The development of a coronary heart disease risk prediction model based on non-invasive myocardial work parameters and lipid metabolism indicators

The development of a coronary heart disease risk prediction model based on non-invasive myocardial work parameters and lipid metabolism indicators | 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 The development of a coronary heart disease risk prediction model based on non-invasive myocardial work parameters and lipid metabolism indicators Shiwen Feng, Meinxin Huang, Chengcai Chen, Tengfang Lai, Shenghao Fu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7778328/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Objective： The aim is to develop a new risk prediction model for coronary heart disease (CHD) by utilizing Non-invasive Myocardial Work（MW）parameters and lipid metabolism indicators, so as to identify and manage populations at high risk of CHD at an early stage. Methods： Patients with suspected CHD who were admitted to the Affiliated Hospital of Youjiang Medical University for Nationalities from October 2024 to June 2025 and scheduled to undergo Coronary Angiography (CAG) were prospectively collected. They were divided into the CHD group and the non-CHD group according to the CAG results. Logistic regression was used to identify factors related to CHD, and R-Studio was employed to construct a nomogram model for predicting CHD based on independent predictors. Results ：Pearson correlation analysis and multivariate logistic regression analysis showed that the Atherogenic Index of Plasma (AIP), Global Work Index (GWI), Global Wasted Work (GWW), and Left Ventricular Posterior Wall （LVPW）at End - Diastole were independent risk factors associated with CHD.Based on the results of multivariate logistic regression, the CHD risk prediction model constructed using R-Studio showed an average AUC of 0.836 and a C-index of 0.847 in the validation set, indicating that the model can well distinguish between high-risk and low-risk populations for CHD. After calibration, the Mean Absolute Error (MAE) of the calibration curve was 0.017, which further verified the robustness and reliability of its discriminative efficacy. Decision curve analysis showed that when the threshold probability was in the range of 10%-95%, the model had a relatively high clinical net benefit value. Conclusion： This study established a CHD risk prediction model based on non-invasive myocardial work parameters and lipid metabolism indicators, which can effectively identify high-risk populations of CHD. It provides a non-invasive, portable and accurate CHD risk assessment tool for most medical institutions in China, assists clinicians in making correct clinical decisions, and thus improves the prognostic effect. Coronary Heart Disease Risk prediction Non-invasive Myocardial Work Lipid metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction CHD is a chronic immune-inflammatory and fibroproliferative disease driven by lipids. When blood lipids remain at a high level, they can activate the serum complement system in the body, producing factors that are harmful to vascular endothelial cells, damaging vascular barrier function, and thereby causing lipid deposition[ 1 ].CHD is a major threat to global health. Therefore, how to early identify people at high risk of CHD for timely and effective intervention has become an urgent problem to be solved[ 2 ]. Previous studies have shown that inflammatory markers such as High-sensitivity C-reactive protein (hs-CRP) and interleukins, as well as traditional blood lipid indicators like High-Density Lipoprotein Cholesterol (HDL-C) and Low-Density Lipoprotein Cholesterol (LDL-C), can be used for early screening and risk prediction of CHD, but their sensitivity is still insufficient.[ 3 ]In recent years, multiple studies have indicated [ 3 ] that the AIP calculated using Triglyceride (TG) and HDL-C, is an independent risk factor for atherosclerotic cardiovascular diseases. Its predictive value is superior to that of individual traditional blood lipid indicators, reflecting the balance between atherogenic and anti-atherogenic processes in the body. In terms of medical imaging, traditional echocardiographic parameters such as Left Ventricle Ejection Fraction (LVEF) have insufficient sensitivity to detect abnormal myocardial activity in patients with early-stage CHD, and abnormalities only appear when myocardial damage is relatively severe. Although Two-dimensional Speckle Tracking can detect myocardial dysfunction relatively well, it is susceptible to the influence of load, leading to errors in the results. As a new technology for evaluating myocardial function, MW technology breaks through traditional limitations. By integrating myocardial longitudinal strain and blood pressure data, it can sensitively detect myocardial metabolic abnormalities in patients with coronary artery stenosis ≥ 50% at an early stage, provide quantitative assessment of left ventricular function, and offer a new perspective for early screening[ 4 , 5 ]. In China, in the early screening for CHD in the outpatient departments of most medical institutions, most outpatient doctors usually assess the risk of early CHD based on patients' symptoms, medical history, electrocardiogram, relevant serological indicators, and echocardiography. For experienced outpatient doctors, it is not difficult to assess people at high risk of CHD through the above methods. However, for doctors with limited clinical experience, it is sometimes difficult to accurately assess the risk of CHD in suspected patients only through the above indicators. If the incidence of CHD in the patient is underestimated and the patient is not admitted to the hospital for further diagnosis and treatment in a timely manner, the opportunity for early intervention may be missed, leading the patient to develop acute coronary syndrome. If the risk of CHD in the patient is overestimated and the patient is admitted to the hospital blindly, it will inevitably result in the waste of medical resources and increase the economic burden on the patient. To address the above-mentioned clinical dilemmas, various clinical scoring systems for CHD are often used as screening tools in outpatient settings. Currently, pre-test probability models related to CHD include the Diamond and Forrester models, among others[ 6 ]. However, these risk assessment tools were developed for Western populations and have certain limitations in terms of universal applicability, making them not entirely suitable for Asian populations. Therefore, there is an urgent need in current clinical practice for a CHD risk assessment tool that is applicable to Asian populations, particularly the Chinese population. Moreover, this tool should assist clinicians in quickly and effectively identifying individuals at high risk of CHD, enabling timely and standardized management of such populations. In light of the above, this study aims to construct a functional-metabolic dual-dimensional nomogram model capable of early predicting the risk of CHD by utilizing MW parameters and lipid metabolism indicators. By quantifying the synergistic diagnostic value of both for CHD, it is intended to provide a more comprehensive non-invasive prediction tool for clinical practice. 2. Materials and Methods 2.1 General Information A total of 129 suspected CHD patients (80 in the CHD group and 49 in the non-CHD group) who were scheduled to undergo CAG examination in our hospital from October 2024 to June 2025 were included. All patients were informed of the study in advance and signed the informed consent form. The diagnosis of CHD was made in accordance with the relevant diagnostic criteria: left main stem diameter stenosis ≥ 50%, or stenosis ≥ 70% in at least one of the left anterior descending artery, left circumflex artery, or right coronary artery with ischemic symptoms, or branch vessel stenosis ≥ 90%. The diagnostic criteria were in line with those specified in the 《2024 ESC Guidelines for the management of chronic coronary syndromes 》[ 7 ]. Inclusion criteria:① Echocardiography and relevant laboratory tests were performed within 3 days before CAG;② Echocardiography showed no obvious segmental wall motion abnormalities;③ The condition was comprehensively analyzed, and combined with CAG, the diagnosis was clearly confirmed as CHD or non-CHD, with complete medical records. Exclusion criteria: ① Patients with congenital heart disease, myocardial infarction, heart failure, chronic pulmonary heart disease, as well as those with severe valvular disease, cardiomyopathy, or mitral regurgitation of moderate degree or above; ② Patients with peripheral vascular diseases where the brachial artery cannot truly reflect central arterial pressure, etc.; ③ Patients with poor image quality that cannot clearly distinguish the endocardium; ④ Patients who have taken drugs including statins that may affect Total Cholesterol (TC), TG, HDL-C, etc. before the visit; those with complicated infectious diseases; severe cardiopulmonary insufficiency; hepatic or renal insufficiency; malignant tumors; connective tissue diseases; ⑤ Patients who have previously undergone Percutaneous Coronary Intervention (PCI) or coronary artery bypass grafting. This study was approved by the Ethics Committee of the Affiliated Hospital of Youjiang Medical University for Nationalities (Approval No.: YYFY-2024-248). 2.2.Measure Collection of patients' general data The general data collected include age, gender, Body Mass Index (BMI), and blood pressure (including Systolic Blood Pressure [SBP] and Diastolic Blood Pressure [DBP]). Collection of patients' serological index tests Fasting peripheral venous blood was collected from the cubital vein in the early morning, and blood biochemical indicators were detected using a fully automatic biochemical analyzer, including: Fasting Plasma Glucose (FPG), TC, TG, HDL-C, and LDL-C. Additionally, the AIP and Triglyceride-Glucose (TyG) were calculated, where AIP = log(TG/HDL-C) and TyG = ln (TG [mg/dL] × FPG [mg/dL] / 2). Collection of echocardiographic parameters A GE Vivid E95 color ultrasound diagnostic instrument with an M5S linear array probe (probe frequency: 1.5–4.6 MHz, frame rate: 70–80 frames/s) was used, equipped with an Echo PAC (Version 203) workstation for offline image analysis and processing. Patients underwent echocardiography to collect images within 3 days before CAG. First, connect the surface electrocardiogram and measure the brachial artery blood pressure of the left upper limb using the cuff method. The patient was placed in the left lateral decubitus position, and conventional echocardiographic parameters were collected under the state of quiet breathing, including standard parasternal left ventricular long-axis view and apical four-chamber view, to obtain Left Atrial diameter (LA), Left Ventricular End Diastolic Dimension (LVEDD), LVEF, Interventricular Septal Thickness at diastole (IVST), LVPW, and Epicardial Adipose Tissue (EAT). The patient was instructed to hold their breath at the end of expiration, and dynamic images of standard apical four-chamber, three-chamber, and two-chamber views were collected for 3–5 cardiac cycles. All images were standard views with clear display of endocardial boundaries. The collected dynamic images were imported into the Echo PAC (Version 203) workstation for offline analysis and processing. According to the prompts, the region of interest was created by automatically tracking and manually correcting the traced endocardial boundaries. The software displayed the results obtained from automatic tracking, and the Global Longitudinal Strain (GLS) value of the left ventricle was acquired. Entering the myocardial work mode, the brachial artery cuff pressure was inputted to obtain the left ventricular pressure-strain loop, and the GWI, GCW, GWE and GWW were directly derived. CAG The patient was placed in a supine position. Routine disinfection was performed, and a sterile drape was applied. The right radial artery was used as the puncture path. After local anesthesia of the puncture site with 1% lidocaine hydrochloride, the right radial artery was punctured using the Seldinger technique, and a sheath was inserted. Unfractionated heparin was injected into the sheath. A 5F Radial TIG multi-purpose angiography catheter was used to perform conventional angiography of the left and right coronary arteries. The coronary artery conditions were evaluated by two senior interventional physicians. CHD was diagnosed if there was ≥ 50% diameter stenosis of the left main stem, or ≥ 70% stenosis of at least one of the left anterior descending artery, left circumflex artery, or right coronary artery with ischemic symptoms, or ≥ 90% stenosis of branch vessels. 2.3.Data Analysis Data were processed using SPSS 24.0 software. Measurement data conforming to a normal distribution were expressed as mean ± standard deviation (x ± s), and comparisons between groups were performed using independent sample t-test; those not conforming to a normal distribution were expressed as median (interquartile range). Comparisons of counting data were conducted using Fisher's exact test and Pearson's chi-square test. To avoid the impact of excessive correlation between variables on the reliability of results, Pearson correlation analysis was used to assess bivariate correlations. Univariate and multivariate Logistic regression analyses were applied to identify independent risk factors for the occurrence of CHD. Based on the independent risk factors identified by logistic analysis, a CHD risk prediction nomogram model was constructed using R-Studio. To evaluate the discriminative ability of the model, a five-fold cross-validation method was used to plot the ROC curve. For calibration, the study plotted the calibration curve using the Bootstrap method (with 1000 repeated samplings). Finally, Decision Curve Analysis (DCA) was employed to evaluate the clinical benefit of the model. 3. Result 3.1.Comparison of general data and echocardiographic parameters . A total of 129 subjects were included in this study, including 80 cases in the CHD group and 49 cases in the non-CHD group. Through comparative analysis of clinical data and ultrasonic parameters ( Tables 1 and 2 ), there were statistically significant differences in LVEF, IVST, GLS, GWI, GCW, GWW, GWE, LVPW, EAT, HDL-C, and AIP between the two groups (P < 0.05). Compared with the non-CHD group, the CHD group had higher levels of LVEF, GLS, GWI, GCW, GWE, and AIP, while lower levels of IVST, GWW, LVPW, EAT, and HDL-C. Table 1 . Comparison of patients' clinical data Characteristic CHD （n=80) non-CHD （n=35) p-value age[M(P25,P75),year] 54(50,57) 53(48,56.5) 0.464 gender 0.604 Male 51(63.8） 29（36.3） Female 29(59.2) 20(40.8) SBP/（mmhg） 133.48±17.64 137.53±15.54 0.188 DBP[M(P25,P75),mmhg] 76.5(70,86) 78（70,86） 0.961 BMI[M(P25,P75)] 23.98(22.60,26.80) 24.78（21.81,27.63） 0.793 LDL-C/（mmol/L) 2.25±0.79 2.28±0.87 0.377 HDL -C [M(P25,P75),mmol/L] 1.13(0.88,1.32) 1.36(1.13,1.57) <0.001 FBG[M(P25,P75),%] 5.25(4.83,5.89) 5.27(4.91,5.87) 0.806 TG[[M(P25,P75),mmol/L] 1.76(1.42,2.14) 1.81(1.18,2.12) 0.301 TC[M(P25,P75),mmol/L] 4.40(3.53,4.92) 4.56(3.87,5.35) 0.188 AIP[M(P25,P75)] 0.18（0.10,0.37） 0.10（-0.01,0.22） 0.003 TyG[M(P25,P75)] 8.88（8.68,9.32） 8.97(8.47,9.15) 0.536 SBP Systolic Blood Pressure，DBP diastolic blood pressure， BMI body mass index， LDL-C Low Density Lipoprotein Cholesterol ， HDL -C High-Density Lipoprotein Cholesterol， FBG fasting plasma glucose， TG Triglyceride，TC total cho-lesterol，AIP Atherogenic Index of Plasma， TyG triglyceride-glucose. Table 2. Comparison of patients' ultrasonic parameters Characteristic CHD （n=80) non-CHD （n=35) p-value LVEF/(mm) 61.38±5.82 63.59±6.11 0.042 LVEDD[M(P25,P75),mm] 48(46,51) 47(45.5,50) 0.184 IVST/(mm) 10.69±1.74 9.82±1.52 0.005 LVPW/[M(P25,P75)，mm] 9.9(9.2,10.8) 9.10(8.75,9.95) <0.001 LA[M(P25,P75),mm] 29.65(27.63,31.60) 30(28.35,33) 0.261 EAT[M(P25,P75),mm] 5.12(4.39,6.35) 4.45(3.99,5.31) 0.018 GLS[M(P25,P75),%] 16.50(14.50,17.88) 18.5（16,19.75） <0.001 GWI[M(P25,P75),mmHg%] 1620(1482.25,1855.25) 1940（1721.75,2193.5） <0.001 GCW[M(P25,P75),mmHg%] 1984.75(1800.63,2188) 2344（2092.5,2606.75) <0.001 GWW[M(P25,P75),mmHg%] 209.50(155.25,263.38) 141(96.75,200) <0.001 GWE[M(P25,P75),mmHg%] 91.50(88.00,93.00) 90.5(93,94) 0.002 LVEF L eft V entricle E jection F raction ， LVEDD Left Ventricular End Diastolic Dimension， IVST interventricular septal thickness at diastole， LVPW Left Ventricular Posterior Wall， LA left atrialdiameter， EAT epicardial adipose tissue， GLS global longitudinal strain，GWI global work index，GCW global constructive work，GWE global work efficiency，GWW global waste work. 3.2.Screening of risk factors for CHD To avoid including redundant variables in the final model, this study conducted a correlation analysis based on the results of univariate analysis. As shown in Table 3 , variable pairs with |r| ≥ 0.7 were screened out: GWI-GCW and GWW-GWE. One of them was randomly retained and the other was eliminated, with GWI and GWW being retained. After eliminating redundant variables, the remaining variables were LVEF, IVST, GLS, GWI, GWW, LVPW, EAT, AIP, and HDL-C. The correlation coefficient matrix of the remaining variables was recalculated to ensure that |r| < 0.7 for all variables in the Table 4 . Finally, the variance inflation factor (VIF) of the remaining variables was calculated using multicollinearity test, and the VIF of all variables was < 5, as shown in Table 5 . Based on the results of univariate analysis, Pearson correlation analysis, and combined with professional expertise, multivariate Logistic regression analysis was performed with factors showing statistically significant differences between groups as independent variables (AIP, GWI, GWW, LVPW) and the occurrence of CHD as the dependent variable. The results showed that AIP, GWI, GWW, and LVPW were independent risk factors for coronary artery stenosis in patients (P < 0.1) ( Table 6 ). AIP[OR 10.312,95%CI(0.747~142.397）,p=0.082], GWI[OR 0.997,95%CI(0.997~0.995）,p=0.002], GWW[OR 1.006,95%CI(1.006~1.013）,p=0.051], LVPW[OR 2.398,95%CI(1.303~4.415）,p=0.005]. Table 3. Correlation analysis LVEF IVST GLS GWI GCW GWW GWE LVPW EAT HDL-C AIP LVEF 1 IVST -.211* 1 GLS .174* -0.162 1 GWI .222* 0.001 .633** 1 GCW .223* -0.069 .665** .875** 1 GWW -0.027 0.098 -.484** -.315** -.209* 1 GWE 0.074 -0.095 .613** .498** .432** -.917** 1 LVPW -.193* .687** -0.113 0.063 -0.019 0.031 0.022 1 EAT -0.118 .344** -.209* -.196* -.267** .195* -.237** .178* 1 HDL-C 0.138 0.118 .204* .240** 0.153 -.224* .207* 0.111 0.008 1 AIP 0.005 -0.076 -0.133 -.189* -.204* 0.104 -0.082 0.013 0.122 -.648** 1 *. Correlation is significant at the 0.05 level (two-tailed). **. Correlation is significant at the 0.01 level (two-tailed). LVEF Left Ventricle Ejection Fraction，IVST interventricular septal thickness at diastole，GLS global longitudinal strain，GWI global work index，GCW global constructive work，GWE global work efficiency，GWW global waste work，LVPW Left Ventricular Posterior Wall，EAT epicardial adipose tissue，HDL-C High-Density Lipoprotein Cholesterol，AIP Atherogenic Index of Plasma. |r| < 0.3 indicates a weak correlation; 0.3 ≤ |r| < 0.7 indicates a moderate correlation; |r| ≥ 0.7 indicates a strong correlation. Table 4 . Correlation analysis of remaining variables after removing redundant variables LVEF IVST GLS GWI GWW LVPW EAT AIP HDL-C LVEF 1 IVST -.211* 1 GLS .174* -.162 1 GWI .222* .001 .633** 1 GWW -.027 .098 -.484** -.315** 1 LVPW -.193* .687** -.113 .063 .031 1 EAT -.118 .344** -.209* -.196* .195* .178* 1 AIP .005 -.076 -.133 -.189* .104 .013 .122 1 HDL .138 .118 .204* .240** -.224* .111 .008 -.648** 1 *. Correlation is significant at the 0.05 level (two-tailed). **. Correlation is significant at the 0.01 level (two-tailed). LVEF Left Ventricle Ejection Fraction，IVST interventricular septal thickness at diastole，GLS global longitudinal strain，GWI global work index，GWW global waste work，LVPW Left Ventricular Posterior Wall，EAT epicardial adipose tissue，HDL-C High-Density Lipoprotein Cholesterol，AIP Atherogenic Index of Plasma. |r| < 0.3 indicates a weak correlation; 0.3 ≤ |r| < 0.7 indicates a moderate correlation; |r| ≥ 0.7 indicates a strong correlation. Table 5 . Multicollinearity test Variable VIF 1-VIF LVEF 1.175 0.851 IVST 2.191 0.456 GLS 2.053 0.487 GWI 1.843 0.543 GWW 1.381 0.724 LVPW 2.026 0.494 EAT 1.262 0.792 HDL 2.008 0.498 AIP 1.907 0.524 LVEF Left Ventricle Ejection Fraction，IVST interventricular septal thickness at diastole，GLS global longitudinal strain，GWI global work index，GWW global waste work，LVPW Left Ventricular Posterior Wall，EAT epicardial adipose tissue，HDL-C High-Density Lipoprotein Cholesterol，AIP Atherogenic Index of Plasma. VIF = 1 indicates that there is no multicollinearity among variables; 1 < VIF < 5 suggests that there is a certain degree of multicollinearity, but it usually does not have a serious impact on the regression results; 5 ≤ VIF < 10 shows that there is a moderate degree of multicollinearity, which requires attention. VIF ≥ 10 means that there is severe multicollinearity, which will have a significant impact on the estimation and interpretation of regression coefficients, and measures need to be taken to deal with it. Table 6 . Multivariate logistic regression analysis of influencing factors for CHD Characteristic β SE WaldX² OR 95%CI p-value AIP 2.333 1.339 3.034 10.312 0.747~142.397 0.082 GWI -0.003 0.001 9.704 0.997 0.997~0.995 0.002 GWW 0.006 0.003 3.812 1.006 1.006~1.013 0.051 LVPW 0.875 0.311 7.892 2.398 1.303~4.415 0.005 AIP Atherogenic Index of Plasma，GWI global work index，GWW global waste work，LVPW Left Ventricular Posterior Wall. 3.3 Construction of the CHD Risk Prediction Nomogram Model Based on the four independent risk factors (GWI, GWW, LVPW, and AIP), a CHD risk prediction nomogram model was constructed using R-Studio. As shown in Figure 1 , the variables included in this nomogram model are GWI, GWW, LVPW, and AIP. The impact of each variable on the occurrence of CHD is reflected in their respective row lengths and corresponding scores. The total score of the model is obtained by summing the scores of each variable, with a higher score indicating a greater contribution to the risk of CHD. GWI represents the overall systolic capacity of the myocardium; the lower the value, the higher the base score on the Points axis, corresponding to a higher risk of CHD. GWW represents the energy consumed by the myocardium; the higher the value, the higher the base score on the Points axis, corresponding to a higher risk of CHD. LVPW is the thickness of the left ventricular wall at the end of diastole; the higher the value, the higher the base score on the Points axis, corresponding to a higher risk of CHD. The higher the AIP value, the higher the base score on the Points axis, corresponding to a higher risk of CHD, suggesting that abnormal lipid metabolism can exacerbate the risk of CHD. For predictive assessment, sum the base scores on the Points axis corresponding to the above risk factors, find the corresponding total score on the Total Points axis after summation, and finally refer to the "The risk of CHD" axis to read the predicted risk of CHD. 3.4 Evaluation of Clinical Performance and Clinical Applicability of the CHD Risk Prediction Nomogram Model To evaluate the discriminative ability of the model, a five-fold cross-validation method was used to plot the ROC curve ( Figure 2 ). The results showed that the model had an average AUC of 0.836 in the validation set, with a 95% CI of 0.759–0.825 and a C-index of 0.864, indicating that the model has good discriminative ability. After determining the optimal cut-off value based on the Youden index, the model had an average sensitivity of 0.838 and specificity of 0.776, which further confirmed the reliability of its classification performance. In terms of calibration, the study plotted a calibration curve ( Figure 3 ) using the Bootstrap method (with 1000 repeated samplings) and calculated a Mean Absolute Error (MAE) of 0.017, indicating a good fit between the model's predicted probabilities and the actual observed rates. In addition, the results of the Hosmer-Lemeshow goodness-of-fit test were X² = 8.492, df = 8, p-value = 0.387, which reached statistical non-significance, suggesting that the model has a good calibration degree with no obvious overfitting. To evaluate the clinical application value of this model under different risk thresholds, a further DCA was performed ( Figure 4 ). The results showed that when the risk threshold range was between 0.05and 0.8, the net benefit brought by the CHD risk prediction nomogram model was higher than that of the "treat-all" and "treat-none" strategies. This indicates that the model has good clinical practicability within a relatively wide range of risk thresholds and can provide an effective auxiliary decision-making basis for the early intervention and management of high-risk populations with CHD. In summary, the CHD risk prediction nomogram model constructed in this study has good discriminative ability, calibration consistency and clinical decision-making value, and thus has certain potential for clinical promotion. 4. Discussion This study is the first to introduce AIP and non-invasive myocardial work parameters into the risk prediction model for CHD. By adding new indicators, the predictive performance of the model for CHD has been improved, enabling a more comprehensive and accurate assessment. The nomogram risk prediction model finally constructed in this study makes the prediction and analysis of clinical events more visual and graphical[ 8 ].All parameters in this model are collectible and easy-to-operate indicators. Their extensive implementation at the primary level is conducive to carrying out disease risk stratification with large sample sizes, avoiding the omission of high-risk groups, and saving unnecessary expenses. The DCA curve analysis in this study also proves the practicability and effectiveness of the model. The model conforms to the "lipid-vessel-myocardium" cascade injury chain theory in the pathophysiological mechanism of coronary heart disease, further indicating that lipid metabolism disorder can drive the development of atherosclerosis[ 9 ]. The model developed in this study is simple to use, accurate and reliable, and features visualization. It can be used for CHD risk stratification in populations still at moderate to high risk. Compared with the simplicity of traditional prediction models, such as relying solely on serological indicators or imaging methods, the prediction model in this study overcomes the limitation of restricted universality, thus meeting the needs of large-scale population screening at the primary level. According to the data from the Global Burden of Disease Study 2021, ischemic heart disease, especially CHD, remains the leading risk factor for death and disease burden worldwide.[ 10 ]Moreover, the continuous increase in the number of CHD patients and the disease burden is mainly caused by metabolic factors such as hypertension, hyperglycemia, high BMI, and LDL-C.[ 11 – 15 ]Previous studies have shown that age, gender, blood pressure, BMI, and age are risk factors for CHD, but they cannot accurately assess the risk level of CHD. TG is the most abundant lipid in human adipose tissue. High levels of TG can lead to lipotoxicity, thereby causing the occurrence and progression of inflammation[ 16 ].HDL-C contains hundreds of lipids and proteins, which are known to perform antioxidant and anti-inflammatory functions in the regulation of metabolic diseases, including diabetes[ 17 ]. An elevated TG/HDL-C ratio has been shown to be associated with an increased risk of cardiovascular events related to cardiometabolic disorders. Particularly in middle- and low-risk populations with stable angina pectoris and no known CHD, a higher TG/HDL-C ratio has been confirmed as an independent predictor of coronary atherosclerotic events and progression[ 18 , 19 ].This finding is consistent with the predictive value of AIP[ 20 ]. AIP combines TG and high-density lipoprotein cholesterol (HDL-C) levels. It not only reflects the ratio of TG to HDL-C but also represents the size of lipoprotein particles. Compared with high TG levels or low HDL-C levels, AIP can more accurately reflect the pathogenicity and specificity of dyslipidemia[ 21 , 22 ]. A subsequent meta-analysis further confirmed that an elevated AIP is a potential prognostic marker for adverse cardiovascular events in patients with CAD, and that increased levels of AIP are consistently associated with an elevated risk of adverse events[ 23 ].Subsequent meta-analyses have further confirmed that an elevated AIP is a potential prognostic marker for adverse cardiovascular events in patients with CAD, and that increased AIP levels are consistently associated with an elevated risk of adverse events. The results of this study support the findings of the aforementioned meta-analyses: the CHD group not only showed significantly lower HDL-C levels than the non-CHD group, but also had a significant elevation in AIP. More importantly, this study confirmed that a significant elevation in AIP is an independent predictor of CHD, which is consistent with previous research conclusions[ 24 ].In summary, AIP can sensitively identify lipid metabolism disorders in the early stage, and its elevation is significantly associated with an increased risk of adverse cardiovascular events. This indirectly indicates the core role of lipoprotein phenotypic imbalance in driving the progression of atherosclerosis. Early non-invasive quantification of myocardial function and structure in high-risk populations with CHD is a major clinical challenge. Previous studies have shown that there is a correlation between myocardial mechanical stress and the severity of coronary artery stenosis in CHD patients with preserved LVEF[ 25 ].Echocardiographic parameter LVPW and two-dimensional speckle tracking parameters have been confirmed as good indicators for identifying abnormal myocardial activity in CHD patients, and our study is consistent with the above-mentioned studies[ 26 ].However, two-dimensional speckle tracking technology is susceptible to afterload, which may lead to errors in the detection results. Compared with other echocardiographic parameters (including LVEF and two-dimensional speckle tracking), non-invasive myocardial work parameters can capture structural remodeling and impairment during myocardial ischemia earlier[ 27 – 29 ].Our research results show that the LVEF and GLS in the CHD group were not significantly lower than those in the non-CHD group, but the myocardial work performance was impaired.A previous study indicated that GWW and GWE are helpful for identifying the systolic function of the heart in patients with coronary artery stenosis and also reflect myocardial oxygen consumption [ 30 , 31 ]. The results of our study are consistent with this conclusion, and the global left ventricular myocardial work parameters may have good clinical significance for screening suspected coronary heart disease. Existing studies have shown that the CHD risk assessment system developed by Diamond and Forrester is based on age, gender, and some of the most basic clinical indicators [ 6 ]. However, this prediction system has certain limitations: it may overestimate the risk of CHD onset and fail to incorporate some important influencing factors. Moreover, the process of obtaining parameters for these two traditional models is cumbersome and poorly operable, making them unavailable for popularization in the majority of outpatient and emergency departments in China, let alone in primary hospitals. Meanwhile, on the basis of domestic and foreign studies, this study is the first to introduce non-invasive myocardial function indicators and AIP into the prediction model, which has greatly improved the prediction accuracy and completeness of the model. In addition, after DCA analysis, the new model in this study can clearly demonstrate its applicability in clinical practice. The nomogram model constructed in this study includes a clinically practical risk scoring system. The parameters of this model are easily accessible, and it has a wide range of applications, which can be used in general hospitals and primary medical and health institutions. This convenient and fast prediction tool can help clinicians make more accurate preventive decisions, control the source of diseases, reduce the burden on the medical system, and at the same time address the current deficiencies in the universality and preventiveness of diagnostic systems. However, this study has certain limitations. Firstly, the study samples were only obtained from a single-center regional hospital, with a limited sample size, and there was insufficient comparative verification with a wider range of medical institutions and a broader clinical population. Secondly, since the model only used the Bootstrap method for internal validation and did not adopt the verification method of multi-center external cohorts, the effect of clinical generalization still needs further exploration. 5. Conclusions The nomogram model established in this study, which integrates indicators related to myocardial function and lipid metabolism, is a convenient and effective tool that can be used for early non-invasive screening of CHD, with an AUC value of 0.836. Whether applied in large hospitals or small community health care centers, this model can accurately predict the risk of high-risk patient groups and help clinicians make accurate judgments on patients' conditions, thereby optimizing clinical decisions and improving patient prognosis. Declarations Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Funding 1)Project approval of Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project (Project No. S2024057);2)2025 Guangxi Graduate Education Innovation Program Project (Project No. YCSW2025605). Author Contribution SWF and MXH: Writing, reviewing, editing, and validation. CCC,TFL: Writing, reviewing, editing, and funding acquisition. YMH and KH: Data collection and analysis. SHF and LTW: Writing, reviewing, and editing. Data Availability Data are contained within the article. References Araujo G, Valencia L M, Martin-Ozimek A, et al. Atherosclerosis: from lipid-lowering and anti-inflammatory therapies to targeting arterial retention of ApoB-containing lipoproteins[J]. Front Immunol, 2025,16:1485801.DOI: 10.3389/fimmu.2025.1485801 . Mensah G A, Fuster V, Murray C, et al. Global Burden of Cardiovascular Diseases and Risks, 1990–2022[J]. J Am Coll Cardiol, 2023,82(25):2350–2473.DOI: 10.1016/j.jacc.2023.11.007 . Huang Q, Liu Z, Wei M, et al. The atherogenic index of plasma and carotid atherosclerosis in a community population: a population-based cohort study in China[J]. Cardiovasc Diabetol, 2023,22(1):125.DOI: 10.1186/s12933-023-01839-y . Edwards N, Scalia G M, Shiino K, et al. Global Myocardial Work Is Superior to Global Longitudinal Strain to Predict Significant Coronary Artery Disease in Patients With Normal Left Ventricular Function and Wall Motion[J]. J Am Soc Echocardiogr, 2019,32(8):947–957.DOI: 10.1016/j.echo.2019.02.014 . Smiseth O A, Donal E, Penicka M, et al. How to measure left ventricular myocardial work by pressure-strain loops[J]. Eur Heart J Cardiovasc Imaging, 2021,22(3):259–261.DOI: 10.1093/ehjci/jeaa301 . Diamond G A, Forrester J S. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease[J]. N Engl J Med, 1979,300(24):1350–1358.DOI: 10.1056/NEJM197906143002402 . Vrints C, Andreotti F, Koskinas K C, et al. 2024 ESC Guidelines for the management of chronic coronary syndromes[J]. Eur Heart J, 2024,45(36):3415–3537.DOI: 10.1093/eurheartj/ehae177 . Deo R, Dubin R F, Ren Y, et al. Proteomic cardiovascular risk assessment in chronic kidney disease[J]. Eur Heart J, 2023,44(23):2095–2110.DOI: 10.1093/eurheartj/ehad115 . Libby P. The changing landscape of atherosclerosis[J]. Nature, 2021,592(7855):524–533.DOI: 10.1038/s41586-021-03392-8 . Wang Y, Li Q, Bi L, et al. Global trends in the burden of ischemic heart disease based on the global burden of disease study 2021: the role of metabolic risk factors[J]. BMC Public Health, 2025,25(1):310.DOI: 10.1186/s12889-025-21588-9 . Wan A W, Rosman A, Bavanandan S, et al. Current Insights on Dyslipidaemia Management for Prevention of Atherosclerotic Cardiovascular Disease: A Malaysian Perspective[J]. Malays J Med Sci, 2023,30(1):67–81.DOI: 10.21315/mjms2023.30.1.6 . Tian J, Dong Y, Xu Z, et al. Association between triglyceride glucose-body mass index and 365-day mortality in patients with critical coronary heart disease[J]. Front Endocrinol (Lausanne), 2025,16:1513898.DOI: 10.3389/fendo.2025.1513898 . Gerdts E, Regitz-Zagrosek V. Sex differences in cardiometabolic disorders[J]. Nat Med, 2019,25(11):1657–1666.DOI: 10.1038/s41591-019-0643-8 . Ananth C V, Rutherford C, Rosenfeld E B, et al. Epidemiologic trends and risk factors associated with the decline in mortality from coronary heart disease in the United States, 1990–2019[J]. Am Heart J, 2023,263:46–55.DOI: 10.1016/j.ahj.2023.05.006 . Zhong D, Cheng H. Application of Mendelian randomization in the discovery of risk factors for coronary heart disease from 2009 to 2023: A bibliometric review[J]. Clin Cardiol, 2024,47(1):e24154.DOI: 10.1002/clc.24154 . Manell H, Kristinsson H, Kullberg J, et al. Hyperglucagonemia in youth is associated with high plasma free fatty acids, visceral adiposity, and impaired glucose tolerance[J]. Pediatr Diabetes, 2019,20(7):880–891.DOI: 10.1111/pedi.12890 . Di Bartolo B A, Cartland S P, Genner S, et al. HDL Improves Cholesterol and Glucose Homeostasis and Reduces Atherosclerosis in Diabetes-Associated Atherosclerosis[J]. J Diabetes Res, 2021,2021:6668506.DOI: 10.1155/2021/6668506 . Caselli C, De Caterina R, Smit J M, et al. Triglycerides and low HDL cholesterol predict coronary heart disease risk in patients with stable angina[J]. Sci Rep, 2021,11(1):20714.DOI: 10.1038/s41598-021-00020-3 . Luo J, Wang J K, Song B L. Lowering low-density lipoprotein cholesterol: from mechanisms to therapies[J]. Life Metab, 2022,1(1):25–38.DOI: 10.1093/lifemeta/loac004 . Cai G, Shi G, Xue S, et al. The atherogenic index of plasma is a strong and independent predictor for coronary artery disease in the Chinese Han population[J]. Medicine (Baltimore), 2017,96(37):e8058.DOI: 10.1097/MD.0000000000008058 . Fernández-Macías J C, Ochoa-Martínez A C, Varela-Silva J A, et al. Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses[J]. Arch Med Res, 2019,50(5):285–294.DOI: 10.1016/j.arcmed.2019.08.009 . Yin B, Wu Z, Xia Y, et al. Non-linear association of atherogenic index of plasma with insulin resistance and type 2 diabetes: a cross-sectional study[J]. Cardiovasc Diabetol, 2023,22(1):157.DOI: 10.1186/s12933-023-01886-5 . Rabiee R M, Ghasempour D G, Darouei B, et al. The association of atherogenic index of plasma with cardiovascular outcomes in patients with coronary artery disease: A systematic review and meta-analysis[J]. Cardiovasc Diabetol, 2024,23(1):119.DOI: 10.1186/s12933-024-02198-y . Mahdavi-Roshan M, Mozafarihashjin M, Shoaibinobarian N, et al. Evaluating the use of novel atherogenicity indices and insulin resistance surrogate markers in predicting the risk of coronary artery disease: a case–control investigation with comparison to traditional biomarkers[J]. Lipids Health Dis, 2022,21(1):126.DOI: 10.1186/s12944-022-01732-9 . Luis S A, Yamada A, Khandheria B K, et al. Use of three-dimensional speckle-tracking echocardiography for quantitative assessment of global left ventricular function: a comparative study to three-dimensional echocardiography[J]. J Am Soc Echocardiogr, 2014,27(3):285–291.DOI: 10.1016/j.echo.2013.11.002 . Uusitalo V, Luotolahti M, Pietilä M, et al. Two-Dimensional Speckle-Tracking during Dobutamine Stress Echocardiography in the Detection of Myocardial Ischemia in Patients with Suspected Coronary Artery Disease[J]. J Am Soc Echocardiogr, 2016,29(5):470–479.DOI: 10.1016/j.echo.2015.12.013 . Boe E, Skulstad H, Smiseth O A. Myocardial work by echocardiography: a novel method ready for clinical testing[J]. Eur Heart J Cardiovasc Imaging, 2019,20(1):18–20.DOI: 10.1093/ehjci/jey156 . Manganaro R, Marchetta S, Dulgheru R, et al. Correlation between non-invasive myocardial work indices and main parameters of systolic and diastolic function: results from the EACVI NORRE study[J]. Eur Heart J Cardiovasc Imaging, 2020,21(5):533–541.DOI: 10.1093/ehjci/jez203 . Li M, Wang Y, Li L, et al. Global myocardial work in coronary artery disease patients without regional wall motion abnormality: Correlation with Gensini-score[J]. Clin Cardiol, 2024,47(2):e24193.DOI: 10.1002/clc.24193 . Lin J, Wu W, Gao L, et al. Global Myocardial Work Combined with Treadmill Exercise Stress to Detect Significant Coronary Artery Disease[J]. J Am Soc Echocardiogr, 2022,35(3):247–257.DOI: 10.1016/j.echo.2021.10.009 . Zhao Y, He F, Guo W, et al. The clinical value of noninvasive left ventricular myocardial work in the diagnosis of myocardial ischemia in coronary heart disease: a comparative study with coronary flow reserve fraction[J]. Int J Cardiovasc Imaging, 2024,40(10):2167–2179.DOI: 10.1007/s10554-024-03208-6 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 13 Oct, 2025 Editor assigned by journal 08 Oct, 2025 Submission checks completed at journal 08 Oct, 2025 First submitted to journal 04 Oct, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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16:33:12","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142341,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7778328/v1/4002a3eee0bf33170ae0b4af.html"},{"id":94484185,"identity":"ad30da74-825e-4ce1-a33a-f89a62e519e3","added_by":"auto","created_at":"2025-10-27 16:34:52","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":263566,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram model for predicting CHD risk\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7778328/v1/2304be4977f2b8ef38b5fb18.jpeg"},{"id":94483821,"identity":"ad286854-acd5-4f95-a612-a826aba603ca","added_by":"auto","created_at":"2025-10-27 16:30:02","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":146647,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve of the nomogram model for predicting the risk of CHD\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7778328/v1/1ee2a308dcd79548bbfd5d52.jpeg"},{"id":94483944,"identity":"6ffde0f2-bbfc-44fa-9c24-10d5d0ff6fa1","added_by":"auto","created_at":"2025-10-27 16:32:16","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46076,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve for the nomogram predicting the risk of confirmed CHD in patients with suspected coronary heart disease\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7778328/v1/bd0ee81754007212579663d7.jpeg"},{"id":94484068,"identity":"fe17925f-d918-4d87-a861-141b32ac3db8","added_by":"auto","created_at":"2025-10-27 16:33:24","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":39159,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve for the nomogram predicting the risk of coronary heart disease\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7778328/v1/c04124e6525fea78642d4b66.jpeg"},{"id":94491682,"identity":"de7f3c37-e133-4d05-beb0-6e4e36790ee1","added_by":"auto","created_at":"2025-10-27 17:27:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2075815,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7778328/v1/b177747f-5cb4-4135-b468-f0d8a7ad0dd5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The development of a coronary heart disease risk prediction model based on non-invasive myocardial work parameters and lipid metabolism indicators","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCHD is a chronic immune-inflammatory and fibroproliferative disease driven by lipids. When blood lipids remain at a high level, they can activate the serum complement system in the body, producing factors that are harmful to vascular endothelial cells, damaging vascular barrier function, and thereby causing lipid deposition[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].CHD is a major threat to global health. Therefore, how to early identify people at high risk of CHD for timely and effective intervention has become an urgent problem to be solved[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies have shown that inflammatory markers such as High-sensitivity C-reactive protein (hs-CRP) and interleukins, as well as traditional blood lipid indicators like High-Density Lipoprotein Cholesterol (HDL-C) and Low-Density Lipoprotein Cholesterol (LDL-C), can be used for early screening and risk prediction of CHD, but their sensitivity is still insufficient.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]In recent years, multiple studies have indicated [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] that the AIP calculated using Triglyceride (TG) and HDL-C, is an independent risk factor for atherosclerotic cardiovascular diseases. Its predictive value is superior to that of individual traditional blood lipid indicators, reflecting the balance between atherogenic and anti-atherogenic processes in the body.\u003c/p\u003e\u003cp\u003eIn terms of medical imaging, traditional echocardiographic parameters such as Left Ventricle Ejection Fraction (LVEF) have insufficient sensitivity to detect abnormal myocardial activity in patients with early-stage CHD, and abnormalities only appear when myocardial damage is relatively severe. Although Two-dimensional Speckle Tracking can detect myocardial dysfunction relatively well, it is susceptible to the influence of load, leading to errors in the results. As a new technology for evaluating myocardial function, MW technology breaks through traditional limitations. By integrating myocardial longitudinal strain and blood pressure data, it can sensitively detect myocardial metabolic abnormalities in patients with coronary artery stenosis\u0026thinsp;\u0026ge;\u0026thinsp;50% at an early stage, provide quantitative assessment of left ventricular function, and offer a new perspective for early screening[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn China, in the early screening for CHD in the outpatient departments of most medical institutions, most outpatient doctors usually assess the risk of early CHD based on patients' symptoms, medical history, electrocardiogram, relevant serological indicators, and echocardiography. For experienced outpatient doctors, it is not difficult to assess people at high risk of CHD through the above methods. However, for doctors with limited clinical experience, it is sometimes difficult to accurately assess the risk of CHD in suspected patients only through the above indicators. If the incidence of CHD in the patient is underestimated and the patient is not admitted to the hospital for further diagnosis and treatment in a timely manner, the opportunity for early intervention may be missed, leading the patient to develop acute coronary syndrome. If the risk of CHD in the patient is overestimated and the patient is admitted to the hospital blindly, it will inevitably result in the waste of medical resources and increase the economic burden on the patient.\u003c/p\u003e\u003cp\u003eTo address the above-mentioned clinical dilemmas, various clinical scoring systems for CHD are often used as screening tools in outpatient settings. Currently, pre-test probability models related to CHD include the Diamond and Forrester models, among others[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, these risk assessment tools were developed for Western populations and have certain limitations in terms of universal applicability, making them not entirely suitable for Asian populations. Therefore, there is an urgent need in current clinical practice for a CHD risk assessment tool that is applicable to Asian populations, particularly the Chinese population. Moreover, this tool should assist clinicians in quickly and effectively identifying individuals at high risk of CHD, enabling timely and standardized management of such populations.\u003c/p\u003e\u003cp\u003eIn light of the above, this study aims to construct a functional-metabolic dual-dimensional nomogram model capable of early predicting the risk of CHD by utilizing MW parameters and lipid metabolism indicators. By quantifying the synergistic diagnostic value of both for CHD, it is intended to provide a more comprehensive non-invasive prediction tool for clinical practice.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 General Information\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA total of 129 suspected CHD patients (80 in the CHD group and 49 in the non-CHD group) who were scheduled to undergo CAG examination in our hospital from October 2024 to June 2025 were included. All patients were informed of the study in advance and signed the informed consent form. The diagnosis of CHD was made in accordance with the relevant diagnostic criteria: left main stem diameter stenosis\u0026thinsp;\u0026ge;\u0026thinsp;50%, or stenosis\u0026thinsp;\u0026ge;\u0026thinsp;70% in at least one of the left anterior descending artery, left circumflex artery, or right coronary artery with ischemic symptoms, or branch vessel stenosis\u0026thinsp;\u0026ge;\u0026thinsp;90%. The diagnostic criteria were in line with those specified in the 《2024 ESC Guidelines for the management of chronic coronary syndromes 》[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInclusion criteria:① Echocardiography and relevant laboratory tests were performed within 3 days before CAG;② Echocardiography showed no obvious segmental wall motion abnormalities;③ The condition was comprehensively analyzed, and combined with CAG, the diagnosis was clearly confirmed as CHD or non-CHD, with complete medical records.\u003c/p\u003e\u003cp\u003eExclusion criteria: ① Patients with congenital heart disease, myocardial infarction, heart failure, chronic pulmonary heart disease, as well as those with severe valvular disease, cardiomyopathy, or mitral regurgitation of moderate degree or above; ② Patients with peripheral vascular diseases where the brachial artery cannot truly reflect central arterial pressure, etc.; ③ Patients with poor image quality that cannot clearly distinguish the endocardium; ④ Patients who have taken drugs including statins that may affect Total Cholesterol (TC), TG, HDL-C, etc. before the visit; those with complicated infectious diseases; severe cardiopulmonary insufficiency; hepatic or renal insufficiency; malignant tumors; connective tissue diseases; ⑤ Patients who have previously undergone Percutaneous Coronary Intervention (PCI) or coronary artery bypass grafting.\u003c/p\u003e\u003cp\u003e This study was approved by the Ethics Committee of the Affiliated Hospital of Youjiang Medical University for Nationalities (Approval No.: YYFY-2024-248).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2.Measure\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eCollection of patients' general data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe general data collected include age, gender, Body Mass Index (BMI), and\u003c/p\u003e\u003cp\u003eblood pressure (including Systolic Blood Pressure [SBP] and Diastolic Blood\u003c/p\u003e\u003cp\u003ePressure [DBP]).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCollection of patients' serological index tests\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFasting peripheral venous blood was collected from the cubital vein in the early\u003c/p\u003e\u003cp\u003emorning, and blood biochemical indicators were detected using a fully automatic\u003c/p\u003e\u003cp\u003ebiochemical analyzer, including: Fasting Plasma Glucose (FPG), TC, TG, HDL-C,\u003c/p\u003e\u003cp\u003eand LDL-C. Additionally, the AIP and Triglyceride-Glucose (TyG) were calculated,\u003c/p\u003e\u003cp\u003ewhere AIP\u0026thinsp;=\u0026thinsp;log(TG/HDL-C) and TyG\u0026thinsp;=\u0026thinsp;ln (TG [mg/dL] \u0026times; FPG [mg/dL] / 2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCollection of echocardiographic parameters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA GE Vivid E95 color ultrasound diagnostic instrument with an M5S linear array\u003c/p\u003e\u003cp\u003eprobe (probe frequency: 1.5\u0026ndash;4.6 MHz, frame rate: 70\u0026ndash;80 frames/s) was used, equipped with an Echo PAC (Version 203) workstation for offline image analysis and processing. Patients underwent echocardiography to collect images within 3 days before CAG.\u003c/p\u003e\u003cp\u003eFirst, connect the surface electrocardiogram and measure the brachial artery blood pressure of the left upper limb using the cuff method. The patient was placed in the left lateral decubitus position, and conventional echocardiographic parameters were collected under the state of quiet breathing, including standard parasternal left ventricular long-axis view and apical four-chamber view, to obtain Left Atrial diameter (LA), Left Ventricular End Diastolic Dimension (LVEDD), LVEF, Interventricular Septal Thickness at diastole (IVST), LVPW, and Epicardial Adipose Tissue (EAT). The patient was instructed to hold their breath at the end of expiration, and dynamic images of standard apical four-chamber, three-chamber, and two-chamber views were collected for 3\u0026ndash;5 cardiac cycles. All images were standard views with clear display of endocardial boundaries.\u003c/p\u003e\u003cp\u003eThe collected dynamic images were imported into the Echo PAC (Version 203) workstation for offline analysis and processing. According to the prompts, the region of interest was created by automatically tracking and manually correcting the traced endocardial boundaries. The software displayed the results obtained from automatic tracking, and the Global Longitudinal Strain (GLS) value of the left ventricle was acquired. Entering the myocardial work mode, the brachial artery cuff pressure was inputted to obtain the left ventricular pressure-strain loop, and the GWI, GCW, GWE and GWW were directly derived.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCAG\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe patient was placed in a supine position. Routine disinfection was performed, and a sterile drape was applied. The right radial artery was used as the puncture path. After local anesthesia of the puncture site with 1% lidocaine hydrochloride, the right radial artery was punctured using the Seldinger technique, and a sheath was inserted. Unfractionated heparin was injected into the sheath. A 5F Radial TIG multi-purpose angiography catheter was used to perform conventional angiography of the left and right coronary arteries. The coronary artery conditions were evaluated by two senior interventional physicians. CHD was diagnosed if there was \u0026ge;\u0026thinsp;50% diameter stenosis of the left main stem, or \u0026ge;\u0026thinsp;70% stenosis of at least one of the left anterior descending artery, left circumflex artery, or right coronary artery with ischemic symptoms, or \u0026ge;\u0026thinsp;90% stenosis of branch vessels.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3.Data Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eData were processed using SPSS 24.0 software. Measurement data conforming to a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s), and comparisons between groups were performed using independent sample t-test; those not conforming to a normal distribution were expressed as median (interquartile range). Comparisons of counting data were conducted using Fisher's exact test and Pearson's chi-square test. To avoid the impact of excessive correlation between variables on the reliability of results, Pearson correlation analysis was used to assess bivariate correlations. Univariate and multivariate Logistic regression analyses were applied to identify independent risk factors for the occurrence of CHD. Based on the independent risk factors identified by logistic analysis, a CHD risk prediction nomogram model was constructed using R-Studio. To evaluate the discriminative ability of the model, a five-fold cross-validation method was used to plot the ROC curve. For calibration, the study plotted the calibration curve using the Bootstrap method (with 1000 repeated samplings). Finally, Decision Curve Analysis (DCA) was employed to evaluate the clinical benefit of the model.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cp\u003e\u003cstrong\u003e3.1.Comparison of general data and echocardiographic parameters\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 129 subjects were included in this study, including 80 cases in the CHD group and 49 cases in the non-CHD group. Through comparative analysis of clinical data and ultrasonic parameters (\u003cstrong\u003eTables 1 and 2\u003c/strong\u003e), there were statistically significant differences in LVEF, IVST, GLS, GWI, GCW, GWW, GWE, LVPW, EAT, HDL-C, and AIP between the two groups (P \u0026lt; 0.05). Compared with the non-CHD group, the CHD group had higher levels of LVEF, GLS, GWI, GCW, GWE, and AIP, while lower levels of IVST, GWW, LVPW, EAT, and HDL-C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003eComparison of patients\u0026apos; clinical data\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHD\u003c/strong\u003e\u003cstrong\u003e（n=80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003enon-CHD\u003c/strong\u003e\u003cstrong\u003e（n=35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eage[M(P25,P75),year]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e54(50,57)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e53(48,56.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.464\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003egender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.604\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51(63.8）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e29（36.3）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e29(59.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20(40.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP/（mmhg）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e133.48\u0026plusmn;17.64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e137.53\u0026plusmn;15.54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.188\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBP[M(P25,P75),mmhg]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e76.5(70,86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e78（70,86）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.961\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI[M(P25,P75)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23.98(22.60,26.80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24.78（21.81,27.63）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.793\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C/（mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.25\u0026plusmn;0.79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.28\u0026plusmn;0.87\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.377\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL\u003c/strong\u003e\u003cstrong\u003e-C\u003c/strong\u003e\u003cstrong\u003e[M(P25,P75),mmol/L]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.13(0.88,1.32)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.36(1.13,1.57)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFBG[M(P25,P75),%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.25(4.83,5.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.27(4.91,5.87)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.806\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG[[M(P25,P75),mmol/L]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.76(1.42,2.14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.81(1.18,2.12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.301\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC[M(P25,P75),mmol/L]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.40(3.53,4.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.56(3.87,5.35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.188\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIP[M(P25,P75)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.18（0.10,0.37）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.10（-0.01,0.22）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3.33919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.0404%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTyG[M(P25,P75)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.6591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.88（8.68,9.32）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8471%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.97(8.47,9.15)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.2478%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.536\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.86643%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003eSBP\u003c/sup\u003e\u003csup\u003e\u0026nbsp;Systolic Blood Pressure，DBP diastolic blood pressure，\u003c/sup\u003e\u003csup\u003eBMI\u003c/sup\u003e\u003csup\u003e\u0026nbsp;body mass index，\u003c/sup\u003e\u003csup\u003eLDL-C\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003eLow Density Lipoprotein Cholesterol\u003c/sup\u003e\u003csup\u003e，\u003c/sup\u003e\u003csup\u003eHDL\u003c/sup\u003e\u003csup\u003e-C High-Density Lipoprotein Cholesterol，\u003c/sup\u003e\u003csup\u003eFBG\u003c/sup\u003e\u003csup\u003e\u0026nbsp;fasting plasma glucose，\u003c/sup\u003e\u003csup\u003eTG\u003c/sup\u003e\u003csup\u003e\u0026nbsp;Triglyceride，TC total cho-lesterol，AIP Atherogenic Index of Plasma，\u003c/sup\u003e\u003csup\u003eTyG\u003c/sup\u003e\u003csup\u003e\u0026nbsp;triglyceride-glucose.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Comparison of patients\u0026apos; ultrasonic parameters\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"t\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHD\u003c/strong\u003e\u003cstrong\u003e（n=80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003enon-CHD\u003c/strong\u003e\u003cstrong\u003e（n=35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF/(mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e61.38\u0026plusmn;5.82\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e63.59\u0026plusmn;6.11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEDD[M(P25,P75),mm]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e48(46,51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e47(45.5,50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.184\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIVST/(mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.69\u0026plusmn;1.74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.82\u0026plusmn;1.52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVPW/[M(P25,P75)，mm]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.9(9.2,10.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.10(8.75,9.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLA[M(P25,P75),mm]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e29.65(27.63,31.60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30(28.35,33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.261\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAT[M(P25,P75),mm]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.12(4.39,6.35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.45(3.99,5.31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLS[M(P25,P75),%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e16.50(14.50,17.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e18.5（16,19.75）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWI[M(P25,P75),mmHg%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1620(1482.25,1855.25)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1940（1721.75,2193.5）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCW[M(P25,P75),mmHg%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1984.75(1800.63,2188)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2344（2092.5,2606.75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWW[M(P25,P75),mmHg%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e209.50(155.25,263.38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e141(96.75,200)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.9155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWE[M(P25,P75),mmHg%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.4085%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e91.50(88.00,93.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.007%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e90.5(93,94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.0352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2.8169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003eLVEF\u003c/sup\u003e\u003csup\u003e\u0026nbsp;L\u003c/sup\u003e\u003csup\u003eeft\u0026nbsp;\u003c/sup\u003e\u003csup\u003eV\u003c/sup\u003e\u003csup\u003eentricle\u0026nbsp;\u003c/sup\u003e\u003csup\u003eE\u003c/sup\u003e\u003csup\u003ejection\u0026nbsp;\u003c/sup\u003e\u003csup\u003eF\u003c/sup\u003e\u003csup\u003eraction\u003c/sup\u003e\u003csup\u003e，\u003c/sup\u003e\u003csup\u003eLVEDD\u003c/sup\u003e\u003csup\u003e\u0026nbsp;Left Ventricular End Diastolic Dimension，\u003c/sup\u003e\u003csup\u003eIVST\u003c/sup\u003e\u003csup\u003e\u0026nbsp;interventricular septal thickness at diastole，\u003c/sup\u003e\u003csup\u003eLVPW\u003c/sup\u003e\u003csup\u003e\u0026nbsp; Left Ventricular Posterior Wall，\u003c/sup\u003e\u003csup\u003eLA\u003c/sup\u003e\u003csup\u003e\u0026nbsp;left atrialdiameter，\u003c/sup\u003e\u003csup\u003eEAT\u003c/sup\u003e\u003csup\u003e\u0026nbsp;epicardial adipose tissue，\u003c/sup\u003e\u003csup\u003eGLS\u003c/sup\u003e\u003csup\u003e\u0026nbsp;global longitudinal strain，GWI global work index，GCW global constructive work，GWE global work efficiency，GWW global waste work.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.Screening of risk factors for CHD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo avoid including redundant variables in the final model, this study conducted a correlation analysis based on the results of univariate analysis. As shown in \u003cstrong\u003eTable 3\u003c/strong\u003e, variable pairs with |r| \u0026ge; 0.7 were screened out: GWI-GCW and GWW-GWE. One of them was randomly retained and the other was eliminated, with GWI and GWW being retained. After eliminating redundant variables, the remaining variables were LVEF, IVST, GLS, GWI, GWW, LVPW, EAT, AIP, and HDL-C. The correlation coefficient matrix of the remaining variables was recalculated to ensure that |r| \u0026lt; 0.7 for all variables in the \u003cstrong\u003eTable 4\u003c/strong\u003e. Finally, the variance inflation factor (VIF) of the remaining variables was calculated using multicollinearity test, and the VIF of all variables was \u0026lt; 5, as shown in \u003cstrong\u003eTable 5\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eBased on the results of univariate analysis, Pearson correlation analysis, and combined with professional expertise, multivariate Logistic regression analysis was performed with factors showing statistically significant differences between groups as independent variables (AIP, GWI, GWW, LVPW) and the occurrence of CHD as the dependent variable. The results showed that AIP, GWI, GWW, and LVPW were independent risk factors for coronary artery stenosis in patients (P \u0026lt; 0.1) (\u003cstrong\u003eTable 6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAIP[OR 10.312,95%CI(0.747~142.397）,p=0.082],\u003c/p\u003e\n\u003cp\u003eGWI[OR 0.997,95%CI(0.997~0.995）,p=0.002],\u003c/p\u003e\n\u003cp\u003eGWW[OR 1.006,95%CI(1.006~1.013）,p=0.051],\u003c/p\u003e\n\u003cp\u003eLVPW[OR 2.398,95%CI(1.303~4.415）,p=0.005].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;3.\u003c/strong\u003eCorrelation analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIVST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVPW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIVST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e-.211*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e.174*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e.222*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e.633**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e.223*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e.665**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e.875**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-.484**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-.315**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-.209*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e-0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e.613**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e.498**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e.432**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-.917**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVPW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e-.193*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e.687**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e.344**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-.209*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-.196*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-.267**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e.195*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-.237**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e.178*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e.204*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e.240**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-.224*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e.207*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.83646%;\"\u003e\n \u003cp\u003e-0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-.189*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-.204*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8586%;\"\u003e\n \u003cp\u003e-0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.51789%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.98467%;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.19932%;\"\u003e\n \u003cp\u003e-.648**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.13288%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*. Correlation is significant at the 0.05 level (two-tailed).\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e**. Correlation is significant at the 0.01 level (two-tailed).\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eLVEF Left Ventricle Ejection Fraction，IVST interventricular septal thickness at diastole，GLS global longitudinal strain，GWI global work index，GCW global constructive work，GWE global work efficiency，GWW global waste work，LVPW \u0026nbsp;Left Ventricular Posterior Wall，EAT epicardial adipose tissue，HDL-C High-Density Lipoprotein Cholesterol，AIP Atherogenic Index of Plasma.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e|r| \u0026lt; 0.3 indicates a weak correlation; 0.3 \u0026le; |r| \u0026lt; 0.7 indicates a moderate correlation; |r| \u0026ge; 0.7 indicates a strong correlation.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eCorrelation analysis of remaining variables after removing redundant variables\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIVST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVPW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.28283%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.28283%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIVST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e-.211*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.28283%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e.174*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e-.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.28283%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e.222*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e.633**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.28283%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e-.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e-.484**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e-.315**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.28283%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVPW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e-.193*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e.687**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e-.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.101%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.28283%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e-.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e.344**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e-.209*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e-.196*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e.195*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.101%;\"\u003e\n \u003cp\u003e.178*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.28283%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e-.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e-.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e-.189*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.101%;\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.28283%;\"\u003e\n \u003cp\u003e.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.101%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.29293%;\"\u003e\n \u003cp\u003e.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e.204*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e.240**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e-.224*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.101%;\"\u003e\n \u003cp\u003e.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.28283%;\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5051%;\"\u003e\n \u003cp\u003e-.648**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9091%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*. Correlation is significant at the 0.05 level (two-tailed).\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e**. Correlation is significant at the 0.01 level (two-tailed).\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eLVEF Left Ventricle Ejection Fraction，IVST interventricular septal thickness at diastole，GLS global longitudinal strain，GWI global work index，GWW global waste work，LVPW \u0026nbsp;Left Ventricular Posterior Wall，EAT epicardial adipose tissue，HDL-C High-Density Lipoprotein Cholesterol，AIP Atherogenic Index of Plasma.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e|r| \u0026lt; 0.3 indicates a weak correlation; 0.3 \u0026le; |r| \u0026lt; 0.7 indicates a moderate correlation; |r| \u0026ge; 0.7 indicates a strong correlation.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003eMulticollinearity test\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1-VIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIVST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e2.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e2.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVPW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e2.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e2.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e1.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003eLVEF Left Ventricle Ejection Fraction，IVST interventricular septal thickness at diastole，GLS global longitudinal strain，GWI global work index，GWW global waste work，LVPW \u0026nbsp;Left Ventricular Posterior Wall，EAT epicardial adipose tissue，HDL-C High-Density Lipoprotein Cholesterol，AIP Atherogenic Index of Plasma.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eVIF = 1 indicates that there is no multicollinearity among variables; 1 \u0026lt; VIF \u0026lt; 5 suggests that there is a certain degree of multicollinearity, but it usually does not have a serious impact on the regression results; 5 \u0026le; VIF \u0026lt; 10 shows that there is a moderate degree of multicollinearity, which requires attention. VIF \u0026ge; 10 means that there is severe multicollinearity, which will have a significant impact on the estimation and interpretation of regression coefficients, and measures need to be taken to deal with it.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003eMultivariate logistic regression analysis of influencing factors for CHD\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4905%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaldX\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8021%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4905%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e2.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e1.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e3.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e10.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8021%;\"\u003e\n \u003cp\u003e0.747~142.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4905%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e9.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8021%;\"\u003e\n \u003cp\u003e0.997~0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4905%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e3.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8021%;\"\u003e\n \u003cp\u003e1.006~1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4905%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVPW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e7.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e2.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8021%;\"\u003e\n \u003cp\u003e1.303~4.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.9415%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003eAIP Atherogenic Index of Plasma，GWI global work index，GWW global waste work，LVPW \u0026nbsp;Left Ventricular Posterior Wall.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Construction of the CHD Risk Prediction Nomogram Model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the four independent risk factors (GWI, GWW, LVPW, and AIP), a CHD risk prediction nomogram model was constructed using R-Studio. As shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e, the variables included in this nomogram model are GWI, GWW, LVPW, and AIP. The impact of each variable on the occurrence of CHD is reflected in their respective row lengths and corresponding scores. The total score of the model is obtained by summing the scores of each variable, with a higher score indicating a greater contribution to the risk of CHD. GWI represents the overall systolic capacity of the myocardium; the lower the value, the higher the base score on the Points axis, corresponding to a higher risk of CHD. GWW represents the energy consumed by the myocardium; the higher the value, the higher the base score on the Points axis, corresponding to a higher risk of CHD. LVPW is the thickness of the left ventricular wall at the end of diastole; the higher the value, the higher the base score on the Points axis, corresponding to a higher risk of CHD. The higher the AIP value, the higher the base score on the Points axis, corresponding to a higher risk of CHD, suggesting that abnormal lipid metabolism can exacerbate the risk of CHD. For predictive assessment, sum the base scores on the Points axis corresponding to the above risk factors, find the corresponding total score on the Total Points axis after summation, and finally refer to the \u0026quot;The risk of CHD\u0026quot; axis to read the predicted risk of CHD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Evaluation of Clinical Performance and Clinical Applicability of the CHD Risk Prediction Nomogram Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the discriminative ability of the model, a five-fold cross-validation method was used to plot the ROC curve (\u003cstrong\u003eFigure 2\u003c/strong\u003e). The results showed that the model had an average AUC of 0.836 in the validation set, with a 95% CI of 0.759\u0026ndash;0.825 and a C-index of 0.864, indicating that the model has good discriminative ability. After determining the optimal cut-off value based on the Youden index, the model had an average sensitivity of 0.838 and specificity of 0.776, which further confirmed the reliability of its classification performance.\u003c/p\u003e\n\u003cp\u003eIn terms of calibration, the study plotted a calibration curve (\u003cstrong\u003eFigure 3\u003c/strong\u003e) using the Bootstrap method (with 1000 repeated samplings) and calculated a Mean Absolute Error (MAE) of 0.017, indicating a good fit between the model\u0026apos;s predicted probabilities and the actual observed rates. In addition, the results of the Hosmer-Lemeshow goodness-of-fit test were X\u0026sup2; = 8.492, df = 8, p-value = 0.387, which reached statistical non-significance, suggesting that the model has a good calibration degree with no obvious overfitting.\u003c/p\u003e\n\u003cp\u003eTo evaluate the clinical application value of this model under different risk thresholds, a further DCA was performed (\u003cstrong\u003eFigure 4\u003c/strong\u003e). The results showed that when the risk threshold range was between 0.05and 0.8, the net benefit brought by the CHD risk prediction nomogram model was higher than that of the \u0026quot;treat-all\u0026quot; and \u0026quot;treat-none\u0026quot; strategies. This indicates that the model has good clinical practicability within a relatively wide range of risk thresholds and can provide an effective auxiliary decision-making basis for the early intervention and management of high-risk populations with CHD.\u003c/p\u003e\n\u003cp\u003eIn summary, the CHD risk prediction nomogram model constructed in this study has good discriminative ability, calibration consistency and clinical decision-making value, and thus has certain potential for clinical promotion.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study is the first to introduce AIP and non-invasive myocardial work parameters into the risk prediction model for CHD. By adding new indicators, the predictive performance of the model for CHD has been improved, enabling a more comprehensive and accurate assessment. The nomogram risk prediction model finally constructed in this study makes the prediction and analysis of clinical events more visual and graphical[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].All parameters in this model are collectible and easy-to-operate indicators. Their extensive implementation at the primary level is conducive to carrying out disease risk stratification with large sample sizes, avoiding the omission of high-risk groups, and saving unnecessary expenses. The DCA curve analysis in this study also proves the practicability and effectiveness of the model. The model conforms to the \"lipid-vessel-myocardium\" cascade injury chain theory in the pathophysiological mechanism of coronary heart disease, further indicating that lipid metabolism disorder can drive the development of atherosclerosis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe model developed in this study is simple to use, accurate and reliable, and features visualization. It can be used for CHD risk stratification in populations still at moderate to high risk. Compared with the simplicity of traditional prediction models, such as relying solely on serological indicators or imaging methods, the prediction model in this study overcomes the limitation of restricted universality, thus meeting the needs of large-scale population screening at the primary level. According to the data from the Global Burden of Disease Study 2021, ischemic heart disease, especially CHD, remains the leading risk factor for death and disease burden worldwide.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]Moreover, the continuous increase in the number of CHD patients and the disease burden is mainly caused by metabolic factors such as hypertension, hyperglycemia, high BMI, and LDL-C.[\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]Previous studies have shown that age, gender, blood pressure, BMI, and age are risk factors for CHD, but they cannot accurately assess the risk level of CHD.\u003c/p\u003e\u003cp\u003eTG is the most abundant lipid in human adipose tissue. High levels of TG can lead to lipotoxicity, thereby causing the occurrence and progression of inflammation[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].HDL-C contains hundreds of lipids and proteins, which are known to perform antioxidant and anti-inflammatory functions in the regulation of metabolic diseases, including diabetes[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. An elevated TG/HDL-C ratio has been shown to be associated with an increased risk of cardiovascular events related to cardiometabolic disorders. Particularly in middle- and low-risk populations with stable angina pectoris and no known CHD, a higher TG/HDL-C ratio has been confirmed as an independent predictor of coronary atherosclerotic events and progression[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].This finding is consistent with the predictive value of AIP[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. AIP combines TG and high-density lipoprotein cholesterol (HDL-C) levels. It not only reflects the ratio of TG to HDL-C but also represents the size of lipoprotein particles. Compared with high TG levels or low HDL-C levels, AIP can more accurately reflect the pathogenicity and specificity of dyslipidemia[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA subsequent meta-analysis further confirmed that an elevated AIP is a potential prognostic marker for adverse cardiovascular events in patients with CAD, and that increased levels of AIP are consistently associated with an elevated risk of adverse events[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].Subsequent meta-analyses have further confirmed that an elevated AIP is a potential prognostic marker for adverse cardiovascular events in patients with CAD, and that increased AIP levels are consistently associated with an elevated risk of adverse events. The results of this study support the findings of the aforementioned meta-analyses: the CHD group not only showed significantly lower HDL-C levels than the non-CHD group, but also had a significant elevation in AIP. More importantly, this study confirmed that a significant elevation in AIP is an independent predictor of CHD, which is consistent with previous research conclusions[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].In summary, AIP can sensitively identify lipid metabolism disorders in the early stage, and its elevation is significantly associated with an increased risk of adverse cardiovascular events. This indirectly indicates the core role of lipoprotein phenotypic imbalance in driving the progression of atherosclerosis.\u003c/p\u003e\u003cp\u003eEarly non-invasive quantification of myocardial function and structure in high-risk populations with CHD is a major clinical challenge. Previous studies have shown that there is a correlation between myocardial mechanical stress and the severity of coronary artery stenosis in CHD patients with preserved LVEF[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].Echocardiographic parameter LVPW and two-dimensional speckle tracking parameters have been confirmed as good indicators for identifying abnormal myocardial activity in CHD patients, and our study is consistent with the above-mentioned studies[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].However, two-dimensional speckle tracking technology is susceptible to afterload, which may lead to errors in the detection results. Compared with other echocardiographic parameters (including LVEF and two-dimensional speckle tracking), non-invasive myocardial work parameters can capture structural remodeling and impairment during myocardial ischemia earlier[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].Our research results show that the LVEF and GLS in the CHD group were not significantly lower than those in the non-CHD group, but the myocardial work performance was impaired.A previous study indicated that GWW and GWE are helpful for identifying the systolic function of the heart in patients with coronary artery stenosis and also reflect myocardial oxygen consumption [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The results of our study are consistent with this conclusion, and the global left ventricular myocardial work parameters may have good clinical significance for screening suspected coronary heart disease.\u003c/p\u003e\u003cp\u003eExisting studies have shown that the CHD risk assessment system developed by Diamond and Forrester is based on age, gender, and some of the most basic clinical indicators [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, this prediction system has certain limitations: it may overestimate the risk of CHD onset and fail to incorporate some important influencing factors. Moreover, the process of obtaining parameters for these two traditional models is cumbersome and poorly operable, making them unavailable for popularization in the majority of outpatient and emergency departments in China, let alone in primary hospitals. Meanwhile, on the basis of domestic and foreign studies, this study is the first to introduce non-invasive myocardial function indicators and AIP into the prediction model, which has greatly improved the prediction accuracy and completeness of the model. In addition, after DCA analysis, the new model in this study can clearly demonstrate its applicability in clinical practice. The nomogram model constructed in this study includes a clinically practical risk scoring system. The parameters of this model are easily accessible, and it has a wide range of applications, which can be used in general hospitals and primary medical and health institutions. This convenient and fast prediction tool can help clinicians make more accurate preventive decisions, control the source of diseases, reduce the burden on the medical system, and at the same time address the current deficiencies in the universality and preventiveness of diagnostic systems.\u003c/p\u003e\u003cp\u003eHowever, this study has certain limitations. Firstly, the study samples were only obtained from a single-center regional hospital, with a limited sample size, and there was insufficient comparative verification with a wider range of medical institutions and a broader clinical population. Secondly, since the model only used the Bootstrap method for internal validation and did not adopt the verification method of multi-center external cohorts, the effect of clinical generalization still needs further exploration.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe nomogram model established in this study, which integrates indicators related to myocardial function and lipid metabolism, is a convenient and effective tool that can be used for early non-invasive screening of CHD, with an AUC value of 0.836. Whether applied in large hospitals or small community health care centers, this model can accurately predict the risk of high-risk patient groups and help clinicians make accurate judgments on patients' conditions, thereby optimizing clinical decisions and improving patient prognosis.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eInformed Consent Statement\u003c/h2\u003e\u003cp\u003e\u003cb\u003e\u003c/b\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003e1)Project approval of Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project (Project No. S2024057);2)2025 Guangxi Graduate Education Innovation Program Project (Project No. YCSW2025605).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSWF and MXH: Writing, reviewing, editing, and validation. CCC,TFL: Writing, reviewing, editing, and funding acquisition. YMH and KH: Data collection and analysis. SHF and LTW: Writing, reviewing, and editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData are contained within the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAraujo G, Valencia L M, Martin-Ozimek A, et al. Atherosclerosis: from lipid-lowering and anti-inflammatory therapies to targeting arterial retention of ApoB-containing lipoproteins[J]. Front Immunol, 2025,16:1485801.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2025.1485801\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2025.1485801\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMensah G A, Fuster V, Murray C, et al. Global Burden of Cardiovascular Diseases and Risks, 1990\u0026ndash;2022[J]. J Am Coll Cardiol, 2023,82(25):2350\u0026ndash;2473.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2023.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2023.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang Q, Liu Z, Wei M, et al. The atherogenic index of plasma and carotid atherosclerosis in a community population: a population-based cohort study in China[J]. Cardiovasc Diabetol, 2023,22(1):125.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-023-01839-y\u003c/span\u003e\u003cspan address=\"10.1186/s12933-023-01839-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEdwards N, Scalia G M, Shiino K, et al. Global Myocardial Work Is Superior to Global Longitudinal Strain to Predict Significant Coronary Artery Disease in Patients With Normal Left Ventricular Function and Wall Motion[J]. J Am Soc Echocardiogr, 2019,32(8):947\u0026ndash;957.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.echo.2019.02.014\u003c/span\u003e\u003cspan address=\"10.1016/j.echo.2019.02.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmiseth O A, Donal E, Penicka M, et al. How to measure left ventricular myocardial work by pressure-strain loops[J]. Eur Heart J Cardiovasc Imaging, 2021,22(3):259\u0026ndash;261.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ehjci/jeaa301\u003c/span\u003e\u003cspan address=\"10.1093/ehjci/jeaa301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiamond G A, Forrester J S. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease[J]. N Engl J Med, 1979,300(24):1350\u0026ndash;1358.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJM197906143002402\u003c/span\u003e\u003cspan address=\"10.1056/NEJM197906143002402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVrints C, Andreotti F, Koskinas K C, et al. 2024 ESC Guidelines for the management of chronic coronary syndromes[J]. Eur Heart J, 2024,45(36):3415\u0026ndash;3537.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehae177\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehae177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeo R, Dubin R F, Ren Y, et al. Proteomic cardiovascular risk assessment in chronic kidney disease[J]. Eur Heart J, 2023,44(23):2095\u0026ndash;2110.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehad115\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehad115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLibby P. The changing landscape of atherosclerosis[J]. Nature, 2021,592(7855):524\u0026ndash;533.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-021-03392-8\u003c/span\u003e\u003cspan address=\"10.1038/s41586-021-03392-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Li Q, Bi L, et al. Global trends in the burden of ischemic heart disease based on the global burden of disease study 2021: the role of metabolic risk factors[J]. BMC Public Health, 2025,25(1):310.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-025-21588-9\u003c/span\u003e\u003cspan address=\"10.1186/s12889-025-21588-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWan A W, Rosman A, Bavanandan S, et al. Current Insights on Dyslipidaemia Management for Prevention of Atherosclerotic Cardiovascular Disease: A Malaysian Perspective[J]. Malays J Med Sci, 2023,30(1):67\u0026ndash;81.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21315/mjms2023.30.1.6\u003c/span\u003e\u003cspan address=\"10.21315/mjms2023.30.1.6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTian J, Dong Y, Xu Z, et al. Association between triglyceride glucose-body mass index and 365-day mortality in patients with critical coronary heart disease[J]. Front Endocrinol (Lausanne), 2025,16:1513898.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2025.1513898\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2025.1513898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGerdts E, Regitz-Zagrosek V. Sex differences in cardiometabolic disorders[J]. Nat Med, 2019,25(11):1657\u0026ndash;1666.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-019-0643-8\u003c/span\u003e\u003cspan address=\"10.1038/s41591-019-0643-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnanth C V, Rutherford C, Rosenfeld E B, et al. Epidemiologic trends and risk factors associated with the decline in mortality from coronary heart disease in the United States, 1990\u0026ndash;2019[J]. Am Heart J, 2023,263:46\u0026ndash;55.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ahj.2023.05.006\u003c/span\u003e\u003cspan address=\"10.1016/j.ahj.2023.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhong D, Cheng H. Application of Mendelian randomization in the discovery of risk factors for coronary heart disease from 2009 to 2023: A bibliometric review[J]. Clin Cardiol, 2024,47(1):e24154.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/clc.24154\u003c/span\u003e\u003cspan address=\"10.1002/clc.24154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManell H, Kristinsson H, Kullberg J, et al. Hyperglucagonemia in youth is associated with high plasma free fatty acids, visceral adiposity, and impaired glucose tolerance[J]. Pediatr Diabetes, 2019,20(7):880\u0026ndash;891.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/pedi.12890\u003c/span\u003e\u003cspan address=\"10.1111/pedi.12890\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDi Bartolo B A, Cartland S P, Genner S, et al. HDL Improves Cholesterol and Glucose Homeostasis and Reduces Atherosclerosis in Diabetes-Associated Atherosclerosis[J]. J Diabetes Res, 2021,2021:6668506.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2021/6668506\u003c/span\u003e\u003cspan address=\"10.1155/2021/6668506\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaselli C, De Caterina R, Smit J M, et al. Triglycerides and low HDL cholesterol predict coronary heart disease risk in patients with stable angina[J]. Sci Rep, 2021,11(1):20714.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-00020-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-00020-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo J, Wang J K, Song B L. Lowering low-density lipoprotein cholesterol: from mechanisms to therapies[J]. Life Metab, 2022,1(1):25\u0026ndash;38.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/lifemeta/loac004\u003c/span\u003e\u003cspan address=\"10.1093/lifemeta/loac004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai G, Shi G, Xue S, et al. The atherogenic index of plasma is a strong and independent predictor for coronary artery disease in the Chinese Han population[J]. Medicine (Baltimore), 2017,96(37):e8058.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MD.0000000000008058\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000008058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Mac\u0026iacute;as J C, Ochoa-Mart\u0026iacute;nez A C, Varela-Silva J A, et al. Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses[J]. Arch Med Res, 2019,50(5):285\u0026ndash;294.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.arcmed.2019.08.009\u003c/span\u003e\u003cspan address=\"10.1016/j.arcmed.2019.08.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYin B, Wu Z, Xia Y, et al. Non-linear association of atherogenic index of plasma with insulin resistance and type 2 diabetes: a cross-sectional study[J]. Cardiovasc Diabetol, 2023,22(1):157.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-023-01886-5\u003c/span\u003e\u003cspan address=\"10.1186/s12933-023-01886-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRabiee R M, Ghasempour D G, Darouei B, et al. The association of atherogenic index of plasma with cardiovascular outcomes in patients with coronary artery disease: A systematic review and meta-analysis[J]. Cardiovasc Diabetol, 2024,23(1):119.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-024-02198-y\u003c/span\u003e\u003cspan address=\"10.1186/s12933-024-02198-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahdavi-Roshan M, Mozafarihashjin M, Shoaibinobarian N, et al. Evaluating the use of novel atherogenicity indices and insulin resistance surrogate markers in predicting the risk of coronary artery disease: a case\u0026ndash;control investigation with comparison to traditional biomarkers[J]. Lipids Health Dis, 2022,21(1):126.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12944-022-01732-9\u003c/span\u003e\u003cspan address=\"10.1186/s12944-022-01732-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuis S A, Yamada A, Khandheria B K, et al. Use of three-dimensional speckle-tracking echocardiography for quantitative assessment of global left ventricular function: a comparative study to three-dimensional echocardiography[J]. J Am Soc Echocardiogr, 2014,27(3):285\u0026ndash;291.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.echo.2013.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.echo.2013.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUusitalo V, Luotolahti M, Pietil\u0026auml; M, et al. Two-Dimensional Speckle-Tracking during Dobutamine Stress Echocardiography in the Detection of Myocardial Ischemia in Patients with Suspected Coronary Artery Disease[J]. J Am Soc Echocardiogr, 2016,29(5):470\u0026ndash;479.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.echo.2015.12.013\u003c/span\u003e\u003cspan address=\"10.1016/j.echo.2015.12.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoe E, Skulstad H, Smiseth O A. Myocardial work by echocardiography: a novel method ready for clinical testing[J]. Eur Heart J Cardiovasc Imaging, 2019,20(1):18\u0026ndash;20.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ehjci/jey156\u003c/span\u003e\u003cspan address=\"10.1093/ehjci/jey156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManganaro R, Marchetta S, Dulgheru R, et al. Correlation between non-invasive myocardial work indices and main parameters of systolic and diastolic function: results from the EACVI NORRE study[J]. Eur Heart J Cardiovasc Imaging, 2020,21(5):533\u0026ndash;541.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ehjci/jez203\u003c/span\u003e\u003cspan address=\"10.1093/ehjci/jez203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi M, Wang Y, Li L, et al. Global myocardial work in coronary artery disease patients without regional wall motion abnormality: Correlation with Gensini-score[J]. Clin Cardiol, 2024,47(2):e24193.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/clc.24193\u003c/span\u003e\u003cspan address=\"10.1002/clc.24193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin J, Wu W, Gao L, et al. Global Myocardial Work Combined with Treadmill Exercise Stress to Detect Significant Coronary Artery Disease[J]. J Am Soc Echocardiogr, 2022,35(3):247\u0026ndash;257.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.echo.2021.10.009\u003c/span\u003e\u003cspan address=\"10.1016/j.echo.2021.10.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao Y, He F, Guo W, et al. The clinical value of noninvasive left ventricular myocardial work in the diagnosis of myocardial ischemia in coronary heart disease: a comparative study with coronary flow reserve fraction[J]. Int J Cardiovasc Imaging, 2024,40(10):2167\u0026ndash;2179.DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10554-024-03208-6\u003c/span\u003e\u003cspan address=\"10.1007/s10554-024-03208-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-heart-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tehj","sideBox":"Learn more about [The Egyptian Heart Journal](https://tehj.springeropen.com)","snPcode":"43044","submissionUrl":"https://submission.springernature.com/new-submission/43044/3","title":"The Egyptian Heart Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Coronary Heart Disease, Risk prediction, Non-invasive Myocardial Work, Lipid metabolism","lastPublishedDoi":"10.21203/rs.3.rs-7778328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7778328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective：\u003c/strong\u003eThe aim is to develop a new risk prediction model for coronary heart disease (CHD) by utilizing Non-invasive Myocardial Work（MW）parameters and lipid metabolism indicators, so as to identify and manage populations at high risk of CHD at an early stage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods：\u003c/strong\u003ePatients with suspected CHD who were admitted to the Affiliated Hospital of Youjiang Medical University for Nationalities from October 2024 to June 2025 and scheduled to undergo Coronary Angiography (CAG) were prospectively collected. They were divided into the CHD group and the non-CHD group according to the CAG results. Logistic regression was used to identify factors related to CHD, and R-Studio was employed to construct a nomogram model for predicting CHD based on independent predictors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e：Pearson correlation analysis and multivariate logistic regression analysis showed that the Atherogenic Index of Plasma (AIP), Global Work Index (GWI), Global Wasted Work (GWW), and Left Ventricular Posterior Wall （LVPW）at End - Diastole were independent risk factors associated with CHD.Based on the results of multivariate logistic regression, the CHD risk prediction model constructed using R-Studio showed an average AUC of 0.836 and a C-index of 0.847 in the validation set, indicating that the model can well distinguish between high-risk and low-risk populations for CHD. After calibration, the Mean Absolute Error (MAE) of the calibration curve was 0.017, which further verified the robustness and reliability of its discriminative efficacy. Decision curve analysis showed that when the threshold probability was in the range of 10%-95%, the model had a relatively high clinical net benefit value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion：\u003c/strong\u003eThis study established a CHD risk prediction model based on non-invasive myocardial work parameters and lipid metabolism indicators, which can effectively identify high-risk populations of CHD. It provides a non-invasive, portable and accurate CHD risk assessment tool for most medical institutions in China, assists clinicians in making correct clinical decisions, and thus improves the prognostic effect.\u003c/p\u003e","manuscriptTitle":"The development of a coronary heart disease risk prediction model based on non-invasive myocardial work parameters and lipid metabolism indicators","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 16:01:36","doi":"10.21203/rs.3.rs-7778328/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-13T11:04:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T08:20:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T08:19:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Egyptian Heart Journal","date":"2025-10-04T08:05:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-heart-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tehj","sideBox":"Learn more about [The Egyptian Heart Journal](https://tehj.springeropen.com)","snPcode":"43044","submissionUrl":"https://submission.springernature.com/new-submission/43044/3","title":"The Egyptian Heart Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca04e191-6f93-481d-8112-33df474d186d","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:01:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 16:01:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7778328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7778328","identity":"rs-7778328","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}