotein (a);\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 ROC curve model\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efor\u0026nbsp;the Gensini score and\u0026nbsp;incidence of myocardial infarction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the predictive performance of the model, ROC curve analysis was performed, which evaluated the model\u0026apos;s specificity and sensitivity. The area under the curve (AUC)\u0026nbsp;was 0.793 (\u0026gt;0.5), and the 95% confidence interval was 0.696\u0026ndash;0.890.\u0026nbsp;indicating acceptable discrimination for predicting the modified Gensini score on\u0026nbsp;the basis of LDL-C and APOE.\u003c/p\u003e\n\u003cp\u003eROC curve analysis revealed good predictive performance, with an area under the curve (AUC) of 0.855 (\u0026gt;0.5) and a 95% confidence interval of 0.774\u0026ndash;0.936; these findings demonstrate the clinical significance of the model in predicting myocardial infarction on the basis of LDL-C level, Lp(a) level, and APOE. See Figure 2.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Key findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; In this study, patients with the APOE gene phenotype E4 presented the highest levels of LDL-C and LP (a). The Gensini score was used as the outcome measure. In the group with a medium Gensini score (GSmid), the proportion of the ε3 allele was the highest (50%). The group with a high Gensini score (GShigh) had the highest proportion of the ε4 allele (66.7%) and the lowest proportion of the ε2 allele (5.9%). Conversely, the group with a low Gensini score (GSlow) presented the highest proportion of the ε2 allele (52.9%) and the lowest proportion of the ε4 allele (11.1%). The distribution of alleles among the three groups was statistically significant (p \u0026lt; 0.05). The Gensini score was highest in patients with the ɛ4 genotype (73.83 ± 36.01), intermediate in those with the ɛ3 genotype (46.73 ± 25.14), and lowest in patients with the ɛ2 genotype (31.35 ± 20.16). These findings indicate that the severity of coronary artery lesions varies according to APOE genotype. Moreover, APOE can be used as an independent risk factor for the Gensini score to predict the extent of coronary artery lesions, which is consistent with the findings of previous studies. There was no significant difference among the three groups when SLCO1B1 genotype distributions were compared with Gensini scores (p = 0.056).\u003c/p\u003e\n\u003cp\u003eFurthermore, subgroup analysis revealed a statistically significant difference in the distribution of the three APOE genotypes among patients with myocardial infarction (p \u0026lt; 0.05). Multivariate analysis revealed that APOE gene polymorphisms and LDL-C levels were independent risk factors for the severity of coronary artery lesions and the occurrence of myocardial infarction.\u003c/p\u003e\n\u003cp\u003eThe prediction model showed excellent predictive performance (area under the curve\u0026nbsp;(AUC):\u0026nbsp;0.793 for the Gensini score prediction model and 0.855 for the myocardial infarction prediction model).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to a detailed and realistic analysis of the relationships between APOE and SLCO1B1 gene polymorphisms and the severity of coronary artery lesions, our study was particularly important because we innovatively analyzed the associations between these gene polymorphisms, LP(a) levels, and myocardial infarction.\u0026nbsp;In addition, we constructed a clinical risk scoring model based on specific indicators, such as APOE genotype information and LDL-C, and the model results suggested high prediction performance.\u0026nbsp;However, the study\u0026nbsp;has\u0026nbsp;limitations due to\u0026nbsp;its\u0026nbsp;small sample size and lack of long-term follow-up data. Future research directions include expanding the sample size and conducting multicenter collaborations.\u0026nbsp;Moreover, it remains necessary to verify the\u0026nbsp;relationships among\u0026nbsp;gene polymorphisms, blood lipid levels, and coronary artery lesions in combination with basic experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Comparison with similar research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCoronary heart disease is a common chronic disease worldwide. In recent years, the incidence of coronary heart disease and myocardial infarction has gradually increased, becoming one of the main causes of death\u003csup\u003e[16]\u003c/sup\u003e. In fact, there is no consensus on the correlation between APOE gene polymorphisms and the severity of coronary artery lesions. Bennett et al. reported an approximately linear relationship between APOE genotype, LDL level, and CAD risk\u003csup\u003e[17]\u003c/sup\u003e. It has been suggested that E2 carriers have a\u0026nbsp;20%\u0026nbsp;lower risk of CAD\u0026nbsp;than do\u0026nbsp;individuals with the E3 genotype,\u0026nbsp;whereas\u0026nbsp;E4 carriers have a slightly higher risk of CAD\u0026nbsp;\u003csup\u003e[18, 19]\u003c/sup\u003e. Other scholars have suggested that APOE gene polymorphisms are not related to the severity of coronary artery lesions\u0026nbsp;or\u0026nbsp;to the occurrence of myocardial infarction\u0026nbsp;\u003csup\u003e[20]\u003c/sup\u003e. The pathogenesis of\u0026nbsp;CHD\u0026nbsp;is complex\u0026nbsp;and involves\u0026nbsp;interactions among genes, genetic factors, and environmental factors, which may explain the differing conclusions of these studies. In our study, the Gensini score was highest in patients with the ɛ4 genotype (73.83 ± 36.01), intermediate in those with the ɛ3 genotype (46.73 ± 25.14), and lowest in patients with the ɛ2 genotype (31.35 ± 20.16).\u0026nbsp;These findings indicate\u0026nbsp;that the severity of coronary artery lesions varies according to APOE\u0026nbsp;genotype. Moreover, APOE can be used as an independent risk factor for the Gensini score to predict the extent of coronary artery lesions, which is consistent with\u0026nbsp;the findings of\u0026nbsp;previous studies. There was no significant difference among the three groups when SLCO1B1 genotype distributions\u0026nbsp;were compared\u0026nbsp;with Gensini scores (p = 0.056). Ghassibe-Sabbagh M et al. reported that the rs4149056 variant in SLCO1B1 was positively associated with hyperhomocysteinemia and might increase the risk of\u0026nbsp;CHD\u0026nbsp;\u003csup\u003e[21]\u003c/sup\u003e; however, their study was limited by a small sample size and lacked basic experimental or animal evidence. Similarly, FF Hao et al.\u0026nbsp;reported\u0026nbsp;no correlation between SLCO1B1 c.388A\u0026gt;G and c.521T\u0026gt;C gene polymorphisms and the incidence of\u0026nbsp;CHD\u0026nbsp;in the Yunnan Bai population\u0026nbsp;\u003csup\u003e[22]\u003c/sup\u003e, which aligns with the findings of the present study.\u003c/p\u003e\n\u003cp\u003eThis study is the first systematic investigation of the associations between the occurrence of coronary artery lesions and myocardial infarction and the polymorphisms of the APOE and SLCO1B1 genes in the human population in East China. It also innovatively analyzes the correlation between Lp(a) and the ApoE gene. We are aware of a number of studies suggesting that ApoE gene polymorphisms modulate LDL-C-mediated atherosclerosis\u0026nbsp;\u003csup\u003e[23]\u003c/sup\u003e;\u0026nbsp;however, few studies have focused\u0026nbsp;on the impact of Lp(a), which is a new research hotspot in blood lipids. Lp(a) is a specialized lipoprotein that is strongly associated with cardiovascular disease and calcified aortic stenosis. Pathophysiological, epidemiological, and genetic studies have shown that elevated plasma Lp(a) levels, regardless of the LDL-C level, independently increase the incidence of cardiovascular events\u0026nbsp;\u003csup\u003e[24, 25]\u003c/sup\u003e. Our study confirmed that Lp(a) was affected by ApoE gene polymorphisms and that the Lp(a) levels of ε4 carriers were significantly\u0026nbsp;greater\u0026nbsp;(293.89 ± 209.79 nmol/L)\u0026nbsp;than those of\u0026nbsp;ε3 carriers, who\u0026nbsp;presented\u0026nbsp;intermediate\u0026nbsp;Lp(a) levels\u0026nbsp;(181.30 ± 157.42 nmol/L), and\u0026nbsp;those of\u0026nbsp;ε2 carriers, who\u0026nbsp;presented\u0026nbsp;the lowest\u0026nbsp;Lp(a)\u0026nbsp;levels (116.82 ± 88.05 nmol/L), p=0.005. Gilliland TC et al. suggested that ApoE gene polymorphisms were the second most important gene for LP(a) levels\u003csup\u003e[25]\u003c/sup\u003e, which further confirmed the validity of our findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Explanations of findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The ε3 allele has been defined as a neutral gene that maintains the normal physiological activities of the body, whereas ε2 and ε4 are considered variant alleles. Among these alleles, ε2 tends to be a protective allele that can reduce the risk of CHD, whereas ε4 is regarded as the main pathogenic allele associated with CHD\u0026nbsp;\u003csup\u003e[26]\u003c/sup\u003e. Many studies have shown that coronary artery lesions result from the interaction of various internal genetic factors and external environmental factors. Atherosclerosis is the fundamental pathological change, and dyslipidemia, as the primary risk factor for atherosclerosis, plays a crucial role in the occurrence, development, and prognosis of coronary artery lesions\u0026nbsp;\u003csup\u003e[27, 28]\u003c/sup\u003e.\u0026nbsp;The\u0026nbsp;ApoE gene has been shown to regulate lipid metabolism in the body through multiple pathways; it significantly influences blood lipid levels and is a susceptibility gene for\u0026nbsp;hyperlipidemia\u0026nbsp;and atherosclerotic vascular diseases (including coronary heart disease, cerebral infarction,\u0026nbsp;and\u0026nbsp;peripheral vascular disease)\u0026nbsp;\u003csup\u003e[12, 29, 30]\u003c/sup\u003e. Consequently, it has become a research hotspot in the clinical management and prognosis evaluation of\u0026nbsp;CHD. The human ApoE gene is located on chromosome 19 (19q13.2) and exhibits significant genetic polymorphisms; its expression varies among different regions and ethnic groups\u003csup\u003e[31]\u003c/sup\u003e. Two\u0026nbsp;nonsynonymous\u0026nbsp;single nucleotide polymorphisms, rs429358 (388T\u0026gt;C) and rs7412 (526C\u0026gt;T), together form three alleles—ε2, ε3, and ε4. These polymorphisms cause differences in amino acid sequence and protein conformation, resulting in varying affinities of the three ApoE isoforms for their receptors, which ultimately lead to different ApoE alleles playing distinct roles in lipid metabolism and cardiovascular diseases\u0026nbsp;\u003csup\u003e[32]\u003c/sup\u003e. APOE allele variants are known to explain up to\u0026nbsp;7% of interindividual\u0026nbsp;differences in LDL-C and total cholesterol levels, and dyslipidemia can be partially attributed to genetic factors\u0026nbsp;\u003csup\u003e[33]\u003c/sup\u003e. In our study, we found that patients with the APOE ε4 genotype had\u0026nbsp;increased\u0026nbsp;serum LDL-C levels, suggesting that the APOE gene is an important factor in the exacerbation of coronary artery disease, as it affects serum lipid levels. Furthermore, APOE may increase the risk of coronary artery disease through its interaction with antioxidants and the immune\u0026nbsp;system\u003csup\u003e[34]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5 Implications and actions needed\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy revealing potential genetic risk factors, these findings provide a theoretical foundation for the development of personalized treatment and prevention strategies for cardiovascular diseases. Research needs to focus on basic studies on the local expression and function of APOE in blood vessel wall cells (endothelial cells, smooth muscle cells, and macrophages). Additionally, how genotypes affect the function and activity of these cells during the process of atherosclerosis should be investigated. From a clinical perspective, advanced imaging techniques such as coronary CT, IVUS, and OCT are being used to conduct thorough investigations of the differences in morphology, composition, and functional characteristics of coronary plaques among patients with different genotypes. The ultimate goal is to explore new intervention strategies targeting the APOE pathway, such as APOE mimetic peptides and/or proteins, gene therapy, and other therapeutic approaches.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, this study revealed a correlation between APOE and SLCO1B1 gene polymorphisms and variations in blood lipid levels, coronary artery lesions, and myocardial infarction, highlighting the important role of genetic factors in the development of cardiovascular diseases. These findings provide a theoretical basis for the development of individualized treatment and prevention strategies for cardiovascular diseases by revealing the underlying genetic risk factors. By linking APOE gene polymorphisms to aberrant serum lipid and inflammation profiles, we found that individuals carrying the ε4 allele exhibit dysregulated lipid metabolism and abnormal inflammatory markers. This dysregulation increases the risk of cardiovascular disease (CVD) and acute myocardial infarction (AMI)\u0026nbsp;\u003csup\u003e[35, 36]\u003c/sup\u003e. Therefore, early detection and timely diagnosis are essential for implementing therapeutic, dietary, and lifestyle interventions to reduce risk and prevent or delay diseases associated with lipids and inflammation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eSupplementary Appendix\u003c/p\u003e\n\u003cp\u003eAPOE:\u0026nbsp;apolipoprotein E;\u0026nbsp;SLCO1B1:\u0026nbsp;solute carrier organic anion transporter family member 1B1;\u0026nbsp;HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol; TG, triglycerides; TC, total cholesterol; BMI:\u0026nbsp;body mass index; LP(a): lipoprotein (a); HbA1C: glycosylated hemoglobin; Hs-cTNI: high-sensitivity cardiac troponin I; LA: left atrial diameter; LVEDD: left ventricular end-diastolic diameter.\u0026nbsp;AMI:\u0026nbsp;Acute myocardial infarction;\u0026nbsp;CAD:\u0026nbsp;coronary artery disease;\u0026nbsp;OATP1B1:\u0026nbsp;organic anion transporting polypeptide 1B1;\u0026nbsp;HbA1c:\u0026nbsp;glycated hemoglobin; CI:\u0026nbsp;confidence interval;\u0026nbsp;OR:\u0026nbsp;odds\u0026nbsp;ratio;\u0026nbsp;CVD:\u0026nbsp;cardiovascular disease;\u0026nbsp;AMI:\u0026nbsp;acute myocardial infarction;\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate:\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003eThe authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved\u003c/em\u003e\u003cem\u003e.\u003c/em\u003eThis study adhered to the ethical standards\u0026nbsp;of the updated version of the Declaration of Helsinki (1964) and was approved by the Human Ethics Committee of the Second Affiliated Hospital of Anhui Medical University (Approval No: YX2024--115). All patients signed informed consent forms.\u003c/h4\u003e\n\u003ch4\u003eConsent for publication\u003c/h4\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch4\u003eAvailability of data and materials\u003c/h4\u003e\n\u003ch4\u003eThe datasets used and analyzed during the current study are available from the corresponding author\u0026nbsp;upon reasonable request.It is noted that the nature of the data for this study are clinical data.\u003c/h4\u003e\n\u003ch4\u003eCompeting interests\u003c/h4\u003e\n\u003cp\u003ehe authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Anhui Provincial Scientific Research Preparation Plan Project. Title: METTL3/IGF2BP2 regulate the stability of Atp6v0d2 mRNA in atherosclerotic macrophage polarization; ID: 2022AH050770\u003c/p\u003e\n\u003ch4\u003eAuthors' contributions\u003c/h4\u003e\n\u003cp\u003eMLL\u0026nbsp;designed the research study and\u0026nbsp;wrote the original drafts; XY\u0026nbsp;and JNZ provided help and advice on conceptual execution. ZW and ZH collected the data and performed the statistical analysis. ZJZ wrote and revised the manuscript\u0026nbsp;and participated in supervision. All the authors contributed to editorial changes in the manuscript. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved its submission. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChamaria S, Ueyama H, Yasumura K, et al. Coronary plaque vulnerability in statin-treated patients with elevated LDL-C and hs-CRP: optical coherence tomography study. The International Journal of Cardiovascular Imaging 2022;38(5):1157\u0026ndash;1167.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFarina FM, Serio S, Hall IF, et al. The epigenetic enzyme DOT1L orchestrates vascular smooth muscle cell\u0026ndash;monocyte crosstalk and protects against atherosclerosis via the NF-κB pathway. European Heart Journal 2022;43(43):4562\u0026ndash;4576.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu D, Xie L, Cheng C, Xue F, Sun C. Triglyceride-rich lipoproteins and cardiovascular diseases. Frontiers in Endocrinology 2024;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaggo SD, Kennedy MA, Clark DW. Clinical implications of pharmacogenetic variation on the effects of statins. Drug Saf 2011;34(1):1\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJofre Monseny L, Minihane AM, Rimbach G. Impact of apoE genotype on oxidative stress, inflammation and disease risk. Molecular Nutrition \u0026amp; Food Research 2008;52(1):131\u0026ndash;145.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePostmus I, Trompet S, Deshmukh HA, et al. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins. Nature Communications 2014;5(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScholz M, Horn K, Pott J, et al. Genome-wide meta-analysis of phytosterols reveals five novel loci and a detrimental effect on coronary atherosclerosis. Nature Communications 2022;13(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu M, Lv C, Wang H, et al. Peanut skin extract ameliorates high-fat diet-induced atherosclerosis by regulating lipid metabolism, inflammation reaction and gut microbiota in ApoE(-/-) mice. Food Res Int 2022;154:111014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiemi M, Pasanen MK, Neuvonen PJ. Organic anion transporting polypeptide 1B1: a genetically polymorphic transporter of major importance for hepatic drug uptake. Pharmacol Rev 2011;63(1):157\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang S, Wang L, Li H, et al. Correlation analysis of plasma lipid profiles and the prognosis of head and neck squamous cell carcinoma. Oral Diseases 2024;30(2):329\u0026ndash;341.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, Gong Y, Peng J, et al. Therapeutic evaluation of rosuvastatin on lipids and endothelial cell functionalities in coronary artery lesions coinciding with hyperlipidemia. American journal of translational research 2023;15(5):3152\u0026ndash;3161.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Yang S, Zhang S, Lu X, Ma W. Apolipoprotein E Gene Polymorphism Effects on Lipid Metabolism and Risk of Cerebral Infarction in Northwest Han Chinese Population. Pharmgenomics Pers Med 2023;16:303\u0026ndash;312.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl Hageh C, Chacar S, Ghassibe-Sabbagh M, et al. Elevated Lp(a) Levels Correlate with Severe and Multiple Coronary Artery Stenotic Lesions. Vascular health and risk management 2023;19:31\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarjalainen JP, Mononen N, Hutri-Kahonen N, et al. New evidence from plasma ceramides links apoE polymorphism to greater risk of coronary artery disease in Finnish adults. J Lipid Res 2019;60(9):1622\u0026ndash;1629.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang ZH, Yue Sun LC, Gu HY, Jiang DC, Yi ZM. Associations BetweenSLCO1B1,APOE and CYP2C9 and Lipid-Lowering Efficacy and Pharmacokinetics of Fluvastatin: A Meta-Analysis. Pharmacogenomics 2023;24(8):475\u0026ndash;484.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenjamin EJ, Blaha MJ, Chiuve SE, et al. Heart Disease and Stroke Statistics\u0026mdash;2017 Update: A Report From the American Heart Association. Circulation (New York, N.Y.) 2017;135(10):e146-e603.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBennet AM, Di Angelantonio E, Ye Z, et al. Association of Apolipoprotein E Genotypes With Lipid Levels and Coronary Risk. JAMA: the journal of the American Medical Association 2007;298(11):1300\u0026ndash;1311.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzuynuk AS, Erkan AF, Dogan N, et al. Examining the effects of the CLU and APOE polymorphisms' combination on coronary artery disease complexed with type 2 diabetes mellitus. Journal of diabetes and its complications 2022;36(1):108078\u0026ndash;108078.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYousuf FA, Iqbal MP. Review: Apolipoprotein E (Apo E) gene polymorphism and coronary heart disease in Asian populations. Pakistan journal of pharmaceutical sciences 2015;28(4):1439.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLarifla L, Armand C, Bangou J, et al. Association of APOE gene polymorphism with lipid profile and coronary artery disease in Afro-Caribbeans. PLOS ONE 2017;12(7):e0181620.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl HC, Alefishat E, Ghassibe-Sabbagh M, et al. Homocysteine levels, H-Hypertension, and the MTHFR C677T genotypes: A complex interaction. Heliyon 2023;9(6):e16444.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Q, Wu H, Yu Z, Huang Q, Zhong Z. APOE gene varepsilon4 allele (388C-526C) effects on serum lipids and risk of coronary artery disease in southern Chinese Hakka population. J Clin Lab Anal 2021;35(9):e23925.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBea AM, Larrea-Sebal A, Marco-Benedi V, et al. Contribution ofAPOE Genetic Variants to Dyslipidemia. Arteriosclerosis, Thrombosis, and Vascular Biology 2023;43(6):1066\u0026ndash;1077.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVolgman AS, Koschinsky ML, Mehta A, Rosenson RS. Genetics and Pathophysiological Mechanisms of Lipoprotein(a)-Associated Cardiovascular Risk. Journal of the American Heart Association 2024;13(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGilliland TC, Liu Y, Mohebi R, et al. Lipoprotein(a), Oxidized Phospholipids, and Coronary Artery Disease Severity and Outcomes. J Am Coll Cardiol 2023;81(18):1780\u0026ndash;1792.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLong Y, Zhao X, Liu C, et al. A Case\u0026ndash;Control Study of the Association of the Polymorphisms of \u003cem\u003eMTHFR\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e with Risk Factors and the Severity of Coronary Artery Disease. Cardiology 2019;142(3):149\u0026ndash;157.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaddox TM, Stanislawski MA, Grunwald GK, et al. Nonobstructive Coronary Artery Disease and Risk of Myocardial Infarction. JAMA 2014;312(17):1754.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElliott J, Bodinier B, Bond TA, et al. Predictive Accuracy of a Polygenic Risk Score\u0026ndash;Enhanced Prediction Model vs a Clinical Risk Score for Coronary Artery Disease. JAMA 2020;323(7):636.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLong L, Sun Q. Analysis of APOE and SLCO1B1 Gene Polymorphism and Correlation with Dyslipidemia in China. Clin Lab 2022;68(11).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlagarsamy J, Jaeschke A, Hui DY. Apolipoprotein E in Cardiometabolic and Neurological Health and Diseases. International Journal of Molecular Sciences 2022;23(17):9892.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiu R, Guo L, Dong X, et al. Analysis of the Difference inSLCO1B1 andAPOE Gene Polymorphisms Between Mongolian and Han Populations. Pharmacogenomics 2022;23(14):783\u0026ndash;790.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOria RB, de Almeida JZ, Moreira CN, Guerrant RL, Figueiredo JR. Apolipoprotein E Effects on Mammalian Ovarian Steroidogenesis and Human Fertility. Trends Endocrinol Metab 2020;31(11):872\u0026ndash;883.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEichner JE DSPG, Aje D. Apolipoprotein E polymorphism and cardiovascular disease: a HuGE review. Am J Epidemiol:2002;155(6):487\u0026ndash;95.doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/155.6.487\u003c/span\u003e\u003cspan address=\"10.1093/aje/155.6.487\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu ZW BZRQ. Oxi-inflamm-aging and its association with the polymorphism of ApoE genes. Sheng Li Xue Bao:2013;65(3):338\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLICASTRO F, CHIAPELLI M, CALDARERA CM, CARUSO C, LIO D, CORDER EH. Acute Myocardial Infarction and Proinflammatory Gene Variants. Annals of the New York Academy of Sciences 2007;1119(1):227\u0026ndash;242.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRanjith N, Pegoraro RJ, Rom L. Lipid Profiles and Associated Gene Polymorphisms in Young Asian Indian Patients With Acute Myocardial Infarction and the Metabolic Syndrome. Metabolic Syndrome and Related Disorders 2009;7(6):571\u0026ndash;578.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"artery-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Artery Research](https://arteryresearch.biomedcentral.com/)","snPcode":"44200","submissionUrl":"https://submission.springernature.com/new-submission/44200/3","title":"Artery Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"APOE, Gensini scores, Lipoprotein(a) [LP(a)], Low-density lipoprotein cholesterol (LDL-C), Myocardial infarction","lastPublishedDoi":"10.21203/rs.3.rs-7094595/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7094595/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e This study aimed to investigate the relationships between the expression of apolipoprotein E (APOE) and gene polymorphisms of solute carrier organic anion transporter family member 1B1 (SLCO1B1) with the blood lipid profile and coronary artery disease severity in Han Chinese individuals living in eastern China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study enrolled 91 patients hospitalized at the Second Affiliated Hospital of Anhui Medical University from June 2024 to December 2024. The serum lipid profiles, including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and lipoprotein(a) [LP(a)], were measured via the Olympus AU5400 system. The genotypes of the APOE and SLCO1B1 genes were determined by Sanger sequencing. The participantswere stratified into three groups on the basis of their Gensini scores. Differences in blood lipid levels and APOE/SLCO1B1 genotype distributions among these groups were statistically analyzed. The Gensini score model and myocardial infarction risk model were subsequently constructed via APOE genotyping, LDL-C levels, and other differential biomarkers identified from the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Patients with the ApoE ε4 genotype had higher LDL-C and Lp(a) levels and higher Gensini scores (F=11.591, P\u0026lt;0.001), indicating more severe coronary artery lesions than the other groups did. Multiple linear regression analysis revealed both LDL-C levels and ApoE genotypes as independent predictors of the severity of coronary artery lesions, whereas SLCO1B1 genotype had a minor effect on lipid levels and coronary artery lesion severity. Notably, this study specifically analyzed the impact of ApoE polymorphisms on the incidence of myocardial infarction and reported a statistically significant difference in the incidence of myocardial infarction among different ApoE genotypes (χ²=6.49, P=0.039). The prediction model showed excellent predictive performance (area under the curve (AUC): 0.793 in the Gensini score prediction model and AUC: 0.855 in the myocardial infarction prediction model).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eAPOE genotype is associated with the concentrations of LDL-C and Lp(a) as well as the severity of coronary artery lesions and the occurrence of myocardial infarction.\u003c/p\u003e","manuscriptTitle":"Impact of the ApoE Genotype on Coronary Artery Disease and the Incidence of Myocardial Infarction: A Clinical Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 06:41:26","doi":"10.21203/rs.3.rs-7094595/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"5915082568055955087706993326174645844","date":"2025-10-23T00:12:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-21T20:00:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179883681094497777856732785478055737282","date":"2025-10-21T03:22:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270323156138584903735612457392417324306","date":"2025-08-16T13:20:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T13:16:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-01T17:13:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-01T17:12:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Artery Research","date":"2025-07-10T15:28:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"artery-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Artery Research](https://arteryresearch.biomedcentral.com/)","snPcode":"44200","submissionUrl":"https://submission.springernature.com/new-submission/44200/3","title":"Artery Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"88c082e4-f4c1-4b9c-8825-39218b5bd4e4","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T11:46:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-20 06:41:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7094595","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7094595","identity":"rs-7094595","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}