Impact of Biguanide Therapy on Hepatic 18F-Fluorodeoxyglucose-Positron Emission Tomography Quantitative Parameters in Patients with Diabetes: A Dynamic Positron Emission Tomography Study

Impact of Biguanide Therapy on Hepatic 18F-Fluorodeoxyglucose-Positron Emission Tomography Quantitative Parameters in Patients with Diabetes: A Dynamic Positron Emission Tomography Study | 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 Impact of Biguanide Therapy on Hepatic 18F-Fluorodeoxyglucose-Positron Emission Tomography Quantitative Parameters in Patients with Diabetes: A Dynamic Positron Emission Tomography Study Akiko Tomiyama, Yu Iwabuchi, Kai Tonda, Yoshiki Owaki, Arashi Fujita, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8770927/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Objective Previous studies have shown that patient characteristics, such as fasting blood glucose, can modulate hepatic 18 F-fluorodeoxyglucose (FDG) uptake; however, the drug-specific effects of different antidiabetic agents on hepatic and whole-body FDG distribution remain poorly understood. Therefore, in this study, we aimed to evaluate the impact of biguanide therapy, as well as insulin and other antidiabetic medications, on hepatic FDG uptake and dynamic positron emission tomography (PET) kinetic parameters [the static mean standardized uptake value normalized by lean body mass (SULmean), dynamic metabolic rate of glucose (Ki), and the FDG distribution volume (DV)] in patients with diabetes, using a multiparametric dynamic FDG-PET/computed tomography (CT) protocol. Methods This retrospective study included 107 patients with diabetes who underwent dynamic whole-body FDG-PET/CT after a 4-h fast and drug withdrawal between January 2022 and May 2025. Patients with hepatic structural abnormalities or artifacts were excluded. PET kinetic parameters (SULmean, Ki, and DV) were measured in the liver and analyzed for associations with clinical variables, including biguanide and insulin therapy, via multivariate regression. Results Among the 107 patients included, 44 and 9 received biguanide and insulin, respectively. Multivariate regression showed that biguanide therapy, but not insulin use or other clinical variables, was significantly associated with increased hepatic SULmean (p = 0.015). No clinical variables were significantly associated with Ki or DV in the corresponding models. These findings suggest that biguanide therapy elevates static hepatic FDG uptake, whereas hepatic glucose metabolic dynamics, as assessed by Ki and DV, remain unaffected by antidiabetic medications under standard pre-scan preparation. Conclusions Our study demonstrates that biguanide therapy increases static hepatic FDG accumulation without interfering with the underlying glucose metabolic dynamics (Ki and DV). The observed increase in SULmean is numerically significant but visually minimal, suggesting its negligible impact on routine diagnostic interpretation. These results support the clinical validity of current European Association of Nuclear Medicine guidelines, confirming that a 4-h fast and drug withdrawal are adequate for accurate PET/CT imaging of patients treated with biguanide. biguanide dynamic positron emission tomography 18F-fluorodeoxyglucose liver diabetes Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Biguanide is the most widely prescribed drug for managing type 2 diabetes mellitus and is well established for its pleiotropic effects on glucose metabolism and safety profile [ 1 ]. However, its unique influence on 18 F-fluorodeoxyglucose (FDG) biodistribution, particularly the pronounced increase in intestinal FDG uptake, has raised critical considerations in the context of interpreting clinical positron emission tomography/computed tomography (PET/CT) imaging [ 2 – 5 ]. FDG-PET/CT enables sensitive detection, staging, and therapeutic monitoring of various malignancies. To ensure robust diagnostic accuracy and reproducibility, standardizing patient preparation and medication regimens is imperative. This is particularly difficult in the case of patients with diabetes due to the metabolic interactions between antidiabetic drugs and glucose metabolism. According to the European Association of Nuclear Medicine (EANM) procedural guidelines, insulin administered before FDG should be rapid-acting, reaching the bloodstream within 15 min post-injection, peaking at 60 min, and remaining effective for 2–4 h. Discontinuation of rapid-acting insulin is recommended at least 4 h prior to minimize hyperinsulinemia-related interference [ 2 , 6 – 11 ]. Oral antidiabetic medications, including metformin, are generally permitted to continue without interruption before PET/CT examination [ 6 ]. However, several randomized controlled trials have shown that discontinuing metformin at least 48 h before PET/CT examination significantly reduces intestinal FDG activity [ 7 ]. This persistence of altered biodistribution, even after short-term withdrawal, suggests that the pharmacological effects of metformin extend beyond its half-life in serum, potentially influencing tissue-level FDG kinetics for an appreciable period of time post-cessation [ 2 – 5 , 12 – 17 ].​ These findings underscore the need to determine the appropriate timing for discontinuing antidiabetic medications before testing. The persistence of metformin-related intestinal uptake may interfere with the detection of intestinal lesions during PET interpretation, making its impact particularly significant. Quantitatively, the liver serves as the standard reference organ in many PET/CT oncology protocols, including the Deauville 5-point scale and Lugano classification for lymphoma assessment. Consequently, drug-induced alterations in hepatic FDG uptake can significantly impact the interpretation of malignant tumors, including malignant lymphoma. Such variations in hepatic FDG uptake can distort lesion-to-liver contrast, potentially compromising the reliability of semi-quantitative assessment tools. Recent advances in PET methodology, particularly dynamic multiparametric FDG-PET, provide new insights into the interaction of clinical variables and hepatic glucose metabolism. Tonda et al. systematically explored patient-related factors affecting liver glucose dynamics and found that fasting blood glucose level and clinical status may modulate hepatic FDG uptake, as measured by the standardized uptake value normalized by lean body mass (SULmean) [ 18 ]. However, that study did not examine differential effects according to the type of antidiabetic medication, nor did it investigate drug-specific modulation of FDG distribution within the liver or throughout the body. Therefore, in the present study, we evaluated the influence of insulin, biguanides, and other antidiabetic agents, administered in accordance with the EANM recommendations, on hepatic FDG uptake and advanced dynamic PET parameters (SULmean, dynamic metabolic rate of glucose [Ki], and the FDG distribution volume [DV]) in patients with diabetes undergoing oncological PET/CT. By leveraging a multiparametric PET imaging protocol, we aimed to clarify whether current guideline-endorsed medication regimens adequately neutralize pharmacological confounding of hepatic metabolic indices. We anticipate our findings to inform the refinement of patient preparation recommendations, balancing the imperative for imaging accuracy with the practicalities of diabetes management and patient safety. Elucidating the impact of different classes of antidiabetic medication (metformin in particular) on hepatic glucose metabolism, as reflected by both static and dynamic PET indices, is critical for interpreting oncologic scans and the broader clinical management of patients with diabetes undergoing molecular imaging. Previous reports have indicated that biguanide use can alter FDG uptake in the intestine and liver, with some studies specifically noting an increase in hepatic FDG uptake. Therefore, we hypothesized that both hepatic SULmean and Ki would be elevated in biguanide users compared with non-users[ 17 , 18 ]. Materials and Methods Ethics Statement All procedures involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. This retrospective study was approved by the Institutional Review Board of Keio University School of Medicine (Approval No. 20211067). The requirement for written informed consent was waived by the board owing to the retrospective nature of the study. Study Design and Patients In total, 671 consecutive patients who underwent dynamic FDG-PET/CT imaging between January 2022 and May 2025 were initially included in this retrospective study. Among these patients, 122 with diabetes mellitus were identified. Ultimately, 107 patients with diabetes were included in the assessment of hepatic FDG tracer accumulation using whole-body dynamic FDG-PET/CT after excluding those with hepatic lesions, structural abnormalities, or imaging artifacts compromising measurement reliability. Comprehensive data on patient demographics and clinical characteristics, including age, sex, body mass index (BMI), fasting blood glucose level, and use of antidiabetic medications (biguanides and insulin) were retrospectively obtained. Protocols All patients underwent dynamic whole-body FDG-PET/CT scans after a 4-h fast and discontinuation of medications, with the blood glucose level measured immediately prior to FDG administration. PET acquisition was performed 60 min after intravenous injection of 4.0 MBq/kg FDG using a fully automated multiparametric PET protocol (FlowMotion Multiparametric PET; Siemens Healthineers) on a Siemens Biograph Vision 600 PET/CT system (Siemens Medical Solutions, USA). A patient-specific population-based input function (PBIF), which eliminates the need for arterial blood sampling, was employed, thereby omitting the typical initial dynamic arterial input function imaging immediately after FDG administration [ 19 ]. Whole-body dynamic PET imaging was performed using three consecutive bed motion passes moving at 5–7 mm/s, covering the region from head to knee[ 12 , 20 , 21 ]. Dynamic datasets were reconstructed using ordered subset expectation maximization (2 iterations, 5 subsets) with point spread function recovery and time-of-flight information at a 440 × 440-matrix size, followed by Gaussian post-reconstruction filtering with a 5-mm full width at half maximum. Low-dose CT (tube voltage: 100 kVp; tube current: 50 mA; rotation time: 0.5 s; slice thickness: 2 mm) was performed for attenuation correction and anatomical co-registration. Image Analysis After PET data acquisition, the multiparametric scanning protocol generated parametric images based on the Patlak graphical model [ 22 ], enabling the quantification of Ki, DV, and the conventional SULmean. The Ki and DV parameters correspond to the Patlak slope, reflecting the rate of irreversible accumulation of FDG-6-phosphate, and intercept, representing the apparent distribution volume of reversibly bound, unmetabolized FDG, respectively [ 23 ]. Multiparametric images, including SULmean, Ki, and DV maps, were reviewed using Syngo.via software (Siemens Medical Solutions, USA). Hepatic quantification was performed by placing a 50-mm spherical volume of interest within the right hepatic lobe, from which the SULmean, Ki, and DV values were extracted. Subsequently, the Ki values were normalized to the circulating blood glucose concentration by dividing the hepatic metabolic rate by the corresponding glucose level. Statistical Analysis Multivariate regression analyses were conducted to assess correlations between the three quantitative PET indices and patient-related factors that may affect hepatic glucose metabolism. The Mann–Whitney U test was used for group comparisons stratified by the following clinical parameters: blood glucose level (< 130 vs. ≥130 mg/dL), use versus non-use of antidiabetic medications, biguanide treatment status, insulin therapy status, and BMI (< 25 vs. ≥25 kg/m²). Statistical significance was set at p < 0.05, and all statistical analyses were conducted using IBM SPSS Statistics for Windows, version 27 (IBM Corp., Armonk, N.Y., USA). Results A total of 671 consecutive patients who underwent dynamic FDG-PET/CT from January 2022 to May 12, 2025 were initially screened in this retrospective study. Among those patients, 122 with diabetes were identified, with 15 excluded owing to the following reasons: rectal cancer with history of percutaneous transhepatic gallbladder drainage for cholecystitis (n = 1), hilar cholangiocarcinoma (n = 1), gallbladder cancer (n = 1), hepatocellular carcinoma (n = 2), unknown medical history (n = 1), hepatic metastases (n = 2), fatty liver disease (n = 2), history of pancreatic surgery (n = 1), and incomplete data (n = 2). Consequently, 107 patients with diabetes were included in the final analysis (Fig. 1 ). The underlying diseases of the patients included in the analysis were as follows: malignant lymphoma (n = 33), lung cancer (n = 27), breast cancer (n = 10), gastrointestinal cancer (n = 8), bone and soft tissue tumors (n = 3), gynecological tumors (n = 4), head and neck tumors (n = 9), sarcoidosis (n = 2), urological tumors (n = 2), and others (n = 9). Table 1 summarizes the patient characteristics, including age, sex, BMI, fasting blood glucose level, and use of antidiabetic medications, such as biguanides and insulin. Table 1 Patient characteristics Characteristic n 107 Age (y, mean±standard deviation) 72.1 ± 9.4 Sex (male/female) 72/35 BMI (kg/m 2 ) 23.8 ± 3.8 Blood glucose level (mg/dL) 136.8 ± 38.7 Patients on biguanides 44 Patients on insulin 9 BMI, body mass index Figure 2 shows a box plot comparing the hepatic PET indices (SULmean, mean Ki, and mean DV) between the biguanide-treated group and the group not treated with biguanide (non-biguanide-treated group). The SULmean values in the biguanide-treated and non-biguanide-treated groups were 1.88 ± 0.28 and 1.76 ± 0.22, respectively (p = 0.015); the corresponding mean Ki values were 9.32 ± 0.82 and 9.25 ± 1.26 (p = 0.740) and the corresponding mean DV values were 14.2 ± 1.51 and 14.4 ± 1.87 (p = 0.660). No significant differences in the Ki or DV values were observed between the groups. Figure 3 shows box-and-whisker plots comparing hepatic PET indices (SULmean, mean Ki, and mean DV) between the insulin users and non-users. The SULmean values in the insulin users and non-users were 1.80 ± 0.256 and 1.82 ± 0.265, respectively (p = 0.876); the respective mean Ki values were 9.33 ± 1.03 and 8.86 ± 8.67 (p = 0.193) and the respective mean DV values were 14.3 ± 1.69 and 13.9 ± 1.29 (p = 0.472). Insulin therapy had no significant effect on any of the hepatic PET parameters assessed (Fig. 3 ). Table 2 shows the multiple regression analysis results for the SULmean values. Biguanide therapy was significantly correlated with the SULmean value (p = 0.037). None of the clinical variables, including sex, age, blood glucose level, insulin use, or BMI, were significantly associated with the SULmean values. Table 2 Multivariate regression analysis for identifying the predictors of the SULmean in patients with diabetes B (unstandardized) SE B Β (standardized) པ p-value Constant 1.709 0.269 - 6.357 < 0.001 Sex -0.064 0.052 -0.118 -1.218 0.226 Age -0.001 0.003 -0.036 -0.360 0.719 Blood glucose level 0.001 0.001 0.168 1.706 0.091 Biguanides 0.109 0.052 0.212 2.119 0.037 Insulin 0.035 0.035 0.075 0.356 0.723 BMI -0.007 0.006 -0.075 -0.940 0.350 BMI, body mass index; SULmean, mean standardized uptake value normalized by lean body mass; SE B, standard error for the unstandardized beta Tables 3 and 4 show the multiple regression analysis results for the Ki and DV values, respectively. None of the clinical variables were significantly associated with the Ki or DV values in the respective models. Table 3 Multivariate regression analysis for identifying the predictors of Ki in patients with diabetes B (unstandardized) SE B Β (standardized) པ p-value Constant 0.009 0.001 - 8.351 < 0.001 Sex -0.000178 0.000099 0.178 1.805 0.074 Age -8.522×10 − 7 0.000 -0.008 -0.078 0.938 Blood glucose level -1.555×10 − 6 0.000 -0.059 -0.590 0.557 Biguanides -5.44×10 − 6 0.000 -0.055 -0.536 0.593 Insulin -1.09×10 − 4 0.0001 -0.109 -1.097 0.275 BMI 1.528×10 − 6 0.000 0.061 0.613 0.541 BMI, body mass index; Ki, dynamic metabolic rate of glucose; SE B, standard error for the unstandardized beta Table 4 Multivariate regression analysis for the predictors of mean DV in patients with diabetes B (unstandardized) SE B Β (standardized) པ p-value Constant 14.582 1.797 - 8.115 < 0.001 Sex -0.211 0.350 -0.60 -0.602 0.548 Age 0.012 0.018 0.071 0.690 0.492 Blood glucose level 0.002 0.004 0.045 0.442 0.660 Biguanides 0.199 0.345 0.059 0.576 0.566 Insulin -0.482 0.597 -0.081 -0.807 0.422 BMI -0.060 0.041 -0.148 -1.479 0.142 BMI, body mass index; DV, distribution volume; SE B, standard error for the unstandardized beta Figure 4 shows representative multiparametric images of two patients (an 84-year-old woman treated with biguanide and an 87-year-old man not treated with biguanide), illustrating differences in the SULmean with minimal variation in the Ki and DV values. Discussion In this retrospective study, we comprehensively evaluated the effects of biguanides (primarily metformin) alongside insulin and other antidiabetic clinical factors on hepatic FDG-PET/CT parameters in patients with diabetes, using a dynamic multiparametric imaging approach. Three quantitative indices (SULmean, Ki, and DV) were specifically assessed. Biguanide treatment independently and significantly increased the hepatic SULmean, whereas insulin and other examined clinical variables had no significant effects on the SULmean, Ki, or DV. This pattern suggests an important divergence in the sensitivity of static versus dynamic PET indices to antidiabetic medication effects, with significant implications for clinical PET imaging and hepatic FDG uptake interpretation in populations with diabetes. The significant increase in the SULmean among biguanide users aligns with our initial hypothesis and previous physiological reports. Metformin is known to accumulate in the liver via organic cation transporters, where it exerts its primary glucose-lowering effect by inhibiting the mitochondrial respiratory chain complex I and activating AMP-activated protein kinase [ 24 , 25 ]. This activation suppresses hepatic gluconeogenesis. Even after the standard 4-h pre-scan drug cessation period, the elevated SULmean in the livers of biguanide users highlights the persistent effect of biguanides (primarily metformin) on the liver background FDG accumulation. This finding is clinically significant, as the liver is routinely used as a reference organ in visual scoring methods, such as the Deauville 5-point scale and Lugano classification in oncological PET/CT, particularly for lymphoma imaging. Metformin-induced elevation of hepatic FDG uptake may compromise lesion-to-background contrast, potentially leading to underestimation of the lesion metabolic activity. However, it is noteworthy that the absolute difference in the SULmean (1.88 vs. 1.76) was small. From a practical radiological perspective, such a subtle difference is unlikely to be discernible during routine visual interpretation, preserving the diagnostic utility of the scans. The most noteworthy finding in our results is that no correlation was observed between biguanide use and the liver Ki. SULmean represents FDG accumulation across all stages of transport and phosphorylation, whereas Ki specifically reflects the rate of irreversible FDG phosphorylation and intracellular uptake; this is contrary to what was initially hypothesized. The pharmacokinetic and pharmacodynamic properties of metformin may explain this difference. The plasma half-life of metformin is approximately 4–6 h. The 4-h washout period for biguanide recommended by current guidelines is likely to prevent any increase in Ki, resulting in a non-significant correlation. Additional key findings indicate that rapid-acting insulin administration, even after the recommended 4-h discontinuation period, did not significantly affect any of the hepatic metabolic markers examined. These results support the current EANM recommendation to discontinue insulin for 4 h before PET/CT examination. Prolonged discontinuation of antidiabetic drugs may be unnecessary, and lead to destabilization of glycemic control and increased risk of hyperglycemia or other adverse events in patients with diabetes. Our findings suggest that, under these guidelines, rapid-acting insulin does not substantially influence dynamic hepatic FDG metabolism in a standardized preparation protocol, supporting the validity of current clinical routines for quantitative PET assessment in populations with diabetes. Our findings also warrant comparison to prior literature. Several previous reports have documented marked metformin-related increases in intestinal FDG uptake, with evidence that a 48-h discontinuation most effectively attenuates bowel accumulation and improves image quality in abdominal and pelvic PET [ 3 – 5 , 26 ]. These findings have prompted some clinical centers to recommend extended biguanide withdrawal, despite the absence of such a requirement in the current EANM guidelines. However, studies specifically focused on the liver are relatively scarce. In a core study, Iozzo et al. reported a significant increase in the hepatic Ki following 26 weeks of metformin monotherapy under strict insulin clamping after prolonged fasting[ 27 ]. The discrepancy between the marked effect of metformin on the hepatic Ki observed in the randomized intervention trial and the absence of such an effect in our observational study likely reflects differences in metformin administration protocols. Furthermore, unlike the study by Iozzo et al. that reported significant increases in the hepatic Ki under rigorous insulin clamp conditions, our study reflected routine clinical practices. The use of the glucose clamp technique, a gold standard for quantifying insulin resistance, often unmasks metabolic changes that are not evident under standard fasting. Our findings indicate that, under the standard preparation protocols recommended by the EANM, acute metabolic dynamics in the liver are not affected by common antidiabetic medications. Another important consideration is the practical and safety dimension of PET/CT preparation in patients with diabetes. Prolonged or unnecessary withdrawal of antidiabetic medications, particularly in patients with labile glycemic control, can cause significant treatment disruptions and increase the risk of adverse events as well as the burden for patients and clinical staff [ 28 ]. The present study demonstrates that a 4-h biguanide withdrawal generally suffices to neutralize dynamic metabolic effects, while a subtle static residual on the SULmean persists. Avoiding unnecessarily prolonged drug discontinuation when not clinically required can reduce the risk of hyperglycemia and help maintain the consistency and safety of diabetes management during the peri-imaging window. From a radiological perspective, this persistent increase in the SULmean, despite being numerically significant, has little effect on the visual assessment and minimal impact on diagnostic imaging. However, this study has certain limitations that should be acknowledged. As a single-center retrospective analysis, the cohort size (n = 107) was sufficient for primary statistical analyses but may lack statistical power for detailed subgroup analyses or dose/duration evaluations. In particular, the small number of insulin-treated patients constrained the ability to verify subtle differences among antidiabetic therapy regimens. Although the use of a PBIF eliminated the need for arterial blood sampling (the gold standard for PET input function derivation), it introduced a minor approximation error. In addition, confounding effects from additional uncontrolled variables such as exercise artifacts or unmeasured drug combinations cannot be completely excluded. Therefore, although the overall results appear robust, large-scale, multicenter prospective studies with detailed pharmacological follow-up are necessary to further clarify the subtle effects of individual drug classes, specific dosages, and discontinuation protocols across diverse populations with diabetes. Looking forward, several future research avenues are suggested. Large-scale, prospective comparative trials should directly assess the effects of differing biguanide withdrawal durations (e.g., 24 vs. 48 h) on static and dynamic hepatic PET indices. Time-course dynamic imaging post-medication cessation could clarify the pharmacodynamic “washout” of the effect of metformin on both FDG uptake and glucose metabolism. The impact of other oral hypoglycemic agents, such as sodium-glucose cotransporter 2 and dipeptidyl peptidase-4 inhibitors, on hepatic and extrahepatic FDG distribution also warrants systematic evaluation [ 29 – 31 ]. Such investigations will ultimately strengthen the evidence base for optimal antidiabetic therapy management in the PET/CT setting and improve the diagnostic reliability and reproducibility of PET indices in oncology and metabolic imaging. In conclusion, this study demonstrated that biguanide therapy remains an independent and significant factor increasing the hepatic SULmean value on static FDG-PET/CT in patients with diabetes, even under standard imaging preparation conditions. Dynamic indicators of the hepatic Ki and DV were minimally affected under standard discontinuation protocols, supporting the practical validity of the current EANM guidelines that allow for continued biguanide use. Although residual static effects should be recognized during quantitative liver-based PET interpretation, their impact on visual analysis is minimal. These findings emphasize the need for mechanism-aware, nuanced image interpretation and patient-centered, individualized PET/CT protocols in populations with diabetes, balancing image integrity with patient safety and metabolic stability. Declarations Sources of funding This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP24K18840. The authors declare that no other funds, grants, or other support were received during the preparation of this manuscript. Conflict -of-interest statement : No potential conflicts of interest were disclosed. Acknowledgments: This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (grant number JP24K18840). The authors thank the staff members of the Division of Nuclear Medicine at the Department of Radiology for their valuable support. References Froldi G. View on Metformin: Antidiabetic and Pleiotropic Effects, Pharmacokinetics, Side Effects, and Sex-Related Differences. Pharmaceuticals (Basel). 2024;17:478. de Groot M, Meeuwis AP, Kok PJ, Corstens FH, Oyen WJ. Influence of blood glucose level, age and fasting period on non-pathological FDG uptake in heart and gut. Eur J Nucl Med Mol Imaging. 2005;32:98–101. Sakaguchi K, Sugawara K, Hosokawa Y, Ito J, Morita Y, Mizuma H, et al. 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Tomiyama","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0008-0572-408X","institution":"Department of Radiology, Keio University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Akiko","middleName":"","lastName":"Tomiyama","suffix":""},{"id":586102289,"identity":"25e583eb-ac4c-4543-96d0-dcbe3c45b1b5","order_by":1,"name":"Yu Iwabuchi","email":"","orcid":"https://orcid.org/0000-0002-9259-6651","institution":"Department of Radiology, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Iwabuchi","suffix":""},{"id":586102290,"identity":"6380d9d4-fd24-49c1-ac09-b7ee8addf87c","order_by":2,"name":"Kai Tonda","email":"","orcid":"","institution":"Department of Radiology, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Tonda","suffix":""},{"id":586102291,"identity":"08b241fe-1b92-4dbb-a5bc-21b69ad1efc5","order_by":3,"name":"Yoshiki Owaki","email":"","orcid":"","institution":"Office of Radiation Technology, Keio University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yoshiki","middleName":"","lastName":"Owaki","suffix":""},{"id":586102292,"identity":"191a3708-ca57-40f1-9e1d-54ecf73df718","order_by":4,"name":"Arashi Fujita","email":"","orcid":"","institution":"Office of Radiation Technology, Keio University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Arashi","middleName":"","lastName":"Fujita","suffix":""},{"id":586102293,"identity":"b3e58d1d-1ce0-41f9-bff3-a78cce6017a5","order_by":5,"name":"Ryosuke Sakurai","email":"","orcid":"","institution":"Department of Radiology, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ryosuke","middleName":"","lastName":"Sakurai","suffix":""},{"id":586102294,"identity":"2187e55b-0e90-46e2-bde2-53677180768f","order_by":6,"name":"Atsushi Shimizu","email":"","orcid":"","institution":"Department of Radiology, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Atsushi","middleName":"","lastName":"Shimizu","suffix":""},{"id":586102295,"identity":"fecc1580-69cd-496b-ac02-8f0caec7bd5b","order_by":7,"name":"Maho Kurihara","email":"","orcid":"","institution":"Department of Radiology, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Maho","middleName":"","lastName":"Kurihara","suffix":""},{"id":586102296,"identity":"bf02f96e-f311-4ec6-8579-c7eca23d4b4b","order_by":8,"name":"Kogo Togo","email":"","orcid":"","institution":"Department of Radiology, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kogo","middleName":"","lastName":"Togo","suffix":""},{"id":586102297,"identity":"a5d2f079-f931-48df-949d-5fc614c13698","order_by":9,"name":"Yuki Iwaita","email":"","orcid":"","institution":"Department of Radiology, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuki","middleName":"","lastName":"Iwaita","suffix":""},{"id":586102298,"identity":"33f674f5-5f7b-42e0-bd46-779e31c92567","order_by":10,"name":"Takehiro Nakahara","email":"","orcid":"","institution":"Department of Radiology, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Takehiro","middleName":"","lastName":"Nakahara","suffix":""},{"id":586102299,"identity":"f7ad4f43-1f55-402f-9f3a-6b3b7711f98a","order_by":11,"name":"Tohru Shiga","email":"","orcid":"","institution":"Advanced Clinical Research Center, Fukushima Global Medical Science Center, Fukushima Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tohru","middleName":"","lastName":"Shiga","suffix":""},{"id":586102300,"identity":"9918c1a6-408d-489a-ac36-726ef7f380cd","order_by":12,"name":"Yoshitake Yamada","email":"","orcid":"","institution":"Department of Radiology, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yoshitake","middleName":"","lastName":"Yamada","suffix":""},{"id":586102301,"identity":"eaf9138c-7351-45bf-8049-83ce6927b0f9","order_by":13,"name":"Masahiro Jinzaki","email":"","orcid":"","institution":"Department of Radiology, Keio University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Masahiro","middleName":"","lastName":"Jinzaki","suffix":""}],"badges":[],"createdAt":"2026-02-03 04:45:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8770927/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8770927/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102239404,"identity":"3d05a27f-86e8-4c61-8526-77822965746a","added_by":"auto","created_at":"2026-02-09 16:45:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8277,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of patient recruitment\u003c/p\u003e\n\u003cp\u003eDM, diabetes mellitus\u003c/p\u003e","description":"","filename":"Fig1Tomiyama.png","url":"https://assets-eu.researchsquare.com/files/rs-8770927/v1/a2da3625850d70f13a035239.png"},{"id":102297439,"identity":"995e9c6b-200e-451d-bbeb-c6a324113131","added_by":"auto","created_at":"2026-02-10 10:27:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12918,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whisker plots comparing the hepatic positron emission tomography indices (SULmean, mean Ki, and mean DV) of biguanide users and non-users\u003c/p\u003e\n\u003cp\u003eThe median, interquartile range, and outliers are depicted. The SULmean is significantly higher in the biguanide-treated group than in the non-biguanide-treatment group (p=0.015), whereas the mean Ki and DV shows no significant group differences.\u003c/p\u003e\n\u003cp\u003eSULmean, standardized uptake value normalized to lean body mass; Ki, glucose metabolic rate; DV, distribution volume\u003c/p\u003e","description":"","filename":"Fig2Tomiyama.png","url":"https://assets-eu.researchsquare.com/files/rs-8770927/v1/3f85178e7d818c878940a0b1.png"},{"id":102239405,"identity":"ad99cf56-1d64-4e50-8d38-d1e932263650","added_by":"auto","created_at":"2026-02-09 16:45:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11406,"visible":true,"origin":"","legend":"\u003cp\u003e-and-whisker plots comparing the hepatic positron emission tomography indices (SULmean, mean Ki, and mean DV) of insulin users and non-users\u003c/p\u003e\n\u003cp\u003eThe median, interquartile range, and outliers are depicted. The SULmean, mean Ki, and mean DV show no significant group differences.\u003c/p\u003e\n\u003cp\u003eSULmean, standardized uptake value normalized to lean body mass; Ki, glucose metabolic rate; DV, distribution volume\u003c/p\u003e","description":"","filename":"Fig3Tomiyama.png","url":"https://assets-eu.researchsquare.com/files/rs-8770927/v1/662a704adb1a2bf7294e44dd.png"},{"id":102239406,"identity":"513e10c0-b50f-4410-9b85-c431c3f866ec","added_by":"auto","created_at":"2026-02-09 16:45:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2424000,"visible":true,"origin":"","legend":"\u003cp\u003eMultiparametric images of representative patients from the two groups\u003c/p\u003e\n\u003cp\u003eFrom left to right, the SULmean, Ki, and DV images are shown. The SUL image is displayed with lower and upper thresholds of 0 and 6, respectively; the Ki image, with lower and upper thresholds of 0 and 4 (mg/min/100 mL), respectively; and the DV image, with lower and upper thresholds of 0 and 50 (%), respectively.\u003c/p\u003e\n\u003cp\u003e(a) Non-biguanide group: a 75-year-old woman\u003c/p\u003e\n\u003cp\u003eThe SULmean, Ki, and DV values are 1.76, 9.53×10\u003csup\u003e-\u003c/sup\u003e³, and 14.53, respectively.\u003c/p\u003e\n\u003cp\u003e(b) Biguanide group: an 80-year-old woman\u003c/p\u003e\n\u003cp\u003eThe SULmean, Ki, and DV values are 1.91, 9.593×10\u003csup\u003e-\u003c/sup\u003e³, and 14.32, respectively.\u003c/p\u003e\n\u003cp\u003eDV, distribution volume; Ki, metabolic glucose rate; SULmean, standardized uptake value normalized by lean body mass\u003c/p\u003e","description":"","filename":"Fig4Tomiyama.png","url":"https://assets-eu.researchsquare.com/files/rs-8770927/v1/7e216577d9547ce1335c9000.png"},{"id":102299508,"identity":"9006c982-7859-4047-9b8d-8d154d6cdcd2","added_by":"auto","created_at":"2026-02-10 11:06:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3251123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8770927/v1/af9e6fce-d841-4bc2-9889-c78f5a9c8e23.pdf"}],"financialInterests":"","formattedTitle":"Impact of Biguanide Therapy on Hepatic 18F-Fluorodeoxyglucose-Positron Emission Tomography Quantitative Parameters in Patients with Diabetes: A Dynamic Positron Emission Tomography Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBiguanide is the most widely prescribed drug for managing type 2 diabetes mellitus and is well established for its pleiotropic effects on glucose metabolism and safety profile [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, its unique influence on \u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose (FDG) biodistribution, particularly the pronounced increase in intestinal FDG uptake, has raised critical considerations in the context of interpreting clinical positron emission tomography/computed tomography (PET/CT) imaging [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. FDG-PET/CT enables sensitive detection, staging, and therapeutic monitoring of various malignancies. To ensure robust diagnostic accuracy and reproducibility, standardizing patient preparation and medication regimens is imperative. This is particularly difficult in the case of patients with diabetes due to the metabolic interactions between antidiabetic drugs and glucose metabolism.\u003c/p\u003e\u003cp\u003eAccording to the European Association of Nuclear Medicine (EANM) procedural guidelines, insulin administered before FDG should be rapid-acting, reaching the bloodstream within 15 min post-injection, peaking at 60 min, and remaining effective for 2\u0026ndash;4 h. Discontinuation of rapid-acting insulin is recommended at least 4 h prior to minimize hyperinsulinemia-related interference [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Oral antidiabetic medications, including metformin, are generally permitted to continue without interruption before PET/CT examination [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, several randomized controlled trials have shown that discontinuing metformin at least 48 h before PET/CT examination significantly reduces intestinal FDG activity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This persistence of altered biodistribution, even after short-term withdrawal, suggests that the pharmacological effects of metformin extend beyond its half-life in serum, potentially influencing tissue-level FDG kinetics for an appreciable period of time post-cessation [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].​\u003c/p\u003e\u003cp\u003eThese findings underscore the need to determine the appropriate timing for discontinuing antidiabetic medications before testing. The persistence of metformin-related intestinal uptake may interfere with the detection of intestinal lesions during PET interpretation, making its impact particularly significant. Quantitatively, the liver serves as the standard reference organ in many PET/CT oncology protocols, including the Deauville 5-point scale and Lugano classification for lymphoma assessment. Consequently, drug-induced alterations in hepatic FDG uptake can significantly impact the interpretation of malignant tumors, including malignant lymphoma. Such variations in hepatic FDG uptake can distort lesion-to-liver contrast, potentially compromising the reliability of semi-quantitative assessment tools.\u003c/p\u003e\u003cp\u003eRecent advances in PET methodology, particularly dynamic multiparametric FDG-PET, provide new insights into the interaction of clinical variables and hepatic glucose metabolism. Tonda et al. systematically explored patient-related factors affecting liver glucose dynamics and found that fasting blood glucose level and clinical status may modulate hepatic FDG uptake, as measured by the standardized uptake value normalized by lean body mass (SULmean) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, that study did not examine differential effects according to the type of antidiabetic medication, nor did it investigate drug-specific modulation of FDG distribution within the liver or throughout the body.\u003c/p\u003e\u003cp\u003e Therefore, in the present study, we evaluated the influence of insulin, biguanides, and other antidiabetic agents, administered in accordance with the EANM recommendations, on hepatic FDG uptake and advanced dynamic PET parameters (SULmean, dynamic metabolic rate of glucose [Ki], and the FDG distribution volume [DV]) in patients with diabetes undergoing oncological PET/CT. By leveraging a multiparametric PET imaging protocol, we aimed to clarify whether current guideline-endorsed medication regimens adequately neutralize pharmacological confounding of hepatic metabolic indices. We anticipate our findings to inform the refinement of patient preparation recommendations, balancing the imperative for imaging accuracy with the practicalities of diabetes management and patient safety. Elucidating the impact of different classes of antidiabetic medication (metformin in particular) on hepatic glucose metabolism, as reflected by both static and dynamic PET indices, is critical for interpreting oncologic scans and the broader clinical management of patients with diabetes undergoing molecular imaging. Previous reports have indicated that biguanide use can alter FDG uptake in the intestine and liver, with some studies specifically noting an increase in hepatic FDG uptake. Therefore, we hypothesized that both hepatic SULmean and Ki would be elevated in biguanide users compared with non-users[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthics Statement\u003c/h2\u003e \u003cp\u003e All procedures involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. This retrospective study was approved by the Institutional Review Board of Keio University School of Medicine (Approval No. 20211067). The requirement for written informed consent was waived by the board owing to the retrospective nature of the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design and Patients\u003c/h3\u003e\n\u003cp\u003eIn total, 671 consecutive patients who underwent dynamic FDG-PET/CT imaging between January 2022 and May 2025 were initially included in this retrospective study. Among these patients, 122 with diabetes mellitus were identified. Ultimately, 107 patients with diabetes were included in the assessment of hepatic FDG tracer accumulation using whole-body dynamic FDG-PET/CT after excluding those with hepatic lesions, structural abnormalities, or imaging artifacts compromising measurement reliability. Comprehensive data on patient demographics and clinical characteristics, including age, sex, body mass index (BMI), fasting blood glucose level, and use of antidiabetic medications (biguanides and insulin) were retrospectively obtained.\u003c/p\u003e\n\u003ch3\u003eProtocols\u003c/h3\u003e\n\u003cp\u003eAll patients underwent dynamic whole-body FDG-PET/CT scans after a 4-h fast and discontinuation of medications, with the blood glucose level measured immediately prior to FDG administration. PET acquisition was performed 60 min after intravenous injection of 4.0 MBq/kg FDG using a fully automated multiparametric PET protocol (FlowMotion Multiparametric PET; Siemens Healthineers) on a Siemens Biograph Vision 600 PET/CT system (Siemens Medical Solutions, USA). A patient-specific population-based input function (PBIF), which eliminates the need for arterial blood sampling, was employed, thereby omitting the typical initial dynamic arterial input function imaging immediately after FDG administration [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Whole-body dynamic PET imaging was performed using three consecutive bed motion passes moving at 5\u0026ndash;7 mm/s, covering the region from head to knee[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Dynamic datasets were reconstructed using ordered subset expectation maximization (2 iterations, 5 subsets) with point spread function recovery and time-of-flight information at a 440 \u0026times; 440-matrix size, followed by Gaussian post-reconstruction filtering with a 5-mm full width at half maximum. Low-dose CT (tube voltage: 100 kVp; tube current: 50 mA; rotation time: 0.5 s; slice thickness: 2 mm) was performed for attenuation correction and anatomical co-registration.\u003c/p\u003e\n\u003ch3\u003eImage Analysis\u003c/h3\u003e\n\u003cp\u003eAfter PET data acquisition, the multiparametric scanning protocol generated parametric images based on the Patlak graphical model [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], enabling the quantification of Ki, DV, and the conventional SULmean. The Ki and DV parameters correspond to the Patlak slope, reflecting the rate of irreversible accumulation of FDG-6-phosphate, and intercept, representing the apparent distribution volume of reversibly bound, unmetabolized FDG, respectively [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Multiparametric images, including SULmean, Ki, and DV maps, were reviewed using Syngo.via software (Siemens Medical Solutions, USA). Hepatic quantification was performed by placing a 50-mm spherical volume of interest within the right hepatic lobe, from which the SULmean, Ki, and DV values were extracted. Subsequently, the Ki values were normalized to the circulating blood glucose concentration by dividing the hepatic metabolic rate by the corresponding glucose level.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eMultivariate regression analyses were conducted to assess correlations between the three quantitative PET indices and patient-related factors that may affect hepatic glucose metabolism. The Mann\u0026ndash;Whitney U test was used for group comparisons stratified by the following clinical parameters: blood glucose level (\u0026lt;\u0026thinsp;130 vs. \u0026ge;130 mg/dL), use versus non-use of antidiabetic medications, biguanide treatment status, insulin therapy status, and BMI (\u0026lt;\u0026thinsp;25 vs. \u0026ge;25 kg/m\u0026sup2;). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and all statistical analyses were conducted using IBM SPSS Statistics for Windows, version 27 (IBM Corp., Armonk, N.Y., USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 671 consecutive patients who underwent dynamic FDG-PET/CT from January 2022 to May 12, 2025 were initially screened in this retrospective study. Among those patients, 122 with diabetes were identified, with 15 excluded owing to the following reasons: rectal cancer with history of percutaneous transhepatic gallbladder drainage for cholecystitis (n\u0026thinsp;=\u0026thinsp;1), hilar cholangiocarcinoma (n\u0026thinsp;=\u0026thinsp;1), gallbladder cancer (n\u0026thinsp;=\u0026thinsp;1), hepatocellular carcinoma (n\u0026thinsp;=\u0026thinsp;2), unknown medical history (n\u0026thinsp;=\u0026thinsp;1), hepatic metastases (n\u0026thinsp;=\u0026thinsp;2), fatty liver disease (n\u0026thinsp;=\u0026thinsp;2), history of pancreatic surgery (n\u0026thinsp;=\u0026thinsp;1), and incomplete data (n\u0026thinsp;=\u0026thinsp;2). Consequently, 107 patients with diabetes were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe underlying diseases of the patients included in the analysis were as follows: malignant lymphoma (n\u0026thinsp;=\u0026thinsp;33), lung cancer (n\u0026thinsp;=\u0026thinsp;27), breast cancer (n\u0026thinsp;=\u0026thinsp;10), gastrointestinal cancer (n\u0026thinsp;=\u0026thinsp;8), bone and soft tissue tumors (n\u0026thinsp;=\u0026thinsp;3), gynecological tumors (n\u0026thinsp;=\u0026thinsp;4), head and neck tumors (n\u0026thinsp;=\u0026thinsp;9), sarcoidosis (n\u0026thinsp;=\u0026thinsp;2), urological tumors (n\u0026thinsp;=\u0026thinsp;2), and others (n\u0026thinsp;=\u0026thinsp;9). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the patient characteristics, including age, sex, BMI, fasting blood glucose level, and use of antidiabetic medications, such as biguanides and insulin.\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\u003ePatient characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (y, mean\u0026plusmn;standard deviation)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (male/female)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72/35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood glucose level (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.8\u0026thinsp;\u0026plusmn;\u0026thinsp;38.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatients on biguanides\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatients on insulin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eBMI, body mass index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a box plot comparing the hepatic PET indices (SULmean, mean Ki, and mean DV) between the biguanide-treated group and the group not treated with biguanide (non-biguanide-treated group). The SULmean values in the biguanide-treated and non-biguanide-treated groups were 1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 and 1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22, respectively (p\u0026thinsp;=\u0026thinsp;0.015); the corresponding mean Ki values were 9.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82 and 9.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26 (p\u0026thinsp;=\u0026thinsp;0.740) and the corresponding mean DV values were 14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51 and 14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87 (p\u0026thinsp;=\u0026thinsp;0.660). No significant differences in the Ki or DV values were observed between the groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows box-and-whisker plots comparing hepatic PET indices (SULmean, mean Ki, and mean DV) between the insulin users and non-users. The SULmean values in the insulin users and non-users were 1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.256 and 1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.265, respectively (p\u0026thinsp;=\u0026thinsp;0.876); the respective mean Ki values were 9.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03 and 8.86\u0026thinsp;\u0026plusmn;\u0026thinsp;8.67 (p\u0026thinsp;=\u0026thinsp;0.193) and the respective mean DV values were 14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69 and 13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29 (p\u0026thinsp;=\u0026thinsp;0.472). Insulin therapy had no significant effect on any of the hepatic PET parameters assessed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the multiple regression analysis results for the SULmean values. Biguanide therapy was significantly correlated with the SULmean value (p\u0026thinsp;=\u0026thinsp;0.037). None of the clinical variables, including sex, age, blood glucose level, insulin use, or BMI, were significantly associated with the SULmean values.\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\u003eMultivariate regression analysis for identifying the predictors of the SULmean in patients with diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB (unstandardized)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΒ (standardized)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eཔ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiguanides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI, body mass index; SULmean, mean standardized uptake value normalized by lean body mass; SE B, standard error for the unstandardized beta\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show the multiple regression analysis results for the Ki and DV values, respectively. None of the clinical variables were significantly associated with the Ki or DV values in the respective models.\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\u003eMultivariate regression analysis for identifying the predictors of Ki in patients with diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB (unstandardized)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΒ (standardized)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eཔ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.000178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.522\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.555\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiguanides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.44\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.09\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.528\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI, body mass index; Ki, dynamic metabolic rate of glucose; SE B, standard error for the unstandardized beta\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate regression analysis for the predictors of mean DV in patients with diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB (unstandardized)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΒ (standardized)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eཔ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiguanides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI, body mass index; DV, distribution volume; SE B, standard error for the unstandardized beta\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows representative multiparametric images of two patients (an 84-year-old woman treated with biguanide and an 87-year-old man not treated with biguanide), illustrating differences in the SULmean with minimal variation in the Ki and DV values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective study, we comprehensively evaluated the effects of biguanides (primarily metformin) alongside insulin and other antidiabetic clinical factors on hepatic FDG-PET/CT parameters in patients with diabetes, using a dynamic multiparametric imaging approach. Three quantitative indices (SULmean, Ki, and DV) were specifically assessed. Biguanide treatment independently and significantly increased the hepatic SULmean, whereas insulin and other examined clinical variables had no significant effects on the SULmean, Ki, or DV. This pattern suggests an important divergence in the sensitivity of static versus dynamic PET indices to antidiabetic medication effects, with significant implications for clinical PET imaging and hepatic FDG uptake interpretation in populations with diabetes.\u003c/p\u003e \u003cp\u003eThe significant increase in the SULmean among biguanide users aligns with our initial hypothesis and previous physiological reports. Metformin is known to accumulate in the liver via organic cation transporters, where it exerts its primary glucose-lowering effect by inhibiting the mitochondrial respiratory chain complex I and activating AMP-activated protein kinase [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This activation suppresses hepatic gluconeogenesis. Even after the standard 4-h pre-scan drug cessation period, the elevated SULmean in the livers of biguanide users highlights the persistent effect of biguanides (primarily metformin) on the liver background FDG accumulation. This finding is clinically significant, as the liver is routinely used as a reference organ in visual scoring methods, such as the Deauville 5-point scale and Lugano classification in oncological PET/CT, particularly for lymphoma imaging. Metformin-induced elevation of hepatic FDG uptake may compromise lesion-to-background contrast, potentially leading to underestimation of the lesion metabolic activity. However, it is noteworthy that the absolute difference in the SULmean (1.88 vs. 1.76) was small. From a practical radiological perspective, such a subtle difference is unlikely to be discernible during routine visual interpretation, preserving the diagnostic utility of the scans.\u003c/p\u003e \u003cp\u003eThe most noteworthy finding in our results is that no correlation was observed between biguanide use and the liver Ki. SULmean represents FDG accumulation across all stages of transport and phosphorylation, whereas Ki specifically reflects the rate of irreversible FDG phosphorylation and intracellular uptake; this is contrary to what was initially hypothesized. The pharmacokinetic and pharmacodynamic properties of metformin may explain this difference. The plasma half-life of metformin is approximately 4\u0026ndash;6 h. The 4-h washout period for biguanide recommended by current guidelines is likely to prevent any increase in Ki, resulting in a non-significant correlation.\u003c/p\u003e \u003cp\u003eAdditional key findings indicate that rapid-acting insulin administration, even after the recommended 4-h discontinuation period, did not significantly affect any of the hepatic metabolic markers examined. These results support the current EANM recommendation to discontinue insulin for 4 h before PET/CT examination. Prolonged discontinuation of antidiabetic drugs may be unnecessary, and lead to destabilization of glycemic control and increased risk of hyperglycemia or other adverse events in patients with diabetes. Our findings suggest that, under these guidelines, rapid-acting insulin does not substantially influence dynamic hepatic FDG metabolism in a standardized preparation protocol, supporting the validity of current clinical routines for quantitative PET assessment in populations with diabetes.\u003c/p\u003e \u003cp\u003eOur findings also warrant comparison to prior literature. Several previous reports have documented marked metformin-related increases in intestinal FDG uptake, with evidence that a 48-h discontinuation most effectively attenuates bowel accumulation and improves image quality in abdominal and pelvic PET [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These findings have prompted some clinical centers to recommend extended biguanide withdrawal, despite the absence of such a requirement in the current EANM guidelines. However, studies specifically focused on the liver are relatively scarce. In a core study, Iozzo et al. reported a significant increase in the hepatic Ki following 26 weeks of metformin monotherapy under strict insulin clamping after prolonged fasting[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The discrepancy between the marked effect of metformin on the hepatic Ki observed in the randomized intervention trial and the absence of such an effect in our observational study likely reflects differences in metformin administration protocols. Furthermore, unlike the study by Iozzo et al. that reported significant increases in the hepatic Ki under rigorous insulin clamp conditions, our study reflected routine clinical practices. The use of the glucose clamp technique, a gold standard for quantifying insulin resistance, often unmasks metabolic changes that are not evident under standard fasting. Our findings indicate that, under the standard preparation protocols recommended by the EANM, acute metabolic dynamics in the liver are not affected by common antidiabetic medications.\u003c/p\u003e \u003cp\u003eAnother important consideration is the practical and safety dimension of PET/CT preparation in patients with diabetes. Prolonged or unnecessary withdrawal of antidiabetic medications, particularly in patients with labile glycemic control, can cause significant treatment disruptions and increase the risk of adverse events as well as the burden for patients and clinical staff [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The present study demonstrates that a 4-h biguanide withdrawal generally suffices to neutralize dynamic metabolic effects, while a subtle static residual on the SULmean persists. Avoiding unnecessarily prolonged drug discontinuation when not clinically required can reduce the risk of hyperglycemia and help maintain the consistency and safety of diabetes management during the peri-imaging window. From a radiological perspective, this persistent increase in the SULmean, despite being numerically significant, has little effect on the visual assessment and minimal impact on diagnostic imaging.\u003c/p\u003e \u003cp\u003eHowever, this study has certain limitations that should be acknowledged. As a single-center retrospective analysis, the cohort size (n\u0026thinsp;=\u0026thinsp;107) was sufficient for primary statistical analyses but may lack statistical power for detailed subgroup analyses or dose/duration evaluations. In particular, the small number of insulin-treated patients constrained the ability to verify subtle differences among antidiabetic therapy regimens. Although the use of a PBIF eliminated the need for arterial blood sampling (the gold standard for PET input function derivation), it introduced a minor approximation error. In addition, confounding effects from additional uncontrolled variables such as exercise artifacts or unmeasured drug combinations cannot be completely excluded. Therefore, although the overall results appear robust, large-scale, multicenter prospective studies with detailed pharmacological follow-up are necessary to further clarify the subtle effects of individual drug classes, specific dosages, and discontinuation protocols across diverse populations with diabetes.\u003c/p\u003e \u003cp\u003eLooking forward, several future research avenues are suggested. Large-scale, prospective comparative trials should directly assess the effects of differing biguanide withdrawal durations (e.g., 24 vs. 48 h) on static and dynamic hepatic PET indices. Time-course dynamic imaging post-medication cessation could clarify the pharmacodynamic \u0026ldquo;washout\u0026rdquo; of the effect of metformin on both FDG uptake and glucose metabolism. The impact of other oral hypoglycemic agents, such as sodium-glucose cotransporter 2 and dipeptidyl peptidase-4 inhibitors, on hepatic and extrahepatic FDG distribution also warrants systematic evaluation [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Such investigations will ultimately strengthen the evidence base for optimal antidiabetic therapy management in the PET/CT setting and improve the diagnostic reliability and reproducibility of PET indices in oncology and metabolic imaging.\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrated that biguanide therapy remains an independent and significant factor increasing the hepatic SULmean value on static FDG-PET/CT in patients with diabetes, even under standard imaging preparation conditions. Dynamic indicators of the hepatic Ki and DV were minimally affected under standard discontinuation protocols, supporting the practical validity of the current EANM guidelines that allow for continued biguanide use. Although residual static effects should be recognized during quantitative liver-based PET interpretation, their impact on visual analysis is minimal. These findings emphasize the need for mechanism-aware, nuanced image interpretation and patient-centered, individualized PET/CT protocols in populations with diabetes, balancing image integrity with patient safety and metabolic stability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eSources of funding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP24K18840. The authors declare that no other funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003cp\u003e\u003cb\u003eConflict -of-interest statement\u003c/b\u003e: No potential conflicts of interest were disclosed.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eThis work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (grant number JP24K18840). The authors thank the staff members of the Division of Nuclear Medicine at the Department of Radiology for their valuable support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFroldi G. View on Metformin: Antidiabetic and Pleiotropic Effects, Pharmacokinetics, Side Effects, and Sex-Related Differences. Pharmaceuticals (Basel). 2024;17:478.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Groot M, Meeuwis AP, Kok PJ, Corstens FH, Oyen WJ. Influence of blood glucose level, age and fasting period on non-pathological FDG uptake in heart and gut. Eur J Nucl Med Mol Imaging. 2005;32:98\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakaguchi K, Sugawara K, Hosokawa Y, Ito J, Morita Y, Mizuma H, et al. Metformin-regulated glucose flux from the circulation to the intestinal lumen. 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Eur J Nucl Med Mol Imaging. 2015;42:328\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamidizadeh R, Eftekhari A, Wiley EA, Wilson D, Alden T, B\u0026eacute;nard F. Metformin Discontinuation prior to FDG PET/CT: A Randomized Controlled Study to Compare 24- and 48-hour Bowel Activity. Radiology. 2018;289:418\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodpaster BH, Bertoldo A, Ng JM, Azuma K, Pencek RR, Kelley C, et al. Interactions among glucose delivery, transport, and phosphorylation that underlie skeletal muscle insulin resistance in obesity and type 2 Diabetes: studies with dynamic PET imaging. Diabetes. 2014;63:1058\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaiser A, Davenport MS, Frey KA, Greenspan B, Brown RKJ. Management of Diabetes Mellitus Before. 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A novel DPP-4 inhibitor Gramcyclin A attenuates cognitive deficits in APP/PS1/tau triple transgenic mice via enhancing brain GLP-1-dependent glucose uptake. Phytother Res. 2022;36:1297\u0026ndash;309.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSperry BW, Almohtasib Y, Ghaly R, Abdel Jawad M, Sauer AJ, Bateman TM. SGLT2 Inhibitor Treatment Is Associated With Reduced Cardiac Glucose Metabolism: A Matched FDG-PET Cohort Study. JACC Adv. 2025;4:102016.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-nuclear-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anme","sideBox":"Learn more about [Annals of Nuclear Medicine](http://link.springer.com/journal/12149)","snPcode":"12149","submissionUrl":"https://www.editorialmanager.com/anme/default2.aspx","title":"Annals of Nuclear Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"biguanide, dynamic positron emission tomography, 18F-fluorodeoxyglucose, liver, diabetes","lastPublishedDoi":"10.21203/rs.3.rs-8770927/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8770927/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003ePrevious studies have shown that patient characteristics, such as fasting blood glucose, can modulate hepatic \u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose (FDG) uptake; however, the drug-specific effects of different antidiabetic agents on hepatic and whole-body FDG distribution remain poorly understood. Therefore, in this study, we aimed to evaluate the impact of biguanide therapy, as well as insulin and other antidiabetic medications, on hepatic FDG uptake and dynamic positron emission tomography (PET) kinetic parameters [the static mean standardized uptake value normalized by lean body mass (SULmean), dynamic metabolic rate of glucose (Ki), and the FDG distribution volume (DV)] in patients with diabetes, using a multiparametric dynamic FDG-PET/computed tomography (CT) protocol.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study included 107 patients with diabetes who underwent dynamic whole-body FDG-PET/CT after a 4-h fast and drug withdrawal between January 2022 and May 2025. Patients with hepatic structural abnormalities or artifacts were excluded. PET kinetic parameters (SULmean, Ki, and DV) were measured in the liver and analyzed for associations with clinical variables, including biguanide and insulin therapy, via multivariate regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 107 patients included, 44 and 9 received biguanide and insulin, respectively. Multivariate regression showed that biguanide therapy, but not insulin use or other clinical variables, was significantly associated with increased hepatic SULmean (p\u0026thinsp;=\u0026thinsp;0.015). No clinical variables were significantly associated with Ki or DV in the corresponding models. These findings suggest that biguanide therapy elevates static hepatic FDG uptake, whereas hepatic glucose metabolic dynamics, as assessed by Ki and DV, remain unaffected by antidiabetic medications under standard pre-scan preparation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study demonstrates that biguanide therapy increases static hepatic FDG accumulation without interfering with the underlying glucose metabolic dynamics (Ki and DV). The observed increase in SULmean is numerically significant but visually minimal, suggesting its negligible impact on routine diagnostic interpretation. These results support the clinical validity of current European Association of Nuclear Medicine guidelines, confirming that a 4-h fast and drug withdrawal are adequate for accurate PET/CT imaging of patients treated with biguanide.\u003c/p\u003e","manuscriptTitle":"Impact of Biguanide Therapy on Hepatic 18F-Fluorodeoxyglucose-Positron Emission Tomography Quantitative Parameters in Patients with Diabetes: A Dynamic Positron Emission Tomography Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 16:45:02","doi":"10.21203/rs.3.rs-8770927/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-02-09T11:05:29+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T05:47:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T08:18:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Nuclear Medicine","date":"2026-02-02T23:45:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-nuclear-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anme","sideBox":"Learn more about [Annals of Nuclear Medicine](http://link.springer.com/journal/12149)","snPcode":"12149","submissionUrl":"https://www.editorialmanager.com/anme/default2.aspx","title":"Annals of Nuclear Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"43f27fb8-01e8-4744-b89c-c5bde18f590c","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T00:55:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 16:45:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8770927","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8770927","identity":"rs-8770927","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}