The Correlation of Pericoronary Adipose Tissue with Coronary artery disease and left ventricular function

The Correlation of Pericoronary Adipose Tissue with Coronary artery disease and left ventricular function | 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 Correlation of Pericoronary Adipose Tissue with Coronary artery disease and left ventricular function Deshu You, Haiyang Yu, Zhiwei Wang, Xiaoyu Wei, Xiangxiang Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1823340/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective: To investigate the correlation of pericoronary adipose tissue with coronary artery disease and left ventricular function. Methods: Participants with clinically suspected coronary artery disease were enrolled. All participants underwent coronary computed tomography angiography (CCTA) and echocardiography followed by invasive coronary angiography (ICA) within 6month. pericoronary adipose tissue (PCAT) was extracted to analyze the correlation with Gensini score and left ventricular function parameter, including IVS, LVPW, LVEDD, LVESD, LVEDV, LVESV, FS, LVEF, LVM, LVMI. The correlation between PCAT and Gensini was assessed by using Spearman correlation analysis, and between PCAT volume or FAI and left ventricular function parameters was assessed by using partial correlation analysis. Results: One hundred and fifty-nine participants (mean age, 64.55years ±10.64, male:65.4%[104/159]) were included for final analysis. The risk factors of coronary artery disease, such as hypertension, diabetes, dyslipidemia, history of smoking and drinking, had no significant association with the PCAT (P>0.05); and there is no correlation between PCAT and Gensini score. However, the LAD-FAI was positively correlated with IVS(r=0.203, P=0.013), LVPW(r=0.218, P=0.008),LVEDD(r=0.317, P＜0.001)，LVESD(r=0.298, P＜0.001), LVEDV(r=0.317, P＜0.001), LVESV(r=0.301, P＜0.001), LVM(r=0.371, P＜0.001), LVMI(r=0.304, P＜0.001). The LCX-FAI was positively correlated with LVEDD(r=0.199, P=0.015)，LVESD(r=0.190, P=0.021), LVEDV(r=0.203, P=0.013), LVESV(r=0.197, P=0.016), LVM(r=0.220, P=0.007), LVMI(r=0.172, P=0.036). The RCA-FAI was positively correlated with LVEDD(r=0.258, P=0.002)，LVESD(r=0.238, P=0.004), LVEDV(r=0.266, P=0.001), LVESV (r=0.249, P=0.002), LVM(r=0.237, P=0.004), LVMI(r=0.218, P=0.008). The total volume was positively correlated with FS (r=0.167, P=0.042). Conclusion: The FAI was positively correlated with the left ventricular function, but was not associated with the severity of coronary artery disease. pericoronary adipose tissue (PCAT) Fat attenuation index (FAI) left ventricular function Gensini score coronary artery disease (CAD) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Key Points: There is a significant correlation between FAI and left ventricular function parameters. Our results suggest that cardiac mortality caused by increased FAI may be due to changes in left ventricular function. PCAT had no correlation with BMI and risk factors of coronary heart disease. 1. Introduction Coronary artery disease（CAD）is referred to a heart disease in which atherosclerotic plaque occurs in the coronary artery, causing coronary lumen stenosis or occlusion, then resulting in myocardial ischemia or necrosis. Coronary atherosclerosis has been considered to be an inflammatory reaction [1] . For patients with CAD, predicting the risk of adverse coronary events is more important than coronary stenosis. The detection of pericoronary inflammation can help patients with coronary heart disease risk stratification and risk prediction early. In recent years, pericoronary fat attenuation index (FAI) measured based on coronary CT angiography (CCTA) image can be used to reflect the inflammation of pericoronary [2-3] . Recent studies had shown that pericoronary adipose tissue (PCAT) has been confirmed to participate in the process of coronary inflammation, and significantly related to the type of plaque and the degree of stenosis [4-6] . One CRISP study showed that FAI> -70.1 HU was associated with the higher risk of cardiac mortality, which can improve the prediction of heart risk [7] . Although PCAT was shown great clinical significance in the occurrence and development of CAD [8] , there are several important questions have to be explored in this popular field. At first, Gensini score is a traditional index used to evaluate the severity of CAD [9] , however, it is unknown that can PCAT assess the severity of CAD. Secondly, some studies showed that CAD is a cause of left ventricular dysfunction and left ventricular dysfunction will increase the mortality of CAD, but the association between PCAT and left ventricular function is unclear [10,11] . The aim of this study is to investigate the association of PCAT with CAD and left ventricular function, which can help clinicians evaluate the prognosis of patients with CAD. 2. Methods This retrospective study was approved by the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University institutional review board, and the need to obtain informed consent was waived. 2.1 Study patient The subjects were patients with treated in the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University from September 2019 to September 2021.We retrospectively included patients with clinically suspected CAD in our institution, who underwent CCTA and echocardiography followed by invasive coronary angiography (ICA) within 6 month. Exclusion criteria include: (a) history of coronary myocardial infarction and cardiac surgery, (b) anatomical variation of heart or coronary artery, (c) diseases that seriously affect heart function such as heart space occupying, cardiomyopathy and severe heart valve disease et al., (d) family history of CAD, and (e) the failure of image reconstruction. (Fig 1) 2.2 Clinical data The clinical data of patients were obtained from the inpatient cases system. Hypertension, diabetes and dyslipidemia were diagnosed according to clinical indicators. Hypertension is defined as systolic blood pressure ≥ 140mmHg and / or diastolic blood pressure ≥ 90mmHg or taking antihypertensive drugs. The diagnosis of diabetes is based on the guideline for prevention and treatment of type 2 diabetes. It is defined as fasting blood glucose > 6.1mmol/L and / or glycosylated hemoglobin > 6.5% or hypoglycemic drugs. Dyslipidemia according to the guidelines for the prevention and treatment of dyslipidemia in adults, dyslipidemia is defined as meeting one or more of the following requirements: (a)total cholesterol (TC)≥ 5.18mmol／L, (b)triglyceride (TG)≥1.70mmol／L, (c)high density lipoprotein cholesterol (HDL-C)＜1.04mmol／L, (d)low density lipoprotein cholesterol (LDL-C)≥ 3.37mmol／L, (e)Taking blood lipid regulating drugs. 2.3 CCTA Image acquisition CCTA was performed on Siemens third generation dual source CT (SOMATOM definition force CT; Siemens AG). Before scanning, the patient rested quietly for at least 15 minutes, and all patients did not take drugs such as β-blocker to slow down their heart rate. The scanning range was from the superior sternal fossa to the diaphragmatic surface of the heart. The prospective electrocardiogram (ECG) gating sequence was used for scanning, and the scanning parameters are as follows: the tube voltage is 100kV, the tube current was 23mA, the CT rotation time was 0.25s, the layer thickness was 0.75mm, the reconstruction interval was 0.50mm, and the display matrix was 512 × 512. Iodixanol injection (100ml: 32g; Qing Liming) was injected through the middle elbow vein. The injection volume was 60 ~ 80ml and the injection speed was5 ~ 6ml / s. 2.4 Coronary artery reconstruction The scanned images were transmitted to Skviewer software (Coronary System, Shukun Technology, Beijing, China). At first, the image was processed for image consistency to eliminate the impact of different window width and window level on the quality of reconstructed image. Then Coronary System was used for longitudinal and axial multiplanar reconstruction (MPR) of coronary artery. Finally, an experienced radiologist evaluates the image quality of coronary artery reconstruction: 1-4 points respectively represent poor image quality (excluded), acceptable image quality, good image quality and excellent image quality. 2.5 Perivascular fat analysis The PCAT was extracted automatically by using Skviewer software FAI intelligent analysis system (Skviewer FAI, Shukun Technology, Beijing, China). Volume and FAI of PCAT were measured by the method described by Oikonomou. et al [7] : The threshold value of PCAT was -190 to -30 HU, the measured length was 40mm, and the extracted radial distance was the average diameter of the target vessel. The segment analyzed was the proximal 40 mm of left anterior descending (LAD) and left circumflex (LCX), and the proximal 10-50 mm of right coronary artery (RCA). The software could extract automatically, and when the automatic extraction was inaccurate, adjusted the extraction range manually. Subsequently, the PCAT volume and FAI were calculated by the software automatically (Fig 2). The above measurement results were completed by an experienced radiologist independently. In order to test the consistency of measurement results, 20% (32) patients were randomly selected one month later, and the second measurement was carried out by two radiologists. The intra observer consistency test was carried out on the results measured by the same doctor, and the inter observer consistency test was carried out on the results measured by two doctors (Supplement 1). 2.6 ICA and Gensini score calculation According to the 2021 ACC / AHA angiography guidelines of the United States, the patients were punctured through the right femoral artery or radial artery with the conventional Seldinger standard method. The left and right coronary arteries were successively imaged with multi-position irradiation imaging [12] . The results of ICA were evaluated by lumen diameter stenosis (DS). The segment score was obtained by multiplying the corresponding scores of the vascular segment where the plaque was located by the score of stenosis. The sum of the total scores of each segment was total Gensini score [13] . (Table 1) 2.7 Left ventricular function parameter acquisition According to the current guidelines and diagnostic criteria by American Society of Echocardiography and European Association of Cardiovascular Imaging [14] ,the echocardiography was acquired by Vivid E9 (GE Vingmed) echocardiography system with an M5S transducer(3.5 MHz).The patient took the left-lying position, laid still for 5 minutes and breathed calmly, then took the long axis section of the left ventricle to obtain the inter ventricular septum(IVS), left ventricular posterior wall (LVPW), left ventricular end-diastole diameter(LVEDD), left ventricular end-systolic diameter( LVESD), 1eft ventricular end-diastolic volume(LVEDV), 1eft ventricular end-systolic volume(LVESV), then calculated the left ventricular fractional shortening (FS), left ventricular ejection fraction (LVEF), left ventricular mass (LVM), left ventricular mass index (LVMI).The calculation formula is as follows: LVEF=（LVEDV-LVESV）/LVEDV×100% FS（%）=（LVEDD-LVESD）/LVEDD LVM（g）=0.8*1.04*[(IVS+LPWT+LVEDD)3-LVESD3]+0.6 LVMI（%）=LVM/ BSA BSA（m2）=0.0061×height（cm）+0.0128×weight(kg)-0.1529 Note:BSA= body surface area 2.8 Statistical analysis All statistical analysis was performed by using SPSS (SPSS Statistics for Windows Version 23.0, IBM, Armonk, NY). The counting data were expressed in quantity and percentage, and the continuous variables were described as mean ± standard deviation (SD) or median (upper quartile, lower quartile). Two subgroups comparisons of continuous variables used the two independent samples Student t test or Mann-Whitney U test. The correlation between PCAT volume or FAI and Gensini score and left ventricular function parameters was assessed by using Spearman correlation analysis. In order to eliminate the influence of interference factors, we used partial correlation analysis to analyze the correlation between PCAT volume or FAI and Gensini score and left ventricular function parameters. P <0.05 was considered to indicate statistical significance. Intra-group correlation coefficient(ICC) was used to evaluate intra and inter observer consistency. 3. Result 3.1 Baseline Characteristics Initially, 197 participants with clinically suspected CAD were included. According to strict exclusion criteria, finally, 159 patients (mean age, 64.55 years ± 10.64; range, 43–89 years) were enrolled in this study (Figure 1). There were 104 males (mean age, 62.03years ±10.58; range, 43-89years) and 55 females (mean age, 69.33years±9.05; range, 51-85 years). Other baseline characteristics are shown in Table 2. 3.2 Relationship between PCAT volume and CAD risk factors The PCAT volumes of all participants were divided into two subgroups according to whether they had hypertension, diabetes, dyslipidemia, smoking, and drinking. Table 3 showed the distribution of volume among the subgroups. The results showed that there were statistically significant differences in the LCX-Volume and Total-Volume between the subgroups ( P =0.028, P =0.025). The other subgroups were no significant. ( P > 0.05). The distribution range of PCAT volume is shown in Figure 3. (The consistency of PCAT intra and inter observer was good [P < 0.001], [Supplement 1]) 3.3 Relationship between FAI and CAD risk factors Table 4 showed the distribution of FAI among different subgroups. The results showed that there was no significant difference in FAI between the subgroups ( P > 0.05). The Figure 4 show the distribution range of FAI. 3.4 Correlation analysis of PCAT volume or FAI and Gensini score or BMI Spearman correlation or Pearson analysis were used to analyze the correlation between PCAT and Gensini score and BMI. The results showed that the volumes of LCX and total were positively correlated with BMI (r = 0.170, P =0.032; r = 0.157, P =0.048). There was no significant correlation between the volume of LAD and RCA and BMI, and no correlation between FAI and BMI. The PCAT was also not correlated with Gensini score. As shown in Figure 5. 3.5 Correlation analysis of PCAT volume or FAI and left ventricular function parameters. We used partial correlation analysis of control variables, excluding confounding factors including age, gender, height, weight, BMI, smoking, drinking, heart rate, hypertension, diabetes and hyperlipidemia, then analyzed the correlation between PCAT and left ventricular function parameters (Figure 6). 4. Discussion While using CCTA to diagnose coronary artery stenosis, we can predict the prognosis of patients with CAD through the analysis of PCAT. We analyzed the relationship between PCAT and CAD, and found that there was no correlation between PCAT volume and Gensini score and cardiac function parameters. However, some studies on the correlation between epicardial fat volume (EFV) and CAD and cardiac function show that the increase of EFV was positively correlated with the risk of CAD and left ventricular function parameters [15-18] . The possible reason is that the measurement methods are different. The extraction range of EFV is all adipose tissue from the aortic root to the apex of the heart, and the radial range of extraction of PCAT is equal to the diameter of the target vessel. Previous studies have shown that EVF is correlated with traditional risk factors of coronary heart disease [19-21] , and EVF is positively correlated with BMI [22] . However, some research results show that EVF can be an independent predictor of CAD [23] and is not associated with the traditional risk factors of CAD [24, 25] . Therefore, we analyzed the correlation between PCAT volume and BMI and risk factor for CAD. Our results showed that PCAT volume had no correlation with BMI and risk factors of coronary heart disease. These differences in these results may be related to different measurement methods, pathological changes in the natural course of the disease and lifestyle and drug intervention. Our results showed that only the results of LCX and total volume were statistically significant(P＜0.05). This may be related to the less adipose tissue around LCX and the method we adopted can cover the more complete volume of vessel. The correlation shown by the total volume may be affected by LCX. In a study of pericoronary epicardial adipose tissue by Vos et al. [26] , only the fat volume around LCX showed different results from LAD and RCA, which was similar to our results. Inflammation is a critical factor not only for the development but also for the progression of atherosclerosis [1, 27] . Inflammatory factors released by arterial wall can induce lipolysis, inhibit lipogenesis and promote perivascular edema. These changes showed attenuation from lipid (close to - 190 Hu) to water (close to - 30 Hu) on CT [28, 29] . Therefore, coronary inflammation can be detected by FAI. One study by Antonopoulos et al. [29] found that FAI was positively correlated with atherosclerotic plaque load, and higher perivascular FAI was associated with higher inflammatory expression levels. But we found that there was no correlation between PCAT FAI and Gensini score (P＞0.05). What triggers this result? It may be that we did not stratify the population according to the Gensini score, and there were only 26 patients (16%) with severe CAD (Gensini score > 60) in our study population. Meanwhile, some studies have shown that FAI can present dynamic changes with the prognosis of the disease, and patients with statins, aspirin or antidiabetic drugs will also change the FAI [30, 31] . These may cause our results to be different with others. Our further analysis found that there was no significant difference between FAI and BMI (LAD r =0.137, P =0.086; LCX r=0.124, P =0.118; RCA r=0.021, P =0.790). And between FAI and risk factors of CAD, the results were no relationship. These results can further support that FAI is a reliable imaging index to quantify coronary artery inflammation and it is not affected by other risk factors. The study of Antonopoulos et al. [29] found that FAI was not associated with traditional cardiovascular risk factors, which was similar to our results. Since the parameters of left ventricular function are affected by individual differences [32, 33] , we analyzed the PCAT and left ventricular function parameters by using partial correlation analysis to exclude the effects of interference factors. There was no correlation between PCAT and left ventricular function parameters, but a significant correlation between FAI and left ventricular function parameters. This may be because coronary artery and myocardium has no obvious boundary, and the PCAT can release cytokines reach myocardium and coronary artery through paracrine signaling mechanisms, which results in the changes of myocardial and left ventricular function [34] . In addition, studies have shown that left ventricular dysfunction and increased FAI (> 70.1HU) can significantly increase the cardiac mortality [31,35] . Our results suggest that cardiac mortality caused by increased FAI may be due to changes in left ventricular function. There are several limitations to this study that should be pointed out. First, this study is a single-center retrospective analysis with a small sample size. Second, in our study, the number of patients with severe CAD was small, and we did not stratify the severity of patients, which may lead our results bias. Third, our study was a retrospective study and failed to obtain the treatment and lifestyle intervention of patients. Fourth, some studies have shown that there are differences in FAI between different genders [36] , moreover women account for less in our study (34.6%), which may also cause deviation to the results. Further research is needed to identify sex-specific variables that explain the correlation between PCAT and left ventricular function accurately. 6. Conclusion The results showed that there was no correlation between PCAT volume and FAI and the severity of CAD, but there was positively correlated between FAI and left ventricular function parameters, and the correlation of LAD was the strongest. Moreover, our study found that FAI has no significant relationship with BMI and traditional risk factors of CAD. It further supports that FAI can be used as a reliable imaging index of coronary artery inflammation, which play a significant role in clinical diagnosis and evaluation of CAD. Abbreviations CAD=coronary artery disease; FAI =pericoronary fat attenuation index; CCTA =coronary computed tomography angiography; PCAT =pericoronary adipose tissue; ICA =invasive coronary angiography; BMI=body mass index; TC=total cholesterol; TG=triglyceride; HDL-C=high density lipoprotein cholesterol; LDL-C=low density lipoprotein cholesterol; ECG=electrocardiogram; LAD=left anterior descending; LCX=left circumflex; RCA=right coronary artery; DS=diameter stenosis; IVS =inter ventricular septum; LVPW =left ventricular posterior wall, LVEDD=left ventricular end-diastole diameter; LVESD=left ventricular end-systolic diameter; LVEDV=1eft ventricular end-diastolic volume; LVESV=1eft ventricular end-systolic volume; FS =fractional shortening; LVEF=left ventricular ejection fraction; LVM =left ventricular mass; LVMI =left ventricular mass index; BSA=body surface area; EFV=epicardial fat volume Declarations Ethics approval and consent to participate This retrospective study was approved by the affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University institutional review board, and the need to obtain informed consent was waived.All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication -Not applicable. Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due protect patient privacy, but are available from the corresponding author on reasonable request. Competing interests The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Funding Major science and technology projects of Changzhou science and Technology Bureau (CE20205047) and Natural Science Foundation of Xinjiang Autonomous Region (2022D01F52). Authors' contributions Deshu You:design,acquisition, analysis, interpretation of data Haiyang Yu: interpretation of data Zhiwei Wang:interpretation of data Xiaoyu Wei:the creation of new software used in the work Xiangxiang Wu:analysis; Changjie Pan: revised manuscript ,Guided the experimental process Acknowledgements ： Thank Professor Changjie Pan for his support to my scientific research.This study has received funding by Major science and technology projects of Changzhou science and Technology Bureau (CE20205047) and Natural Science Foundation of Xinjiang Autonomous Region (2022D01F52).Institutional Review Board approval was obtained by the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University institutional. References Ridker PM, Libby P, MacFadyen JG, et al. Modulation of the interleukin-6 signalling pathway and incidence rates of atherosclerotic events and all-cause mortality: analyses from the Canakinumab Anti-Inflammatory Thrombosis Outcomes Study (CANTOS). Eur Heart J. 2018;39(38):3499-3507. Antonopoulos AS, Angelopoulos A, Tsioufis K, et al. Cardiovascular risk stratification by coronary computed tomography angiography imaging: current state-of-the-art. Eur J Prev Cardiol. 2022;29(4):608-624. Antoniades C, Antonopoulos AS, Deanfield J. Imaging residual inflammatory cardiovascular risk. Eur Heart J. 2020;41(6):748-758. Ma R, van Assen M, Ties D, et al. Focal pericoronary adipose tissue attenuation is related to plaque presence, plaque type, and stenosis severity in coronary CTA. Eur Radiol. 2021;31(10):7251-7261. Zhou Y, Wei Y, Wang L, et al. Decreased adiponectin and increased inflammation expression in epicardial adipose tissue in coronary artery disease.Cardiovasc Diabetol, 2011; 10: 2. Zhu X, Chen X, Ma S, et al. Dual-layer spectral detector CT to study the correlation between pericoronary adipose tissue and coronary artery stenosis. J Cardiothorac Surg. 2021;16(1):325. Oikonomou EK, Marwan M, Desai MY, et al. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet. 2018;392(10151):929-939. Konwerski M, Gromadka A, Arendarczyk A, et al. Atherosclerosis Pathways are Activated in Pericoronary Adipose Tissue of Patients with Coronary Artery Disease. J Inflamm Res. 2021; 14: 5419-5431. Yao Y, Li X, Wang Z, et al. Interaction of Lipids, Mean Platelet Volume, and the Severity of Coronary Artery Disease Among Chinese Adults: A Mediation Analysis. Front Cardiovasc Med. 2022; 9:753171. Published 2022 Jan 31. Park S, Ahn JM, Kim TO, et al. Revascularization in Patients With Left Main Coronary Artery Disease and Left Ventricular Dysfunction. J Am Coll Cardiol. 2020;76(12):1395-1406. He XY, Gao CQ. Peri-operative application of intra-aortic balloon pumping reduced in-hospital mortality of patients with coronary artery disease and left ventricular dysfunction. Chin Med J (Engl). 2019;132(8):935-942. Writing Committee Members, Lawton JS, Tamis-Holland JE, et al. 2021 ACC/AHA/SCAI Guideline for Coronary Artery Revascularization: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2022; 79(2):197-215. Gensini GG. A more meaningful scoring system for determining the severity of coronary heart disease. Am J Cardiol. 1983;51(3):606. Schiller NB, Shah PM, Crawford M, et al. Recommendations for quantitation of the left ventricle by two-dimensional echocardiography. American Society of Echocardiography Committee on Standards, Subcommittee on Quantitation of Two-Dimensional Echocardiograms. J Am Soc Echocardiogr. 1989;2(5):358-367. Mohammadzadeh M, Mohammadzadeh V, Shakiba M, et al. Assessing the Relation of Epicardial Fat Thickness and Volume, Quantified by 256-Slice Computed Tomography Scan, With Coronary Artery Disease and Cardiovascular Risk Factors. Arch Iran Med. 2018;21(3):95-100. Yu W, Liu B, Zhang F, et al. Association of Epicardial Fat Volume With Increased Risk of Obstructive Coronary Artery Disease in Chinese Patients With Suspected Coronary Artery Disease. J Am Heart Assoc. 2021; 10(6): e018080. Liu J, Li J, Pu H, et al. Cardiac remodeling and subclinical left ventricular dysfunction in adults with uncomplicated obesity: a cardiovascular magnetic resonance study. Quant Imaging Med Surg. 2022;12(3):2035-2050. de Wit-Verheggen VHW, Altintas S, Spee RJM, et al. Pericardial fat and its influence on cardiac diastolic function. Cardiovasc Diabetol. 2020;19(1):129. Azab M, Al-Shudifat AE, Johannessen A, et al. Are Risk Factors for Coronary Artery Disease Different in Persons With and Without Obesity? Metab Syndr Relat Disord. 2018;16(8):440-445. Qu Y, Yang J, Zhang F, et al. Relationship between body mass index and outcomes of coronary artery disease in Asian population: Insight from the FOCUS registry. Int J Cardiol. 2020; 300:262-267. Guan B, Liu L, Li X, et al. Association between epicardial adipose tissue and blood pressure: A systematic review and meta-analysis. Nutr Metab Cardiovasc Dis. 2021;31(9):2547-2556. Rabkin SW. The relationship between epicardial fat and indices of obesity and the metabolic syndrome: a systematic review and meta-analysis. Metab Syndr Relat Disord. 2014;12(1):31-42. Xie Z, Zhu J, Li W, et al. Relationship of epicardial fat volume with coronary plaque characteristics, coronary artery calcification score, coronary stenosis, and CT-FFR for lesion-specific ischemia in patients with known or suspected coronary artery disease. Int J Cardiol. 2021; 332:8-14. Yin R, Tang X, Wang T, et al. Cardiac CT scanning in coronary artery disease: Epicardial fat volume and its correlation with coronary artery lesions and left ventricular function. Exp Ther Med. 2020;20(4):2961-2968. Mahabadi AA, Berg MH, Lehmann N, et al. Association of epicardial fat with cardiovascular risk factors and incident myocardial infarction in the general population: the Heinz Nixdorf Recall Study. J Am Coll Cardiol. 2013;61(13):1388-1395. de Vos AM, Prokop M, Roos CJ, et al. Peri-coronary epicardial adipose tissue is related to cardiovascular risk factors and coronary artery calcification in post-menopausal women. Eur Heart J. 2008;29(6):777-783. Williams KJ, Tabas I. Atherosclerosis--an inflammatory disease. N Engl J Med. 1999;340(24):1928-1929. Oikonomou EK, Antonopoulos AS, Schottlander D, et al. Standardized measurement of coronary inflammation using cardiovascular computed tomography: integration in clinical care as a prognostic medical device. Cardiovasc Res. 2021;117(13):2677-2690. Antonopoulos AS, Sanna F, Sabharwal N, et al. Detecting human coronary inflammation by imaging perivascular fat. Sci Transl Med. 2017;9(398): eaal2658. Liu Y, Sun Y, Hu C, et al. Perivascular Adipose Tissue as an Indication, Contributor to, and Therapeutic Target for Atherosclerosis. Front Physiol. 2020; 11:615503. Dai X, Yu L, Lu Z, Shen C, Tao X, Zhang J. Serial change of perivascular fat attenuation index after statin treatment: Insights from a coronary CT angiography follow-up study. Int J Cardiol. 2020; 319:144-149. Appiah D, Nwabuo CC, Ebong IA, et al. The association of age at natural menopause with pre- to postmenopausal changes in left ventricular structure and function: the Coronary Artery Risk Development in Young Adults (CARDIA) study. Menopause. 2022;10.1097/GME.0000000000001950. Kwok CS, Bachmann MO, Mamas MA, et al. Effect of age on the prognostic value of left ventricular function in patients with acute coronary syndrome: A prospective registry study. Eur Heart J Acute Cardiovasc Care. 2017;6(2):191-198. Siontis GC, Branca M, Serruys P, et al. Impact of left ventricular function on clinical outcomes among patients with coronary artery disease. Eur J Prev Cardiol. 2019;26(12):1273-1284. Honold S, Wildauer M, Beyer C, et al. Reciprocal communication of pericoronary adipose tissue and coronary atherogenesis. Eur J Radiol. 2021; 136:109531. Bengs S, Haider A, Warnock GI, et al. Quantification of perivascular inflammation does not provide incremental prognostic value over myocardial perfusion imaging and calcium scoring. Eur J Nucl Med Mol Imaging. 2021;48(6):1806-1812. Tables Table 1. Gensini scoring system. DS（%） Score Location Corresponding scores 0-25 1 LM 5.0 26-50 2 Proximal segment of LAD or LCX 2.5 51-75 4 Middle segment of LAD 1.5 76-90 8 Distal segment of LAD or LCX 1 91-99 16 RCA, PDA 1 100 100 D1, OM 1 D2、PLV 0.5 LM= left main, LAD= left anterior descending, LCX= left circumflex, RCA=right coronary artery, PDA=posterior descending artery, OM=obtuse marginal, D1=first diagonal, D2=second diagonal, PLV= Posterior left ventricular branch. Table 2. Baseline Characteristics of Study Participants Characteristics Result Age（y） * 64.55±10.64 Gender Males 104（65.4％） Females 55（34.6％） Height + 165（160-170） Weight 66.69±11.21 BMI 24.42±3.12 Heart rate + 74（66-80） Gensini score + 19（10, 41） Risk factors of CAD Hypertension 116（73.0％） Diabetes 63（39.6％） Dyslipidemia 95（59.7％） Smoking 58（36.5％） Drinking 24（15.1％） Note * Date are means ± standard deviations. +Data are the median, with the interquartile range in parentheses. Table 3. Relationship between PCAT volume and CAD related risk factors. group n LAD LCX RCA Total-Volume volume P volume P volume P lume P total 159 1508.05±466.01 808.35(600.79, 1136.64) 1771.94(1415.12, 2110.19) 4185.25±1128.80 hypertension 0.678 0.954 0.175 0.599 yes 116 1498.66±429.52 871.45±351.15 1843.89±547.27 4214.00±1045.14 no 43 1533.38±557.58 867.76±390.23 1706.54±609.47 4107.68±1339.43 diabetes 0.705 0.304 0.563 0.840 yes 63 1490.72±391.45 1490.72±391.45 1838.03±523.76 4162.77±910.17 no 96 1519.43±510.73 1519.43±510.73 1786.21±594.06 4200.01±1256.18 dyslipidemia 0.654 0.499 0.071 0.506 yes 95 1521.69±444.34 854.50±330.34 1858.07±550.06 4234.26±1073.34 no 64 1487.81±499.34 894.14±403.53 1680.80(1335.61, 1915.44) 4112.50±1211.47 smoking 0.327 0.415 0.150 0.145 yes 58 1556.01±497.75 776.65(635.17, 1235.13) 1892.12±548.84 4357.67±1120.09 no 101 1480.51±446.99 848.01±360.07 1757.72±572.72 4086.24±1127.37 drinking 0.130 0.028 0.103 0.025 yes 24 1640.77±579.42 1037.84±375.47 1980.53±652.73 4659.15±1202.05 no 135 1484.46±441.27 791.59(585.98, 1093.43) 1775.85±546.24 4101.00±1098.65 The data with normal distribution were described as mean ± standard deviation (SD) and tested by independent-samples Student t test, while data with non-normal distribution were described as median (upper quartile, lower quartile) and tested by Mann-Whitney U test. Table 4. Relationship between FAI and CAD related risk factors. x group n LAD LCX RCA FAI P FAI P FAI P Total 159 -83.00(-88.00, -79.00) -76.03±6.86 -79.58±8.70 hypertension 0.808 0.809 0.511 yes 116 -82.97±7.49 -76.11±7.07 -79.86±8.56 no 43 -83.30±8.45 -75.81±6.35 -78.84±9.11 diabetes 0.875 0.466 0.423 yes 63 -82.94±6.95 -75.54±7.08 -80.27±7.72 no 96 -83.14±8.25 -76.35±6.74 -79.14±9.30 dyslipidemia 0.474 0.073 0.584 yes 95 -82.69±7.78 -75.23±6.60 -79.27±8.54 no 64 -83.59±7.71 -77.22±7.13 -80.05±8.97 smoking 0.429 0.826 0.671 yes 58 -82.41±8.08 -76.19±7.52 -79.00±8.42 no 101 -83.43±7.55 -75.94±6.50 -80.50(-86.00, -73.75) drinking 0.746 0.670 0.880 yes 24 -82.58±9.41 -76.58±8.02 -79.83±8.11 no 135 -83.14±7.44 -75.93±6.67 -79.54±8.83 The statistical parameters are consistent with table 3 Additional Declarations No competing interests reported. Supplementary Files Supplement1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 28 Jul, 2022 Reviews received at journal 26 Jul, 2022 Reviewers agreed at journal 26 Jul, 2022 Reviewers agreed at journal 21 Jul, 2022 Reviewers invited by journal 20 Jul, 2022 Editor assigned by journal 19 Jul, 2022 Editor invited by journal 11 Jul, 2022 Submission checks completed at journal 11 Jul, 2022 First submitted to journal 04 Jul, 2022 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1823340","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":120086393,"identity":"bee62363-b4dd-44f0-ad02-be605d082f73","order_by":0,"name":"Deshu You","email":"","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Deshu","middleName":"","lastName":"You","suffix":""},{"id":120086398,"identity":"059fe569-378c-4a78-bf2b-5a97adbd8571","order_by":1,"name":"Haiyang Yu","email":"","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haiyang","middleName":"","lastName":"Yu","suffix":""},{"id":120086400,"identity":"b205c4bd-bcf0-4446-9a5e-79a14ad66f76","order_by":2,"name":"Zhiwei Wang","email":"","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Wang","suffix":""},{"id":120086405,"identity":"d8ccfac7-7d44-4938-ae77-ac63b08f6fd3","order_by":3,"name":"Xiaoyu Wei","email":"","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Wei","suffix":""},{"id":120086406,"identity":"8ada7b4e-9e85-4eef-b3c3-6f6eca523737","order_by":4,"name":"Xiangxiang Wu","email":"","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiangxiang","middleName":"","lastName":"Wu","suffix":""},{"id":120086409,"identity":"68a5c4be-be55-4331-af8e-d94577fbc13a","order_by":5,"name":"Changjie Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYJACZhDB2MzA+ICBByxgQLQWZgPStAABmwSUgV+LOfvZg58Lau7YNbczH6sukNmW2MDevE2CoeYOTi2WPXnJ0jOOPUtubGZLuz2D53ZiA8+xMgmGY89wajE4kGPGzMN2OJmxmcfsNg9Ii0SOmQRjw2HcWs6/AWr5B9LC/60YrEX+DQEtN4C28LYdtgPawsYMsYUHvxbLGW+MpXn7DicwNrMZSwO1GLfxpBVbJBzDrcWcP8fwM8+3w/aG/YcffubtuS3bz354440PNXgcBqUTNzYAScYeYOyAuAk4NSC02MuDqR94lI6CUTAKRsGIBQDIRVC/l5LZOwAAAABJRU5ErkJggg==","orcid":"","institution":"Changzhou No.2 People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Changjie","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2022-07-04 10:29:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1823340/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1823340/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24202368,"identity":"7d96d69e-6541-4747-baaa-79be7e614e4f","added_by":"auto","created_at":"2022-07-22 16:20:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43154,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study inclusion and exclusion.\u0026nbsp;\t\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1823340/v1/eb2d3b0511405e4c7d588290.png"},{"id":24202367,"identity":"b10f752b-5b67-48bd-9c12-9564a0e67d5c","added_by":"auto","created_at":"2022-07-22 16:20:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":342862,"visible":true,"origin":"","legend":"\u003cp\u003eThe PCAT extracted by Skviewer software automatically. d1= diameter of the target vessel; d2=extracted radial distance of PCAT\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1823340/v1/e12953159eec719739065a66.png"},{"id":24202365,"identity":"477fe771-fe60-4bc0-87e4-68926b17848f","added_by":"auto","created_at":"2022-07-22 16:20:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49965,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution range of PCAT volume.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1823340/v1/dbd935ceb25ee16e1be3cea7.png"},{"id":24202362,"identity":"485b4894-32a6-4bf1-ad24-f7c1d9ee3306","added_by":"auto","created_at":"2022-07-22 16:20:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47799,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution range of FAI.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1823340/v1/4f81e6b45fff1bd10ad314d7.png"},{"id":24202366,"identity":"6fa3a0ec-3932-4e5e-abc5-d0284940692d","added_by":"auto","created_at":"2022-07-22 16:20:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":173671,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of PCAT volume or FAI and Gensini score or BMI.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1823340/v1/1b098abaa87a9a4cf4a43ded.png"},{"id":24202708,"identity":"55b77751-73e2-4c08-bf95-aa6c2246c00b","added_by":"auto","created_at":"2022-07-22 16:25:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88901,"visible":true,"origin":"","legend":"\u003cp\u003ePartial correlation analysis results of PCAT and left ventricular function parameters. The results were presented in the form of a matrix. The LAD-FAI was positively correlated with IVS(r=0.203, P=0.013),LVPW(r=0.218, P=0.008),LVEDD(r=0.317, P＜0.001), LVESD(r=0.298, P＜0.001), LVEDV(r=0.317, P＜0.001), LVESV(r=0.301, P＜0.001), LVM(r=0.371, P＜0.001)and LVMI(r=0.304, P＜0.001). The LCX-FAI was positively correlated with LVEDD(r=0.199, P=0.015)，LVESD(r=0.190, P=0.021), LVEDV(r=0.203, P=0.013), LVESV(r=0.197, P=0.016), LVM(r=0.220, P=0.007)and LVMI(r=0.172, P=0.036). The RCA-FAI was positively correlated with LVEDD(r=0.258, P=0.002)，LVESD(r=0.238, P=0.004), LVEDV(r=0.266, P=0.001), LVESV(r=0.249, P=0.002), LVM(r=0.237, P=0.004), and LVMI(r=0.218, P=0.008). The total volume was positively correlated with FS (r=0.167, P=0.042).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-1823340/v1/984a5ea5d3fc4cb30190094e.png"},{"id":24203771,"identity":"6adcdef7-9d2f-410c-93d1-ac2e88a97c0b","added_by":"auto","created_at":"2022-07-22 16:30:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1090682,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1823340/v1/02ef2b6e-e610-428b-8c35-40deeb83505e.pdf"},{"id":24203765,"identity":"b01e9990-c2bb-4067-8564-7dbd32a2f395","added_by":"auto","created_at":"2022-07-22 16:30:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16157,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1.docx","url":"https://assets-eu.researchsquare.com/files/rs-1823340/v1/f48c5c07111264e9fcb9fc31.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Correlation of Pericoronary Adipose Tissue with Coronary artery disease and left ventricular function","fulltext":[{"header":"Key Points:","content":"\u003col\u003e\n \u003cli\u003eThere is a significant correlation between FAI and left ventricular function parameters.\u003c/li\u003e\n \u003cli\u003eOur results suggest that cardiac mortality caused by increased FAI may be due to changes in left ventricular function.\u003c/li\u003e\n \u003cli\u003ePCAT had no correlation with BMI and risk factors of coronary heart disease.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"1.\tIntroduction","content":"\u003cp\u003eCoronary\u0026nbsp;artery\u0026nbsp;disease（CAD）is referred to a heart disease in which atherosclerotic plaque occurs in the coronary artery, causing coronary lumen stenosis or occlusion, then resulting in myocardial ischemia or necrosis. Coronary atherosclerosis has been considered to be an inflammatory reaction \u003csup\u003e[1]\u003c/sup\u003e.\u0026nbsp;For patients with CAD, predicting the risk of adverse coronary events is more important than coronary stenosis. The detection of pericoronary inflammation can help patients with coronary heart disease risk stratification and risk prediction early.\u003c/p\u003e\n\u003cp\u003eIn recent years, pericoronary fat attenuation index (FAI) measured based on coronary CT angiography (CCTA) image can be used to reflect the inflammation of pericoronary \u003csup\u003e[2-3]\u003c/sup\u003e.\u0026nbsp;Recent studies had shown that\u0026nbsp;pericoronary adipose tissue\u0026nbsp;(PCAT) has been confirmed to participate in the process of coronary inflammation, and significantly related to the type of plaque and the degree of stenosis\u0026nbsp;\u003csup\u003e[4-6]\u003c/sup\u003e. One CRISP study showed that FAI\u0026gt; -70.1 HU was associated with the higher risk of cardiac mortality, which can improve the prediction of heart risk\u0026nbsp;\u003csup\u003e[7]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough PCAT was shown great clinical significance in the occurrence and development of CAD \u003csup\u003e[8]\u003c/sup\u003e, there are\u0026nbsp;several important questions have to be explored in this\u0026nbsp;popular field.\u0026nbsp;At first, Gensini score is a traditional index used to evaluate the severity of CAD \u003csup\u003e[9]\u003c/sup\u003e, however, it is unknown that can PCAT assess the severity of CAD. Secondly, some studies showed that CAD is a cause of left ventricular dysfunction and left ventricular dysfunction will increase the mortality of CAD, but the association between PCAT and left ventricular function is unclear\u0026nbsp;\u003csup\u003e[10,11]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe aim of this study is to investigate the association of PCAT with CAD and left ventricular function, which can help clinicians evaluate the prognosis of patients with CAD.\u003c/p\u003e"},{"header":"2.\tMethods","content":"\u003cp\u003eThis retrospective study was approved by\u0026nbsp;the Affiliated Changzhou No. 2 People\u0026apos;s Hospital of Nanjing Medical University\u0026nbsp;institutional\u0026nbsp;review board, and the need to obtain informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cem\u003e2.1\u0026nbsp;Study patient\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe subjects were patients with treated in\u0026nbsp;the Affiliated Changzhou No. 2 People\u0026apos;s Hospital of Nanjing Medical University\u0026nbsp;from September 2019 to September 2021.We retrospectively included patients with clinically suspected CAD in our institution, who underwent\u0026nbsp;CCTA and echocardiography followed by invasive coronary angiography (ICA) within 6 month.\u0026nbsp;Exclusion criteria include: (a) history of coronary\u0026nbsp;myocardial infarction and cardiac surgery, (b) anatomical variation of heart or coronary artery, (c) diseases that seriously affect heart function such as\u0026nbsp;heart space occupying, cardiomyopathy and severe heart valve disease et al., (d) family history of CAD, and\u0026nbsp;(e) the failure of image reconstruction. (Fig 1)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cem\u003e2.2\u0026nbsp;Clinical data\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical data of patients were obtained from the inpatient cases system. Hypertension, diabetes and dyslipidemia were diagnosed according to clinical indicators. Hypertension is defined as systolic blood pressure \u0026ge; 140mmHg and / or diastolic blood pressure \u0026ge; 90mmHg or taking antihypertensive drugs. The diagnosis of diabetes is based on the guideline for prevention and treatment of type 2 diabetes. It is defined as fasting blood glucose \u0026gt; 6.1mmol/L and / or glycosylated hemoglobin \u0026gt; 6.5% or hypoglycemic drugs. Dyslipidemia according to the guidelines for the prevention and treatment of dyslipidemia in adults, dyslipidemia is defined as meeting one or more of the following requirements:\u0026nbsp;(a)total cholesterol (TC)\u0026ge; 5.18mmol／L, (b)triglyceride (TG)\u0026ge;1.70mmol／L, (c)high density lipoprotein cholesterol (HDL-C)＜1.04mmol／L, (d)low density lipoprotein cholesterol (LDL-C)\u0026ge; 3.37mmol／L, (e)Taking blood lipid regulating drugs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;2.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eCCTA Image acquisition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCCTA was performed on Siemens third generation dual source CT (SOMATOM definition force CT; Siemens AG). Before scanning, the patient rested quietly for at least 15 minutes, and all patients did not take drugs such as \u0026beta;-blocker to slow down their heart rate. The scanning range was from the superior sternal fossa to the diaphragmatic surface of the heart. The prospective electrocardiogram (ECG) gating sequence was used for scanning, and the scanning parameters are as follows: the tube voltage is 100kV, the tube current was 23mA, the CT rotation time was 0.25s, the layer thickness was 0.75mm, the reconstruction interval was 0.50mm, and the display matrix was 512 \u0026times; 512. Iodixanol injection (100ml: 32g; Qing Liming) was injected through the middle elbow vein. The injection volume was 60 ~ 80ml and the injection speed was5 ~ 6ml / s.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cem\u003e2.4\u0026nbsp;Coronary artery reconstruction\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scanned images were transmitted to Skviewer software (Coronary System, Shukun Technology, Beijing, China). At first, the image was processed for image consistency to eliminate the impact of different window width and window level on the quality of reconstructed image. Then Coronary System was used for longitudinal and axial multiplanar reconstruction (MPR) of coronary artery. Finally, an experienced radiologist evaluates the image quality of coronary artery reconstruction: 1-4 points respectively represent poor image quality (excluded), acceptable image quality, good image quality and excellent image quality.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cem\u003e2.5\u0026nbsp;Perivascular fat analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PCAT was extracted automatically by using Skviewer software FAI intelligent analysis system (Skviewer FAI, Shukun Technology, Beijing, China). Volume and FAI of PCAT were measured by the method described by Oikonomou. et al \u003csup\u003e[7]\u003c/sup\u003e: The threshold value of PCAT was -190 to -30 HU, the measured length was 40mm, and the extracted radial distance was the average diameter of the target vessel. The segment analyzed was the proximal 40 mm of left anterior descending (LAD) and left circumflex (LCX), and the\u0026nbsp;proximal 10-50 mm of right coronary artery (RCA).\u0026nbsp;The software could extract automatically, and when the automatic extraction was inaccurate, adjusted the extraction range manually. Subsequently, the PCAT volume and FAI were calculated by the software automatically (Fig 2). The above measurement results were completed by an experienced radiologist independently. In order to test the consistency of measurement results, 20% (32) patients were randomly selected one month later, and the second measurement was carried out by two radiologists. The intra observer consistency test was carried out on the results measured by the same doctor, and the inter observer consistency test was carried out on the results measured by two doctors\u0026nbsp;(Supplement 1).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cem\u003e2.6\u0026nbsp;ICA and Gensini score calculation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the 2021 ACC / AHA angiography guidelines of the United States, the patients were punctured through the right femoral artery or radial artery with the conventional Seldinger standard method. The left and right coronary arteries were successively imaged with multi-position irradiation imaging \u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The results of ICA were evaluated by lumen diameter stenosis (DS). The segment score was obtained by multiplying the corresponding scores of the vascular segment where the plaque was located by the score of stenosis. The sum of the total scores of each segment was total Gensini score \u003csup\u003e[13]\u003c/sup\u003e. (Table 1)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cem\u003e2.7\u0026nbsp;Left ventricular function parameter acquisition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the current guidelines and diagnostic criteria by American Society of Echocardiography and European Association of Cardiovascular Imaging\u003csup\u003e[14]\u003c/sup\u003e,the echocardiography was acquired by Vivid E9 (GE Vingmed) echocardiography system with an M5S transducer(3.5 MHz).The patient took the left-lying position, laid still for 5 minutes and breathed calmly, then took the long axis section of the left ventricle to obtain the inter ventricular septum(IVS), left ventricular posterior wall (LVPW), left ventricular end-diastole diameter(LVEDD), left ventricular end-systolic diameter( LVESD), 1eft ventricular end-diastolic volume(LVEDV), 1eft ventricular end-systolic volume(LVESV), then calculated the left ventricular fractional shortening (FS), left ventricular ejection fraction (LVEF), \u0026nbsp;left ventricular mass (LVM), left ventricular mass index (LVMI).The calculation formula is as follows:\u003c/p\u003e\n\u003cp\u003eLVEF=（LVEDV-LVESV）/LVEDV\u0026times;100%\u003c/p\u003e\n\u003cp\u003eFS（%）=（LVEDD-LVESD）/LVEDD\u003c/p\u003e\n\u003cp\u003eLVM（g）=0.8*1.04*[(IVS+LPWT+LVEDD)3-LVESD3]+0.6\u003c/p\u003e\n\u003cp\u003eLVMI（%）=LVM/ BSA\u003c/p\u003e\n\u003cp\u003eBSA（m2）=0.0061\u0026times;height（cm）+0.0128\u0026times;weight(kg)-0.1529\u003c/p\u003e\n\u003cp\u003eNote:BSA= body surface area\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.8\u0026nbsp;Statistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analysis was performed by using SPSS\u0026nbsp;(SPSS Statistics for Windows Version 23.0, IBM,\u0026nbsp;Armonk, NY). The counting data were expressed in quantity and percentage, and the continuous variables\u0026nbsp;were described as mean \u0026plusmn; standard deviation (SD) or median (upper quartile, lower quartile).\u0026nbsp;Two subgroups comparisons of continuous variables used the two independent samples Student\u0026nbsp;\u003cem\u003et\u003c/em\u003e test or Mann-Whitney \u003cem\u003eU\u003c/em\u003e test.\u0026nbsp; The\u0026nbsp;correlation between PCAT volume or FAI and\u0026nbsp;Gensini score and left ventricular function parameters\u0026nbsp;was assessed by using Spearman correlation analysis.\u0026nbsp;In order to eliminate the influence of interference factors, we used partial correlation analysis to analyze the correlation between PCAT volume or FAI and Gensini score and left ventricular function parameters.\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.05 was considered to indicate statistical significance. Intra-group correlation coefficient(ICC) was used to evaluate intra and inter observer consistency.\u003c/p\u003e"},{"header":"3. Result","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1\u0026nbsp;Baseline Characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInitially, 197 participants with clinically suspected CAD were included. According to strict exclusion criteria, finally, 159 patients (mean age, 64.55 years \u0026plusmn; 10.64; range, 43\u0026ndash;89 years) were enrolled in this study (Figure 1). There were 104 males (mean age, 62.03years \u0026plusmn;10.58; range, 43-89years) and 55 females (mean age, 69.33years\u0026plusmn;9.05; range, 51-85 years). Other baseline characteristics are shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 Relationship between PCAT volume and CAD risk factors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PCAT volumes of all participants were divided into two subgroups according to whether they had hypertension, diabetes,\u0026nbsp;dyslipidemia, smoking, and drinking.\u0026nbsp;Table 3 showed the distribution of volume among the subgroups. The results showed that there were statistically significant differences in the LCX-Volume and Total-Volume between the subgroups (\u003cem\u003eP\u003c/em\u003e =0.028, \u003cem\u003eP\u003c/em\u003e=0.025). The other subgroups were no significant. (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). The distribution range of PCAT volume is shown in Figure 3.\u0026nbsp;(The consistency of PCAT\u0026nbsp;intra\u0026nbsp;and\u0026nbsp;inter observer\u0026nbsp;was good [P \u0026lt; 0.001], [Supplement 1])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3 Relationship between FAI and CAD risk factors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 showed the distribution of FAI among different subgroups. The results showed that there was no significant difference in FAI between the subgroups (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). The Figure 4 show the distribution range of FAI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4 Correlation analysis of PCAT volume or FAI and Gensini score or BMI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman correlation or Pearson analysis were used to analyze the correlation between PCAT and Gensini score and BMI. The results showed that the volumes of LCX and total were positively correlated with BMI\u0026nbsp;(r = 0.170, \u003cem\u003eP\u003c/em\u003e =0.032; r = 0.157, \u003cem\u003eP\u003c/em\u003e =0.048). There was no significant correlation between the volume of LAD and RCA and BMI, and no correlation between FAI and BMI. The PCAT was also not correlated with Gensini score. As shown in Figure 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.5 Correlation analysis of PCAT volume or FAI and left ventricular function parameters.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used partial correlation analysis of control variables, excluding confounding factors including age, gender, height, weight, BMI, smoking, drinking, heart rate, hypertension, diabetes and hyperlipidemia, then analyzed the correlation between PCAT and left ventricular function parameters (Figure 6).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWhile using CCTA to diagnose coronary artery stenosis, we can predict the prognosis of patients with CAD through the analysis of PCAT. We analyzed the relationship between PCAT and CAD, and found that there was no correlation between PCAT volume and Gensini score and cardiac function parameters. However, some studies on the correlation between epicardial fat volume (EFV)\u0026nbsp;and CAD and cardiac function show that the increase of EFV was positively correlated with the risk of CAD and left ventricular function parameters \u003csup\u003e[15-18]\u003c/sup\u003e. The possible reason is that the measurement methods are different. The extraction range of EFV is all adipose tissue from the aortic root to the apex of the heart, and the radial range of extraction of PCAT is equal to the diameter of the target vessel.\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that EVF is correlated with traditional risk factors of coronary heart disease \u003csup\u003e[19-21]\u003c/sup\u003e, and EVF is positively correlated with BMI \u003csup\u003e[22]\u003c/sup\u003e. However, some research results show that EVF can be an independent predictor of CAD \u003csup\u003e[23]\u003c/sup\u003eand is not associated with the traditional risk factors of CAD \u003csup\u003e[24, 25]\u003c/sup\u003e. Therefore, we analyzed the correlation between PCAT volume and BMI and risk factor for CAD. Our results showed that PCAT volume had no correlation with BMI and risk factors of coronary heart disease. These differences in these results may be related to different measurement methods, pathological changes in the natural course of the disease and lifestyle and drug intervention. Our results showed that only the results of LCX and total volume were statistically significant(P＜0.05). This may be related to the less adipose tissue around LCX and the method we adopted can cover the more complete volume of vessel. The correlation shown by the total volume may be affected by LCX. In a study of pericoronary epicardial adipose tissue by Vos et al. \u003csup\u003e[26]\u003c/sup\u003e, only the fat volume around LCX showed different results from LAD and RCA, which was similar to our results.\u003c/p\u003e\n\u003cp\u003eInflammation is a critical factor not only for the development but also for the progression of atherosclerosis \u003csup\u003e[1, 27]\u003c/sup\u003e. Inflammatory factors released by arterial wall can induce lipolysis, inhibit lipogenesis and promote perivascular edema. These changes showed attenuation from lipid (close to - 190 Hu) to water (close to - 30 Hu) on CT \u003csup\u003e[28, 29]\u003c/sup\u003e. Therefore, coronary inflammation can be detected by FAI.\u003c/p\u003e\n\u003cp\u003eOne study by Antonopoulos et al. \u003csup\u003e[29]\u003c/sup\u003e found that FAI was positively correlated with atherosclerotic plaque load, and higher perivascular FAI was associated with higher inflammatory expression levels. But we found that there was no correlation between PCAT FAI and Gensini score (P＞0.05). What triggers this result? It may be that we did not stratify the population according to the Gensini score, and there were only 26 patients (16%) with severe CAD (Gensini score \u0026gt; 60) in our study population. Meanwhile, some studies have shown that FAI can present dynamic changes with the prognosis of the disease, and patients with statins, aspirin or antidiabetic drugs will also change the FAI \u003csup\u003e[30, 31]\u003c/sup\u003e. These may cause our results to be different with others.\u003c/p\u003e\n\u003cp\u003eOur further analysis found that there was no significant difference between FAI and BMI (LAD r =0.137, \u003cem\u003eP\u003c/em\u003e =0.086; LCX r=0.124, \u003cem\u003eP\u003c/em\u003e=0.118; RCA r=0.021, \u003cem\u003eP\u003c/em\u003e=0.790). And between FAI and risk factors of CAD, the results were no relationship. These results can further support that FAI is a reliable imaging index to quantify coronary artery inflammation and it is not affected by other risk factors. The study of Antonopoulos et al. \u003csup\u003e[29]\u003c/sup\u003e found that FAI was not associated with traditional cardiovascular risk factors, which was similar to our results.\u003c/p\u003e\n\u003cp\u003eSince the parameters of left ventricular function are affected by individual differences \u003csup\u003e[32, 33]\u003c/sup\u003e, we analyzed the PCAT and left ventricular function parameters by using partial correlation analysis to exclude the effects of interference factors. There was no correlation between PCAT and left ventricular function parameters, but a significant correlation between FAI and left ventricular function parameters. This may be because coronary artery and myocardium has no obvious boundary, and the PCAT can release cytokines reach myocardium and coronary artery through paracrine signaling mechanisms, which results in the changes of myocardial and left ventricular function \u003csup\u003e[34]\u003c/sup\u003e. In addition, studies have shown that left ventricular dysfunction and increased FAI (\u0026gt; 70.1HU) can significantly increase the cardiac mortality \u003csup\u003e[31,35]\u003c/sup\u003e. Our results suggest that cardiac mortality caused by increased FAI may be due to changes in left ventricular function.\u003c/p\u003e\n\u003cp\u003eThere are several limitations to this study that should be pointed out. First, this study is a single-center retrospective analysis with a small sample size. Second, in our study, the number of patients with severe CAD was small, and we did not stratify the severity of patients, which may lead our results bias. Third, our study was a retrospective study and failed to obtain the treatment and lifestyle intervention of patients. Fourth, some studies have shown that there are differences in FAI between different genders \u003csup\u003e[36]\u003c/sup\u003e, moreover women account for less in our study (34.6%), which may also cause deviation to the results. Further research is needed to identify sex-specific variables that explain the correlation between PCAT and left ventricular function accurately.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe results showed that there was no correlation between PCAT volume and FAI and the severity of CAD, but there was positively correlated between FAI and left ventricular function parameters, and the correlation of LAD was the strongest. Moreover, our study found that FAI has no significant relationship with BMI and traditional risk factors of CAD. It further supports that FAI can be used as a reliable imaging index of coronary artery inflammation, which play a significant role in clinical diagnosis and evaluation of CAD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCAD=coronary artery disease; FAI =pericoronary fat attenuation index; CCTA =coronary computed tomography angiography; PCAT =pericoronary adipose tissue; ICA =invasive coronary angiography; BMI=body mass index; TC=total cholesterol; TG=triglyceride; HDL-C=high density lipoprotein cholesterol; LDL-C=low density lipoprotein cholesterol; ECG=electrocardiogram; LAD=left anterior descending; LCX=left circumflex; RCA=right coronary artery; DS=diameter stenosis; IVS =inter ventricular septum; LVPW =left ventricular posterior wall, LVEDD=left ventricular end-diastole diameter; LVESD=left ventricular end-systolic diameter; LVEDV=1eft ventricular end-diastolic volume; LVESV=1eft ventricular end-systolic volume; FS =fractional shortening; LVEF=left ventricular ejection fraction; LVM =left ventricular mass; LVMI =left ventricular mass index; BSA=body surface area; EFV=epicardial fat volume\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective\u0026nbsp;study was\u0026nbsp;approved by the affiliated Changzhou No. 2 People\u0026apos;s Hospital of Nanjing Medical University\u0026nbsp;institutional review board, and the need to obtain informed consent was waived.All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e-Not applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due\u0026nbsp;protect patient privacy,\u0026nbsp;but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMajor science and technology projects of Changzhou science and Technology Bureau (CE20205047) and Natural Science Foundation of Xinjiang Autonomous Region (2022D01F52).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeshu You:design,acquisition, analysis,\u0026nbsp;interpretation of data\u003c/p\u003e\n\u003cp\u003eHaiyang Yu:\u0026nbsp;interpretation of data\u003c/p\u003e\n\u003cp\u003eZhiwei Wang:interpretation of data\u003c/p\u003e\n\u003cp\u003eXiaoyu Wei:the creation of new software used in the work\u003c/p\u003e\n\u003cp\u003eXiangxiang Wu:analysis;\u003c/p\u003e\n\u003cp\u003eChangjie Pan:\u0026nbsp;revised\u0026nbsp;manuscript\u0026nbsp;,Guided the experimental process\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank Professor Changjie Pan for his support to my scientific research.This study has received funding by Major science and technology projects of Changzhou science and Technology Bureau (CE20205047) and Natural Science Foundation of Xinjiang Autonomous Region (2022D01F52).Institutional Review Board approval was obtained by the Affiliated Changzhou No. 2 People\u0026apos;s Hospital of Nanjing Medical University institutional.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRidker PM, Libby P, MacFadyen JG, et al. Modulation of the interleukin-6 signalling pathway and incidence rates of atherosclerotic events and all-cause mortality: analyses from the Canakinumab Anti-Inflammatory Thrombosis Outcomes Study (CANTOS). Eur Heart J. 2018;39(38):3499-3507.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAntonopoulos AS, Angelopoulos A, Tsioufis K, et al. Cardiovascular risk stratification by coronary computed tomography angiography imaging: current state-of-the-art.\u0026nbsp;Eur J Prev Cardiol. 2022;29(4):608-624.\u003c/li\u003e\n \u003cli\u003eAntoniades C, Antonopoulos AS, Deanfield J. Imaging residual inflammatory cardiovascular risk. Eur Heart J. 2020;41(6):748-758.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMa R, van Assen M, Ties D, et al. Focal pericoronary adipose tissue attenuation is related to plaque presence, plaque type, and stenosis severity in coronary CTA.\u0026nbsp;Eur Radiol. 2021;31(10):7251-7261.\u003c/li\u003e\n \u003cli\u003eZhou Y, Wei Y, Wang L, et al. Decreased adiponectin and increased inflammation expression in epicardial adipose tissue in coronary artery disease.Cardiovasc Diabetol, 2011; 10: 2.\u003c/li\u003e\n \u003cli\u003eZhu X, Chen X, Ma S, et al. Dual-layer spectral detector CT to study the correlation between pericoronary adipose tissue and coronary artery stenosis. J Cardiothorac Surg. 2021;16(1):325.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOikonomou EK, Marwan M, Desai MY, et al. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data.\u0026nbsp;Lancet. 2018;392(10151):929-939.\u003c/li\u003e\n \u003cli\u003eKonwerski M, Gromadka A, Arendarczyk A, et al. Atherosclerosis Pathways are Activated in Pericoronary Adipose Tissue of Patients with Coronary Artery Disease. J Inflamm Res. 2021; 14: 5419-5431.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYao Y, Li X, Wang Z, et al. Interaction of Lipids, Mean Platelet Volume, and the Severity of Coronary Artery Disease Among Chinese Adults: A Mediation Analysis.\u0026nbsp;Front Cardiovasc Med. 2022; 9:753171. Published 2022 Jan 31.\u003c/li\u003e\n \u003cli\u003ePark S, Ahn JM, Kim TO, et al. Revascularization in Patients With Left Main Coronary Artery Disease and Left Ventricular Dysfunction. J Am Coll Cardiol. 2020;76(12):1395-1406.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHe XY, Gao CQ. Peri-operative application of intra-aortic balloon pumping reduced in-hospital mortality of patients with coronary artery disease and left ventricular dysfunction. Chin Med J (Engl). 2019;132(8):935-942.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWriting Committee Members, Lawton JS, Tamis-Holland JE, et al. 2021 ACC/AHA/SCAI Guideline for Coronary Artery Revascularization: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint\u0026nbsp;Committee\u0026nbsp;on\u0026nbsp;Clinical Practice Guidelines.\u0026nbsp;J Am Coll Cardiol. 2022; 79(2):197-215.\u003c/li\u003e\n \u003cli\u003eGensini GG. A more meaningful scoring system for determining the severity of coronary heart disease. Am J Cardiol. 1983;51(3):606.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSchiller NB, Shah PM, Crawford M, et al. Recommendations for quantitation of the left ventricle by two-dimensional echocardiography. American Society of Echocardiography Committee on Standards, Subcommittee on Quantitation of Two-Dimensional Echocardiograms. J Am Soc Echocardiogr. 1989;2(5):358-367.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMohammadzadeh M, Mohammadzadeh V, Shakiba M, et al. Assessing the Relation of Epicardial Fat Thickness and Volume, Quantified by 256-Slice Computed Tomography Scan, With Coronary Artery Disease and Cardiovascular Risk Factors. Arch Iran Med. 2018;21(3):95-100.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYu W, Liu B, Zhang F, et al. Association of Epicardial Fat Volume With Increased Risk of Obstructive Coronary Artery Disease in Chinese Patients With Suspected Coronary Artery Disease. J Am Heart Assoc. 2021; 10(6): e018080.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu J, Li J, Pu H, et al. Cardiac remodeling and subclinical left ventricular dysfunction in adults with uncomplicated obesity: a cardiovascular magnetic resonance study.\u0026nbsp;Quant Imaging Med Surg. 2022;12(3):2035-2050.\u003c/li\u003e\n \u003cli\u003ede Wit-Verheggen VHW, Altintas S, Spee RJM, et al. Pericardial fat and its influence on cardiac diastolic function. Cardiovasc Diabetol. 2020;19(1):129.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAzab M, Al-Shudifat AE, Johannessen A, et al. Are Risk Factors for Coronary Artery Disease Different in Persons With and Without Obesity? Metab Syndr Relat Disord. 2018;16(8):440-445.\u003c/li\u003e\n \u003cli\u003eQu Y, Yang J, Zhang F, et al. Relationship between body mass index and outcomes of coronary artery disease in Asian population: Insight from the FOCUS registry. Int J Cardiol. 2020; 300:262-267.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGuan B, Liu L, Li X, et al. Association between epicardial adipose tissue and blood pressure: A systematic review and meta-analysis.\u0026nbsp;Nutr Metab Cardiovasc Dis. 2021;31(9):2547-2556.\u003c/li\u003e\n \u003cli\u003eRabkin SW. The relationship between epicardial fat and indices of obesity and the metabolic syndrome: a systematic review and meta-analysis.\u0026nbsp;Metab Syndr Relat Disord. 2014;12(1):31-42.\u003c/li\u003e\n \u003cli\u003eXie Z, Zhu J, Li W, et al. Relationship of epicardial fat volume with coronary plaque characteristics, coronary artery calcification score, coronary stenosis, and CT-FFR for lesion-specific ischemia in patients with known or suspected coronary artery disease.\u0026nbsp;Int J Cardiol. 2021; 332:8-14.\u003c/li\u003e\n \u003cli\u003eYin R, Tang X, Wang T, et al. Cardiac CT scanning in coronary artery disease: Epicardial fat volume and its correlation with coronary artery lesions and left ventricular function. Exp Ther Med. 2020;20(4):2961-2968.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMahabadi AA, Berg MH, Lehmann N, et al. Association of epicardial fat with cardiovascular risk factors and incident myocardial infarction in the general population: the Heinz Nixdorf Recall Study. J Am Coll Cardiol. 2013;61(13):1388-1395.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ede Vos AM, Prokop M, Roos CJ, et al. Peri-coronary epicardial adipose tissue is related to cardiovascular risk factors and coronary artery calcification in post-menopausal women. Eur Heart J. 2008;29(6):777-783.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWilliams KJ, Tabas I. Atherosclerosis--an inflammatory disease.\u0026nbsp;N Engl J Med. 1999;340(24):1928-1929.\u003c/li\u003e\n \u003cli\u003eOikonomou EK, Antonopoulos AS, Schottlander D, et al. Standardized measurement of coronary inflammation using cardiovascular computed tomography: integration in clinical care as a prognostic medical device.\u0026nbsp;Cardiovasc Res. 2021;117(13):2677-2690.\u003c/li\u003e\n \u003cli\u003eAntonopoulos AS, Sanna F, Sabharwal N, et al. Detecting human coronary inflammation by imaging perivascular fat. Sci Transl Med. 2017;9(398): eaal2658.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu Y, Sun Y, Hu C, et al. Perivascular Adipose Tissue as an Indication, Contributor to, and Therapeutic Target for Atherosclerosis. Front Physiol. 2020; 11:615503.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDai X, Yu L, Lu Z, Shen C, Tao X, Zhang J. Serial change of perivascular fat attenuation index after statin treatment: Insights from a coronary CT angiography follow-up study. Int J Cardiol. 2020; 319:144-149.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAppiah D, Nwabuo CC, Ebong IA, et al. The association of age at natural menopause with pre- to postmenopausal changes in left ventricular structure and function: the Coronary Artery Risk Development in Young Adults (CARDIA) study. Menopause. 2022;10.1097/GME.0000000000001950.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKwok CS, Bachmann MO, Mamas MA, et al. Effect of age on the prognostic value of left ventricular function in patients with acute coronary syndrome: A prospective registry study. Eur Heart J Acute Cardiovasc Care. 2017;6(2):191-198.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSiontis GC, Branca M, Serruys P, et al. Impact of left ventricular function on clinical outcomes among patients with coronary artery disease. Eur J Prev Cardiol. 2019;26(12):1273-1284.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHonold S, Wildauer M, Beyer C, et al. Reciprocal communication of pericoronary adipose tissue and coronary atherogenesis. Eur J Radiol. 2021; 136:109531.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBengs S, Haider A, Warnock GI, et al. Quantification of perivascular inflammation does not provide incremental prognostic value over myocardial perfusion imaging and calcium scoring. Eur J Nucl Med Mol Imaging. 2021;48(6):1806-1812. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Gensini scoring system.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.044728434504794%\"\u003e\n \u003cp\u003eDS（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.057507987220447%\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.26198083067093%\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.635782747603834%\"\u003e\n \u003cp\u003eCorresponding scores\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.044728434504794%\"\u003e\n \u003cp\u003e0-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.057507987220447%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.26198083067093%\"\u003e\n \u003cp\u003eLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.635782747603834%\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.044728434504794%\"\u003e\n \u003cp\u003e26-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.057507987220447%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.26198083067093%\"\u003e\n \u003cp\u003eProximal segment of LAD or LCX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.635782747603834%\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.044728434504794%\"\u003e\n \u003cp\u003e51-75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.057507987220447%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.26198083067093%\"\u003e\n \u003cp\u003eMiddle segment of LAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.635782747603834%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.044728434504794%\"\u003e\n \u003cp\u003e76-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.057507987220447%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.26198083067093%\"\u003e\n \u003cp\u003eDistal segment of LAD or LCX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.635782747603834%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.044728434504794%\"\u003e\n \u003cp\u003e91-99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.057507987220447%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.26198083067093%\"\u003e\n \u003cp\u003eRCA, PDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.635782747603834%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.044728434504794%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.057507987220447%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.26198083067093%\"\u003e\n \u003cp\u003eD1, OM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.635782747603834%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.044728434504794%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.057507987220447%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.26198083067093%\"\u003e\n \u003cp\u003eD2、PLV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.635782747603834%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;LM= left main, LAD= left anterior descending, LCX= left circumflex, RCA=right coronary artery, PDA=posterior descending artery, OM=obtuse marginal, D1=first diagonal, D2=second diagonal, PLV= Posterior left ventricular branch.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eBaseline Characteristics of Study Participants\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eResult\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eAge（y）\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e64.55\u0026plusmn;10.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Males\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e104（65.4％）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp; Females\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e55（34.6％）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eHeight\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e165（160-170）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e66.69\u0026plusmn;11.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e24.42\u0026plusmn;3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eHeart rate\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e74（66-80）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eGensini score\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e19（10, 41）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eRisk factors of CAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e116（73.0％）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e63（39.6％）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e95（59.7％）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e58（36.5％）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e24（15.1％）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e* Date are means \u0026plusmn; standard deviations.\u003c/p\u003e\n\u003cp\u003e+Data are the median, with the interquartile range in parentheses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Relationship between PCAT volume and CAD related risk factors.\u0026nbsp;\u003c/p\u003e\n\u003ctable align=\"left\" border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 10.5143%;\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003egroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 3.9022%;\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20.8118%;\" valign=\"top\" width=\"19.240506329113924%\"\u003e\n \u003cp\u003eLAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 21.0285%;\" valign=\"top\" width=\"19.240506329113924%\"\u003e\n \u003cp\u003eLCX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 21.8957%;\" valign=\"top\" width=\"20.253164556962027%\"\u003e\n \u003cp\u003eRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 17.3431%;\" valign=\"top\" width=\"20.126582278481013%\"\u003e\n \u003cp\u003eTotal-Volume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"15.868263473053892%\"\u003e\n \u003cp\u003evolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"6.88622754491018%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"15.868263473053892%\"\u003e\n \u003cp\u003evolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"6.88622754491018%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"17.065868263473053%\"\u003e\n \u003cp\u003evolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"6.88622754491018%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"16.91616766467066%\"\u003e\n \u003cp\u003elume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"6.88622754491018%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1508.05\u0026plusmn;466.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e808.35(600.79, 1136.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e1771.94(1415.12, 2110.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e4185.25\u0026plusmn;1128.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003ehypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1498.66\u0026plusmn;429.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e871.45\u0026plusmn;351.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e1843.89\u0026plusmn;547.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e4214.00\u0026plusmn;1045.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1533.38\u0026plusmn;557.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e867.76\u0026plusmn;390.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e1706.54\u0026plusmn;609.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e4107.68\u0026plusmn;1339.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1490.72\u0026plusmn;391.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1490.72\u0026plusmn;391.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e1838.03\u0026plusmn;523.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e4162.77\u0026plusmn;910.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1519.43\u0026plusmn;510.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1519.43\u0026plusmn;510.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e1786.21\u0026plusmn;594.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e4200.01\u0026plusmn;1256.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003edyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1521.69\u0026plusmn;444.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e854.50\u0026plusmn;330.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e1858.07\u0026plusmn;550.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e4234.26\u0026plusmn;1073.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1487.81\u0026plusmn;499.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e894.14\u0026plusmn;403.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e1680.80(1335.61, 1915.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e4112.50\u0026plusmn;1211.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003esmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1556.01\u0026plusmn;497.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e776.65(635.17, 1235.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e1892.12\u0026plusmn;548.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e4357.67\u0026plusmn;1120.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1480.51\u0026plusmn;446.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e848.01\u0026plusmn;360.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e1757.72\u0026plusmn;572.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e4086.24\u0026plusmn;1127.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003edrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1640.77\u0026plusmn;579.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1037.84\u0026plusmn;375.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e1980.53\u0026plusmn;652.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e4659.15\u0026plusmn;1202.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.5143%;\" valign=\"top\" width=\"11.012658227848101%\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.9022%;\" valign=\"top\" width=\"4.430379746835443%\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e1484.46\u0026plusmn;441.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7417%;\" valign=\"top\" width=\"13.417721518987342%\"\u003e\n \u003cp\u003e791.59(585.98, 1093.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2869%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8256%;\" valign=\"top\" width=\"14.430379746835444%\"\u003e\n \u003cp\u003e1775.85\u0026plusmn;546.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.0701%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.0073%;\" valign=\"top\" width=\"14.30379746835443%\"\u003e\n \u003cp\u003e4101.00\u0026plusmn;1098.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3358%;\" valign=\"top\" width=\"5.822784810126582%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe data with normal distribution were described as mean \u0026plusmn; standard deviation (SD) and tested by independent-samples Student t test, while data with non-normal distribution were described as median (upper quartile, lower quartile) and tested by Mann-Whitney U test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Relationship between FAI and CAD related risk factors. x\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13.2482%;\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003egroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7.0073%;\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 30.3285%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eLAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 30.219%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eLCX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 15%;\" valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"22.36842105263158%\"\u003e\n \u003cp\u003eFAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"9.210526315789474%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"22.36842105263158%\"\u003e\n \u003cp\u003eFAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"9.210526315789474%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"22.36842105263158%\"\u003e\n \u003cp\u003eFAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"9.210526315789474%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-83.00(-88.00, -79.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-76.03\u0026plusmn;6.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-79.58\u0026plusmn;8.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003ehypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-82.97\u0026plusmn;7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-76.11\u0026plusmn;7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-79.86\u0026plusmn;8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-83.30\u0026plusmn;8.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-75.81\u0026plusmn;6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-78.84\u0026plusmn;9.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-82.94\u0026plusmn;6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-75.54\u0026plusmn;7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-80.27\u0026plusmn;7.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-83.14\u0026plusmn;8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-76.35\u0026plusmn;6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-79.14\u0026plusmn;9.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003edyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-82.69\u0026plusmn;7.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-75.23\u0026plusmn;6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-79.27\u0026plusmn;8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-83.59\u0026plusmn;7.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-77.22\u0026plusmn;7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-80.05\u0026plusmn;8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003esmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-82.41\u0026plusmn;8.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-76.19\u0026plusmn;7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-79.00\u0026plusmn;8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-83.43\u0026plusmn;7.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-75.94\u0026plusmn;6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-80.50(-86.00, -73.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003edrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-82.58\u0026plusmn;9.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-76.58\u0026plusmn;8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-79.83\u0026plusmn;8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.2482%;\" valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.0073%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4599%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-83.14\u0026plusmn;7.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.3504%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-75.93\u0026plusmn;6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.8686%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7299%;\" valign=\"top\" width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-79.54\u0026plusmn;8.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.2701%;\" valign=\"top\" width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe statistical parameters are consistent with table 3\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pericoronary adipose tissue (PCAT), Fat attenuation index (FAI), left ventricular function, Gensini score, coronary artery disease (CAD)","lastPublishedDoi":"10.21203/rs.3.rs-1823340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1823340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To investigate the correlation\u0026nbsp;of pericoronary adipose tissue with coronary artery disease and left ventricular function.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Participants with clinically suspected coronary artery disease were enrolled. All participants underwent\u0026nbsp;coronary computed tomography angiography (CCTA) and\u0026nbsp;echocardiography followed by\u0026nbsp;invasive coronary angiography (ICA)\u0026nbsp;within 6month. pericoronary adipose tissue (PCAT) was extracted to analyze the correlation with Gensini score and left ventricular function parameter, including IVS, LVPW, LVEDD, LVESD, LVEDV, LVESV, FS, LVEF, LVM, LVMI. The\u0026nbsp;correlation between PCAT and Gensini\u0026nbsp;was assessed by using Spearman correlation analysis, and between PCAT volume or FAI and left ventricular function parameters was assessed by using partial correlation analysis.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e One hundred and fifty-nine participants (mean age, 64.55years ±10.64, male:65.4%[104/159]) were included for final analysis. The risk factors of coronary artery disease, such as hypertension, diabetes, dyslipidemia, history of smoking and drinking, had no significant association with the PCAT (P\u0026gt;0.05); and there is no correlation between PCAT and Gensini score. However, the LAD-FAI was positively correlated with IVS(r=0.203, P=0.013), LVPW(r=0.218, P=0.008),LVEDD(r=0.317, P＜0.001)，LVESD(r=0.298, P＜0.001), LVEDV(r=0.317, P＜0.001), LVESV(r=0.301, P＜0.001), LVM(r=0.371, P＜0.001), LVMI(r=0.304, P＜0.001). The LCX-FAI was positively correlated with LVEDD(r=0.199, P=0.015)，LVESD(r=0.190, P=0.021), LVEDV(r=0.203, P=0.013), LVESV(r=0.197, P=0.016), LVM(r=0.220, P=0.007), LVMI(r=0.172, P=0.036). The RCA-FAI was positively correlated with LVEDD(r=0.258, P=0.002)，LVESD(r=0.238, P=0.004), LVEDV(r=0.266, P=0.001), LVESV (r=0.249, P=0.002), LVM(r=0.237, P=0.004), LVMI(r=0.218, P=0.008). The total volume was positively correlated with FS (r=0.167, P=0.042).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The FAI was positively correlated with the left ventricular function, but was not associated with the severity of coronary artery disease.\u003c/p\u003e","manuscriptTitle":"The Correlation of Pericoronary Adipose Tissue with Coronary artery disease and left ventricular function","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-22 16:20:51","doi":"10.21203/rs.3.rs-1823340/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-07-28T10:58:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-07-26T13:46:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e4a5c6be-1579-4dae-9c81-13f6b7aba054","date":"2022-07-26T13:19:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91034b6a-c373-45de-a275-9d9fce866b32","date":"2022-07-21T09:05:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-07-20T08:52:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-07-19T20:36:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-07-11T08:26:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-07-11T08:21:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2022-07-04T10:24:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac4cf2e7-d785-4728-a2fe-d974f047dff2","owner":[],"postedDate":"July 22nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-08-31T17:44:22+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-22 16:20:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1823340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1823340","identity":"rs-1823340","version":["v1"]},"buildId":"2u56kwukJI3zHK-uzyFNs","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}