Association between Nonalcoholic Fatty Liver Disease on CT and Myocardial Infarct Size using SPECT-MPI in patients with ST-elevation Myocardial Infarction

Association between Nonalcoholic Fatty Liver Disease on CT and Myocardial Infarct Size using SPECT-MPI in patients with ST-elevation Myocardial Infarction | 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 Association between Nonalcoholic Fatty Liver Disease on CT and Myocardial Infarct Size using SPECT-MPI in patients with ST-elevation Myocardial Infarction Weiwei Cui, Ningjun Li, Xiao Gao, Xuehuan Liu, Qingshuang Bai, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4357262/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background: We aim to explore the association between nonalcoholic fatty liver disease (NAFLD), intrathoracic fat (IF), pericardial fat (PF) and myocardial infarct size (MIS) in patients with ST-elevation myocardial infarction (STEMI). Methods: SPECT-MPI was used to detect MIS, while CT scans were used to measure IF, PF, and NAFLD in patients with STEMI. Firstly, we categorised the patients into two groups (those with measurable and nonmeasurable MIS). The difference in fat between the two groups was compared using a two-sample t-test to determine which type of fat might be correlated with MIS. Secondly, the association between the related fats obtained in the aforementioned steps and MIS was evaluated using linear regression analysis. Third, to further verify this association at the molecular level, we explored the potential shared genes associated with related fat obtained in the above steps and acute myocardial infarction via bioinformatics analysis using the Gene Expression Omnibus (GEO) database. Finally, the association between the expression of shared genes in the serum of patients with STEMI and related fat was confirmed using Pearson’s correlation analysis. Results: The volume and fat attenuation index of IF and PF showed no difference between patients with MIS and those without. However, the L/S of NAFLD on CT reduced significantly in patients with MIS ( P =0.001). The L/S of NAFLD on CT was an independent predictor of MIS on SPECT-MPI in patients with STEMI ( P =0.042). We identified ST2, THBD, LEPR, and CEBP-α in NAFLD and acute myocardial infarction cases from the GEO database ( P <0.05). Compared to patients with STEMI without NAFLD, those with NAFLD exhibited a reduction in sST2 levels ( P =0.042); however, no differences were observed in THBD, LEPR, and CEBP-α levels. Correlation analysis showed a positive correlation between L/S and sST2 levels (r=0.459, P =0.032). Conclusions: Among patients with STEMI, the L/S of NAFLD, but not IF or PF, was associated with MIS on SPECT-MPI. Additionally, the L/S of NAFLD on CT emerged as an independent predictor of MIS. The expression of sST2, a biomarker associated with NAFLD and STEMI, positively correlated with the L/S on CT imaging. Intrathoracic fat Nonalcoholic fatty liver disease Pericardial fat ST-elevation myocardial infarction Soluble suppression of tumorigenicity-2 Single-photon emission computed tomography myocardial perfusion imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Introduction Accumulation of fat tissue in vital organs is closely related to the onset and progression of cardiovascular diseases. Nonalcoholic fatty liver disease (NAFLD) is a prevalent symptom of excessive fat accumulation in the liver. Currently, it stands as the predominant chronic liver disease, affecting approximately 30% of the general population[ 1 , 2 ]. A previous study suggested that NAFLD may be an independent risk factor for cardiovascular morbidity and mortality[ 3 ]. Paradoxically, Sattar et al. found no discernable difference in the occurrence of acute myocardial infarction (AMI) between patients with NAFLD and their healthy counterparts[ 4 ]. This observation led us to consider whether the effect of NAFLD on the prognosis of AMI was different from its effect on cardiovascular diseases. However, studies on how NAFLD affects the prognosis of AMI remain scarce. The accumulation of ectopic fat around the heart, such as pericardial fat (PF) or intrathoracic fat (IF), may have a stronger correlation with both clinical and subclinical cardiovascular conditions compared to other types of fat stores, such as abdominal visceral fat[ 5 ]. Quantification of PF and IF correlates with major adverse cardiac events and improves risk stratification beyond traditional cardiovascular risk factors[ 6 ]. Patients with myocardial ischaemia exhibit a significant increase in IF volume, which could serve as a reliable predictor of myocardial infarction risk in patients with metabolic syndrome[ 7 ]. In Chen's study, PF and IF were correlated with the occurrence and severity of coronary heart disease; however, subsequent observations revealed no disparity in PF and IF between patients with and without AMI[ 8 ]. Therefore, we hypothesised that IF and PF are not associated with the occurrence of AMI but rather with its severity. Resting-gated single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) is one of the most reliable and non-invasive methods for detecting myocardial ischaemia. SPECT imaging with (99m) Tc-sestamibi is the best tool for detecting myocardial infarct size (MIS) and can be used as an endpoint for early pilot studies evaluating potential efficacy and dose-range studies[ 9 ]. A recent study showed a strong association between MIS measured by SPECT-MPI within 1 month in patients with ST-elevation myocardial infarction (STEMI)who underwent primary percutaneous coronary intervention (PCI) and 1-year all-cause mortality or hospitalization due to heart failure[ 10 ]. Furthermore, long-term follow-up of clinical outcomes in patients with STEMI showed that MIS was associated with the occurrence of cardiac death or reinfarction[ 11 ]. Therefore, the MIS could serve as a useful endpoint in clinical trials and as an important prognostic indicator when caring for patients with STEMI. Two transcripts of interleukin-1 receptor-like 1 (IL1RL1) are important: a truncated soluble receptor(sST2 or IL1RL1-a) circulating in the serum and a transmembrane receptor (ST2L or IL1RL1-b)[ 12 ]. Cardiac fibroblasts, cardiomyocytes, and non-cardiomyocytes, including those within large human blood vessels (such as the aorta and coronary arteries) and cardiac microvascular endothelial cells, can generate sST2 in response to injury or stress[ 13 ]. Normally, in cardiomyocytes, IL-33 exerts a protective effect by binding to ST2L. However, sST2 can competitively bind IL-33 to ST2L, thus obstructing the IL-33/ST2L pathway[ 14 ]. Elevated sST2 levels have been previously reported to predict heart failure and mortality in patients with AMI[ 15 , 16 ]. ST2L is predominantly expressed in haematopoietic cells and was also detected in the healthy liver. It was up-regulated within 1 h and peaked at 4 h after I/R[ 17 , 18 ]. Furthermore, ST2-deficient mice exhibited more severe hepatitis[ 19 ]. However, the alterations in sST2 levels in patients with NAFLD and AMI remain unclear. Our study aimed to assesse the relationship between NAFLD, IF, PF, and MIS in patients with STEMI using CT scans and SPECT-MPI. At the molecular level, our objective was to explore the common genes between the two diseases in the Gene Expression Omnibus (GEO) database and to assess the expression levels of these genes in the serum samples, thereby shedding light on the molecular mechanisms underlying this association and further verifying the association between NAFLD and MIS. Materials and Methods Study population In this study, we retrospectively collected data from 406 patients who underwent myocardial perfusion imaging and chest CT at Tianjin Fourth Central Hospital between September 2021 and August 2023. The inclusion criteria were as follows: (1) Diagnosis of STEMI; (2) successful PCI performed within 24 h of symptom emergence; and (3) patients who underwent SPECT-MPI for more than 4 days following the emergence of myocardial infarction symptoms. The exclusion criteria were as follows: (1) individuals diagnosed with unstable angina, (2) patients with a history of acute myocardial infarction, (3) individuals without STEMI, and (4) individuals with reported alcohol consumption or abuse and liver diseases. STEMI was diagnosed according to the 2019 Chinese Society of Cardiology Guidelines for the Diagnosis and Management of Patients with ST-elevation myocardial infarction diagnosis[ 20 ]. Thrombolysis in myocardial infarction grade 3 coronary flow within the treated vessel was defined as Successful PCI [ 21 ]. The infarct size remained unaffected by PCI within 24 h [ 22 ]. To mitigate the effects of reversible ischaemic foci, we ensured that only patients who underwent myocardial perfusion imaging for no less than 4 days after the emergence of myocardial infarction symptoms were selected. The detailed procedure is illustrated in Fig. 1 . CT image acquisition All the patients underwent scanning via GE LightSpeed VCT 64-slice spiral CT scanner and Vitrea2 workstation, utilising retrospective electrocardiogram-gated technology. The scanning parameters were set at 120 kV and 440 mA, with a slice thickness of 5 mm. The scan was conducted over a 360° rotation in 0.4 s, featuring an automated and smart pitch adjustment. The scanning range extended from 3.2 (0.2) to 4.8 (0.3°), covering the area from the subcarinal region to the subdiaphragm. Fat tissue measurements In our study, NAFLD was defined as hepatic steatosis in individuals without any reported history of alcohol consumption, abuse, or liver disease. Non-enhanced CT scan was utilized to measure CT attenuation in Hounsfield units, focusing on liver and spleen regions with a region of interest of 80 mm 2 or more, as depicted in Fig. 2 . Within the right lobe, two measurements were recorded in different planes, and the average of these measurements from each patient was used in the analysis. NAFLD on CT scans was determined when the ratio of the average liver CT readings to the average splenic CT readings was ≤ 1. Fat volume was measured through semi-automated segmentation using the SliceOmatic software (TomoVision, Magog, QC, Canada) managed by a pair of experienced radiologists. Initially, a step-by-step semi-manual mapping of the fat areas in the chest CT cross-sectional images was conducted, with the fat threshold set between − 190 and − 30 HU. Consequently, the software autonomously determined the average fat attenuation index (FAI) and the aggregate fat volume within the threshold. The total IF volume encompasses all adipose tissue found in the thorax, stretching from the level of the right pulmonary artery to the diaphragm and from the chest wall to the descending aorta, along with fat in the pericardial sac. The term 'PF volume' refers to any fat tissue located in the pericardial sac, as illustrated in Fig. 3 . SPECT-MPI The SPECT-MPI procedure was conducted for a minimum of 4 days following the PCI for STEMI. The chosen imaging medium was 99Tcm-methoxy isobutyl acetonitrile, which possessed a radiological purity exceeding 90% and an injectable quantity ranging from 15–20 mCi. The injection was administered via the middle elbow vein, followed by the oral administration of 250 ml of milk. After 1.5 h, images were acquired using a gated method. SPECT-MPI utilized a GE Discovery NM630 SPECT device and a MedExMIM medical imaging workstation. Acquisition conditions included a 180° rotation, 6° per frame, 64×64 matrix, and 1.4 magnification. Subsequently, the images were transferred to a workstation for the reconstruction of short-axis, vertical long-axis, and horizontal long-axis views of the left ventricle. The vascular territories were delineated using the 17-segment model [ 23 , 24 ]. Tracer uptake for each segment was rated on a 5-point scale (0 = normal, 1 = mild reduction, 2 = moderate reduction, 3 = severe reduction, and 4 = absent tracer uptake). Infarct size was measured by setting a threshold value of 60% of the peak counts, while defect size was quantified as a proportion of the left ventricle. Two experienced deputy chief physicians from the Department of Nuclear Medicine independently analysed the images in a double-blind manner. GEO data collection NAFLD, first acute myocardial infarction (FAMI), and control datasets were retrieved from the GEO database ( http://www.ncbi.nlm.nih.gov/geo ). The GSE89632 dataset encompasses gene expression profiles of 39 patients with NAFLD and 24 healthy individuals. Additionally, the GEO24519 dataset included data from 17 patients with FAMI and four healthy individuals. Identification of differentially expressed genes (DEGs) in NAFLD and AMI DEGs were screened and identified using the GEO2R tool ( https://www.ncbi.nlm.nih.gov/geo/geo2r ) in GSE89632 and GEO24519. The thresholds for DEGs were set at P 1. Subsequently, we input the DEGs of both diseases into the Venn online analysis tool ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ) to identify overlapping genes. Twenty DEGs were identified, and further analysis was conducted via two digital platforms, metascape ( https://metascape.org/gp/index.html#/main/step3 ) and Microbiology Letter ( https://www.bioinformatics.com.cn/ ), for Gene Ontology (GO) analysis. Measurement of the expression of DEGs in the serum Following the aforementioned inclusion and exclusion criteria, we prospectively collected serum from 22 patients with STEMI(7 with NAFLD and 15 without NAFLD) at Tianjin Fourth Central Hospital between December 2023 and March 2024. Serum samples from each patient, which were otherwise discarded from the clinical laboratory, were collected on the day the symptoms of AMI first appeared. This study was approved by the Ethics Committee of Tianjin Fourth Central Hospital and the ethics approval number was SZXLL-2023-K007. Plasma concentrations of CCAAT/enhancer-binding protein alpha (CEBP-α), sST2, leptin receptor (LEPR), and thrombomodulin (THBD)were measured using a solid-phase sandwich enzyme-linked immunosorbent assay (ELISA) kit (MeiMian, Jiangsu, China) in accordance with manufacturer guidelines. Statistical Analysis Continuous variables are represented either as mean ± standard deviation (SD) or as the median in a range of quartiles. Categorical variables are represented as frequencies (percentages). The liver-to-spleen (L/S) ratios on CT and MIS were treated as continuous variables. For intergroup comparisons, the independent sample Student’s t-test or Mann–Whitney U test for the continuous variable was used, while categorical variables were analysed using either the Chi-square or Fisher exact tests. Pearson's and Spearman's correlation methods were used to assess the correlations. Statistical analyses were performed using the IBM SPSS Statistics 26.0 (IBM Corporation). A two-tailed P < 0.05 was considered statistical significance. Using MIS as the dependent variable, both univariate and multivariate linear regression methods were used to examine the association between NAFLD and MIS. The L/S ratio of NAFLD on CT and traditional coronary artery disease risk factors were defined as independent variables. Results Clinical Characteristics The clinical characteristics of the 117 patients with STEMI, comprising 23 with NAFLD and 94 without NAFLD, are presented in Table 1 . In comparison to the group without NAFLD, those with NAFLD exhibited elevated triglyceride levels ( P = 0.005), and infarction in the posterior wall of the left ventricle was less common ( P = 0.029). No significant differences were observed in the other parameters between the two groups. Table 1 Clinical characteristics of study patients Total(n = 117) NAFLD Present(n = 23) NAFLD Absent(n = 94) P Age(y) 65.0(58.0,70.0) 63.0 (55.0,71.0) 65.5 (58.0,70.0) 0.508 Man (%) 96(82.1%) 21 (91.3%) 75 (79.8%) 0.324 Body mass index, kg/m2 25.6(23.7,27.8) 25.7 (24.2,29.1) 25.5 (23.4,27.8) 0.126 Systolic blood pressure (mm Hg) 160 (140,180) 160 (150,180) 160 (140,180) 0.535 Diastolic blood pressure (mm Hg) 100 (80,110) 100 (90,110) 100 (80,110) 0.213 Fasting blood glucose (mmol/L) 6.0 (5.2,8.8) 7.6 (5.4,10.1) 5.9 (5.1,8.0) 0.095 Total cholesterol (mmol/L) 4.8 (4.1,5.4) 5.2 (4.2,5.4) 4.7 (4.0,5.4) 0.340 Triglyceride (mmol/L) 1.6 (1.2,2.4) 2.1 (1.4,3.0) 1.5 (1.1,2.1) 0.005 Low density lipoprotein (mmol/L) 3.1 ± 0.8 3.2 ± 0.2 3.0 ± 0.1 0.324 High density lipoprotein (mmol/L) 0.9 (0.8,1.1) 0.9 (08,1.0) 1.0 (0.8,1.1) 0.256 Hear trate (beats/min) 76.1 ± 13.5 79.3 ± 11.9 74.9 ± 12.3 0.559 Cardiovascular risk factors, n (%) Hypertension 82(70.1%) 18(78.3%) 64(68.1%) 0.339 Dyslipidemia 92(78.6%) 21(91.3%) 71(75.5%) 0.171 Diabetes 39(33.3%) 7(30.4%) 32(34.0%) 0.742 Smoking 30(0,40) 30(0,40) 30(0,40) 0.718 Family history of premature CAD 14(12.0%) 1(4.3%) 13(13.8%) 0.369 Symptom, n (%) Typical angina 97(82.9%) 19(82.6%) 78(83.0%) 1.000 Electrocardiogram, n (%) Infarct location Anterior 57(48.7%) 9(39.1%) 48(51.1%) 0.305 Inferior 59(50.4%) 12(52.2%) 47(50.0%) 0.852 Posterior 30(25.6%) 10(43.5%) 20(26.6%) 0.029 Lateral 15(12.8%) 4(17.4%) 11(11.7%) 0.701 Infarct related coronary artery, n(%) 0.161 LAD 60(51.3%) 8(34.8%) 52(55.3%) LCX 15(12.8%) 5(21.7%) 10(10.6%) RCA 42(35.9%) 10(43.5%) 32(34.0%) TIMI flow grade before intervention, n (%) 0.175 0 68(58.1%) 15(65.2%) 53(56.4%) 1 1(0.9%) 1(4.3%) 0(0.0%) 2 11(9.4%) 4(17.4%) 7(7.4%) 3 37(31.6%) 3(13.0%) 34(36.2%) Grade of Killip, n (%) 0.100 Ⅰ 49(41.9%) 13(56.5%) 36(38.3%) Ⅱ 66(56.4%) 10(43.5%) 56(59.6%) Ⅲ 2(1.7%) 0(0.0%) 2(2.1%) Ⅳ 0(0.0%) 0(0.0%) 0(0.0%) Values are mean ± SD, median (interquartile range), or n (%). CAD, coronary artery disease; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NAFLD, non-alcoholic fatty liver disease; RCA, right coronary artery; TIMI, thrombolysis in myocardial Infarction. Fat characteristics of vital organs in patients with and without MIS Among the 117 patients with STEMI, the majority of MIS cases fell within the range of 0–10% (Fig. 4 ). According to the SPECT-MPI results, patients with STEMI were categorised into two groups: those with MIS (MIS > 0%) and those without MIS (MIS for 0%). We found that in patients with MIS, the L/S was lower compared to that in those without MIS (Fig. 5 , P = 0.001). However, there was no notable difference in the volume and FAI of the IF and PF (Fig. 6 ). Characteristics of rest SPECT-MPI in Patients with and without NAFLD Figure 7 shows an example of no measurable infarct size, while Fig. 8 demonstrates a measurable infarct size. In comparison to the group without NAFLD, the group with NAFLD had a larger MIS ( P = 0.037). Regarding bandwidth, left ventricle volume, and ejection fraction, there were no statistically significant differences between the two groups (Table 2 ). Table 2 Characteristics of rest SPECT MPI in Patients with and without NAFLD Total(n = 117) NAFLD Present(n = 23) NAFLD Absent(n = 94) P LV volumes and ejection fraction LV EDVI (mL) 102(82.5,125) 102(86,117) 101.5(81.75,129.5) 0.888 LV ESVI (mL) 42(33.5,56) 42(36,54) 42(33,56.5) 0.648 LVEF (%), n (%) 58(52,62) 56(50,61) 58(52,63) 0.163 Bandwidth 58(52,62) 79(53,133) 73(50,133.75) 0.709 Infarct size 6(0,15) 9(3,17) 4(0,13.5) 0.037 Values are median (interquartile range). EDVI, indexed end-diastolic volume; ESVI, indexed end-systolic volume; LV, left ventricle; LVEF, left ventricular ejection fraction; NAFLD, non-alcoholic fatty liver disease. Infarct size was quantitated using a threshold value of 60% of peak counts. The defect size was expressed as a percentage of the left ventricle. Association between NAFLD at CT and MIS at SPECT-MPI Correlation analysis revealed a significant negative correlation between the L/S ratio and MIS (Fig. 9 ; r=-0.328, p < 0.001). In the univariate analysis, MIS was associated with fasting blood glucose ( P = 0.038) and L/S ( P = 0.016). Multiple linear regression analysis identified the L/S ratio as an independent predictor of MIS, after adjusting for blood glucose and triglyceride (Table 3 , P = 0.042). Table 3 Predictors of infarct size Univariable Analysis Multivariable Step-forward Selection Analysis Parameter β Value P Value β Value P Value Age(y) -0.154 0.097 Man (%) 0.072 0.441 BMI, kg/m2 0.037 0.694 Systolic blood pressure (mm Hg) 0.016 0.865 Diastolic blood pressure (mm Hg) 0.126 0.177 Fasting blood glucose (mmol/L) 0.192 0.038 0.160 0.094 Total cholesterol (mmol/L) -0.097 0.299 Triglyceride (mmol/L) -0.026 0.785 -0.083 0.374 Low density lipoprotein (mmol/L) -0.130 0.163 High density lipoprotein (mmol/L) 0.109 0.242 Heart rate (beats/min) 0.101 0.278 Smoking -0.146 0.117 Family history of premature CAD 0.060 0.522 Live/Spleen -0.222 0.016 -0.193 0.042 BMI, body mass index; CAD, coronary artery disease; Live/Spleen, the CT value ratio of live to spleen. Differential expression analysis of DEGs in NAFLD and FAMI In the NAFLD GSE89632 dataset, we compared 39 patients with NAFLD to 24 healthy controls and identified 474 DEGs, which are presented as heat maps in Fig. 10 . Additionally, we analysed the GSE24519 dataset for the FAMI, comprising 17 patients with FAMI and four healthy controls. From this dataset, we identified 837 DEGs, which are presented as heat maps in Fig. 11 . Using the Venn Diagram online tool, we determined the intersection of DEGs between the two diseases, revealing 20 genes common to both conditions, as shown in Fig. 12 . Changes of sST2 in patients with NAFLD and FAMI GO classification was performed on the previously identified 20 DEGs, and the results are shown in Fig. 13 . The alterations in GO biological processes predominantly encompass cytokine-mediated signalling pathways, lipid biosynthetic processes, alcohol metabolic processes, and circulatory system processes. Due to the exclusion of patients with a history of alcohol use, we selected the cytokine-mediated signalling pathway with the minimal P-value for further study. This pathway’s DEGs included IL1RL1(the approved symbol for ST2), THBD, LEPR, and CEBP-α. Subsequently, serum was prospectively collected from patients with STEMI in the clinic, and an ELISA was used to confirm the differences in the expression of these four genes at the protein level among patients with and without NAFLD. The experimental results showed a decrease in the expression of sST2 in the NAFLD group (Fig. 14 , P = 0.042). Correlation analysis indicated a significant positive correlation between L/S and sST2 levels (Fig. 15 ; r = 0.459, P = 0.032). Discussion Previous studies have confirmed a higher incidence of myocardial ischaemia and coronary microvascular dysfunction among patients with NAFLD by combining CT with CT-MPI or SPECT-MPI. The conclusion drawn was that NAFLD is an independent predictor of myocardial ischemia and coronary microvascular dysfunction[ 25 , 26 ]. In our study, we combined SPECT-MPI with CT imaging to demonstrate for the first time that NAFLD is associated with MIS, rather than IF or PF, and that the L/S of NAFLD on CT was an independent predictor of MIS in patients experiencing primary STEMI. In addition, previous studies have indicated that the levels of sST2, which increase in the early stage of STEMI, inversely correlate with MIS[ 27 ]. Our results suggest that sST2 is associated with both STEMI and NAFLD. Furthermore, the expression of sST2 positively correlated with the L/S of NAFLD as observed on CT in patients with STEMI. This finding further confirmed the association between NAFLD detected on CT and MIS on SPECT-MPI in STEMI at the molecular level. John et al. showed that IF and PF in patients with coronary heart disease may not correlate with the subsequent occurrence of AMI[ 28 ]. Conversely, Amir et al. revealed a correlation between PF and long-term cardiovascular events in healthy individuals[ 29 ]. Therefore, the impact of IF and PF on MIS among patients with STEMI still requires further investigation. Our results revealed no significant differences in the volume and FAI of the IF and PF between the groups with or without MIS. However, the L/S of NAFLD differed significantly. Regarding AMI, MIS correlated with coronary microvascular dysfunction and prompt reperfusion[ 30 , 31 ]. Furthermore, NAFLD is an independent predictor of coronary microvascular dysfunction[ 26 ]. Therefore, we hypothesise that these findings may be attributed to coronary microvascular dysfunction, which is closely related to NAFLD and aggravates the MIS. In comparison with one previous study[ 32 ], we observed that 19.7% (23 of 117) of patients with STEMI in our study had NAFLD. This variation may be attributed to our utilization of an L/S ratio < 1 to diagnose NAFLD instead of ultrasound. As widely acknowledged, NAFLD is associated with insulin resistance, obesity, dyslipidemia, and diabetes[ 33 ]. Our findings revealed elevated triglyceride levels in patients with STEMI and NAFLD. However, this coincides with traditional cardiovascular risk factors, thus failing to verify an association between NAFLD and STEMI. Research indicates that NAFLD can serve as a microvascular dysfunction predictor, which is more powerful than the burden of individual risk factors [ 26 ]. Our results suggest that the L/S ratio of NAFLD on CT is an independent predictor of MIS in patients with STEMI. Through analysis of the NAFLD and FAMI GEO databases, we screened 20 DEGs shared by the two diseases. Following GO analysis, four DEGs were selected and confirmed in patients with FAMI using ELISA. The results indicated a significant decrease in sST2 levels among patients with FAMI in the NAFLD group than in those in the non-NAFLD group. Zhong et al. also identified DEGs associated with these two diseases through GEO database analysis[ 34 ]. However, compared to previous research, our study included a larger patient cohort in the database, with the AMI database being the first AMI patient group, potentially explaining the discrepancies observed in our findings. The IL33/ST2L pathway has a protective effect on the myocardium, and sST2 can compete with ST2L to bind to IL33, thereby inhibiting this pathway[ 14 ]. The rapid increase in sST2 levels in the early stages of AMI predicts heart failure and mortality in patients with AMI[ 15 , 16 ]. Our analysis revealed a reduction in sST2 levels among patients with STEMI with NAFLD than in those without NAFLD (P < 0.05), which suggests that NAFLD may contribute to decreased sST2 levels. Therefore, further investigation on the role of ST2 in NAFLD is warranted. Additionally, the expression of sST2 positively correlated with the L/S of NAFLD as observed on CT in patients with STEMI. The level of sST2, which was negatively correlated with MIS in [ 27 ]the early STEMI stage[ 27 ], was found to be reduced in the serum of STEMI patients with NAFLD, thus reinforcing the association between NAFLD and MIS at the molecular level. This indicates the need to prioritise this group when using sST2 levels to assess the prognosis of patients with STEMI and NAFLD. However, further investigation is needed to elucidate the underlying mechanism. Our study has several limitations. Firstly, this was a single-centre retrospective study with a limited number of patients, which may have hampered the robustness of our findings. Additionally, it was unnecessary for patients who met the inclusion criteria to collect serum prospectively to verify the DEGs before receiving SPECT-MPI, resulting in a lack of SPECT-MPI data for these patients. Although an existing study supports our results, direct evidence is still lacking. Finally, the limitations associated with visual evaluation of perfusion and function are well known. However, for an experienced observer, the strong correlation observed between MIS calculated through visual segmental scoring and the reference assessment employing a circumferential profile and an objective threshold method supports the reliability of visual analysis[ 35 ]. In summary, our study is the first to demonstrate the association between NAFLD and MIS in patients with STEMI rather than IF or PF. The L/S ratio of NAFLD on CT may serve as an independent predictor of MIS in patients with STEMI. Moreover, the sST2 level, which negatively correlates with MIS in the early STEMI stage and positively correlates with the L/S of NAFLD on CT in patients with STEMI, thus reinforcing the association between NAFLD and MIS at the molecular level. Abbreviations AMI, acute myocardial infarction; CEBP-α, CCAAT/enhancer-binding protein alpha; CT, computed tomography; DEGs, differentially expressed genes; ELISA, enzyme-linked immunosorbent assay; FAI, fat attenuation index; FAMI, first acute myocardial infarction; GEO, Gene Expression Omnibus; GO, Gene Ontology; IF, intrathoracic fat; IL1RL1, interleukin-1 receptor-like 1; LEPR, leptin receptor; L/S, liver-to-spleen ratio; MIS, myocardial infarct size; NAFLD, nonalcoholic fatty liver disease; PCI, percutaneous coronary intervention; PF, pericardial fat; SD, standard deviation; SPECT-MPI, single-photon emission computed tomography myocardial perfusion imaging; STEMI, ST-elevation myocardial infarction; THBD, thrombomodulin. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Tianjin Fourth Central Hospital and the ethics approval number was SZXLL-2023-K007. Consent for publication Not applicable. Availability of data and materials Not applicable. Competing interests The authors declare no competing interests. Funding Sources This study was supported by the National Natural Science Foundation of China (12174203), Scientific and Technological Projects of Tianjin (21JCYBJC00120), Tianjin Education Commission Scientific Research Project (2022KJ268), Tianjin Health Science and Technology Project (TJWJ2022MS023). Author contributions Weiwei Cui: clinical data collection, data analysis, writing original draft. Ningjun Li: clinical data collection, data analysis, fat tissue measurements. Xiao Gao: clinical data collection, supervision, writing review and editing. Xuehuan Liu: clinical data collection, supervision, writing review and editing. Qingshuang Bai: analysing the SPECT-MPI images. Zuoxi Li: analysing the SPECT-MPI images. Zhibo Zhou: fat tissue measurements. Hong Yu: fat tissue measurements. Li Yu: clinical data collection. Can Li: clinical data collection.Xinying Lian: clinical data collection. Jun Liu: Conceptualization, Supervision, Writing review and editing. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Younossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C, Henry L. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review. Hepatology. 2023;77:1335–47. Alon L, Corica B, Raparelli V, Cangemi R, Basili S, Proietti M, et al. Risk of cardiovascular events in patients with non-alcoholic fatty liver disease: a systematic review and meta-analysis. EUR J PREV CARDIOL. 2022;29:938–46. Mantovani A, Csermely A, Petracca G, Beatrice G, Corey KE, Simon TG, et al. Non-alcoholic fatty liver disease and risk of fatal and non-fatal cardiovascular events: an updated systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2021;6:903–13. Alexander M, Loomis AK, van der Lei J, Duarte-Salles T, Prieto-Alhambra D, Ansell D, et al. Non-alcoholic fatty liver disease and risk of incident acute myocardial infarction and stroke: findings from matched cohort study of 18 million European adults. BMJ. 2019;367:l5367. Konishi M, Sugiyama S, Sugamura K, Nozaki T, Ohba K, Matsubara J, et al. Association of pericardial fat accumulation rather than abdominal obesity with coronary atherosclerotic plaque formation in patients with suspected coronary artery disease. ATHEROSCLEROSIS. 2010;209:573–8. Possner M, Liga R, Gaisl T, Vontobel J, Clerc OF, Mikulicic F, et al. Quantification of epicardial and intrathoracic fat volume does not provide an added prognostic value as an adjunct to coronary artery calcium score and myocardial perfusion single-photon emission computed tomography. Eur Heart J Cardiovasc Imaging. 2016;17:885–91. Jolly US, Soliman A, McKenzie C, Peters T, Stirrat J, Nevis I, et al. Intra-thoracic fat volume is associated with myocardial infarction in patients with metabolic syndrome. J Cardiovasc Magn Reson. 2013;15:77. Chen O, Sharma A, Ahmad I, Bourji N, Nestoiter K, Hua P, et al. Correlation between pericardial, mediastinal, and intrathoracic fat volumes with the presence and severity of coronary artery disease, metabolic syndrome, and cardiac risk factors. Eur Heart J Cardiovasc Imaging. 2015;16:37–46. Gibbons RJ, Miller TD, Christian TF. Infarct size measured by single photon emission computed tomographic imaging with (99m)Tc-sestamibi: A measure of the efficacy of therapy in acute myocardial infarction. Circulation. 2000;101:101–8. Stone GW, Selker HP, Thiele H, Patel MR, Udelson JE, Ohman EM, et al. Relationship Between Infarct Size and Outcomes Following Primary PCI: Patient-Level Analysis From 10 Randomized Trials. J AM COLL CARDIOL. 2016;67:1674–83. Smit JM, Hermans MP, Dimitriu-Leen AC, van Rosendael AR, Dibbets-Schneider P, de Geus-Oei LF, et al. Long-term prognostic value of single-photon emission computed tomography myocardial perfusion imaging after primary PCI for STEMI. Eur Heart J Cardiovasc Imaging. 2018;19:1287–93. Dale M, Nicklin MJ. Interleukin-1 receptor cluster: gene organization of IL1R2, IL1R1, IL1RL2 (IL-1Rrp2), IL1RL1 (T1/ST2), and IL18R1 (IL-1Rrp) on human chromosome 2q. Genomics. 1999;57:177–9. Demyanets S, Kaun C, Pentz R, Krychtiuk KA, Rauscher S, Pfaffenberger S, et al. Components of the interleukin-33/ST2 system are differentially expressed and regulated in human cardiac cells and in cells of the cardiac vasculature. J MOL CELL CARDIOL. 2013;60:16–26. Pascual-Figal DA, Januzzi JL. The biology of ST2: the International ST2 Consensus Panel. AM J CARDIOL. 2015;115:B3–7. Weinberg EO, Shimpo M, De Keulenaer GW, MacGillivray C, Tominaga S, Solomon SD, et al. Expression and regulation of ST2, an interleukin-1 receptor family member, in cardiomyocytes and myocardial infarction. Circulation. 2002;106:2961–6. Weinberg EO, Shimpo M, Hurwitz S, Tominaga S, Rouleau JL, Lee RT. Identification of serum soluble ST2 receptor as a novel heart failure biomarker. Circulation. 2003;107:721–6. Kakkar R, Lee RT. The IL-33/ST2 pathway: therapeutic target and novel biomarker. NAT REV DRUG DISCOV. 2008;7:827–40. Sakai N, Van Sweringen HL, Quillin RC, Schuster R, Blanchard J, Burns JM, et al. Interleukin-33 is hepatoprotective during liver ischemia/reperfusion in mice. Hepatology. 2012;56:1468–78. Volarevic V, Mitrovic M, Milovanovic M, Zelen I, Nikolic I, Mitrovic S, et al. Protective role of IL-33/ST2 axis in Con A-induced hepatitis. J HEPATOL. 2012;56:26–33. [2019 Chinese. Society of Cardiology (CSC) guidelines for the diagnosis and management of patients with ST-segment elevation myocardial infarction]. Zhonghua xin xue guan bing za zhi. 2019;47:766–83. The Thrombolysis in Myocardial. Infarction (TIMI) trial. Phase I findings. N Engl J Med. 1985;312:932–6. Sager HB, Husser O, Steffens S, Laugwitz KL, Schunkert H, Kastrati A, et al. Time-of-day at symptom onset was not associated with infarct size and long-term prognosis in patients with ST-segment elevation myocardial infarction. J TRANSL MED. 2019;17:180. Imaging guidelines for nuclear cardiology procedures, part 2. American Society of Nuclear Cardiology. J NUCL CARDIOL. 1999;6:G47–84. Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation. 2002;105:539–42. Ren Z, Wen D, Xue R, Li S, Wang J, Li J, et al. Nonalcoholic fatty liver disease is associated with myocardial ischemia by CT myocardial perfusion imaging, independent of clinical and coronary CT angiography characteristics. EUR RADIOL. 2023;33:3857–66. Vita T, Murphy DJ, Osborne MT, Bajaj NS, Keraliya A, Jacob S, et al. Association between Nonalcoholic Fatty Liver Disease at CT and Coronary Microvascular Dysfunction at Myocardial Perfusion PET/CT. Volume 291. RADIOLOGY; 2019. pp. 330–7. Weir RA, Miller AM, Murphy GE, Clements S, Steedman T, Connell JM, et al. Serum soluble ST2: a potential novel mediator in left ventricular and infarct remodeling after acute myocardial infarction. J AM COLL CARDIOL. 2010;55:243–50. Chen O, Sharma A, Ahmad I, Bourji N, Nestoiter K, Hua P, et al. Correlation between pericardial, mediastinal, and intrathoracic fat volumes with the presence and severity of coronary artery disease, metabolic syndrome, and cardiac risk factors. Eur Heart J Cardiovasc Imaging. 2015;16:37–46. Mahabadi AA, Berg MH, Lehmann N, Kalsch H, Bauer M, Kara K, 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;61:1388–95. Redfors B, Mohebi R, Giustino G, Chen S, Selker HP, Thiele H, et al. Time Delay, Infarct Size, and Microvascular Obstruction After Primary Percutaneous Coronary Intervention for ST-Segment-Elevation Myocardial Infarction. Circ Cardiovasc Interv. 2021;14:e9879. Bonfig NL, Soukup CR, Shah AA, Olet S, Davidson SJ, Schmidt CW, et al. Increasing myocardial edema is associated with greater microvascular obstruction in ST-segment elevation myocardial infarction. Am J Physiol Heart Circ Physiol. 2022;323:H818–24. Younossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C, Henry L. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review. Hepatology. 2023;77:1335–47. Lomonaco R, Sunny NE, Bril F, Cusi K. Nonalcoholic fatty liver disease: current issues and novel treatment approaches. DRUGS. 2013;73:1–14. Dai W, Sun Y, Jiang Z, Du K, Xia N, Zhong G. Key genes associated with non-alcoholic fatty liver disease and acute myocardial infarction. Med Sci Monit. 2020;26:e922492. O'Connor MK, Hammell T, Gibbons RJ. In vitro validation of a simple tomographic technique for estimation of percentage myocardium at risk using methoxyisobutyl isonitrile technetium 99m (sestamibi). Eur J Nucl Med. 1990;17:69–76. Additional Declarations No competing interests reported. Supplementary Files Graphicalabstractimage.tif Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 03 May, 2024 Submission checks completed at journal 03 May, 2024 First submitted to journal 02 May, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4357262","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":298463622,"identity":"7ae66b3d-770c-4b4c-ae9d-dc03440996c2","order_by":0,"name":"Weiwei Cui","email":"","orcid":"","institution":"The Fourth Central Clinical School, Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Cui","suffix":""},{"id":298463625,"identity":"be021c3e-3cc6-4aeb-9ee1-9bc8274e6cdb","order_by":1,"name":"Ningjun Li","email":"","orcid":"","institution":"The Fourth Central Clinical School, Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ningjun","middleName":"","lastName":"Li","suffix":""},{"id":298463627,"identity":"bf2a886a-c747-48d1-ad38-bbc3e19a3d5b","order_by":2,"name":"Xiao Gao","email":"","orcid":"","institution":"Tianjin Fourth Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Gao","suffix":""},{"id":298463629,"identity":"366daaf4-87fc-45a5-ae17-b45ca9e7f471","order_by":3,"name":"Xuehuan Liu","email":"","orcid":"","institution":"Tianjin Union Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Xuehuan","middleName":"","lastName":"Liu","suffix":""},{"id":298463631,"identity":"c4327d32-8541-48a5-a412-22b289afb2cf","order_by":4,"name":"Qingshuang Bai","email":"","orcid":"","institution":"Tianjin Fourth Central 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University","correspondingAuthor":false,"prefix":"","firstName":"Xinying","middleName":"","lastName":"Lian","suffix":""},{"id":298463642,"identity":"edf9d592-0a14-4527-89e1-b17911c3b5b5","order_by":11,"name":"Jun Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie2RQUoDMRSG/yFQN7HZZsBDPFciFr1KhgHdVPAIkUJXHmA8R6F0+YbCuGn3A7qIeIHpRhBc2KRuG7IUzLf4IeT/eC8EyGT+JAw4ggAEwwBSKZugmKCMjFfOyoYTBpmQknxOyJp4Wz1uHczD+oRenz71+8qLXAy76XFF8wvtF1sLetsudbWBvBBWlM/L4wqhw0Hp7xdUzSEvLY/EaZoy/QgKsUlXCpekaO7Ahu5E2d+eu2qjZdm0s+hbVNMVbvi+qsd97dqv1eRGqVk77CLKfoz/TNThUFgdMtL3Yw73179KvJzJZDL/kx//ylHei8BSJgAAAABJRU5ErkJggg==","orcid":"","institution":"The Fourth Central Clinical School, Tianjin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-05-02 06:48:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4357262/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4357262/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56395505,"identity":"f18ebcf8-7545-4344-8cb8-2d0c032e3b27","added_by":"auto","created_at":"2024-05-13 15:41:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":152364,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the patient collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNAFLD, nonalcoholic fatty liver disease; IF, intrathoracic fat; PF, pericardial fat.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/ec8b1015cff07d327c0d33a4.png"},{"id":56397423,"identity":"001f99de-efb1-4384-a585-ad80f7962d8e","added_by":"auto","created_at":"2024-05-13 15:49:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":860260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNonalcoholic fatty liver disease (NAFLD) in an axial image\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNAFLD on CT scans is defined as follows: the average liver CT readings/splenic average CT readings ≤ 1.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/948a1676a2dbfbc37ae092e8.png"},{"id":56395508,"identity":"2f61d05c-da2d-4917-b3df-77d3ade50b15","added_by":"auto","created_at":"2024-05-13 15:41:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":328750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePericardial fat, pericardial sac, and intrathoracic fat in an axial image\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pericardial sac is defined as the border between pericardial and intrathoracic fat.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/5934b8c62e48a9f0a623e502.png"},{"id":56398593,"identity":"8efa1777-6fd8-4158-9ea5-6cd0f38827bc","added_by":"auto","created_at":"2024-05-13 15:57:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of SPECT-MPI infarct size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe figure\u003cstrong\u003e \u003c/strong\u003eshows that almost half of the patients had an infarct size of 0%-10%.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/952053ffbd026e26575558c4.png"},{"id":56397419,"identity":"73d6eabc-2666-4afe-b856-385669e6739a","added_by":"auto","created_at":"2024-05-13 15:49:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":98932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe CT ratio of liver to spleen\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ratio of CT value of the liver to the spleen was lower in patients with MIS than in those without MIS. Values are presented as medians (interquartile ranges). Boxplots depict the minimum, upper quartile, median, lower quartile, and maximum. **\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01. MIS, myocardial infarct size.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/9599a5f9da5d50283bfc4d14.png"},{"id":56397421,"identity":"a6765c15-78a4-4f4b-ba74-816a6f9f0866","added_by":"auto","created_at":"2024-05-13 15:49:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":181716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntrathoracic fat and pericardial fat with and without MIS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003e There was no difference in the intrathoracic fat volume between patients with and without MIS. \u003cstrong\u003eb.\u003c/strong\u003e The intrathoracic FAI in the group with MIS was lower than those without MIS, but there was no statistical difference. \u003cstrong\u003ec.\u003c/strong\u003e There was no difference in pericardial fat volume between patients with and without MIS. \u003cstrong\u003ed.\u003c/strong\u003e The pericardiac FAI in the group with MIS was lower than that in the group without MIS, but there was no statistical difference.\u003c/p\u003e\n\u003cp\u003eBar plots represent means ± SD, while boxplots represent the minimum, upper quartile, median, lower quartile, and maximum. FAI, fat attenuation index; MIS, myocardial infarct size; ns, not significant.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/e4fa17b465b23f644d480b4e.png"},{"id":56395519,"identity":"cbbb5c08-6579-4e3d-b418-38f3592eb074","added_by":"auto","created_at":"2024-05-13 15:41:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":326356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAn example of no measurable infarct size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePresented is a 77-year-old male patient. Resting gated myocardial perfusion imaging showed that the tracer distribution was sparse in part of the anterior wall and the low-posterior wall of the left ventricle. No obvious abnormal tracer distribution or defect area was detected. LVEF was measured at 64%. (SA = short axis; VLA = vertical long axis; HLA = horizontal long axis)\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/6aac5b28baa597dac69fc51e.png"},{"id":56395520,"identity":"439c43ce-535b-4705-913b-07a8c596b652","added_by":"auto","created_at":"2024-05-13 15:41:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":330815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAn example of a measurable infarct size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePresented is a 65-year-old female patient. Resting gated myocardial perfusion imaging revealed visible short-axis, vertical long-axis, and horizontal long-axis, demonstrating deficient myocardial tracer distribution in part of the low-posterior wall of the left ventricle, part of the lateral and apex wall. In addition, Myocardial tracer distribution was sparse in part of the anterior wall of the left ventricle. Polar coordinates of the target heart map showed that the myocardium with more than 60% LV perfusion reduction accounted for 29% of the total LV area. The LVEF was calculated at 56%.\u003c/p\u003e\n\u003cp\u003e(SA = short axis; VLA = vertical long axis; HLA = horizontal long axis)\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/db308b260807a5fd812e06dc.png"},{"id":56395513,"identity":"50c70549-02a7-47fe-a4d0-a3fb5494f213","added_by":"auto","created_at":"2024-05-13 15:41:09","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":140012,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe association between MIS and the NAFLD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScatter plot of infarct size in patients with nonalcoholic fatty liver disease (NAFLD) and without NAFLD\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/340effc8e49d147b0c630178.png"},{"id":56395509,"identity":"bc9cbd8f-dc1f-4f1e-ac2e-0e660984c829","added_by":"auto","created_at":"2024-05-13 15:41:09","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":356543,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression changes in NAFLD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmap showing the expression changes in nonalcoholic fatty liver disease (NAFLD)\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/668ed3b90c1a7afd29647b22.png"},{"id":56395515,"identity":"fd9ff5af-78da-431b-a477-992339d482ae","added_by":"auto","created_at":"2024-05-13 15:41:10","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":173799,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression changes in FAMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmap showing the expression changes in first acute myocardial infarction (FAMI)\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/b160fe74716f0a8548888dd1.png"},{"id":56395517,"identity":"55323cf2-9484-49eb-bb2f-e024316f7ed1","added_by":"auto","created_at":"2024-05-13 15:41:10","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":130002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShared genes associated with NAFLD and FAMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenn diagram of intersecting common genes identified by differential genes from nonalcoholic fatty liver disease (NAFLD) and first acute myocardial infarction (FAMI).\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/10c0f2307b8f6395b3d27149.png"},{"id":56395514,"identity":"afd3a1e8-c294-4dac-9f5f-e0dc7053fadb","added_by":"auto","created_at":"2024-05-13 15:41:10","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":83469,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontologyanalysis of biological processes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/7ceb6510c72ac9d4a32b8300.png"},{"id":56398594,"identity":"c839f972-3b5a-42cb-a496-697c7a89cb7e","added_by":"auto","created_at":"2024-05-13 15:57:09","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":113298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDEGs in patients with and without NAFLD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003e No significant difference was observed in the expression of CEBP-α between patients with and without NAFLD. \u003cstrong\u003eb.\u003c/strong\u003e The expression of sST2 was lower in the group with NAFLD than in the control. \u003cstrong\u003ec.\u003c/strong\u003eThere was no difference in the expression of Lepr between patients with and without NAFLD. \u003cstrong\u003ed.\u003c/strong\u003e The expression of THBD in the NAFLD group was lower than that in the control, although the difference was not statistically significant.\u003c/p\u003e\n\u003cp\u003eBar plots represent means ± SD, while boxplots represent the minimum, upper quartile, median, lower quartile, and maximum. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; DEGs, differentially expressed genes;\u003cstrong\u003e \u003c/strong\u003eNAFLD, nonalcoholic fatty liver disease; ns, not significant.\u003c/p\u003e","description":"","filename":"Figure14.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/65a5c524fedbd9051bf54893.png"},{"id":56397424,"identity":"9d566927-8aff-4d0c-a0c3-ec8586daae7a","added_by":"auto","created_at":"2024-05-13 15:49:10","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":103513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe association between the sST2 and the NAFLD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScatter plot of the sST2 and L/S in patients with STEMI. L/S, the liver-to-spleen ratio (L/S), NAFLD, nonalcoholic fatty liver disease.\u003c/p\u003e","description":"","filename":"Figure15.png","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/22e0ec6ad7619fbb2f8dc2f1.png"},{"id":56398649,"identity":"3724af7b-bfd8-44dd-adc8-008cb426425e","added_by":"auto","created_at":"2024-05-13 15:57:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5318672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/be0db182-ba34-4bf7-aec4-2b650ee57bb8.pdf"},{"id":56395512,"identity":"79b29814-ed99-4b5c-8c1b-222c5475d381","added_by":"auto","created_at":"2024-05-13 15:41:09","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3216164,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstractimage.tif","url":"https://assets-eu.researchsquare.com/files/rs-4357262/v1/17713db4f041ceca50f59999.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Nonalcoholic Fatty Liver Disease on CT and Myocardial Infarct Size using SPECT-MPI in patients with ST-elevation Myocardial Infarction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccumulation of fat tissue in vital organs is closely related to the onset and progression of cardiovascular diseases. Nonalcoholic fatty liver disease (NAFLD) is a prevalent symptom of excessive fat accumulation in the liver. Currently, it stands as the predominant chronic liver disease, affecting approximately 30% of the general population[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A previous study suggested that NAFLD may be an independent risk factor for cardiovascular morbidity and mortality[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Paradoxically, Sattar et al. found no discernable difference in the occurrence of acute myocardial infarction (AMI) between patients with NAFLD and their healthy counterparts[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This observation led us to consider whether the effect of NAFLD on the prognosis of AMI was different from its effect on cardiovascular diseases. However, studies on how NAFLD affects the prognosis of AMI remain scarce.\u003c/p\u003e \u003cp\u003eThe accumulation of ectopic fat around the heart, such as pericardial fat (PF) or intrathoracic fat (IF), may have a stronger correlation with both clinical and subclinical cardiovascular conditions compared to other types of fat stores, such as abdominal visceral fat[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Quantification of PF and IF correlates with major adverse cardiac events and improves risk stratification beyond traditional cardiovascular risk factors[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Patients with myocardial ischaemia exhibit a significant increase in IF volume, which could serve as a reliable predictor of myocardial infarction risk in patients with metabolic syndrome[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In Chen's study, PF and IF were correlated with the occurrence and severity of coronary heart disease; however, subsequent observations revealed no disparity in PF and IF between patients with and without AMI[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, we hypothesised that IF and PF are not associated with the occurrence of AMI but rather with its severity.\u003c/p\u003e \u003cp\u003eResting-gated single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) is one of the most reliable and non-invasive methods for detecting myocardial ischaemia. SPECT imaging with (99m) Tc-sestamibi is the best tool for detecting myocardial infarct size (MIS) and can be used as an endpoint for early pilot studies evaluating potential efficacy and dose-range studies[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A recent study showed a strong association between MIS measured by SPECT-MPI within 1 month in patients with ST-elevation myocardial infarction (STEMI)who underwent primary percutaneous coronary intervention (PCI) and 1-year all-cause mortality or hospitalization due to heart failure[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, long-term follow-up of clinical outcomes in patients with STEMI showed that MIS was associated with the occurrence of cardiac death or reinfarction[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, the MIS could serve as a useful endpoint in clinical trials and as an important prognostic indicator when caring for patients with STEMI.\u003c/p\u003e \u003cp\u003eTwo transcripts of interleukin-1 receptor-like 1 (IL1RL1) are important: a truncated soluble receptor(sST2 or IL1RL1-a) circulating in the serum and a transmembrane receptor (ST2L or IL1RL1-b)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Cardiac fibroblasts, cardiomyocytes, and non-cardiomyocytes, including those within large human blood vessels (such as the aorta and coronary arteries) and cardiac microvascular endothelial cells, can generate sST2 in response to injury or stress[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Normally, in cardiomyocytes, IL-33 exerts a protective effect by binding to ST2L. However, sST2 can competitively bind IL-33 to ST2L, thus obstructing the IL-33/ST2L pathway[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Elevated sST2 levels have been previously reported to predict heart failure and mortality in patients with AMI[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. ST2L is predominantly expressed in haematopoietic cells and was also detected in the healthy liver. It was up-regulated within 1 h and peaked at 4 h after I/R[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, ST2-deficient mice exhibited more severe hepatitis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the alterations in sST2 levels in patients with NAFLD and AMI remain unclear.\u003c/p\u003e \u003cp\u003eOur study aimed to assesse the relationship between NAFLD, IF, PF, and MIS in patients with STEMI using CT scans and SPECT-MPI. At the molecular level, our objective was to explore the common genes between the two diseases in the Gene Expression Omnibus (GEO) database and to assess the expression levels of these genes in the serum samples, thereby shedding light on the molecular mechanisms underlying this association and further verifying the association between NAFLD and MIS.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eIn this study, we retrospectively collected data from 406 patients who underwent myocardial perfusion imaging and chest CT at Tianjin Fourth Central Hospital between September 2021 and August 2023. The inclusion criteria were as follows: (1) Diagnosis of STEMI; (2) successful PCI performed within 24 h of symptom emergence; and (3) patients who underwent SPECT-MPI for more than 4 days following the emergence of myocardial infarction symptoms. The exclusion criteria were as follows: (1) individuals diagnosed with unstable angina, (2) patients with a history of acute myocardial infarction, (3) individuals without STEMI, and (4) individuals with reported alcohol consumption or abuse and liver diseases. STEMI was diagnosed according to the 2019 Chinese Society of Cardiology Guidelines for the Diagnosis and Management of Patients with ST-elevation myocardial infarction diagnosis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thrombolysis in myocardial infarction grade 3 coronary flow within the treated vessel was defined as Successful PCI [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The infarct size remained unaffected by PCI within 24 h [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To mitigate the effects of reversible ischaemic foci, we ensured that only patients who underwent myocardial perfusion imaging for no less than 4 days after the emergence of myocardial infarction symptoms were selected. The detailed procedure is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCT image acquisition\u003c/h2\u003e \u003cp\u003eAll the patients underwent scanning via GE LightSpeed VCT 64-slice spiral CT scanner and Vitrea2 workstation, utilising retrospective electrocardiogram-gated technology. The scanning parameters were set at 120 kV and 440 mA, with a slice thickness of 5 mm. The scan was conducted over a 360\u0026deg; rotation in 0.4 s, featuring an automated and smart pitch adjustment. The scanning range extended from 3.2 (0.2) to 4.8 (0.3\u0026deg;), covering the area from the subcarinal region to the subdiaphragm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFat tissue measurements\u003c/h2\u003e \u003cp\u003eIn our study, NAFLD was defined as hepatic steatosis in individuals without any reported history of alcohol consumption, abuse, or liver disease. Non-enhanced CT scan was utilized to measure CT attenuation in Hounsfield units, focusing on liver and spleen regions with a region of interest of 80 mm\u003csup\u003e2\u003c/sup\u003e or more, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Within the right lobe, two measurements were recorded in different planes, and the average of these measurements from each patient was used in the analysis. NAFLD on CT scans was determined when the ratio of the average liver CT readings to the average splenic CT readings was \u0026le;\u0026thinsp;1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFat volume was measured through semi-automated segmentation using the SliceOmatic software (TomoVision, Magog, QC, Canada) managed by a pair of experienced radiologists. Initially, a step-by-step semi-manual mapping of the fat areas in the chest CT cross-sectional images was conducted, with the fat threshold set between \u0026minus;\u0026thinsp;190 and \u0026minus;\u0026thinsp;30 HU. Consequently, the software autonomously determined the average fat attenuation index (FAI) and the aggregate fat volume within the threshold.\u003c/p\u003e \u003cp\u003eThe total IF volume encompasses all adipose tissue found in the thorax, stretching from the level of the right pulmonary artery to the diaphragm and from the chest wall to the descending aorta, along with fat in the pericardial sac. The term 'PF volume' refers to any fat tissue located in the pericardial sac, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSPECT-MPI\u003c/h2\u003e \u003cp\u003eThe SPECT-MPI procedure was conducted for a minimum of 4 days following the PCI for STEMI. The chosen imaging medium was 99Tcm-methoxy isobutyl acetonitrile, which possessed a radiological purity exceeding 90% and an injectable quantity ranging from 15\u0026ndash;20 mCi. The injection was administered via the middle elbow vein, followed by the oral administration of 250 ml of milk. After 1.5 h, images were acquired using a gated method. SPECT-MPI utilized a GE Discovery NM630 SPECT device and a MedExMIM medical imaging workstation. Acquisition conditions included a 180\u0026deg; rotation, 6\u0026deg; per frame, 64\u0026times;64 matrix, and 1.4 magnification. Subsequently, the images were transferred to a workstation for the reconstruction of short-axis, vertical long-axis, and horizontal long-axis views of the left ventricle.\u003c/p\u003e \u003cp\u003eThe vascular territories were delineated using the 17-segment model [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Tracer uptake for each segment was rated on a 5-point scale (0\u0026thinsp;=\u0026thinsp;normal, 1\u0026thinsp;=\u0026thinsp;mild reduction, 2\u0026thinsp;=\u0026thinsp;moderate reduction, 3\u0026thinsp;=\u0026thinsp;severe reduction, and 4\u0026thinsp;=\u0026thinsp;absent tracer uptake). Infarct size was measured by setting a threshold value of 60% of the peak counts, while defect size was quantified as a proportion of the left ventricle. Two experienced deputy chief physicians from the Department of Nuclear Medicine independently analysed the images in a double-blind manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGEO data collection\u003c/h2\u003e \u003cp\u003eNAFLD, first acute myocardial infarction (FAMI), and control datasets were retrieved from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GSE89632 dataset encompasses gene expression profiles of 39 patients with NAFLD and 24 healthy individuals. Additionally, the GEO24519 dataset included data from 17 patients with FAMI and four healthy individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of differentially expressed genes (DEGs) in NAFLD and AMI\u003c/h2\u003e \u003cp\u003eDEGs were screened and identified using the GEO2R tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/geo2r\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/geo2r\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in GSE89632 and GEO24519. The thresholds for DEGs were set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt;1. Subsequently, we input the DEGs of both diseases into the Venn online analysis tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify overlapping genes. Twenty DEGs were identified, and further analysis was conducted via two digital platforms, metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/gp/index.html#/main/step3\u003c/span\u003e\u003cspan address=\"https://metascape.org/gp/index.html#/main/step3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Microbiology Letter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), for Gene Ontology (GO) analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of the expression of DEGs in the serum\u003c/h2\u003e \u003cp\u003e Following the aforementioned inclusion and exclusion criteria, we prospectively collected serum from 22 patients with STEMI(7 with NAFLD and 15 without NAFLD) at Tianjin Fourth Central Hospital between December 2023 and March 2024. Serum samples from each patient, which were otherwise discarded from the clinical laboratory, were collected on the day the symptoms of AMI first appeared. This study was approved by the Ethics Committee of Tianjin Fourth Central Hospital and the ethics approval number was SZXLL-2023-K007.\u003c/p\u003e \u003cp\u003e Plasma concentrations of CCAAT/enhancer-binding protein alpha (CEBP-α), sST2, leptin receptor (LEPR), and thrombomodulin (THBD)were measured using a solid-phase sandwich enzyme-linked immunosorbent assay (ELISA) kit (MeiMian, Jiangsu, China) in accordance with manufacturer guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are represented either as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or as the median in a range of quartiles. Categorical variables are represented as frequencies (percentages). The liver-to-spleen (L/S) ratios on CT and MIS were treated as continuous variables. For intergroup comparisons, the independent sample Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test for the continuous variable was used, while categorical variables were analysed using either the Chi-square or Fisher exact tests. Pearson's and Spearman's correlation methods were used to assess the correlations. Statistical analyses were performed using the IBM SPSS Statistics 26.0 (IBM Corporation). A two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistical significance. Using MIS as the dependent variable, both univariate and multivariate linear regression methods were used to examine the association between NAFLD and MIS. The L/S ratio of NAFLD on CT and traditional coronary artery disease risk factors were defined as independent variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics\u003c/h2\u003e \u003cp\u003eThe clinical characteristics of the 117 patients with STEMI, comprising 23 with NAFLD and 94 without NAFLD, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In comparison to the group without NAFLD, those with NAFLD exhibited elevated triglyceride levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), and infarction in the posterior wall of the left ventricle was less common (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). No significant differences were observed in the other parameters between the two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of study patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD Present(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNAFLD Absent(n\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.0(58.0,70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0 (55.0,71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.5 (58.0,70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMan (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96(82.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (91.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (79.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.6(23.7,27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.7 (24.2,29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.5 (23.4,27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160 (140,180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (150,180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160 (140,180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (80,110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (90,110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (80,110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting blood glucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (5.2,8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.6 (5.4,10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9 (5.1,8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8 (4.1,5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2 (4.2,5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7 (4.0,5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6 (1.2,2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (1.4,3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (1.1,2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow density lipoprotein (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh density lipoprotein (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.8,1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (08,1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (0.8,1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHear trate (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCardiovascular risk factors, n (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82(70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(78.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64(68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(91.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71(75.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(0,40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(0,40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(0,40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of premature CAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSymptom, n (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypical angina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97(82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(82.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78(83.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eElectrocardiogram, n (%)\u003c/p\u003e \u003cp\u003eInfarct location\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnterior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57(48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48(51.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosterior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfarct related coronary artery, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(51.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52(55.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMI flow grade before intervention, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(58.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(56.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade of Killip, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(41.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(56.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(56.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(59.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median (interquartile range), or n (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCAD, coronary artery disease; LAD, left anterior descending coronary artery; LCX, left circumflex coronary artery; NAFLD, non-alcoholic fatty liver disease; RCA, right coronary artery; TIMI, thrombolysis in myocardial Infarction.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFat characteristics of vital organs in patients with and without MIS\u003c/h2\u003e \u003cp\u003eAmong the 117 patients with STEMI, the majority of MIS cases fell within the range of 0\u0026ndash;10% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to the SPECT-MPI results, patients with STEMI were categorised into two groups: those with MIS (MIS\u0026thinsp;\u0026gt;\u0026thinsp;0%) and those without MIS (MIS for 0%). We found that in patients with MIS, the L/S was lower compared to that in those without MIS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, P\u0026thinsp;=\u0026thinsp;0.001). However, there was no notable difference in the volume and FAI of the IF and PF (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of rest SPECT-MPI in Patients with and without NAFLD\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows an example of no measurable infarct size, while Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e demonstrates a measurable infarct size. In comparison to the group without NAFLD, the group with NAFLD had a larger MIS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037). Regarding bandwidth, left ventricle volume, and ejection fraction, there were no statistically significant differences between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of rest SPECT MPI in Patients with and without NAFLD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAFLD Present(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNAFLD Absent(n\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLV volumes and ejection\u003c/p\u003e \u003cp\u003efraction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV EDVI (mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102(82.5,125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102(86,117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.5(81.75,129.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLV ESVI (mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(33.5,56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(36,54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42(33,56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(52,62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(50,61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58(52,63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBandwidth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(52,62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(53,133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73(50,133.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfarct size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(0,15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(3,17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(0,13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are median (interquartile range).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eEDVI, indexed end-diastolic volume; ESVI, indexed end-systolic volume; LV, left ventricle; LVEF, left ventricular ejection fraction; NAFLD, non-alcoholic fatty liver disease.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eInfarct size was quantitated using a threshold value of 60% of peak counts. The defect size was expressed as a percentage of the left ventricle.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between NAFLD at CT and MIS at SPECT-MPI\u003c/h2\u003e \u003cp\u003eCorrelation analysis revealed a significant negative correlation between the L/S ratio and MIS (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e; r=-0.328, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the univariate analysis, MIS was associated with fasting blood glucose (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) and L/S (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). Multiple linear regression analysis identified the L/S ratio as an independent predictor of MIS, after adjusting for blood glucose and triglyceride (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, P\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictors of infarct size\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariable Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eMultivariable Step-forward\u003c/p\u003e \u003cp\u003eSelection Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eβ Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMan (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting blood glucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow density lipoprotein (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh density lipoprotein (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of premature CAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLive/Spleen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI, body mass index; CAD, coronary artery disease; Live/Spleen, the CT value ratio of live to spleen.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression analysis of DEGs in NAFLD and FAMI\u003c/h2\u003e \u003cp\u003eIn the NAFLD GSE89632 dataset, we compared 39 patients with NAFLD to 24 healthy controls and identified 474 DEGs, which are presented as heat maps in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Additionally, we analysed the GSE24519 dataset for the FAMI, comprising 17 patients with FAMI and four healthy controls. From this dataset, we identified 837 DEGs, which are presented as heat maps in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. Using the Venn Diagram online tool, we determined the intersection of DEGs between the two diseases, revealing 20 genes common to both conditions, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eChanges of sST2 in patients with NAFLD and FAMI\u003c/h2\u003e \u003cp\u003eGO classification was performed on the previously identified 20 DEGs, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e. The alterations in GO biological processes predominantly encompass cytokine-mediated signalling pathways, lipid biosynthetic processes, alcohol metabolic processes, and circulatory system processes. Due to the exclusion of patients with a history of alcohol use, we selected the cytokine-mediated signalling pathway with the minimal P-value for further study. This pathway\u0026rsquo;s DEGs included IL1RL1(the approved symbol for ST2), THBD, LEPR, and CEBP-α. Subsequently, serum was prospectively collected from patients with STEMI in the clinic, and an ELISA was used to confirm the differences in the expression of these four genes at the protein level among patients with and without NAFLD. The experimental results showed a decrease in the expression of sST2 in the NAFLD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e, P\u0026thinsp;=\u0026thinsp;0.042). Correlation analysis indicated a significant positive correlation between L/S and sST2 levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e; r\u0026thinsp;=\u0026thinsp;0.459, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies have confirmed a higher incidence of myocardial ischaemia and coronary microvascular dysfunction among patients with NAFLD by combining CT with CT-MPI or SPECT-MPI. The conclusion drawn was that NAFLD is an independent predictor of myocardial ischemia and coronary microvascular dysfunction[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In our study, we combined SPECT-MPI with CT imaging to demonstrate for the first time that NAFLD is associated with MIS, rather than IF or PF, and that the L/S of NAFLD on CT was an independent predictor of MIS in patients experiencing primary STEMI. In addition, previous studies have indicated that the levels of sST2, which increase in the early stage of STEMI, inversely correlate with MIS[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our results suggest that sST2 is associated with both STEMI and NAFLD. Furthermore, the expression of sST2 positively correlated with the L/S of NAFLD as observed on CT in patients with STEMI. This finding further confirmed the association between NAFLD detected on CT and MIS on SPECT-MPI in STEMI at the molecular level.\u003c/p\u003e \u003cp\u003eJohn et al. showed that IF and PF in patients with coronary heart disease may not correlate with the subsequent occurrence of AMI[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Conversely, Amir et al. revealed a correlation between PF and long-term cardiovascular events in healthy individuals[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, the impact of IF and PF on MIS among patients with STEMI still requires further investigation. Our results revealed no significant differences in the volume and FAI of the IF and PF between the groups with or without MIS. However, the L/S of NAFLD differed significantly. Regarding AMI, MIS correlated with coronary microvascular dysfunction and prompt reperfusion[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, NAFLD is an independent predictor of coronary microvascular dysfunction[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, we hypothesise that these findings may be attributed to coronary microvascular dysfunction, which is closely related to NAFLD and aggravates the MIS.\u003c/p\u003e \u003cp\u003eIn comparison with one previous study[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], we observed that 19.7% (23 of 117) of patients with STEMI in our study had NAFLD. This variation may be attributed to our utilization of an L/S ratio\u0026thinsp;\u0026lt;\u0026thinsp;1 to diagnose NAFLD instead of ultrasound. As widely acknowledged, NAFLD is associated with insulin resistance, obesity, dyslipidemia, and diabetes[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our findings revealed elevated triglyceride levels in patients with STEMI and NAFLD. However, this coincides with traditional cardiovascular risk factors, thus failing to verify an association between NAFLD and STEMI. Research indicates that NAFLD can serve as a microvascular dysfunction predictor, which is more powerful than the burden of individual risk factors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our results suggest that the L/S ratio of NAFLD on CT is an independent predictor of MIS in patients with STEMI.\u003c/p\u003e \u003cp\u003eThrough analysis of the NAFLD and FAMI GEO databases, we screened 20 DEGs shared by the two diseases. Following GO analysis, four DEGs were selected and confirmed in patients with FAMI using ELISA. The results indicated a significant decrease in sST2 levels among patients with FAMI in the NAFLD group than in those in the non-NAFLD group. Zhong et al. also identified DEGs associated with these two diseases through GEO database analysis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, compared to previous research, our study included a larger patient cohort in the database, with the AMI database being the first AMI patient group, potentially explaining the discrepancies observed in our findings.\u003c/p\u003e \u003cp\u003eThe IL33/ST2L pathway has a protective effect on the myocardium, and sST2 can compete with ST2L to bind to IL33, thereby inhibiting this pathway[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The rapid increase in sST2 levels in the early stages of AMI predicts heart failure and mortality in patients with AMI[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our analysis revealed a reduction in sST2 levels among patients with STEMI with NAFLD than in those without NAFLD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which suggests that NAFLD may contribute to decreased sST2 levels. Therefore, further investigation on the role of ST2 in NAFLD is warranted. Additionally, the expression of sST2 positively correlated with the L/S of NAFLD as observed on CT in patients with STEMI. The level of sST2, which was negatively correlated with MIS in [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]the early STEMI stage[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], was found to be reduced in the serum of STEMI patients with NAFLD, thus reinforcing the association between NAFLD and MIS at the molecular level. This indicates the need to prioritise this group when using sST2 levels to assess the prognosis of patients with STEMI and NAFLD. However, further investigation is needed to elucidate the underlying mechanism.\u003c/p\u003e \u003cp\u003eOur study has several limitations. Firstly, this was a single-centre retrospective study with a limited number of patients, which may have hampered the robustness of our findings. Additionally, it was unnecessary for patients who met the inclusion criteria to collect serum prospectively to verify the DEGs before receiving SPECT-MPI, resulting in a lack of SPECT-MPI data for these patients. Although an existing study supports our results, direct evidence is still lacking. Finally, the limitations associated with visual evaluation of perfusion and function are well known. However, for an experienced observer, the strong correlation observed between MIS calculated through visual segmental scoring and the reference assessment employing a circumferential profile and an objective threshold method supports the reliability of visual analysis[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, our study is the first to demonstrate the association between NAFLD and MIS in patients with STEMI rather than IF or PF. The L/S ratio of NAFLD on CT may serve as an independent predictor of MIS in patients with STEMI. Moreover, the sST2 level, which negatively correlates with MIS in the early STEMI stage and positively correlates with the L/S of NAFLD on CT in patients with STEMI, thus reinforcing the association between NAFLD and MIS at the molecular level.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAMI, acute myocardial infarction;\u003c/p\u003e\n\u003cp\u003eCEBP-\u0026alpha;, CCAAT/enhancer-binding protein alpha;\u003c/p\u003e\n\u003cp\u003eCT, computed tomography;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDEGs, differentially expressed genes;\u003c/p\u003e\n\u003cp\u003eELISA, enzyme-linked immunosorbent assay;\u003c/p\u003e\n\u003cp\u003eFAI, fat attenuation index;\u003c/p\u003e\n\u003cp\u003eFAMI,\u0026nbsp;first acute myocardial infarction;\u003c/p\u003e\n\u003cp\u003eGEO,\u0026nbsp;Gene Expression Omnibus;\u003c/p\u003e\n\u003cp\u003eGO, Gene Ontology;\u003c/p\u003e\n\u003cp\u003eIF, intrathoracic fat;\u003c/p\u003e\n\u003cp\u003eIL1RL1, interleukin-1 receptor-like 1;\u003c/p\u003e\n\u003cp\u003eLEPR, leptin receptor;\u003c/p\u003e\n\u003cp\u003eL/S, liver-to-spleen ratio;\u003c/p\u003e\n\u003cp\u003eMIS, myocardial infarct size;\u003c/p\u003e\n\u003cp\u003eNAFLD, nonalcoholic fatty liver disease;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCI, percutaneous coronary intervention;\u003c/p\u003e\n\u003cp\u003ePF, pericardial fat;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSD, standard deviation;\u003c/p\u003e\n\u003cp\u003eSPECT-MPI, single-photon emission computed tomography myocardial perfusion imaging; STEMI, ST-elevation myocardial infarction;\u003c/p\u003e\n\u003cp\u003eTHBD, thrombomodulin.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of\u0026nbsp;Tianjin Fourth Central Hospital and the ethics approval number was SZXLL-2023-K007.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (12174203), Scientific and Technological Projects of Tianjin (21JCYBJC00120), Tianjin Education Commission Scientific Research Project (2022KJ268), Tianjin Health Science and Technology Project (TJWJ2022MS023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeiwei Cui: clinical data collection, data analysis, writing original draft. Ningjun Li: clinical data collection, data analysis, fat tissue measurements. Xiao Gao: clinical data collection, supervision, writing review and editing. Xuehuan Liu: clinical data collection, supervision, writing review and editing. Qingshuang Bai: analysing the SPECT-MPI images. Zuoxi Li: analysing the SPECT-MPI images. Zhibo Zhou: fat tissue measurements. Hong Yu: fat tissue measurements. Li Yu: clinical data collection. Can Li: clinical data collection.Xinying Lian: clinical data collection. Jun Liu: Conceptualization, Supervision, Writing review and editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYounossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C, Henry L. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review. Hepatology. 2023;77:1335\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlon L, Corica B, Raparelli V, Cangemi R, Basili S, Proietti M, et al. Risk of cardiovascular events in patients with non-alcoholic fatty liver disease: a systematic review and meta-analysis. EUR J PREV CARDIOL. 2022;29:938\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMantovani A, Csermely A, Petracca G, Beatrice G, Corey KE, Simon TG, et al. Non-alcoholic fatty liver disease and risk of fatal and non-fatal cardiovascular events: an updated systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2021;6:903\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander M, Loomis AK, van der Lei J, Duarte-Salles T, Prieto-Alhambra D, Ansell D, et al. Non-alcoholic fatty liver disease and risk of incident acute myocardial infarction and stroke: findings from matched cohort study of 18 million European adults. BMJ. 2019;367:l5367.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonishi M, Sugiyama S, Sugamura K, Nozaki T, Ohba K, Matsubara J, et al. Association of pericardial fat accumulation rather than abdominal obesity with coronary atherosclerotic plaque formation in patients with suspected coronary artery disease. ATHEROSCLEROSIS. 2010;209:573\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePossner M, Liga R, Gaisl T, Vontobel J, Clerc OF, Mikulicic F, et al. Quantification of epicardial and intrathoracic fat volume does not provide an added prognostic value as an adjunct to coronary artery calcium score and myocardial perfusion single-photon emission computed tomography. Eur Heart J Cardiovasc Imaging. 2016;17:885\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJolly US, Soliman A, McKenzie C, Peters T, Stirrat J, Nevis I, et al. Intra-thoracic fat volume is associated with myocardial infarction in patients with metabolic syndrome. J Cardiovasc Magn Reson. 2013;15:77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen O, Sharma A, Ahmad I, Bourji N, Nestoiter K, Hua P, et al. Correlation between pericardial, mediastinal, and intrathoracic fat volumes with the presence and severity of coronary artery disease, metabolic syndrome, and cardiac risk factors. Eur Heart J Cardiovasc Imaging. 2015;16:37\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibbons RJ, Miller TD, Christian TF. Infarct size measured by single photon emission computed tomographic imaging with (99m)Tc-sestamibi: A measure of the efficacy of therapy in acute myocardial infarction. Circulation. 2000;101:101\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStone GW, Selker HP, Thiele H, Patel MR, Udelson JE, Ohman EM, et al. Relationship Between Infarct Size and Outcomes Following Primary PCI: Patient-Level Analysis From 10 Randomized Trials. J AM COLL CARDIOL. 2016;67:1674\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmit JM, Hermans MP, Dimitriu-Leen AC, van Rosendael AR, Dibbets-Schneider P, de Geus-Oei LF, et al. Long-term prognostic value of single-photon emission computed tomography myocardial perfusion imaging after primary PCI for STEMI. Eur Heart J Cardiovasc Imaging. 2018;19:1287\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDale M, Nicklin MJ. Interleukin-1 receptor cluster: gene organization of IL1R2, IL1R1, IL1RL2 (IL-1Rrp2), IL1RL1 (T1/ST2), and IL18R1 (IL-1Rrp) on human chromosome 2q. Genomics. 1999;57:177\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemyanets S, Kaun C, Pentz R, Krychtiuk KA, Rauscher S, Pfaffenberger S, et al. Components of the interleukin-33/ST2 system are differentially expressed and regulated in human cardiac cells and in cells of the cardiac vasculature. J MOL CELL CARDIOL. 2013;60:16\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePascual-Figal DA, Januzzi JL. The biology of ST2: the International ST2 Consensus Panel. AM J CARDIOL. 2015;115:B3\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeinberg EO, Shimpo M, De Keulenaer GW, MacGillivray C, Tominaga S, Solomon SD, et al. Expression and regulation of ST2, an interleukin-1 receptor family member, in cardiomyocytes and myocardial infarction. Circulation. 2002;106:2961\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeinberg EO, Shimpo M, Hurwitz S, Tominaga S, Rouleau JL, Lee RT. Identification of serum soluble ST2 receptor as a novel heart failure biomarker. Circulation. 2003;107:721\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKakkar R, Lee RT. The IL-33/ST2 pathway: therapeutic target and novel biomarker. NAT REV DRUG DISCOV. 2008;7:827\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakai N, Van Sweringen HL, Quillin RC, Schuster R, Blanchard J, Burns JM, et al. Interleukin-33 is hepatoprotective during liver ischemia/reperfusion in mice. Hepatology. 2012;56:1468\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolarevic V, Mitrovic M, Milovanovic M, Zelen I, Nikolic I, Mitrovic S, et al. Protective role of IL-33/ST2 axis in Con A-induced hepatitis. J HEPATOL. 2012;56:26\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e[2019 Chinese. Society of Cardiology (CSC) guidelines for the diagnosis and management of patients with ST-segment elevation myocardial infarction]. Zhonghua xin xue guan bing za zhi. 2019;47:766\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe Thrombolysis in Myocardial. Infarction (TIMI) trial. Phase I findings. N Engl J Med. 1985;312:932\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSager HB, Husser O, Steffens S, Laugwitz KL, Schunkert H, Kastrati A, et al. Time-of-day at symptom onset was not associated with infarct size and long-term prognosis in patients with ST-segment elevation myocardial infarction. J TRANSL MED. 2019;17:180.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImaging guidelines for nuclear cardiology procedures, part 2. American Society of Nuclear Cardiology. J NUCL CARDIOL. 1999;6:G47\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK, et al. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. Circulation. 2002;105:539\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen Z, Wen D, Xue R, Li S, Wang J, Li J, et al. Nonalcoholic fatty liver disease is associated with myocardial ischemia by CT myocardial perfusion imaging, independent of clinical and coronary CT angiography characteristics. EUR RADIOL. 2023;33:3857\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVita T, Murphy DJ, Osborne MT, Bajaj NS, Keraliya A, Jacob S, et al. Association between Nonalcoholic Fatty Liver Disease at CT and Coronary Microvascular Dysfunction at Myocardial Perfusion PET/CT. Volume 291. RADIOLOGY; 2019. pp. 330\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeir RA, Miller AM, Murphy GE, Clements S, Steedman T, Connell JM, et al. Serum soluble ST2: a potential novel mediator in left ventricular and infarct remodeling after acute myocardial infarction. J AM COLL CARDIOL. 2010;55:243\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen O, Sharma A, Ahmad I, Bourji N, Nestoiter K, Hua P, et al. Correlation between pericardial, mediastinal, and intrathoracic fat volumes with the presence and severity of coronary artery disease, metabolic syndrome, and cardiac risk factors. Eur Heart J Cardiovasc Imaging. 2015;16:37\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahabadi AA, Berg MH, Lehmann N, Kalsch H, Bauer M, Kara K, 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;61:1388\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRedfors B, Mohebi R, Giustino G, Chen S, Selker HP, Thiele H, et al. Time Delay, Infarct Size, and Microvascular Obstruction After Primary Percutaneous Coronary Intervention for ST-Segment-Elevation Myocardial Infarction. Circ Cardiovasc Interv. 2021;14:e9879.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonfig NL, Soukup CR, Shah AA, Olet S, Davidson SJ, Schmidt CW, et al. Increasing myocardial edema is associated with greater microvascular obstruction in ST-segment elevation myocardial infarction. Am J Physiol Heart Circ Physiol. 2022;323:H818\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYounossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C, Henry L. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review. Hepatology. 2023;77:1335\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLomonaco R, Sunny NE, Bril F, Cusi K. Nonalcoholic fatty liver disease: current issues and novel treatment approaches. DRUGS. 2013;73:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai W, Sun Y, Jiang Z, Du K, Xia N, Zhong G. Key genes associated with non-alcoholic fatty liver disease and acute myocardial infarction. Med Sci Monit. 2020;26:e922492.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Connor MK, Hammell T, Gibbons RJ. In vitro validation of a simple tomographic technique for estimation of percentage myocardium at risk using methoxyisobutyl isonitrile technetium 99m (sestamibi). Eur J Nucl Med. 1990;17:69\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Intrathoracic fat, Nonalcoholic fatty liver disease, Pericardial fat, ST-elevation myocardial infarction, Soluble suppression of tumorigenicity-2, Single-photon emission computed tomography myocardial perfusion imaging","lastPublishedDoi":"10.21203/rs.3.rs-4357262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4357262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eWe aim to explore the association between nonalcoholic fatty liver disease (NAFLD), intrathoracic fat (IF), pericardial fat (PF) and myocardial infarct size (MIS) in patients with ST-elevation myocardial infarction (STEMI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eSPECT-MPI was used to detect MIS, while CT scans were used to measure IF, PF, and NAFLD in patients with STEMI. Firstly, we categorised the patients into two groups (those with measurable and nonmeasurable MIS). The difference in fat between the two groups was compared using a two-sample t-test to determine which type of fat might be correlated with MIS. Secondly, the association between the related fats obtained in the aforementioned steps and MIS was evaluated using linear regression analysis. Third, to further verify this association at the molecular level, we explored the potential shared genes associated with related fat obtained in the above steps and acute myocardial infarction via bioinformatics analysis using the Gene Expression Omnibus (GEO) database. Finally, the association between the expression of shared genes in the serum of patients with STEMI and related fat was confirmed using Pearson’s correlation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe volume and fat attenuation index of IF and PF showed no difference between patients with MIS and those without. However, the L/S of NAFLD on CT reduced significantly in patients with MIS (\u003cem\u003eP \u003c/em\u003e=0.001). The L/S of NAFLD on CT was an independent predictor of MIS on SPECT-MPI in patients with STEMI (\u003cem\u003eP \u003c/em\u003e=0.042). We identified ST2, THBD, LEPR, and CEBP-α in NAFLD and acute myocardial infarction cases from the GEO database (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05). Compared to patients with STEMI without NAFLD, those with NAFLD exhibited a reduction in sST2 levels (\u003cem\u003eP\u003c/em\u003e=0.042); however, no differences were observed in THBD, LEPR, and CEBP-α levels. Correlation analysis showed a positive correlation between L/S and sST2 levels (r=0.459, \u003cem\u003eP\u003c/em\u003e =0.032).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eAmong patients with STEMI, the L/S of NAFLD, but not IF or PF, was associated with MIS on SPECT-MPI. Additionally, the L/S of NAFLD on CT emerged as an independent predictor of MIS. The expression of sST2, a biomarker associated with NAFLD and STEMI, positively correlated with the L/S on CT imaging.\u003c/p\u003e","manuscriptTitle":"Association between Nonalcoholic Fatty Liver Disease on CT and Myocardial Infarct Size using SPECT-MPI in patients with ST-elevation Myocardial Infarction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-13 15:41:04","doi":"10.21203/rs.3.rs-4357262/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-05-03T15:31:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-03T04:12:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2024-05-02T06:47:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f336f750-8f81-4492-a9b6-b906903f9fe0","owner":[],"postedDate":"May 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-13T15:41:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-13 15:41:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4357262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4357262","identity":"rs-4357262","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}