Kinetic Modeling of 18 F-FDG Myocardial Metabolism: Elucidating the Mechanistic Superiority of Insulin Loading in Viability Assessment

Kinetic Modeling of 18 F-FDG Myocardial Metabolism: Elucidating the Mechanistic Superiority of Insulin Loading in Viability Assessment | 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 Kinetic Modeling of 18 F-FDG Myocardial Metabolism: Elucidating the Mechanistic Superiority of Insulin Loading in Viability Assessment Yangchun Chen, Jianjia Jiang, Ping Yuan, Jianwei Chen, Ruozhu Dai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9249612/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 This study investigates the mechanism by which insulin loading enhances myocardial 18 F-fluorodeoxyglucose ( 18 F-FDG) uptake compared to glucose loading. Using dynamic positron emission tomography and two-compartment irreversible kinetic modeling, 18 patients with ischemic heart disease were analyzed. Results demonstrate that insulin significantly increases the net influx rate ( Ki ) by accelerating blood-to-tissue transport ( K1 ), while concurrently reducing tracer efflux ( k2 ) and phosphorylation ( k3 ). These findings provide a quantitative metabolic basis for the clinical superiority of insulin-enhanced imaging in detecting viable myocardium. viable myocardium insulin glucose transporter 4 kinetic modeling positron emission tomography Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ischemic heart disease (IHD) remains a formidable challenge to global public health, with coronary revascularization standing as a primary therapeutic intervention [ 1 ]. The identification of viable myocardium distal to coronary stenoses or occlusions is a critical determinant of clinical outcomes [ 2 ], as revascularization of salvaged tissue significantly improves overall cardiac function [ 3 ]. Under ischemic conditions, myocardial survival shifts predominantly toward anaerobic glycolysis. Consequently, 18 F-FDG positron emission tomography (PET) has been established as the FDA-approved gold standard for the non-invasive assessment of myocardial viability [ 4 ]. Achieving optimal 18 F-FDG uptake in viable myocardium is essential for high-quality PET imaging; however, this remains a complex clinical challenge. While various pharmacological interventions—including the hyperinsulinemic-euglycemic clamp [ 5 ], glucose loading [ 6 ], nicotinic acid [ 5 , 7 ], and trimetazidine[ 8 ]—have been employed to augment tracer uptake, their efficacy and efficiency vary. Our previous research demonstrated that an insulin-loading protocol rapidly enhances 18 F-FDG uptake in viable myocardium, yielding superior imaging success rates and contrast ratios compared to standardized glucose loading [ 9 ]. Despite these practical advancements, the precise kinetic mechanisms by which insulin modulates myocardial 18 F-FDG metabolism remain insufficiently characterized. Dynamic 18 F-FDG PET imaging offers a powerful framework for quantifying metabolic flux through compartmental modeling. Within the two-compartment irreversible model, the rate constant K1 reflects the density and activity of glucose transporter 4 (GLUT4) translocated to the myocardial cell membrane [ 10 ]. Therefore, kinetic analysis provides essential evidence to elucidate the mechanisms underlying the rapid enhancement of 18 F-FDG uptake following insulin loading. In the present study, we utilized the dPetBrainQuantification module within the 3D Slicer platform to process dynamic PET datasets [ 11 ]. By deriving the two-compartment irreversible model parameters, we aim to provide a quantitative metabolic basis for the superior performance of insulin-enhanced myocardial imaging. Experimental Patient Selection and Study Design Between July and September 2018, we enrolled patients with confirmed or suspected ischemic heart disease (IHD) who were scheduled for 18 F-FDG PET imaging to evaluate myocardial viability. Inclusion required the availability of complete dynamic PET acquisition data. Patients were excluded if the image quality was suboptimal, defined by the myocardium-to-liver uptake ratio < 1.0. This retrospective analysis was approved by the Institutional Review Board of Quanzhou First Hospital Affiliated with Fujian Medical University (Approval No. 2020 − 106), which waived the requirement for written informed consent due to the study's retrospective nature. Imaging Preparation and Protocols All participants fasted overnight prior to the imaging procedure. Baseline blood glucose (BG) levels were measured before the initiation of either the insulin or standardized glucose loading protocols. In the insulin group, patients received an intravenous bolus of 3–4 units of regular insulin approximately 20 minutes before 18 F-FDG administration. In the glucose group, patients were orally administered 10–50 g of glucose, with the specific dosage titrated according to their fasting BG. For the glucose group, BG levels were re-evaluated 45–60 minutes post-ingestion. If necessary, supplemental regular insulin (1–3 units) was administered based on real-time BG monitoring to achieve a target glycemic range of 5.55–7.77 mmol/L prior to the 18 F-FDG injection [ 4 ]. PET/CT Acquisition Protocol Patients were positioned supine with arms raised above the head. A low-dose, non-contrast CT scan was performed during shallow breathing for anatomic localization and attenuation correction (Biograph mCT Flow 64, Siemens, USA). Immediately following the initiation of electrocardiogram (ECG)-gated list-mode PET data acquisition, patients received an intravenous bolus of 18 F-FDG (3.7 MBq/kg). Dynamic acquisition was performed with a 200 × 200 matrix at 15 minutes per bed position. The dynamic sequence was reconstructed into 21 frames (10s/frame × 6, 30s/frame × 2, and 60s/frame × 13) using an iterative TrueX algorithm (3 iterations, 24 subsets). A subsequent static PET acquisition was performed 45 minutes post-injection and reconstructed using the same iterative parameters. Image Analysis and Kinetic Modeling Image processing was conducted using 3D Slicer software (version 5.5.0) equipped with the dPetBrainQuantification module [ 11 , 12 ]. To ensure anatomical precision, automated segmentation of the descending aorta (DA), left ventricle (LV), and liver was performed on co-registered CT images. For dynamic analysis, the LV myocardial mask was generated from the 20th frame of the cardiac PET sequence using an automated segmentation approach. Time-activity curves (TACs) were extracted from the DA to derive image-derived input functions (IDIFs). These were integrated with hematocrit (HCT) values obtained from electronic medical records within five days of the PET examination to refine kinetic calculations. A three-parameter, two-compartment irreversible model was employed to estimate myocardial and hepatic kinetic constants: K1 (blood-to-tissue transport), k2 (tissue-to-blood transport), and k3 (phosphorylation rate) [ 10 ]. The macroparameter Ki (net influx rate) was calculated to quantify global glucose metabolic flux. Static Image Analysis and SUV Quantification Static PET/CT analysis was likewise performed in 3D Slicer. Images underwent preprocessing, including resampling, partial volume effect correction, and rigid co-registration with CT datasets. We employed the TotalSegmentator AI model for fully automated whole-body CT segmentation to derive masks for the aorta, autochthonous muscle, torso and non-torso fat, liver, and spleen [ 13 ]. For the LV myocardium, a semi-automated segmentation approach was used; the segmentation threshold was defined as the mean SUV of the DA plus two standard deviations (SD) [ 14 ]. Myocardial SUVs were subsequently quantified, and the LV was partitioned into a 17-segment model with American Heart Association (AHA) guidelines. Viable myocardium was defined by a mean SUV (SUVmean) exceeding that of the liver. Statistical Analysis Continuous variables are expressed as mean ± SD. Inter-group comparisons were performed using independent two-tailed t-tests. Correlations were assessed using Pearson’s or Spearman’s rank correlation coefficients as appropriate. All statistical analyses were conducted in R (version 4.2.1), with a two-sided P ≤ 0.05 considered statistically significant. Results and discussion Patient Characteristics A total of 18 patients with known or suspected IHD were enrolled. Notably, 15 patients underwent the insulin loading protocol, while 3 followed the glucose loading protocol, primarily due to the significantly shorter preparation time associated with insulin. Demographic and clinical baseline characteristics—including sex ratio, age, weight, height, fasting BG, BG at the time of 18 F-FDG injection, and history of prior myocardial infarction—were comparable between the two groups. However, the insulin group exhibited significantly lower insulin dosages and hematocrit (HCT) levels compared to the glucose group (Table 1 ). Table 1 The patients’ clinical characteristics num total Glu Ins P-value 18 3 15 Sex F 3, M 15 M 3 F 3, M 12 0.40 Age(y) 57.4 (14.7) 54.7 (19.6) 57.9 (14.3) 0.74 Weight(kg) 67.1 (12.2) 70.7 (13.7) 66.4 (12.3) 0.59 Height(cm) 170.3 (6.2) 170.3 (2.1) 170.3 (6.8) 1 Fast BG(mmol/L) 5.7 (1.0) 5.8 (1.1) 5.7 (1.0) 0.92 HCT 0.42 (0.04) 0.47 (0.01) 0.41 (0.04) 0.04 DM 2 0 2 0.53 Prior MI 11 3 8 0.13 Insulin dose(U) 3.9 (1.1) 5.3 (2.3) 3.6 (0.5) 0.01 Last BG(mmol/L) 4.7 (1.0) 5.8 (1.6) 4.5 (0.9) 0.10 Delay time(min) 26.1 (18.8) 38.0 (49.0) 23.7 (7.1) 0.66 Note: *: P ≤ 0.05, # : P ≤ 0.01, HCT: hematocrit, DM: diabetic mellitus, MI: myocardial infarction Tracer Distribution in Insulin-Sensitive Tissues High signal-to-noise ratio (SNR) PET images were achieved for all participants. All myocardial segments were confirmed as viable, with SUVmean values consistently exceeding those of the liver. Table 2 details the 18 F-FDG distribution across insulin-sensitive tissues one hour post-injection. At the patient level, myocardial SUVmean was significantly higher in the insulin group compared to the glucose group (6.6 ± 3.1 vs. 3.9 ± 1.8, P = 0.05). In contrast, no significant differences in SUVmean were observed between groups for other insulin-sensitive tissues (autochthonous muscle, liver, and fat) or non-insulin-sensitive tissues (aorta, spleen). Furthermore, at the segment level, myocardial SUVmean remained notably higher in the insulin group (6.6 ± 1.9 vs. 3.8 ± 1.3, P < 0.01). Table 2 The different 18 F-FDG distribution in the insulin sensitive tissues between glucose loading and insulin loading groups after 18 F-FDG intravenous injection 1 hour later. aorta Glu Ins P-value 1.1 (0.3) 1.3 (0.5) 0.52 Autochthon muscle 0.8 (0.0) 0.9 (0.3) 0.44 Myocardium at patient level 3.9 (1.8) 6.6 (3.1) 0.05 Myocardium at segment level 3.8 (1.3) 6.6 (1.9) < 0.01 liver 1.6 (0.5) 1.8 (0.7) 0.59 spleen 1.1 (0.3) 1.4 (0.4) 0.36 Non_Torso-fat 0.7 (0.1) 0.6 (0.1) 0.11 Torso-fat 0.8 (0.2) 1.0 (0.2) 0.08 Note: *: P ≤ 0.05, # : P ≤ 0.01 Pharmacokinetic Analyses of the LV Myocardium Typical 18 F-FDG time-activity curves (TACs) for the LV myocardium following insulin (Patient 12) and glucose (Patient 3) loading are compared in Fig. 1 . The mean SUV of the LV myocardium was markedly elevated in Patient 12 (5.2) relative to Patient 3 (3.8), highlighting the efficacy of insulin in augmenting net 18 F-FDG influx compared to oral glucose. The estimated kinetic parameters are summarized in Table 3 . At the patient level, the myocardial k3 rate constant was significantly lower in the insulin group (0.28 ± 0.08 vs. 0.40 ± 0.08 min − 1 , P = 0.02). At the segment level, both K1 and Ki were significantly elevated in the insulin group (Fig. 2 a) compared to the glucose group (Fig. 3 a) (0.60 ± 0.17 and 0.37 ± 0.11 vs. 0.50 ± 0.10 and 0.30 ± 0.05 mL/cm 3 /min, respectively; P < 0.01). Conversely, the rate constants k2 and k3 were significantly slower in the insulin group (Fig. 2 b–c) than in the glucose group (Fig. 3 b–c) (0.15 ± 0.14 and 0.23 ± 0.14 vs. 0.23 ± 0.14 and 0.37 ± 0.13 min − 1 , respectively; P < 0.01). Additionally, myocardial SUVs were higher across multiple segments in the insulin group (Fig. 2 d) compared to the glucose group (Fig. 3 d). Specifically, k3 in the apical-inferior segment was remarkably reduced in the insulin group (0.25 ± 0.15 vs. 0.49 ± 0.21 min-1, P = 0.03). Table 3 Kinetic parameters of 18 F-FDG during either glucose or insulin loading protocol for viable myocardium detection. K1(mL/cm 3 /min) Myo by patients Myo by segment Liv Glu 0.50 (0.05) 0.50 (0.10) 0.72 (0.32) Ins 0.57 (0.09) 0.60 (0.17 # ) 0.60 (0.26) k2(min − 1 ) Glu 0.24 (0.01) 0.23 (0.14) 0.52 (0.45) Ins 0.19 (0.12) 0.15 (0.14 # ) 0.34 (0.34) k3(min − 1 ) Glu 0.40 (0.08) 0.37 (0.13) 0.42 (0.27) Ins 0.28 (0.08*) 0.23 (0.14 # ) 0.44 (0.22) Ki(mL/cm 3 /min) Glu 0.31 (0.04) 0.30 (0.05) 0.27 (0.07) Ins 0.34 (0.08) 0.37 (0.11 # ) 0.28 (0.08) Note: *: P ≤ 0.05, # : P ≤ 0.01, Myo: myocardium, Liv: liver Correlation of Kinetic Parameters with SUV At the patient level, correlation analysis revealed no significant associations between K1 , k2 , k3 , Ki , and SUVmean. However, at the segment level, significant Spearman rank correlations (Fig. 4 a–c) were observed between SUVmean and K1 ( ρ = 0.15, P < 0.01), k2 ( ρ = -0.26, P < 0.01), and k3 ( ρ = -0.19, P < 0.01). Additionally, a significant Pearson correlation (Fig. 4 d) was identified between Ki and segmental myocardial SUVmean ( r = 0.38, P < 0.01). Discussion The present study demonstrates that exogenous insulin administration significantly enhances 18 F-FDG uptake in the myocardium, at both the patient and segmental levels, compared to oral glucose loading. Crucially, our kinetic analysis revealed a marked augmentation of the K1 parameter in the insulin group. These findings support the hypothesis that insulin effectively promotes the translocation of GLUT4 to the cytomembrane [ 15 ], thereby accelerating the initial transport of 18 F-FDG from the blood to the myocyte. Mechanistic discussion K1 is primarily governed by two factors: the density of GLUT4 on the myocyte membrane and the transsarcolemmal glucose gradient (TSGG) [ 16 ]. Our observed increase in K1 aligns with previous reports by Zuo et al., underscoring the role of insulin in modulating early-stage glucose transport [ 17 ]. Furthermore, our results indicate that insulin influences the subsequent phosphorylation and efflux kinetics of 18 F-FDG. Compared to historical data on oral glucose loading, both protocols in our study exhibited a reduced k2 and an elevated k3 , suggesting that insulin-stimulated phosphorylation traps the tracer more effectively within the tissue, thereby decreasing the tissue-to-blood reflux rate [ 17 ]. Interestingly, we observed a lower k3 in the insulin group compared to the glucose group. While the precise mechanism for this specific observation remains to be elucidated, it may reflect complex regulatory feedback in high-insulin states, necessitating further biochemical investigation. Our findings confirm that insulin augments Ki , representing a significant improvement over the fasting state. This enhancement in Ki is largely driven by the insulin-mediated reduction in k2 . These data provide a robust metabolic foundation for the clinical superiority of insulin loading over glucose loading for myocardial viability detection, as previously suggested by several clinical trials [ 9 , 18 , 19 ]. From a methodological standpoint, we utilized the DA rather than the LV cavity as the input function for several reasons. First, the TotalSegmentator AI model provides more reliable and reproducible DA masks compared to the LV cavity. Second, the proximity of the LV wall and cavity makes boundary delineation challenging on non-contrast CT and PET. Third, cardiac motion-induced blurring further complicates LV cavity segmentation. Finally, established literature supports the use of the aortic time-activity curve as a valid image-derived input function for calculating myocardial kinetic parameters [ 20 ]. Limitations Several limitations of this study warrant acknowledgment. First, the relatively small sample size (N = 18) may have limited our statistical power to detect significant differences at the individual patient level. Second, due to the inherent fluctuations in blood glucose during PET acquisition, K1 values were not normalized, which might introduce variability. Lastly, the numerical imbalance between the insulin and glucose groups could potentially introduce bias, though the baseline characteristics remained comparable. In conclusion, this study provides clinical evidence that insulin loading significantly boosts the net 18 F-FDG influx rate by accelerating blood-to-tissue delivery while concurrently reducing tracer efflux. These kinetic shifts culminate in superior myocardial tracer uptake, reinforcing the insulin loading protocol as an optimal preparatory strategy for the assessment of myocardial viability in clinical practice. Declarations Acknowledgments This study was supported by Natural Science Foundation of Fujian Province (2015J01516, 2018J01202), Natural Science Foundation of Quanzhou (2019C023R). Conflict of interest disclosure : None Data availability statement All data supporting the findings of this study are available from the corresponding author on justified request. Author Contributions Statement YC conceptualized and designed the study, performed the formal analysis, and drafted the original manuscript. JJ and RD provided clinical oversight and technical support. JC was responsible for data acquisition, while PY performed data analysis. Funding acquisition was led by YC and RD. All authors participated in the critical revision of the manuscript and approved the final version for submission. References Tsao C, Aday A, Almarzooq Z, Alonso A, Beaton A, Bittencourt M et al (2022) Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. 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J Nucl Med 64:1821–1830 Chen Y, Pan M, Wang Q, Wang Y, Zhuo H, Dai R (2022) Intravenous insulin injection supplemented with subsequent milk consumption is a safer formulation for cardiac viability 18F-FDG imaging. J Nucl Cardiol 29:1985–1991 Zhang X, Xie Z, Berg E, Judenhofer M, Liu W, Xu T et al (2020) Total-Body Dynamic Reconstruction and Parametric Imaging on the uEXPLORER. J Nucl Med 61:285–291 Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9249612","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620305594,"identity":"1ac7d489-3f48-4515-96e2-a326ef498dd2","order_by":0,"name":"Yangchun Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3QsYrCMBjA8ZRAe0PQNRLQV/hKoDoIvkrkIC4KfYNTHFziXt/i3uA8CroU524qt9yYo6twfh6H4JJ2PLj8h0Da/CD5CPH5/mDtaLGyFobdnx0HXLY1pGPyXZClWrLGBEqt6ZPNx+b+qY6Q7TT5IEAnb9E6/xykpNsqVVClDhHMi75MIZwZdtADvJjslIqKzEFoYEBkwGaGTxNAMn4tVUiZg+BfEAz4hP2Sl1rCwlAjAYVEnpAoqCOc0TzOQMWmKBIcMo83xXkpXGR0PC9O9vLdi1ZGVvwy7LX2z++Vizy8S3DCyW2KDQFO78s2Puvz+Xz/qStmiEehmmnYFAAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Pulmonary Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yangchun","middleName":"","lastName":"Chen","suffix":""},{"id":620305595,"identity":"fa221e92-7d73-45a6-9371-e3176124db7f","order_by":1,"name":"Jianjia Jiang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jianjia","middleName":"","lastName":"Jiang","suffix":""},{"id":620305596,"identity":"213d4d31-9793-4203-b9e7-bdab06c6dcd5","order_by":2,"name":"Ping Yuan","email":"","orcid":"","institution":"Shanghai Pulmonary Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Yuan","suffix":""},{"id":620305597,"identity":"d6025463-0746-4bcb-a940-f8033e16cd04","order_by":3,"name":"Jianwei Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jianwei","middleName":"","lastName":"Chen","suffix":""},{"id":620305598,"identity":"e06bcd0d-e44a-4704-be80-e9a6f4b319da","order_by":4,"name":"Ruozhu Dai","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ruozhu","middleName":"","lastName":"Dai","suffix":""}],"badges":[],"createdAt":"2026-03-28 04:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9249612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9249612/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106870037,"identity":"44ffd9c0-8070-432a-9278-c387826141bb","added_by":"auto","created_at":"2026-04-14 09:41:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":167988,"visible":true,"origin":"","legend":"\u003cp\u003eComparative time-activity curves (TACs) of myocardial and descending aortic \u003csup\u003e18\u003c/sup\u003eF-FDG uptake. Representative data are shown for Patient 12 (insulin group) and Patient 3 (glucose group)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9249612/v1/097e038906297205722a899c.png"},{"id":106870131,"identity":"6adc6280-6891-496a-8270-bf7eeb227153","added_by":"auto","created_at":"2026-04-14 09:41:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":374597,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of \u003csup\u003e18\u003c/sup\u003eF-FDG kinetic parameters (\u003cem\u003eK1\u003c/em\u003e, \u003cem\u003ek2\u003c/em\u003e, \u003cem\u003ek3\u003c/em\u003e) and SUVmean across the 17 AHA myocardial segments for Patient 12 (insulin group)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9249612/v1/da7d19d7811da23b25727e89.png"},{"id":106870019,"identity":"eb42daa0-d66a-4ecd-ad79-6da9363e9e2a","added_by":"auto","created_at":"2026-04-14 09:41:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":361561,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of \u003csup\u003e18\u003c/sup\u003eF-FDG kinetic parameters (\u003cem\u003eK1\u003c/em\u003e, \u003cem\u003ek2\u003c/em\u003e, \u003cem\u003ek3\u003c/em\u003e) and SUVmean across the 17 AHA myocardial segments for Patient 3 (glucose group)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9249612/v1/36022a58a22fcf1eeeb69816.png"},{"id":106870146,"identity":"7927724b-92af-4098-99bc-8b40982aa72b","added_by":"auto","created_at":"2026-04-14 09:41:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":347245,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between myocardial SUVmean and kinetic parameters at the segment level. Significant correlations were observed for \u003cem\u003eK1\u003c/em\u003e (\u003cem\u003eρ\u003c/em\u003e = 0.15), \u003cem\u003ek2\u003c/em\u003e(\u003cem\u003eρ\u003c/em\u003e= -0.26), \u003cem\u003ek3\u003c/em\u003e (\u003cem\u003eρ\u003c/em\u003e= -0.19), and \u003cem\u003eKi\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e = 0.38) (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9249612/v1/82d2c8c885caa5e7461b7802.png"},{"id":106870834,"identity":"246be67f-1f51-4d53-bbdf-d16bf2f2a2ef","added_by":"auto","created_at":"2026-04-14 09:43:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1992549,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9249612/v1/2a1c12a4-e5a7-4b2d-93d3-5b7f0d424726.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Kinetic Modeling of 18 F-FDG Myocardial Metabolism: Elucidating the Mechanistic Superiority of Insulin Loading in Viability Assessment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIschemic heart disease (IHD) remains a formidable challenge to global public health, with coronary revascularization standing as a primary therapeutic intervention [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The identification of viable myocardium distal to coronary stenoses or occlusions is a critical determinant of clinical outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], as revascularization of salvaged tissue significantly improves overall cardiac function [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Under ischemic conditions, myocardial survival shifts predominantly toward anaerobic glycolysis. Consequently, \u003csup\u003e18\u003c/sup\u003eF-FDG positron emission tomography (PET) has been established as the FDA-approved gold standard for the non-invasive assessment of myocardial viability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAchieving optimal \u003csup\u003e18\u003c/sup\u003eF-FDG uptake in viable myocardium is essential for high-quality PET imaging; however, this remains a complex clinical challenge. While various pharmacological interventions\u0026mdash;including the hyperinsulinemic-euglycemic clamp [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], glucose loading [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], nicotinic acid [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and trimetazidine[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u0026mdash;have been employed to augment tracer uptake, their efficacy and efficiency vary. Our previous research demonstrated that an insulin-loading protocol rapidly enhances \u003csup\u003e18\u003c/sup\u003eF-FDG uptake in viable myocardium, yielding superior imaging success rates and contrast ratios compared to standardized glucose loading [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite these practical advancements, the precise kinetic mechanisms by which insulin modulates myocardial \u003csup\u003e18\u003c/sup\u003eF-FDG metabolism remain insufficiently characterized.\u003c/p\u003e \u003cp\u003eDynamic \u003csup\u003e18\u003c/sup\u003eF-FDG PET imaging offers a powerful framework for quantifying metabolic flux through compartmental modeling. Within the two-compartment irreversible model, the rate constant \u003cem\u003eK1\u003c/em\u003e reflects the density and activity of glucose transporter 4 (GLUT4) translocated to the myocardial cell membrane [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, kinetic analysis provides essential evidence to elucidate the mechanisms underlying the rapid enhancement of \u003csup\u003e18\u003c/sup\u003eF-FDG uptake following insulin loading. In the present study, we utilized the dPetBrainQuantification module within the 3D Slicer platform to process dynamic PET datasets [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By deriving the two-compartment irreversible model parameters, we aim to provide a quantitative metabolic basis for the superior performance of insulin-enhanced myocardial imaging.\u003c/p\u003e"},{"header":"Experimental","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Selection and Study Design\u003c/h2\u003e \u003cp\u003eBetween July and September 2018, we enrolled patients with confirmed or suspected ischemic heart disease (IHD) who were scheduled for \u003csup\u003e18\u003c/sup\u003eF-FDG PET imaging to evaluate myocardial viability. Inclusion required the availability of complete dynamic PET acquisition data. Patients were excluded if the image quality was suboptimal, defined by the myocardium-to-liver uptake ratio\u0026thinsp;\u0026lt;\u0026thinsp;1.0. This retrospective analysis was approved by the Institutional Review Board of Quanzhou First Hospital Affiliated with Fujian Medical University (Approval No. 2020\u0026thinsp;\u0026minus;\u0026thinsp;106), which waived the requirement for written informed consent due to the study's retrospective nature.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImaging Preparation and Protocols\u003c/h3\u003e\n\u003cp\u003eAll participants fasted overnight prior to the imaging procedure. Baseline blood glucose (BG) levels were measured before the initiation of either the insulin or standardized glucose loading protocols.\u003c/p\u003e \u003cp\u003eIn the insulin group, patients received an intravenous bolus of 3\u0026ndash;4 units of regular insulin approximately 20 minutes before \u003csup\u003e18\u003c/sup\u003eF-FDG administration. In the glucose group, patients were orally administered 10\u0026ndash;50 g of glucose, with the specific dosage titrated according to their fasting BG. For the glucose group, BG levels were re-evaluated 45\u0026ndash;60 minutes post-ingestion. If necessary, supplemental regular insulin (1\u0026ndash;3 units) was administered based on real-time BG monitoring to achieve a target glycemic range of 5.55\u0026ndash;7.77 mmol/L prior to the \u003csup\u003e18\u003c/sup\u003eF-FDG injection [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003ePET/CT Acquisition Protocol\u003c/h3\u003e\n\u003cp\u003ePatients were positioned supine with arms raised above the head. A low-dose, non-contrast CT scan was performed during shallow breathing for anatomic localization and attenuation correction (Biograph mCT Flow 64, Siemens, USA). Immediately following the initiation of electrocardiogram (ECG)-gated list-mode PET data acquisition, patients received an intravenous bolus of \u003csup\u003e18\u003c/sup\u003eF-FDG (3.7 MBq/kg). Dynamic acquisition was performed with a 200 \u0026times; 200 matrix at 15 minutes per bed position. The dynamic sequence was reconstructed into 21 frames (10s/frame \u0026times; 6, 30s/frame \u0026times; 2, and 60s/frame \u0026times; 13) using an iterative TrueX algorithm (3 iterations, 24 subsets). A subsequent static PET acquisition was performed 45 minutes post-injection and reconstructed using the same iterative parameters.\u003c/p\u003e\n\u003ch3\u003eImage Analysis and Kinetic Modeling\u003c/h3\u003e\n\u003cp\u003eImage processing was conducted using 3D Slicer software (version 5.5.0) equipped with the dPetBrainQuantification module [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To ensure anatomical precision, automated segmentation of the descending aorta (DA), left ventricle (LV), and liver was performed on co-registered CT images. For dynamic analysis, the LV myocardial mask was generated from the 20th frame of the cardiac PET sequence using an automated segmentation approach.\u003c/p\u003e \u003cp\u003eTime-activity curves (TACs) were extracted from the DA to derive image-derived input functions (IDIFs). These were integrated with hematocrit (HCT) values obtained from electronic medical records within five days of the PET examination to refine kinetic calculations. A three-parameter, two-compartment irreversible model was employed to estimate myocardial and hepatic kinetic constants: \u003cem\u003eK1\u003c/em\u003e (blood-to-tissue transport), \u003cem\u003ek2\u003c/em\u003e (tissue-to-blood transport), and \u003cem\u003ek3\u003c/em\u003e (phosphorylation rate) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The macroparameter \u003cem\u003eKi\u003c/em\u003e (net influx rate) was calculated to quantify global glucose metabolic flux.\u003c/p\u003e\n\u003ch3\u003eStatic Image Analysis and SUV Quantification\u003c/h3\u003e\n\u003cp\u003eStatic PET/CT analysis was likewise performed in 3D Slicer. Images underwent preprocessing, including resampling, partial volume effect correction, and rigid co-registration with CT datasets. We employed the TotalSegmentator AI model for fully automated whole-body CT segmentation to derive masks for the aorta, autochthonous muscle, torso and non-torso fat, liver, and spleen [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the LV myocardium, a semi-automated segmentation approach was used; the segmentation threshold was defined as the mean SUV of the DA plus two standard deviations (SD) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Myocardial SUVs were subsequently quantified, and the LV was partitioned into a 17-segment model with American Heart Association (AHA) guidelines. Viable myocardium was defined by a mean SUV (SUVmean) exceeding that of the liver.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Inter-group comparisons were performed using independent two-tailed t-tests. Correlations were assessed using Pearson\u0026rsquo;s or Spearman\u0026rsquo;s rank correlation coefficients as appropriate. All statistical analyses were conducted in R (version 4.2.1), with a two-sided P\u0026thinsp;\u0026le;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 18 patients with known or suspected IHD were enrolled. Notably, 15 patients underwent the insulin loading protocol, while 3 followed the glucose loading protocol, primarily due to the significantly shorter preparation time associated with insulin. Demographic and clinical baseline characteristics\u0026mdash;including sex ratio, age, weight, height, fasting BG, BG at the time of \u003csup\u003e18\u003c/sup\u003eF-FDG injection, and history of prior myocardial infarction\u0026mdash;were comparable between the two groups. However, the insulin group exhibited significantly lower insulin dosages and hematocrit (HCT) levels compared to the glucose group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe patients\u0026rsquo; clinical characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003enum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003etotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIns\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF 3, M 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF 3, M 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.4 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.7 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.9 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.1 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.7 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.4 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170.3 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.3 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170.3 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFast BG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin dose(U)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLast BG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay time(min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.1 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.0 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.7 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *: P\u0026thinsp;\u0026le;\u0026thinsp;0.05, \u003csup\u003e#\u003c/sup\u003e: P\u0026thinsp;\u0026le;\u0026thinsp;0.01, HCT: hematocrit, DM: diabetic mellitus, MI: myocardial infarction\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTracer Distribution in Insulin-Sensitive Tissues\u003c/h2\u003e \u003cp\u003eHigh signal-to-noise ratio (SNR) PET images were achieved for all participants. All myocardial segments were confirmed as viable, with SUVmean values consistently exceeding those of the liver. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e details the \u003csup\u003e18\u003c/sup\u003eF-FDG distribution across insulin-sensitive tissues one hour post-injection. At the patient level, myocardial SUVmean was significantly higher in the insulin group compared to the glucose group (6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1 vs. 3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05). In contrast, no significant differences in SUVmean were observed between groups for other insulin-sensitive tissues (autochthonous muscle, liver, and fat) or non-insulin-sensitive tissues (aorta, spleen). Furthermore, at the segment level, myocardial SUVmean remained notably higher in the insulin group (6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 vs. 3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe different \u003csup\u003e18\u003c/sup\u003eF-FDG distribution in the insulin sensitive tissues between glucose loading and insulin loading groups after \u003csup\u003e18\u003c/sup\u003eF-FDG intravenous injection 1 hour later.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eaorta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIns\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (0.5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutochthon muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardium at patient level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.9 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.6 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardium at segment level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.8 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.6 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eliver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.6 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003espleen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon_Torso-fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTorso-fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: *: P\u0026thinsp;\u0026le;\u0026thinsp;0.05, \u003csup\u003e#\u003c/sup\u003e: P\u0026thinsp;\u0026le;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePharmacokinetic Analyses of the LV Myocardium\u003c/h2\u003e \u003cp\u003eTypical \u003csup\u003e18\u003c/sup\u003eF-FDG time-activity curves (TACs) for the LV myocardium following insulin (Patient 12) and glucose (Patient 3) loading are compared in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean SUV of the LV myocardium was markedly elevated in Patient 12 (5.2) relative to Patient 3 (3.8), highlighting the efficacy of insulin in augmenting net \u003csup\u003e18\u003c/sup\u003eF-FDG influx compared to oral glucose. The estimated kinetic parameters are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. At the patient level, the myocardial \u003cem\u003ek3\u003c/em\u003e rate constant was significantly lower in the insulin group (0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 vs. 0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08 min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). At the segment level, both \u003cem\u003eK1\u003c/em\u003e and \u003cem\u003eKi\u003c/em\u003e were significantly elevated in the insulin group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) compared to the glucose group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) (0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 and 0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 vs. 0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 and 0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 mL/cm\u003csup\u003e3\u003c/sup\u003e/min, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conversely, the rate constants \u003cem\u003ek2\u003c/em\u003e and \u003cem\u003ek3\u003c/em\u003e were significantly slower in the insulin group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u0026ndash;c) than in the glucose group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb\u0026ndash;c) (0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 and 0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 vs. 0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 and 0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, myocardial SUVs were higher across multiple segments in the insulin group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) compared to the glucose group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Specifically, \u003cem\u003ek3\u003c/em\u003e in the apical-inferior segment was remarkably reduced in the insulin group (0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 vs. 0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 min-1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKinetic parameters of \u003csup\u003e18\u003c/sup\u003eF-FDG during either glucose or insulin loading protocol for viable myocardium detection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eK1(mL/cm\u003csup\u003e3\u003c/sup\u003e/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMyo by patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMyo by segment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLiv\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50 (0.05)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72 (0.32)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.57 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60 (0.17\u003csup\u003e#\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60 (0.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ek2(min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52 (0.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15 (0.14\u003csup\u003e#\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34 (0.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ek3(min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42 (0.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28 (0.08*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23 (0.14\u003csup\u003e#\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44 (0.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi(mL/cm\u003csup\u003e3\u003c/sup\u003e/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27 (0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37 (0.11\u003csup\u003e#\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28 (0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *: P\u0026thinsp;\u0026le;\u0026thinsp;0.05, \u003csup\u003e#\u003c/sup\u003e: P\u0026thinsp;\u0026le;\u0026thinsp;0.01, Myo: myocardium, Liv: liver\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of Kinetic Parameters with SUV\u003c/h2\u003e \u003cp\u003eAt the patient level, correlation analysis revealed no significant associations between \u003cem\u003eK1\u003c/em\u003e, \u003cem\u003ek2\u003c/em\u003e, \u003cem\u003ek3\u003c/em\u003e, \u003cem\u003eKi\u003c/em\u003e, and SUVmean. However, at the segment level, significant Spearman rank correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;c) were observed between SUVmean and \u003cem\u003eK1\u003c/em\u003e (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), \u003cem\u003ek2\u003c/em\u003e (\u003cem\u003eρ\u003c/em\u003e = -0.26, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and \u003cem\u003ek3\u003c/em\u003e (\u003cem\u003eρ\u003c/em\u003e = -0.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, a significant Pearson correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed) was identified between \u003cem\u003eKi\u003c/em\u003e and segmental myocardial SUVmean (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrates that exogenous insulin administration significantly enhances \u003csup\u003e18\u003c/sup\u003eF-FDG uptake in the myocardium, at both the patient and segmental levels, compared to oral glucose loading. Crucially, our kinetic analysis revealed a marked augmentation of the \u003cem\u003eK1\u003c/em\u003e parameter in the insulin group. These findings support the hypothesis that insulin effectively promotes the translocation of GLUT4 to the cytomembrane [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], thereby accelerating the initial transport of \u003csup\u003e18\u003c/sup\u003eF-FDG from the blood to the myocyte.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMechanistic discussion\u003c/h2\u003e \u003cp\u003e\u003cem\u003eK1\u003c/em\u003e is primarily governed by two factors: the density of GLUT4 on the myocyte membrane and the transsarcolemmal glucose gradient (TSGG) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our observed increase in \u003cem\u003eK1\u003c/em\u003e aligns with previous reports by Zuo et al., underscoring the role of insulin in modulating early-stage glucose transport [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, our results indicate that insulin influences the subsequent phosphorylation and efflux kinetics of \u003csup\u003e18\u003c/sup\u003eF-FDG. Compared to historical data on oral glucose loading, both protocols in our study exhibited a reduced \u003cem\u003ek2\u003c/em\u003e and an elevated \u003cem\u003ek3\u003c/em\u003e, suggesting that insulin-stimulated phosphorylation traps the tracer more effectively within the tissue, thereby decreasing the tissue-to-blood reflux rate [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Interestingly, we observed a lower \u003cem\u003ek3\u003c/em\u003e in the insulin group compared to the glucose group. While the precise mechanism for this specific observation remains to be elucidated, it may reflect complex regulatory feedback in high-insulin states, necessitating further biochemical investigation.\u003c/p\u003e \u003cp\u003eOur findings confirm that insulin augments \u003cem\u003eKi\u003c/em\u003e, representing a significant improvement over the fasting state. This enhancement in \u003cem\u003eKi\u003c/em\u003e is largely driven by the insulin-mediated reduction in \u003cem\u003ek2\u003c/em\u003e. These data provide a robust metabolic foundation for the clinical superiority of insulin loading over glucose loading for myocardial viability detection, as previously suggested by several clinical trials [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. From a methodological standpoint, we utilized the DA rather than the LV cavity as the input function for several reasons. First, the TotalSegmentator AI model provides more reliable and reproducible DA masks compared to the LV cavity. Second, the proximity of the LV wall and cavity makes boundary delineation challenging on non-contrast CT and PET. Third, cardiac motion-induced blurring further complicates LV cavity segmentation. Finally, established literature supports the use of the aortic time-activity curve as a valid image-derived input function for calculating myocardial kinetic parameters [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations of this study warrant acknowledgment. First, the relatively small sample size (N\u0026thinsp;=\u0026thinsp;18) may have limited our statistical power to detect significant differences at the individual patient level. Second, due to the inherent fluctuations in blood glucose during PET acquisition, \u003cem\u003eK1\u003c/em\u003e values were not normalized, which might introduce variability. Lastly, the numerical imbalance between the insulin and glucose groups could potentially introduce bias, though the baseline characteristics remained comparable.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides clinical evidence that insulin loading significantly boosts the net \u003csup\u003e18\u003c/sup\u003eF-FDG influx rate by accelerating blood-to-tissue delivery while concurrently reducing tracer efflux. These kinetic shifts culminate in superior myocardial tracer uptake, reinforcing the insulin loading protocol as an optimal preparatory strategy for the assessment of myocardial viability in clinical practice.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was supported by Natural Science Foundation of Fujian Province (2015J01516, 2018J01202), Natural Science Foundation of Quanzhou (2019C023R).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure\u003c/strong\u003e: None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available from the corresponding author on justified request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYC conceptualized and designed the study, performed the formal analysis, and drafted the original manuscript. JJ and RD provided clinical oversight and technical support. JC was responsible for data acquisition, while PY performed data analysis. Funding acquisition was led by YC and RD. All authors participated in the critical revision of the manuscript and approved the final version for submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTsao C, Aday A, Almarzooq Z, Alonso A, Beaton A, Bittencourt M et al (2022) Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation 145:e153\u0026ndash;e639\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong L, Qiao S, Guan C, Bai Y, Zou T, Wu F et al (2021) Association of symptom status, myocardial viability, and clinical/anatomic risk on long-term outcomes after chronic total occlusion percutaneous coronary intervention. Catheter Cardiovasc Interv 97:996\u0026ndash;1008\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanza J, Ellis A, Al-Khalidi H, Holly T, Berman D, Oh J et al (2019) Myocardial Viability and Long-Term Outcomes in Ischemic Cardiomyopathy. N Engl J Med 381:739\u0026ndash;748\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDilsizian V, Bacharach S, Beanlands R, Bergmann S, Delbeke D, Dorbala S et al (2016) ASNC imaging guidelines/SNMMI procedure standard for positron emission tomography (PET) nuclear cardiology procedures. J Nucl Cardiol 23:1187\u0026ndash;1226\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVitale G, deKemp R, Ruddy T, Williams K, Beanlands R (2001) Myocardial glucose utilization and optimization of (18)F-FDG PET imaging in patients with non-insulin-dependent diabetes mellitus, coronary artery disease, and left ventricular dysfunction. 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J Thorac Imaging 38:247\u0026ndash;259\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuiken J, Nabben M, Neumann D, Glatz J (2020) Understanding the distinct subcellular trafficking of CD36 and GLUT4 during the development of myocardial insulin resistance. Biochim Biophys Acta Mol Basis Dis 1866:165775\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026lsquo;Doherty R, Halseth A, Granner D, Bracy D, Wasserman D (1998) Analysis of insulin-stimulated skeletal muscle glucose uptake in conscious rat using isotopic glucose analogs. Am J Physiol 274:E287\u0026ndash;E296\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo Y, L\u0026oacute;pez J, Smith T, Foster C, Carson R, Badawi R et al (2021) Multiparametric cardiac18F-FDG PET in humans: pilot comparison of FDG delivery rate with82Rb myocardial blood flow. Phys Med Biol 66:155014\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Nardo L, Spencer B, Abdelhafez Y, Li E, Omidvari N et al (2023) Total-Body Multiparametric PET Quantification of 18F-FDG Delivery and Metabolism in the Study of Coronavirus Disease 2019 Recovery. J Nucl Med 64:1821\u0026ndash;1830\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Pan M, Wang Q, Wang Y, Zhuo H, Dai R (2022) Intravenous insulin injection supplemented with subsequent milk consumption is a safer formulation for cardiac viability 18F-FDG imaging. J Nucl Cardiol 29:1985\u0026ndash;1991\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Xie Z, Berg E, Judenhofer M, Liu W, Xu T et al (2020) Total-Body Dynamic Reconstruction and Parametric Imaging on the uEXPLORER. J Nucl Med 61:285\u0026ndash;291\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"viable myocardium, insulin, glucose transporter 4, kinetic modeling, positron emission tomography","lastPublishedDoi":"10.21203/rs.3.rs-9249612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9249612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the mechanism by which insulin loading enhances myocardial \u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose (\u003csup\u003e18\u003c/sup\u003eF-FDG) uptake compared to glucose loading. Using dynamic positron emission tomography and two-compartment irreversible kinetic modeling, 18 patients with ischemic heart disease were analyzed. Results demonstrate that insulin significantly increases the net influx rate (\u003cem\u003eKi\u003c/em\u003e) by accelerating blood-to-tissue transport (\u003cem\u003eK1\u003c/em\u003e), while concurrently reducing tracer efflux (\u003cem\u003ek2\u003c/em\u003e) and phosphorylation (\u003cem\u003ek3\u003c/em\u003e). These findings provide a quantitative metabolic basis for the clinical superiority of insulin-enhanced imaging in detecting viable myocardium.\u003c/p\u003e","manuscriptTitle":"Kinetic Modeling of 18 F-FDG Myocardial Metabolism: Elucidating the Mechanistic Superiority of Insulin Loading in Viability Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 09:39:12","doi":"10.21203/rs.3.rs-9249612/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":"5d2ac0c2-b0e4-437a-9103-65dd2284f376","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T09:39:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 09:39:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9249612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9249612","identity":"rs-9249612","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}