Evaluation of clinical variables affecting myocardial glucose uptake in cardiac FDG PET

Evaluation of clinical variables affecting myocardial glucose uptake in cardiac FDG PET | 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 Evaluation of clinical variables affecting myocardial glucose uptake in cardiac FDG PET Yeongjoo Lee, Sae Jung Na, Sungmin Lim, Jaehyuk Jang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4209144/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Cardiac 2-deoxy-2-[F-18]fluoro-D-glucose positron emission tomography (FDG PET) is widely used to assess myocardial viability in patients with ischemic heart disease. While sufficient glucose uptake is a prerequisite for accurate interpretation of cardiac viability, there is a lack of data on which clinical variables have the most significant impact on myocardial glucose metabolism. Therefore, this study was designed to evaluate several clinical variables that could affect myocardial glucose metabolism. Methods Between May 2018 and November 2022, a total of 214 consecutive cases were retrospectively enrolled in this study. All subjects were fasted for at least 8 hours. They received 250 mg of acipimox and underwent glucose loading as preparation for cardiac FDG PET/CT. Three-dimensional regions of interest (ROI) were drawn on PET/CT fusion images. SUV ratio (SUVmax of LV myocardium/SUVmean of liver) was then calculated. Clinical variables of age, sex, height, weight, body mass index (BMI), fasting blood glucose level, administered insulin dosage, blood glucose level at FDG injection, total cholesterol, high-density lipoprotein, low-density lipoprotein, cardiac markers, creatinine, hemoglobin A1c, and ejection fraction were measured and analyzed for correlation with myocardial glucose uptake. Participants were divided into an obese group and a non-obese group based on a BMI of 25. Whether there was a difference in myocardial glucose uptake between the two groups was then determined. Pearson correlation coefficient and Student’s t-test were used for statistical analysis. Results Myocardial uptake showed significant correlations with BMI (r = -0.162, p = 0.018), HbA1c (r = -0.150, p = 0.030), and triglyceride levels (r = -0.137, p = 0.046). No other clinical variables showed a significant correlation with myocardial glucose uptake. In group analysis, after dividing patients based on BMI, the obese group showed significantly lower myocardial uptake than the non-obese group (3.8 ± 1.9 vs. 4.4 ± 2.1, p = 0.031). Conclusions Among several clinical variables, BMI, HbA1c, and triglyceride levels exhibited negative correlations with myocardial glucose uptake. Patients with higher BMI, HbA1c, and triglyceride levels might require more thorough preparation or consideration during cardiac FDG PET exams to ensure optimal glucose uptake. Cardiac FDG PET BMI obesity myocardial glucose uptake Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The prevalence of ischemic heart disease is rising worldwide, driven primarily by an aging population[ 1 ]. A recent report has indicated that ischemic heart disease affects approximately 126 million individuals worldwide, accounting for 1.72% of global population[ 2 ]. For cardiologists facing an increasing number of patients with ischemic heart disease, accurately evaluating the extent and severity of viable myocardial tissue is of utmost importance. Identifying viable myocardial tissue enables clinicians to anticipate functional recovery after revascularization. Numerous previous reports have indicated that identifying myocardial viability can benefit patients with ischemic heart disease undergoing revascularization[ 3 – 5 ]. Conversely, if the tissue is non-viable, it can reduce unnecessary invasive procedures, benefiting patients both medically and economically. The assessment of myocardial viability has been a challenging issue for clinicians as variable degrees of ischemia can occur at different time points, resulting in coexistence of different phases of acute, subacute, and chronic conditions. The Food and Drug Administration (FDA) has approved cardiac 2-deoxy-2-[F-18]fluoro-D-glucose positron emission tomography (FDG PET) imaging as the imaging technique for assessing myocardial viability[ 6 ]. Cardiac FDG PET has been widely used for viability assessment and proven to be beneficial for patients' recovery in previous studies[ 7 , 8 ]. A notable example was the large randomized study called ‘Positron emission tomography and recovery following revascularization (PARR-2)’, which demonstrated a significant advantage in terms of cardiac death, myocardial infarction, and cardiac hospitalization when cardiac FDG PET was performed compared to cases where it was not conducted[ 9 ]. The current imaging guidelines from the American Society of Nuclear Cardiology (ASNC) and the procedure standards from the Society of Nuclear Medicine and Molecular Imaging (SNMMI) in 2016 focus on the metabolism of glucose over fatty acids[ 10 ]. Patient preparation is determined solely based on blood glucose level. For optimal myocardial glucose uptake, the current guideline recommends maintaining a basal glucose level of approximately 100–140 mg/dL (5.55–7.77 mmol/L) at the time of FDG injection and adjusting insulin dosage based on blood glucose level after glucose loading. However, some patients achieve acceptable imaging quality with the same amount of insulin administration, while others fail to achieve sufficient myocardial glucose uptake despite receiving an adequate amount of insulin. An overestimated insulin dose may cause unexpected hypoglycemia that requires immediate glucose replacement followed by careful monitoring. This situation may require additional delayed images or even necessitate re-examination. We assume that various clinical factors including baseline blood glucose levels might influence myocardial glucose metabolism. A previous report has suggested that both peripheral insulin resistance and increasing age may independently affect FDG uptake in the myocardium[ 11 ], although other clinical features related to glucose metabolism need to be verified. Therefore, this study aimed to investigate correlations of various clinical factors including baseline blood glucose levels with myocardial glucose uptake in patients with ischemic heart disease undergoing cardiac FDG PET. Materials and methods Subjects Medical records of patients with ischemic cardiopathy who underwent cardiac FDG PET/CT at Uijeongbu St. Mary Hospital between May 2018 and November 2022 were retrospectively reviewed. All patients had previous records of coronary angiography of chronic total occlusion in at least one of the cardiac coronary vessels. They were referred to our department for cardiac FDG PET/CT to assess myocardial viability. Patients with inflammatory conditions such as infective endocarditis and pericarditis were excluded. A total of 214 consecutive cases of cardiac FDG PET/CT were enrolled in this cohort. Clinical parameters such as patient’s age, sex, height, weight, fasting blood sugar test (BST), BST after glucose loading, and BST at FDG injection were recorded. If necessary, regular insulin was administered based on blood sugar level after oral glucose loading and its dosage was recorded. Laboratory data including hemoglobin A1c, total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride, troponin T, creatinine (Cr), and creatine kinase-myoglobin binding (CK-MB) were also recorded. All lab data were acquired within a month prior to cardiac FDG PET, with an average of 2.8 ± 13.6 days from PET. Ejection fraction was measured and recorded based on the echocardiogram taken within a month of the cardiac FDG PET. Results from coronary angiography conducted prior to cardiac FDG PET were also recorded. Image Acquisition and Measurement Cardiac FDG PET/CT images were acquired using a single PET/CT scanner (40 TruePoint with True V, Siemens Medical Solutions, Knoxville, TN, USA) following administration of 370 MBq (10.4 ± 0.6 mCi) of FDG. All patients were administered 250 mg of acipimox, a nicotinic acid derivative known to reduce plasma free fatty acid levels and enhance myocardial glucose uptake. Regular insulin was administrated as needed based on blood sugar level prior to exam. A CT scan was performed for attenuation correction before PET acquisition. Images of cardiac FDG PET were obtained in a single bed for fifteen minutes. After injection of radiotracer, images were taken between 45 and 60 minutes with patients in a supine position and their arms raised bilaterally. The time interval between radiotracer injection and scanning was recorded for analysis. Each cardiac PET image was reconstructed with a 256 x 256 matrix and a 3 mm Gaussian filter. Attenuation correction was performed immediately after CT transmission imaging. An iterative reconstruction algorithm of 3D ordered-subsets expectation maximization (OSEM) was applied with 6 iterations and 16 subsets. A flowchart of patient preparation and imaging process is summarized in Fig. 1 . For analysis of myocardial uptake, 3 cm-sized spheres of Region of Interest (ROI) were drawn in the wall of the left ventricle (LV) and liver, respectively. The maximum standardized uptake value (SUVmax) of LV and the mean standardized uptake value (SUVmean) of liver were measured and used to calculate myocardial glucose uptake ratio (MGUR). Data Analysis Pearson correlation test and linear regression analysis were applied to examine linear associations of clinical factors with MGUR. Fitting results of linear regression were compared to test whether slopes and intercepts differed significantly. For factors showing significance with a p -value of up to 0.2 in the univariable analysis, additional multivariable analysis was conducted. According to the Asia-Pacific criteria of the World Health Organization guidelines, obesity in East Asian adults is defined as a Body Mass Index (BMI) of 25 kg/m² or greater. For group analysis, subjects were dichotomized into an obese group (BMI ≥ 25) and a non-obese group (BMI < 25) based on their BMI. Binary groups dichotomized by BMI were then compared in terms of their clinical factors including age, gender, height, weight, BMI, ejection fraction, fasting BST, loading BST, injection BST, total cholesterol, triglyceride, HDL, LDL, troponin T, Cr, CK-MB, and HbA1c. Student's t-test was employed to compare clinical factors between binary groups. Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 24.0 (IBM Corporation, Armonk, New York, USA). A p -value of less than 0.05 was considered statistically significant. Results Patient Characteristics A total of 214 patients were included in this study for analysis. The average age of all included patients was 64.8 (± 11.4) years, ranging from 39 to 87 years. Of these, 101 patients were previously diagnosed with diabetes, while 113 were not diagnosed as diabetic at the time of examination. The mean (± SD) BMI was 24.8 ± 3.5. Forty-two (19.6%) patients had a one-vessel disease (VD), 87 (40.7%) patients had a two-vessel disease, and 84 (39.9%) patients had a three-vessel disease. One patient had a prior history of Coronary Artery Bypass Grafting (CABG). All subjects underwent an echocardiogram prior to cardiac PET, revealing an average ejection fraction of 47.3% (±12.7). The mean fasting blood sugar test (BST) after at least 8 hours of fasting (nil per os, NPO) was 121.7 (± 32.9) mg/dl, while the BST after glucose loading was 200.2 (± 54.2) mg/dl. Of all patients, 201 underwent an oral glucose loading of 50 g and 15 had a loading of 25 g. BST at the time of FDG injection was 194.5 ± 49.0 mg/dl. On average, 2.9 (± 2.1) IU of insulin was administered to 103 patients before cardiac PET based on their basal glucose levels. The average HbA1c level was 6.7 ± 1.5 % and the time between the cardiac PET and laboratory data was 2.8 ± 13.6 days. The average injection to image time was 52.8 ± 8.0 minutes. The SUVmax of the left ventricle was 9.2 (± 3.6). The SUVmean of the liver was 2.4 (± 1.2) and the average myocardial glucose uptake ratio (MGUR) calculated was 4.2 (± 2.0). Other detailed patient characteristics are summarized in Table 1. Correlation Analysis with MGUR Correlation analysis of clinical factors and MGUR revealed significant negative correlations of MGUR with BMI (r = -0.162, p = 0.018), HbA1c (r = -0.150, p = 0.030), and triglycerides (r = -0.137, p = 0.046). Scatter plots of these significant factors are depicted in Figure 2 (panels a, b, and c). No significant correlations were observed between MGUR and other clinical factors such as age, height, weight, ejection fraction (EF), troponin T, CK-MB, fasting BST, loading BST, injection BST, insulin, creatinine, total cholesterol, HDL, or LDL. Correlation coefficient data are summarized in Table 2. Group Analysis According to BMI and Imaging Time After dichotomizing subjects based on their BMI, the non-obese group (BMI < 25) consisted of 119 subjects (male: female = 99:20), while the obese group (BMI ≥ 25) included 95 subjects (male: female = 79:16). The non-obese group had a significantly higher MGUR than the obese group (4.4 ± 2.1 vs. 3.8 ± 1.9, p = 0.036) as illustrated in Figure 3. Regarding other clinical data, the non-obese group exhibited a significantly higher HDL level than the obese group (43.1 ± 12.7 vs. 38.9 ± 8.4, p = 0.006). The two groups showed no significant differences in age, height, ejection fraction (EF), troponin T, CK-MB, fasting BST, loading BST, injection BST, insulin, HbA1c, creatinine, total cholesterol, triglycerides, or LDL. Detailed comparisons of clinical factors between the two groups are summarized in Table 3. Examples of myocardial glucose metabolism at different body mass index (BMI) levels, emphasizing the influence of BMI on myocardial glucose metabolism, are presented in Figure 4. Results of multiple linear regression analysis are presented in Table 4, detailing correlations between clinical factors and MGUR. BMI and HbA1c were found to be independently correlated with MGUR, with p-values of 0.009 for both variables. The regression model accounted for 4.4% of the variance in MGUR, as indicated by an R-squared value of 0.044. The adjusted R-squared value of the model was 0.035. The F-statistic for the model was 4.775, with a corresponding p -value of 0.009, indicating an overall statistical significance of the regression model. Discussion As the prevalence of ischemic heart disease increases, the assessment of myocardial viability is increasingly important. Given the challenge of confirming myocardial viability through coronary angiography in chronic total occlusion (CTO), imaging modalities such as cardiac magnetic resonance (CMR) and cardiac FDG PET are considered as preferred methods for viability assessment. CMR offers high spatial resolution, enabling detection of size and assessment of the transmural extent of myocardial scar. It also provides structural information without the risk of radiation exposure. Late gadolinium enhancement less than 50% of wall thickness is regarded as a viable tissue. CMR shows comparatively high sensitivities, specificities, and substantial evidence base[ 12 , 13 ]. However, CMR is limited for patients with pacemakers or internal cardiac defibrillators and gadolinium-based contrast is contraindicated for those with reduced renal function (eGFR < 30 ml/min/1.73 m²) or severe claustrophobia. In comparison, cardiac FDG PET has emerged as a significant functional imaging tool for predicting left ventricular (LV) functional recovery[ 14 ]. Regions in which cardiac FDG uptake is increased relative to a perfusion defect are called 'perfusion-metabolism mismatch'. They represent viable hibernating myocardium. With accumulated clinical data, the role of cardiac FDG PET has been considered as the reference standard for myocardial viability. Schinkel et al. have reported that weighted mean sensitivity and specificity of cardiac FDG PET are 92% and 63%, respectively[ 3 ]. Evaluating the hibernating myocardium holds significance in identifying patients for whom revascularization enhances prognosis. Kandolin et al. have suggested that viability assessment should be performed for high-risk patients with comorbidity before revascularization[ 15 ]. Multiple other studies have shown a connection between viability identified through cardiac FDG PET and different clinical outcomes[ 16 – 19 ]. While viability assessment using cardiac FDG PET is known to be beneficial, achieving proper patient preparation is not always readily achievable due to ‘dual metabolism’ of myocardial tissue involving both fatty acids and glucose. The myocardium exhibits 'metabolic plasticity', allowing it to utilize various energy sources even under different metabolic conditions, including ischemia. Typically, it prefers fatty acid metabolism due to its higher carbon content per molecule, resulting in more ATP production. For instance, palmitate and oleate as fatty acid metabolites contains 16 and 18 carbons respectively, while glucose contains only 6 carbons. However, in cells that are viable but at risk, there is an increase in FDG uptake due to a transition towards anaerobic metabolism and a preference for glucose metabolism over fatty acid metabolism[ 11 ]. The duration of fasting and the level of endogenous insulin at the time of radiotracer injection can significantly influence myocardial glucose uptake. Thus, accumulated clinical experience is needed to optimize image quality. This study aimed to evaluate several clinical variables that might influence myocardial glucose metabolism in patients of ischemic heart disease. Each clinical factor, including BMI, HbA1c, and triglyceride, demonstrated a negative correlation with myocardial glucose uptake. In group comparison, the obese group demonstrated significantly lower myocardial uptake than the non-obese group, suggesting altered myocardial metabolism in obesity. This aligns with previous reports indicating an association between obesity and increased myocardial fatty acid metabolism at the expense of glucose metabolism[ 20 ]. The decrease of glucose uptake in obese patients can be partially explained by excessive uptake and utilization of fatty acids. This process can inhibit full glucose oxidation more than glycolysis and glucose uptake[ 21 ]. Previous reports have also have demonstrated excessive delivery and utilization of fatty acids to the myocardium using C-11 palmitate PET[ 22 , 23 ]. Herrero et al. have reported that patients with type I diabetes exhibit higher myocardial fatty acid uptake, utilization, and oxidative stress but lower glucose utilization than normal controls[ 24 ]. Previous studies have reported that subjects undergoing bariatric surgery show reduced fatty acid metabolism[ 25 ] but increased glucose metabolism[ 26 ]. These findings imply that the degree of obesity might have an impact on myocardial glucose uptake at an individual level. A previous study using mouse models showed a significant increase in myocardial cannabinoid type 1 receptor (CB1-R) expression in advanced obesity compared to normal weight controls[ 27 ]. It appears that activation of the endocannabinoid system in obesity could stimulate the expression and upregulation of myocardial CB1-R, which could reflect altered metabolism. Hence, we could assume that obesity somehow contributes to altered myocardial metabolism via excessive delivery of free fatty acids and triglycerides. Although the pathophysiology of type I and type II diabetes may differ, it is anticipated that the overall mechanism of glucose metabolism in each type of diabetes could be similar. Our presented study considered HbA1c levels at the time of cardiac FDG PET as an alternative objective value, as many non-diabetic patients might have unknown insulin resistance, while diabetic patients on medication might have well-controlled blood glucose levels at the time of radiotracer injection. This study has some limitations. Firstly, it was a single-center retrospective study. Secondly, not all patients had previous perfusion images to investigate perfusion-metabolism mismatch. Instead, all patients underwent coronary angiography and had a history of complete total occlusion in their coronary vessels. Thirdly, while SUVmax is a convenient value for tumor metabolism, its representation of myocardial metabolism is debatable. Although the SUVmax value does not fully represent the entire spectrum of cardiac metabolism, it can provide insights into glucose uptake and utilization. A future multicenter prospective study with a large cohort is necessary to ascertain the relationship between glucose metabolism and clinical factors to determine optimal conditions for maximizing myocardial uptake. Conclusion Among several clinical variables, BMI, HbA1c, and triglyceride levels were negatively correlated with myocardial glucose uptake. For accurate myocardial viability assessment using cardiac FDG PET, patients who are obese or have high HbA1c levels might require more thorough preparation and sufficient scanning time. Declarations Sources of funding for the article: No funding Informed consent: The institutional review board of our institute approved this study (UC23RASI0179), and the requirement to obtain informed consent was waived. Ethical statement: The study was approved by an institutional review board or equivalent and has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Ethical approvement: This article does not contain any studies with human participants or animals performed by any of the authors. Conflict of interest: There is no conflict of interest to disclose. Acknowledgments : No acknowledgement References North BJ, Sinclair DA. The intersection between aging and cardiovascular disease. Circ Res. 2012;110(8):1097-108. Khan MA, Hashim MJ, Mustafa H, Baniyas MY, Al Suwaidi S, AlKatheeri R, et al. Global Epidemiology of Ischemic Heart Disease: Results from the Global Burden of Disease Study. Cureus. 2020;12(7):e9349. Schinkel AF, Bax JJ, Poldermans D, Elhendy A, Ferrari R, Rahimtoola SH. Hibernating myocardium: diagnosis and patient outcomes. Curr Probl Cardiol. 2007;32(7):375-410. Gerber BL, Rousseau MF, Ahn SA, le Polain de Waroux JB, Pouleur AC, Phlips T, et al. Prognostic value of myocardial viability by delayed-enhanced magnetic resonance in patients with coronary artery disease and low ejection fraction: impact of revascularization therapy. 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Tables Table 1 Patient characteristics (n=214) Characteristics Value Male/Female 178/36 Diabetic/Non-diabetic 101/113 Age (years) 64.8 ± 11.4 Height (cm) 164.8 ± 8.8 Weight (kg) 67.5 ± 12.2 BMI (kg/m 2 ) 24.8 ± 3.5 Ischemic cardiomyopathy 1VD 2VD 3VD 42 (19.6%) 87 (40.7%) 84 (39.3%) Ejection Fraction (%) 47.3 ± 12.7 Cardiac markers Troponin (ng/mL) CK-MB (ng/ml) 0.88 ± 2.14 21.0 ± 55.5 HbA1c (%) 6.7 ± 1.5 Kidney function Creatinine (mg/dl) 1.4 ± 1.5 Lipid profile (mg/dl) Total cholesterol Triglyceride HDL LDL 154.2 ± 49.2 136.9 ± 86.5 41.2 ± 11.2 88.6 ± 45.7 Blood Sugar Test (mg/dl) fasting BST BST after glucose loading BST at FDG injection 121.7 ± 32.9 200.2 ± 54.2 194.5 ± 49.0 Injected insulin (IU) 2.9 ± 2.1 Injection to image time (minutes) 52.8 ± 8.0 SUVmax of left ventricle 9.2 ± 3.6 SUVmean of liver 2.4 ± 1.2 Myocardial Glucose Uptake Ratio 4.2 ± 2.0 VD, vessel disease Table 2 Pearson correlation analysis of the clinical factors with Myocardial Glucose Uptake Ratio Clinical factors Coefficient of correlation (r) p-value Age -0.105 0.127 Height 0.044 0.525 Weight -0.110 0.107 BMI -0.162 0.018* Ejection Fraction -0.083 0.226 Troponin T -0.006 0.935 CK-MB 0.003 0.961 Fasting BST -0.016 0.821 BST after glucose loading -0.040 0.561 BST at FDG injection -0.091 0.186 Insulin 0.051 0.461 HbA1c -0.150 0.030* Creatinine 0.013 0.850 Total cholesterol 0.069 0.319 Triglyceride -0.137 0.046* HDL 0.133 0.052 LDL 0.091 0.185 * Statistically significant Table 3 Comparison of variables between the non-obese and obese groups (n=214) Variables Non-obese group (BMI < 25, n=119) Obese group (BMI ≥ 25, n=95) p -value Weight 61.3 ± 9.2 75.3 ± 11.0 <0.001* MGUR 4.4 ± 2.1 3.8 ± 1.9 0.036 * Sex (male: female) 99: 20 79: 16 N/A Age (year) 65.1 ± 10.9 64.3 ± 12.0 0.598 Height 165.4 ± 8.2 164.2 ± 9.6 0.397 Ejection Fraction 47.1±13.2 47.4±12.1 0.901 Troponin T 0.88 ± 2.1 0.87 ± 2.2 0.958 CK-MB 24.3 ± 62.7 16.9 ± 45.2 0.336 Fasting BST 122.6 ± 33.7 120.6 ± 33.2 0.674 BST after glucose loading 197.8 ± 57.4 203.2 ± 50.2 0.469 BST at FDG injection 191.7 ± 52.6 203.2 ± 50.2 0.363 Insulin (IU) 1.7 ± 2.1 1.7 ± 2.0 0.803 HbA1c 6.6 ± 1.5 6.8 ± 1.5 0.349 Injection to image time 53.1 ± 8.5 52.4 ± 7.5 0.528 Creatinine 1.4 ± 1.3 1.4 ± 1.7 0.873 Total cholesterol 155.3 ± 50.9 152.7 ± 47.6 0.699 Triglyceride 129.5 ± 80.2 146.0 ± 93.7 0.169 HDL 43.1 ± 12.7 38.9 ± 8.4 0.006* LDL 88.9 ± 43.9 88.2 ± 48.3 0.909 *Statistically significant Table 4 Multiple linear regression analysis of variables affecting MGUR Variables Unstandardized Coefficient Standardized Coefficient t p-value B Standard error (constant) 7.488 1.096 6.829 0.000 BMI -0.084 0.039 -0.148 -2.163 0.032 HbA1c -0.182 0.092 -0.135 -1.974 0.050 R: 0.210 Adjusted R-squared: 0.035 F-statistics: 4.775, p=0.009 Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4209144","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288116066,"identity":"a23a51cf-8542-4b60-ad91-294095bf22e1","order_by":0,"name":"Yeongjoo Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYLCCDxU2cvwk6WCccSbNWLKBFC3MvG2HEzccIFa5fHvzswc8bMyMm68df/aAocYmmqAWgzPHzA0kgHrMbueYGzAcS8sl6EADiQQzCZAeoBY2CcaGw4S1yM9I/yaRANRjPDv9GXFaGG7kmEkcAOoxkAZaR5QWgzNnyg0bQHpuA/UmEOMX+fb2bY///vtf3w9y2IcaGyIcxsDAhmAmEKEcTcsoGAWjYBSMAmwAAPv4PM0HbSvwAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2978-4913","institution":"The Catholic University of Korea Uijeongbu St Mary's Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yeongjoo","middleName":"","lastName":"Lee","suffix":""},{"id":288116067,"identity":"b5ea14e2-32af-49c0-9884-ee5bd90feb91","order_by":1,"name":"Sae Jung Na","email":"","orcid":"","institution":"Uijeongbu St. Mary’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sae","middleName":"Jung","lastName":"Na","suffix":""},{"id":288116068,"identity":"3b27afec-fa8f-435c-8ce3-efab03e9c480","order_by":2,"name":"Sungmin Lim","email":"","orcid":"","institution":"Uijeongbu St. Mary’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sungmin","middleName":"","lastName":"Lim","suffix":""},{"id":288116069,"identity":"3661be4c-5f95-4eef-b864-d4e96648a9e5","order_by":3,"name":"Jaehyuk Jang","email":"","orcid":"","institution":"Uijeongbu St. Mary’s Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jaehyuk","middleName":"","lastName":"Jang","suffix":""}],"badges":[],"createdAt":"2024-04-03 00:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4209144/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4209144/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54450891,"identity":"e97acae0-af38-4155-81b0-a923c3e05861","added_by":"auto","created_at":"2024-04-10 17:51:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79363,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the patient preparation and cardiac FDG PET\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4209144/v1/e88b0f7787782de0925c1fc1.png"},{"id":54450198,"identity":"6f55ca62-ce0f-45ae-af2f-fbf42a6035d3","added_by":"auto","created_at":"2024-04-10 17:43:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59559,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots that showed correlation with MGUR (Myocardial Glucose Uptake Ratio). (a) BMI (Body Mass Index), (b) HbA1c (%), (c) Triglyceride (mg/dl)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4209144/v1/708611aed0eeac07a0fdae61.png"},{"id":54450201,"identity":"7779bbf9-6a8e-4b0b-b08a-2fb33916129b","added_by":"auto","created_at":"2024-04-10 17:43:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35070,"visible":true,"origin":"","legend":"\u003cp\u003eA box plot that shows significantly lower MGUR in the obese group (BMI ≥25, MGUR; 3.8±1.9) than the non-obese group (BMI\u0026lt;25, 4.4±2.1\u003cem\u003e, p\u003c/em\u003e=0.031)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4209144/v1/96580a91264b29edf927f804.png"},{"id":54450200,"identity":"f6da85d5-bd1a-4b83-940a-e4c82e07c6a1","added_by":"auto","created_at":"2024-04-10 17:43:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":389643,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent myocardial glucose metabolism in accordance with BMI; Note the patient of higher BMI (a) demonstrated lower glucose uptake, compared to the patient of lower BMI (b). (a) M/69, BMI: 30.6, fasting BST: 126, BST at FDG injection: 242, HbA1c: 6.5, FDG: 11.5 mCi, 60 min image without insulin, SUVmax: 4.8, MGUR: 1.9; (b) M/68, BMI: 21.1, fasting BST: 109, BST at FDG injection: 220, HbA1c: 5.8, FDG: 10.5 mCi, 60 min image without insulin, SUVmax: 9.7, MGUR: 4.4\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4209144/v1/6779bf50d2517110ca4ab567.png"},{"id":55694289,"identity":"f694be47-d06a-4478-a63e-5394499fd221","added_by":"auto","created_at":"2024-05-02 00:41:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1129940,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4209144/v1/6f2b6eba-5ec3-403b-b77e-b817fda23ec4.pdf"}],"financialInterests":"","formattedTitle":"Evaluation of clinical variables affecting myocardial glucose uptake in cardiac FDG PET","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe prevalence of ischemic heart disease is rising worldwide, driven primarily by an aging population[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A recent report has indicated that ischemic heart disease affects approximately 126\u0026nbsp;million individuals worldwide, accounting for 1.72% of global population[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For cardiologists facing an increasing number of patients with ischemic heart disease, accurately evaluating the extent and severity of viable myocardial tissue is of utmost importance. Identifying viable myocardial tissue enables clinicians to anticipate functional recovery after revascularization. Numerous previous reports have indicated that identifying myocardial viability can benefit patients with ischemic heart disease undergoing revascularization[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Conversely, if the tissue is non-viable, it can reduce unnecessary invasive procedures, benefiting patients both medically and economically. The assessment of myocardial viability has been a challenging issue for clinicians as variable degrees of ischemia can occur at different time points, resulting in coexistence of different phases of acute, subacute, and chronic conditions.\u003c/p\u003e \u003cp\u003eThe Food and Drug Administration (FDA) has approved cardiac 2-deoxy-2-[F-18]fluoro-D-glucose positron emission tomography (FDG PET) imaging as the imaging technique for assessing myocardial viability[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Cardiac FDG PET has been widely used for viability assessment and proven to be beneficial for patients' recovery in previous studies[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A notable example was the large randomized study called \u0026lsquo;Positron emission tomography and recovery following revascularization (PARR-2)\u0026rsquo;, which demonstrated a significant advantage in terms of cardiac death, myocardial infarction, and cardiac hospitalization when cardiac FDG PET was performed compared to cases where it was not conducted[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current imaging guidelines from the American Society of Nuclear Cardiology (ASNC) and the procedure standards from the Society of Nuclear Medicine and Molecular Imaging (SNMMI) in 2016 focus on the metabolism of glucose over fatty acids[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Patient preparation is determined solely based on blood glucose level. For optimal myocardial glucose uptake, the current guideline recommends maintaining a basal glucose level of approximately 100\u0026ndash;140 mg/dL (5.55\u0026ndash;7.77 mmol/L) at the time of FDG injection and adjusting insulin dosage based on blood glucose level after glucose loading. However, some patients achieve acceptable imaging quality with the same amount of insulin administration, while others fail to achieve sufficient myocardial glucose uptake despite receiving an adequate amount of insulin. An overestimated insulin dose may cause unexpected hypoglycemia that requires immediate glucose replacement followed by careful monitoring. This situation may require additional delayed images or even necessitate re-examination.\u003c/p\u003e \u003cp\u003eWe assume that various clinical factors including baseline blood glucose levels might influence myocardial glucose metabolism. A previous report has suggested that both peripheral insulin resistance and increasing age may independently affect FDG uptake in the myocardium[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], although other clinical features related to glucose metabolism need to be verified. Therefore, this study aimed to investigate correlations of various clinical factors including baseline blood glucose levels with myocardial glucose uptake in patients with ischemic heart disease undergoing cardiac FDG PET.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eMedical records of patients with ischemic cardiopathy who underwent cardiac FDG PET/CT at Uijeongbu St. Mary Hospital between May 2018 and November 2022 were retrospectively reviewed. All patients had previous records of coronary angiography of chronic total occlusion in at least one of the cardiac coronary vessels. They were referred to our department for cardiac FDG PET/CT to assess myocardial viability. Patients with inflammatory conditions such as infective endocarditis and pericarditis were excluded. A total of 214 consecutive cases of cardiac FDG PET/CT were enrolled in this cohort.\u003c/p\u003e \u003cp\u003eClinical parameters such as patient\u0026rsquo;s age, sex, height, weight, fasting blood sugar test (BST), BST after glucose loading, and BST at FDG injection were recorded. If necessary, regular insulin was administered based on blood sugar level after oral glucose loading and its dosage was recorded. Laboratory data including hemoglobin A1c, total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride, troponin T, creatinine (Cr), and creatine kinase-myoglobin binding (CK-MB) were also recorded. All lab data were acquired within a month prior to cardiac FDG PET, with an average of 2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6 days from PET. Ejection fraction was measured and recorded based on the echocardiogram taken within a month of the cardiac FDG PET. Results from coronary angiography conducted prior to cardiac FDG PET were also recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eImage Acquisition and Measurement\u003c/h2\u003e \u003cp\u003eCardiac FDG PET/CT images were acquired using a single PET/CT scanner (40 TruePoint with True V, Siemens Medical Solutions, Knoxville, TN, USA) following administration of 370 MBq (10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 mCi) of FDG. All patients were administered 250 mg of acipimox, a nicotinic acid derivative known to reduce plasma free fatty acid levels and enhance myocardial glucose uptake. Regular insulin was administrated as needed based on blood sugar level prior to exam. A CT scan was performed for attenuation correction before PET acquisition. Images of cardiac FDG PET were obtained in a single bed for fifteen minutes. After injection of radiotracer, images were taken between 45 and 60 minutes with patients in a supine position and their arms raised bilaterally. The time interval between radiotracer injection and scanning was recorded for analysis. Each cardiac PET image was reconstructed with a 256 x 256 matrix and a 3 mm Gaussian filter. Attenuation correction was performed immediately after CT transmission imaging. An iterative reconstruction algorithm of 3D ordered-subsets expectation maximization (OSEM) was applied with 6 iterations and 16 subsets. A flowchart of patient preparation and imaging process is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor analysis of myocardial uptake, 3 cm-sized spheres of Region of Interest (ROI) were drawn in the wall of the left ventricle (LV) and liver, respectively. The maximum standardized uptake value (SUVmax) of LV and the mean standardized uptake value (SUVmean) of liver were measured and used to calculate myocardial glucose uptake ratio (MGUR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003ePearson correlation test and linear regression analysis were applied to examine linear associations of clinical factors with MGUR. Fitting results of linear regression were compared to test whether slopes and intercepts differed significantly. For factors showing significance with a \u003cem\u003ep\u003c/em\u003e-value of up to 0.2 in the univariable analysis, additional multivariable analysis was conducted. According to the Asia-Pacific criteria of the World Health Organization guidelines, obesity in East Asian adults is defined as a Body Mass Index (BMI) of 25 kg/m\u0026sup2; or greater. For group analysis, subjects were dichotomized into an obese group (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25) and a non-obese group (BMI\u0026thinsp;\u0026lt;\u0026thinsp;25) based on their BMI. Binary groups dichotomized by BMI were then compared in terms of their clinical factors including age, gender, height, weight, BMI, ejection fraction, fasting BST, loading BST, injection BST, total cholesterol, triglyceride, HDL, LDL, troponin T, Cr, CK-MB, and HbA1c. Student's t-test was employed to compare clinical factors between binary groups. Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 24.0 (IBM Corporation, Armonk, New York, USA). A \u003cem\u003ep\u003c/em\u003e-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003ePatient Characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 214 patients were included in this study for analysis. The average age of all included patients was 64.8 (± 11.4) years, ranging from 39 to 87 years. Of these, 101 patients were previously diagnosed with diabetes, while 113 were not diagnosed as diabetic at the time of examination. The mean (± SD) BMI was 24.8 ± 3.5. Forty-two (19.6%) patients had a one-vessel disease (VD), 87 (40.7%) patients had a two-vessel disease, and 84 (39.9%) patients had a three-vessel disease. One patient had a prior history of Coronary Artery Bypass Grafting (CABG). All subjects underwent an echocardiogram prior to cardiac PET, revealing an average ejection fraction of 47.3% (±12.7). The mean fasting blood sugar test (BST) after at least 8 hours of fasting (nil per os, NPO) was 121.7 (± 32.9) mg/dl, while the BST after glucose loading was 200.2 (± 54.2) mg/dl. Of all patients, 201 underwent an oral glucose loading of 50 g and 15 had a loading of 25 g. BST at the time of FDG injection was 194.5 ± 49.0 mg/dl. On average, 2.9 (± 2.1) IU of insulin was administered to 103 patients before cardiac PET based on their basal glucose levels. The average HbA1c level was 6.7 ± 1.5 % and the time between the cardiac PET and laboratory data was 2.8 ± 13.6 days. The average injection to image time was 52.8 ± 8.0 minutes. The SUVmax of the left ventricle was 9.2 (± 3.6). The SUVmean of the liver was 2.4 (± 1.2) and the average myocardial glucose uptake ratio (MGUR) calculated was 4.2 (± 2.0). Other detailed patient characteristics are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrelation Analysis with MGUR\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis of clinical factors and MGUR revealed significant negative correlations of MGUR with BMI (r = -0.162, \u003cem\u003ep\u003c/em\u003e = 0.018), HbA1c (r = -0.150, \u003cem\u003ep\u003c/em\u003e = 0.030), and triglycerides (r = -0.137, \u003cem\u003ep\u003c/em\u003e = 0.046). Scatter plots of these significant factors are depicted in Figure 2 (panels a, b, and c). No significant correlations were observed between MGUR and other clinical factors such as age, height, weight, ejection fraction (EF), troponin T, CK-MB, fasting BST, loading BST, injection BST, insulin, creatinine, total cholesterol, HDL, or LDL. Correlation coefficient data are summarized in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGroup Analysis According to BMI and Imaging Time\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAfter dichotomizing subjects based on their BMI, the non-obese group (BMI \u0026lt; 25) consisted of 119 subjects (male: female = 99:20), while the obese group (BMI ≥ 25) included 95 subjects (male: female = 79:16). The non-obese group had a significantly higher MGUR than the obese group (4.4 ± 2.1 vs. 3.8 ± 1.9, \u003cem\u003ep\u003c/em\u003e = 0.036) as illustrated in Figure 3. Regarding other clinical data, the non-obese group exhibited a significantly higher HDL level than the obese group (43.1 ± 12.7 vs. 38.9 ± 8.4, \u003cem\u003ep\u003c/em\u003e = 0.006). The two groups showed no significant differences in age, height, ejection fraction (EF), troponin T, CK-MB, fasting BST, loading BST, injection BST, insulin, HbA1c, creatinine, total cholesterol, triglycerides, or LDL. Detailed comparisons of clinical factors between the two groups are summarized in Table 3. Examples of myocardial glucose metabolism at different body mass index (BMI) levels, emphasizing the influence of BMI on myocardial glucose metabolism, are presented in Figure 4.\u003c/p\u003e\n\u003cp\u003eResults of multiple linear regression analysis are presented in Table 4, detailing correlations between clinical factors and MGUR. BMI and HbA1c were found to be independently correlated with MGUR, with p-values of 0.009 for both variables. The regression model accounted for 4.4% of the variance in MGUR, as indicated by an R-squared value of 0.044. The adjusted R-squared value of the model was 0.035. The F-statistic for the model was 4.775, with a corresponding\u003cem\u003e\u0026nbsp;p\u003c/em\u003e-value of 0.009, indicating an overall statistical significance of the regression model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs the prevalence of ischemic heart disease increases, the assessment of myocardial viability is increasingly important. Given the challenge of confirming myocardial viability through coronary angiography in chronic total occlusion (CTO), imaging modalities such as cardiac magnetic resonance (CMR) and cardiac FDG PET are considered as preferred methods for viability assessment. CMR offers high spatial resolution, enabling detection of size and assessment of the transmural extent of myocardial scar. It also provides structural information without the risk of radiation exposure. Late gadolinium enhancement less than 50% of wall thickness is regarded as a viable tissue. CMR shows comparatively high sensitivities, specificities, and substantial evidence base[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, CMR is limited for patients with pacemakers or internal cardiac defibrillators and gadolinium-based contrast is contraindicated for those with reduced renal function (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 ml/min/1.73 m\u0026sup2;) or severe claustrophobia.\u003c/p\u003e \u003cp\u003eIn comparison, cardiac FDG PET has emerged as a significant functional imaging tool for predicting left ventricular (LV) functional recovery[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Regions in which cardiac FDG uptake is increased relative to a perfusion defect are called 'perfusion-metabolism mismatch'. They represent viable hibernating myocardium. With accumulated clinical data, the role of cardiac FDG PET has been considered as the reference standard for myocardial viability. Schinkel et al. have reported that weighted mean sensitivity and specificity of cardiac FDG PET are 92% and 63%, respectively[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Evaluating the hibernating myocardium holds significance in identifying patients for whom revascularization enhances prognosis. Kandolin et al. have suggested that viability assessment should be performed for high-risk patients with comorbidity before revascularization[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Multiple other studies have shown a connection between viability identified through cardiac FDG PET and different clinical outcomes[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile viability assessment using cardiac FDG PET is known to be beneficial, achieving proper patient preparation is not always readily achievable due to \u0026lsquo;dual metabolism\u0026rsquo; of myocardial tissue involving both fatty acids and glucose. The myocardium exhibits 'metabolic plasticity', allowing it to utilize various energy sources even under different metabolic conditions, including ischemia. Typically, it prefers fatty acid metabolism due to its higher carbon content per molecule, resulting in more ATP production. For instance, palmitate and oleate as fatty acid metabolites contains 16 and 18 carbons respectively, while glucose contains only 6 carbons. However, in cells that are viable but at risk, there is an increase in FDG uptake due to a transition towards anaerobic metabolism and a preference for glucose metabolism over fatty acid metabolism[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The duration of fasting and the level of endogenous insulin at the time of radiotracer injection can significantly influence myocardial glucose uptake. Thus, accumulated clinical experience is needed to optimize image quality.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate several clinical variables that might influence myocardial glucose metabolism in patients of ischemic heart disease. Each clinical factor, including BMI, HbA1c, and triglyceride, demonstrated a negative correlation with myocardial glucose uptake. In group comparison, the obese group demonstrated significantly lower myocardial uptake than the non-obese group, suggesting altered myocardial metabolism in obesity. This aligns with previous reports indicating an association between obesity and increased myocardial fatty acid metabolism at the expense of glucose metabolism[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The decrease of glucose uptake in obese patients can be partially explained by excessive uptake and utilization of fatty acids. This process can inhibit full glucose oxidation more than glycolysis and glucose uptake[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Previous reports have also have demonstrated excessive delivery and utilization of fatty acids to the myocardium using C-11 palmitate PET[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Herrero et al. have reported that patients with type I diabetes exhibit higher myocardial fatty acid uptake, utilization, and oxidative stress but lower glucose utilization than normal controls[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Previous studies have reported that subjects undergoing bariatric surgery show reduced fatty acid metabolism[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] but increased glucose metabolism[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These findings imply that the degree of obesity might have an impact on myocardial glucose uptake at an individual level.\u003c/p\u003e \u003cp\u003eA previous study using mouse models showed a significant increase in myocardial cannabinoid type 1 receptor (CB1-R) expression in advanced obesity compared to normal weight controls[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It appears that activation of the endocannabinoid system in obesity could stimulate the expression and upregulation of myocardial CB1-R, which could reflect altered metabolism. Hence, we could assume that obesity somehow contributes to altered myocardial metabolism via excessive delivery of free fatty acids and triglycerides.\u003c/p\u003e \u003cp\u003eAlthough the pathophysiology of type I and type II diabetes may differ, it is anticipated that the overall mechanism of glucose metabolism in each type of diabetes could be similar. Our presented study considered HbA1c levels at the time of cardiac FDG PET as an alternative objective value, as many non-diabetic patients might have unknown insulin resistance, while diabetic patients on medication might have well-controlled blood glucose levels at the time of radiotracer injection.\u003c/p\u003e \u003cp\u003eThis study has some limitations. Firstly, it was a single-center retrospective study. Secondly, not all patients had previous perfusion images to investigate perfusion-metabolism mismatch. Instead, all patients underwent coronary angiography and had a history of complete total occlusion in their coronary vessels. Thirdly, while SUVmax is a convenient value for tumor metabolism, its representation of myocardial metabolism is debatable. Although the SUVmax value does not fully represent the entire spectrum of cardiac metabolism, it can provide insights into glucose uptake and utilization. A future multicenter prospective study with a large cohort is necessary to ascertain the relationship between glucose metabolism and clinical factors to determine optimal conditions for maximizing myocardial uptake.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAmong several clinical variables, BMI, HbA1c, and triglyceride levels were negatively correlated with myocardial glucose uptake. For accurate myocardial viability assessment using cardiac FDG PET, patients who are obese or have high HbA1c levels might require more thorough preparation and sufficient scanning time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSources of funding for the article:\u0026nbsp;\u003c/strong\u003eNo funding\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe institutional review board of our institute approved this study (UC23RASI0179), and the requirement to obtain informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by an institutional review board or equivalent and has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approvement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict of interest to disclose.\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: No acknowledgement\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNorth BJ, Sinclair DA. The intersection between aging and cardiovascular disease. Circ Res. 2012;110(8):1097-108.\u003c/li\u003e\n\u003cli\u003eKhan MA, Hashim MJ, Mustafa H, Baniyas MY, Al Suwaidi S, AlKatheeri R, et al. Global Epidemiology of Ischemic Heart Disease: Results from the Global Burden of Disease Study. Cureus. 2020;12(7):e9349.\u003c/li\u003e\n\u003cli\u003eSchinkel AF, Bax JJ, Poldermans D, Elhendy A, Ferrari R, Rahimtoola SH. Hibernating myocardium: diagnosis and patient outcomes. Curr Probl Cardiol. 2007;32(7):375-410.\u003c/li\u003e\n\u003cli\u003eGerber BL, Rousseau MF, Ahn SA, le Polain de Waroux JB, Pouleur AC, Phlips T, et al. Prognostic value of myocardial viability by delayed-enhanced magnetic resonance in patients with coronary artery disease and low ejection fraction: impact of revascularization therapy. J Am Coll Cardiol. 2012;59(9):825-35.\u003c/li\u003e\n\u003cli\u003eAllman KC, Shaw LJ, Hachamovitch R, Udelson JE. Myocardial viability testing and impact of revascularization on prognosis in patients with coronary artery disease and left ventricular dysfunction: a meta-analysis. J Am Coll Cardiol. 2002;39(7):1151-8.\u003c/li\u003e\n\u003cli\u003eRieves D, Jacobs P. The Use of Published Clinical Study Reports to Support U.S. Food and Drug Administration Approval of Imaging Agents. J Nucl Med. 2016;57(12):2022-6.\u003c/li\u003e\n\u003cli\u003eAbraham A, Nichol G, Williams KA, Guo A, deKemp RA, Garrard L, et al. 18F-FDG PET imaging of myocardial viability in an experienced center with access to 18F-FDG and integration with clinical management teams: the Ottawa-FIVE substudy of the PARR 2 trial. J Nucl Med. 2010;51(4):567-74.\u003c/li\u003e\n\u003cli\u003eGhesani M, Depuey EG, Rozanski A. Role of F-18 FDG positron emission tomography (PET) in the assessment of myocardial viability. Echocardiography. 2005;22(2):165-77.\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Egidio G, Nichol G, Williams KA, Guo A, Garrard L, deKemp R, et al. Increasing benefit from revascularization is associated with increasing amounts of myocardial hibernation: a substudy of the PARR-2 trial. JACC Cardiovasc Imaging. 2009;2(9):1060-8.\u003c/li\u003e\n\u003cli\u003eDilsizian V, Bacharach SL, Beanlands RS, Bergmann SR, Delbeke D, Dorbala S, et al. ASNC imaging guidelines/SNMMI procedure standard for positron emission tomography (PET) nuclear cardiology procedures. J Nucl Cardiol. 2016;23(5):1187-226.\u003c/li\u003e\n\u003cli\u003eHansen AK, Gejl M, Bouchelouche K, Tolbod LP, Gormsen LC. Reverse Mismatch Pattern in Cardiac 18F-FDG Viability PET/CT Is Not Associated With Poor Outcome of Revascularization: A Retrospective Outcome Study of 91 Patients With Heart Failure. Clin Nucl Med. 2016;41(10):e428-35.\u003c/li\u003e\n\u003cli\u003eSandstede JJ. Assessment of myocardial viability by MR imaging. Eur Radiol. 2003;13(1):52-61.\u003c/li\u003e\n\u003cli\u003eSandstede JJ, Lipke C, Beer M, Harre K, Pabst T, Kenn W, et al. Analysis of first-pass and delayed contrast-enhancement patterns of dysfunctional myocardium on MR imaging: use in the prediction of myocardial viability. AJR Am J Roentgenol. 2000;174(6):1737-40.\u003c/li\u003e\n\u003cli\u003eHunold P, Jakob H, Erbel R, Barkhausen J, Heilmaier C. Accuracy of myocardial viability imaging by cardiac MRI and PET depending on left ventricular function. World J Cardiol. 2018;10(9):110-8.\u003c/li\u003e\n\u003cli\u003eKandolin RM, Wiefels CC, Mesquita CT, Chong AY, Boland P, Glineur D, et al. The Current Role of Viability Imaging to Guide Revascularization and Therapy Decisions in Patients With Heart Failure and Reduced Left Ventricular Function. Can J Cardiol. 2019;35(8):1015-29.\u003c/li\u003e\n\u003cli\u003eCarrel T, Jenni R, Haubold-Reuter S, von Schulthess G, Pasic M, Turina M. Improvement of severely reduced left ventricular function after surgical revascularization in patients with preoperative myocardial infarction. Eur J Cardiothorac Surg. 1992;6(9):479-84.\u003c/li\u003e\n\u003cli\u003eGrandin C, Wijns W, Melin JA, Bol A, Robert AR, Heyndrickx GR, et al. Delineation of myocardial viability with PET. J Nucl Med. 1995;36(9):1543-52.\u003c/li\u003e\n\u003cli\u003eTillisch J, Brunken R, Marshall R, Schwaiger M, Mandelkern M, Phelps M, et al. Reversibility of cardiac wall-motion abnormalities predicted by positron tomography. N Engl J Med. 1986;314(14):884-8.\u003c/li\u003e\n\u003cli\u003eBeanlands RS, Nichol G, Huszti E, Humen D, Racine N, Freeman M, et al. F-18-fluorodeoxyglucose positron emission tomography imaging-assisted management of patients with severe left ventricular dysfunction and suspected coronary disease: a randomized, controlled trial (PARR-2). J Am Coll Cardiol. 2007;50(20):2002-12.\u003c/li\u003e\n\u003cli\u003eMcGill JB, Peterson LR, Herrero P, Saeed IM, Recklein C, Coggan AR, et al. Potentiation of abnormalities in myocardial metabolism with the development of diabetes in women with obesity and insulin resistance. J Nucl Cardiol. 2011;18(3):421-9; quiz 32-3.\u003c/li\u003e\n\u003cli\u003eTaegtmeyer H, Stanley WC. Too much or not enough of a good thing? Cardiac glucolipotoxicity versus lipoprotection. J Mol Cell Cardiol. 2011;50(1):2-5.\u003c/li\u003e\n\u003cli\u003ePeterson LR, Herrero P, Schechtman KB, Racette SB, Waggoner AD, Kisrieva-Ware Z, et al. Effect of obesity and insulin resistance on myocardial substrate metabolism and efficiency in young women. Circulation. 2004;109(18):2191-6.\u003c/li\u003e\n\u003cli\u003ePeterson LR, Soto PF, Herrero P, Mohammed BS, Avidan MS, Schechtman KB, et al. Impact of gender on the myocardial metabolic response to obesity. JACC Cardiovasc Imaging. 2008;1(4):424-33.\u003c/li\u003e\n\u003cli\u003eHerrero P, Peterson LR, McGill JB, Matthew S, Lesniak D, Dence C, et al. Increased myocardial fatty acid metabolism in patients with type 1 diabetes mellitus. J Am Coll Cardiol. 2006;47(3):598-604.\u003c/li\u003e\n\u003cli\u003eLin CH, Kurup S, Herrero P, Schechtman KB, Eagon JC, Klein S, et al. Myocardial oxygen consumption change predicts left ventricular relaxation improvement in obese humans after weight loss. Obesity (Silver Spring). 2011;19(9):1804-12.\u003c/li\u003e\n\u003cli\u003eMorbelli S, Marini C, Adami GF, Kudomi N, Camerini G, Iozzo P, et al. Tissue specificity in fasting glucose utilization in slightly obese diabetic patients submitted to bariatric surgery. Obesity (Silver Spring). 2013;21(3):E175-81.\u003c/li\u003e\n\u003cli\u003eValenta I, Varga ZV, Valentine H, Cinar R, Horti A, Mathews WB, et al. Feasibility Evaluation of Myocardial Cannabinoid Type 1 Receptor Imaging in Obesity: A Translational Approach. JACC Cardiovasc Imaging. 2018;11(2 Pt 2):320-32.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003ePatient characteristics (n=214)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eMale/Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e178/36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetic/Non-diabetic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e101/113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e64.8 \u0026plusmn; 11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e164.8 \u0026plusmn; 8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e67.5 \u0026plusmn; 12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e24.8 \u0026plusmn; 3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eIschemic cardiomyopathy\u003c/p\u003e\n \u003cp\u003e1VD\u003c/p\u003e\n \u003cp\u003e2VD\u003c/p\u003e\n \u003cp\u003e3VD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42 (19.6%)\u003c/p\u003e\n \u003cp\u003e87 (40.7%)\u003c/p\u003e\n \u003cp\u003e84 (39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eEjection Fraction (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e47.3 \u0026plusmn; 12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eCardiac markers\u003c/p\u003e\n \u003cp\u003eTroponin (ng/mL)\u003c/p\u003e\n \u003cp\u003eCK-MB (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.88 \u0026plusmn; 2.14\u003c/p\u003e\n \u003cp\u003e21.0 \u0026plusmn; 55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e6.7 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eKidney function\u003c/p\u003e\n \u003cp\u003eCreatinine (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.4 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eLipid profile (mg/dl)\u003c/p\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003cp\u003eTriglyceride\u003c/p\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e154.2 \u0026plusmn; 49.2\u003c/p\u003e\n \u003cp\u003e136.9 \u0026plusmn; 86.5\u003c/p\u003e\n \u003cp\u003e41.2 \u0026plusmn; 11.2\u003c/p\u003e\n \u003cp\u003e88.6 \u0026plusmn; 45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eBlood Sugar Test (mg/dl)\u003c/p\u003e\n \u003cp\u003efasting BST\u003c/p\u003e\n \u003cp\u003eBST after glucose loading\u003c/p\u003e\n \u003cp\u003eBST at FDG injection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e121.7 \u0026plusmn; 32.9\u003c/p\u003e\n \u003cp\u003e200.2 \u0026plusmn; 54.2\u003c/p\u003e\n \u003cp\u003e194.5 \u0026plusmn; 49.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eInjected insulin (IU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e2.9 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eInjection to image time (minutes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e52.8 \u0026plusmn; 8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eSUVmax of left ventricle\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e9.2 \u0026plusmn; 3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eSUVmean of liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e2.4 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"62.414965986394556%\" valign=\"top\"\u003e\n \u003cp\u003eMyocardial Glucose Uptake Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.585034013605444%\" valign=\"top\"\u003e\n \u003cp\u003e4.2 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eVD, vessel\u0026nbsp;disease\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003ePearson correlation analysis of the clinical factors with\u0026nbsp;Myocardial Glucose Uptake Ratio\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient of correlation (r)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e-0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eEjection Fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eTroponin T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eCK-MB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eFasting BST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eBST after glucose loading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eBST at FDG injection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e-0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.030*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eTriglyceride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e-0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.046*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.71137521222411%\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.463497453310694%\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.825127334465193%\" valign=\"top\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003c/strong\u003eStatistically significant\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 \u0026nbsp;\u003c/strong\u003eComparison of variables between the non-obese and obese groups (n=214)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-obese group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(BMI \u0026lt; 25, n=119)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eObese group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(BMI \u0026ge; 25, n=95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e61.3 \u0026plusmn; 9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e75.3 \u0026plusmn; 11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eMGUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e4.4 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e3.8 \u0026plusmn; 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eSex (male: female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e99: 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e79: 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e65.1 \u0026plusmn; 10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e64.3 \u0026plusmn; 12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e165.4 \u0026plusmn; 8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e164.2 \u0026plusmn; 9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eEjection Fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e47.1\u0026plusmn;13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e47.4\u0026plusmn;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eTroponin T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e0.88 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e0.87 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eCK-MB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e24.3 \u0026plusmn; 62.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e16.9 \u0026plusmn; 45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eFasting BST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e122.6 \u0026plusmn; 33.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e120.6 \u0026plusmn; 33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eBST after glucose loading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e197.8 \u0026plusmn; 57.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e203.2 \u0026plusmn; 50.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eBST at FDG injection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e191.7 \u0026plusmn; 52.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e203.2 \u0026plusmn; 50.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin (IU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e1.7 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e1.7 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e6.6 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e6.8 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eInjection to image time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e53.1 \u0026plusmn; 8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e52.4 \u0026plusmn; 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e1.4 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e1.4 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e155.3 \u0026plusmn; 50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e152.7 \u0026plusmn; 47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eTriglyceride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e129.5 \u0026plusmn; 80.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e146.0 \u0026plusmn; 93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e43.1 \u0026plusmn; 12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e38.9 \u0026plusmn; 8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.69387755102041%\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e88.9 \u0026plusmn; 43.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" valign=\"top\"\u003e\n \u003cp\u003e88.2 \u0026plusmn; 48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"top\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Statistically significant\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u0026nbsp; \u0026nbsp;Multiple linear regression analysis of variables affecting MGUR\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.449664429530202%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.919463087248324%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnstandardized Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.80536912751678%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandardized Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073825503355705%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.449664429530202%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(constant)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.959731543624162%\" valign=\"top\"\u003e\n \u003cp\u003e7.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.959731543624162%\" valign=\"top\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.80536912751678%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\" valign=\"top\"\u003e\n \u003cp\u003e6.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073825503355705%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.449664429530202%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.959731543624162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.959731543624162%\" valign=\"top\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.80536912751678%\" valign=\"top\"\u003e\n \u003cp\u003e-0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\" valign=\"top\"\u003e\n \u003cp\u003e-2.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073825503355705%\" valign=\"top\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.449664429530202%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.959731543624162%\" valign=\"top\"\u003e\n \u003cp\u003e-0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.959731543624162%\" valign=\"top\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.80536912751678%\" valign=\"top\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.751677852348994%\" valign=\"top\"\u003e\n \u003cp\u003e-1.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.073825503355705%\" valign=\"top\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eR: 0.210\u003c/p\u003e\n\u003cp\u003eAdjusted R-squared: 0.035\u003c/p\u003e\n\u003cp\u003eF-statistics: 4.775, p=0.009\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiac FDG PET, BMI, obesity, myocardial glucose uptake","lastPublishedDoi":"10.21203/rs.3.rs-4209144/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4209144/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eCardiac 2-deoxy-2-[F-18]fluoro-D-glucose positron emission tomography (FDG PET) is widely used to assess myocardial viability in patients with ischemic heart disease. While sufficient glucose uptake is a prerequisite for accurate interpretation of cardiac viability, there is a lack of data on which clinical variables have the most significant impact on myocardial glucose metabolism. Therefore, this study was designed to evaluate several clinical variables that could affect myocardial glucose metabolism.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBetween May 2018 and November 2022, a total of 214 consecutive cases were retrospectively enrolled in this study. All subjects were fasted for at least 8 hours. They received 250 mg of acipimox and underwent glucose loading as preparation for cardiac FDG PET/CT. Three-dimensional regions of interest (ROI) were drawn on PET/CT fusion images. SUV ratio (SUVmax of LV myocardium/SUVmean of liver) was then calculated. Clinical variables of age, sex, height, weight, body mass index (BMI), fasting blood glucose level, administered insulin dosage, blood glucose level at FDG injection, total cholesterol, high-density lipoprotein, low-density lipoprotein, cardiac markers, creatinine, hemoglobin A1c, and ejection fraction were measured and analyzed for correlation with myocardial glucose uptake. Participants were divided into an obese group and a non-obese group based on a BMI of 25. Whether there was a difference in myocardial glucose uptake between the two groups was then determined. Pearson correlation coefficient and Student\u0026rsquo;s t-test were used for statistical analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMyocardial uptake showed significant correlations with BMI (r = -0.162, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), HbA1c (r = -0.150, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), and triglyceride levels (r = -0.137, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046). No other clinical variables showed a significant correlation with myocardial glucose uptake. In group analysis, after dividing patients based on BMI, the obese group showed significantly lower myocardial uptake than the non-obese group (3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 vs. 4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAmong several clinical variables, BMI, HbA1c, and triglyceride levels exhibited negative correlations with myocardial glucose uptake. Patients with higher BMI, HbA1c, and triglyceride levels might require more thorough preparation or consideration during cardiac FDG PET exams to ensure optimal glucose uptake.\u003c/p\u003e","manuscriptTitle":"Evaluation of clinical variables affecting myocardial glucose uptake in cardiac FDG PET","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 17:43:25","doi":"10.21203/rs.3.rs-4209144/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6740d1e-fa11-459e-81be-9a02ba5d6c2e","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-01T02:43:07+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 17:43:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4209144","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4209144","identity":"rs-4209144","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}