Changes in blood glucose profile before and after kidney transplantation: a prospective cohort study using continuous glucose monitoring

Changes in blood glucose profile before and after kidney transplantation: a prospective cohort study using continuous glucose monitoring | 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 Article Changes in blood glucose profile before and after kidney transplantation: a prospective cohort study using continuous glucose monitoring Jiyoung Shin, Eun-Ah Jo, Ara Cho, Myeonghyeon Ko, Sangwan Kim, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4589321/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Post-transplantation diabetes mellitus (PTDM) negatively affects graft and patient survival after kidney transplantation (KT). This prospective study used continuous glucose monitoring (CGM) to evaluate perioperative blood glucose dynamics, identify PTDM risk factors, and compare predictive accuracy with capillary blood glucose monitoring (CBGM) in 60 non-diabetic living-donor KT recipients. Patients underwent 2-week pre- and postoperative CGM, including routine CBGM during their in-hospital stays. PTDM-related risk factors and glucose profiles were analyzed with postoperative CGM and CBG. PTDM developed in 14 (23.3%) patients and was associated with older age, male sex, higher baseline HbA1c, high-density lipoprotein cholesterol, and 3-month cumulative tacrolimus exposure levels. Male sex and postoperative time above the range (TAR) of 180 mg/dL by CGM were PTDM-related risk factors in the multivariate analysis. For predictive power, the CGM model with postoperative glucose profiles exhibited higher accuracy compared with the CBGM model (areas under the curves of 0.916, and 0.865 respectively). Therefore, we found that male patients with a higher postoperative TAR of 180 mg/dL have an increased risk of PTDM. Postoperative CGM provides detailed glucose dynamics and demonstrates superior predictive potential for PTDM than CBGM. Health sciences/Endocrinology Health sciences/Medical research Health sciences/Nephrology kidney transplantation diabetes mellitus continuous glucose monitoring blood glucose postoperative care follow up Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Post-transplantation diabetes mellitus (PTDM) refers to the development of diabetes mellitus (DM) in individuals without DM prior to organ transplantation. The reported occurrence rate of PTDM ranges from 2–52%, and the highest prevalence occurs in cases of kidney transplantation (KT). [ 1 , 2 ] The development of PTDM amplifies the risk of cardiovascular disease and infections, diminishes quality of life, and ultimately leads to lower overall patient and graft survival. Risk factors for PTDM overlap with conventional risk factors for type 2 DM, encompassing age, ethnicity, obesity, family history, genetic predisposition, and metabolic syndromes. Additionally, transplant-related factors such as hepatitis C, immunosuppressive medications, and cytomegalovirus infection contribute to the onset of PTDM. [ 3 , 4 ] Physicians strive to manage these risk factors through various strategies, including adjustments to immunosuppressive medication and the implementation of rigorous glucose monitoring and regulation. [ 5 ] However, reducing the use of calcineurin inhibitors and glucocorticoids, which serve as essential immunosuppressive agents for preventing rejection but are also diabetogenic, remains challenging. [ 6 , 7 ] Therefore, the best effective modifiable strategy is an early prediction of PTDM, which facilitates the reduction of complications through stringent blood glucose management and ensures long-term patient and graft survival after KT. The commonly used method, skin-prick capillary blood glucose monitoring (CBGM) is invasive and makes frequent monitoring difficult. Consequently, it is not sensitive enough to adequately monitor for dysglycemia. However, continuous glucose monitoring (CGM) in the form of a wearable device is non-invasive and provides complete 24-h data along with detailed glucose profiles, offering insights into changes in patients’ glucose levels. [ 8 ] Recent study have reported that incorporating CGM into standard diagnostic methods allows for earlier identification of individuals with diabetes or pre-diabetes from healthy individuals. [ 9 ] This study aimed to identify changes in glucose levels pre- and post-KT using CGM and investigate the risk factors associated with the incidence of PTDM. Furthermore, we compared the predictive efficacy of CBGM and postoperative CGM in relation to PTDM occurrence. METHODS Study population This was a single-center, prospective, observational study (ClinicalTrials.gov NCT) conducted from June 2021 to September 2022. All patients scheduled for living-donor KT were identified as potentially eligible participants. The exclusion criteria included: patients younger than 18 years, prior renal transplant recipients, those undergoing deceased-donor or multi-organ transplantation, and those diagnosed with DM prior to transplantation. All potential candidates provided informed consent prior to enrollment. Enrolled patients who completed glucose monitoring and assessment up to 6 months post-transplantation were included in the final analysis. Study design Once consent was obtained, preoperative glucose data were collected 14 days before the scheduled surgery using a CGM sensor (Freestyle Libre 1; Abbott Diabetes Care Ltd., Maidenhead, UK). The device was placed on the patient’s upper arm according to the manufacturer’s instructions. The patients installed the corresponding application on their smartphones and were instructed to scan the sensors using their smartphones at least once every 8 h. The sensor collected glucose data every min and automatically stored a reading every 15 min. In addition to the daily glucose levels, the CGM system provided calculated parameters based on these daily levels, including glucose management index (GMI) (%), coefficient of variation (%), time within the range of 70–180 mg/dL (%), time above the range (TAR) of 180 mg/dL (%), time below the range of 70 mg/dL (%), and nadir and peak glucose levels. To collect postoperative glucose data, a new sensor was applied and used for an additional 14 days following surgery. Throughout the hospitalization period for transplantation, patients adhered to the established protocol for CBGM, which was performed daily before each meal and bedtime. Interventions for abnormal glucose levels were based on the CBGM rather than CGM readings. Preoperative waist-to-hip circumference ratio and fat-to-muscle ratio measurements using bioimpedance analysis (InBody970; InBody Co., Ltd., Seoul, Korea) were also collected in the enrolled patients. Further laboratory examinations were conducted for fasting plasma glucose (FPG), insulin, hemoglobin A1c (HbA1c), C-peptide, and lipid profiles (including total cholesterol, high-density lipoprotein [HDL], low-density lipoprotein [LDL], and triglycerides) at baseline and 1, 2, 3, and 6 months postoperatively. The homeostasis model assessment of insulin resistance (HOMA-IR) and beta-cell function (HOMA-B) were used to estimate insulin resistance and secretion by analyzing insulin levels. [ 10 ] Definition of PTDM PTDM was defined based on the American Diabetes Association (ADA) diagnostic criteria at 6 months post-transplantation in patients without a preoperative diagnosis of DM. [ 11 ] Study patients who developed PTDM were categorized as the PTDM group. Those who did not develop PTDM during the study period were defined as the non-PTDM group. Immunosuppressive regimen and postoperative glucose control protocol The immunosuppressive regimen consisted of induction therapy and triple maintenance agents. Induction therapy included basiliximab or rabbit anti-human thymocyte immunoglobulin. The maintenance regimen consisted of tacrolimus, antimetabolites (mycophenolate mofetil and mycophenolic acid), and corticosteroids. Tacrolimus was administered twice a day. During the first 3 months post-transplantation, trough concentration levels were maintained within the range of 8–12 ng/mL, followed by a target range of 6–10 ng/mL from 3 to 6 months. Cumulative exposure to tacrolimus (CET) for 3 months was calculated as the area under the concentration–time curve (AUC) based on trough concentrations. All measured tacrolimus concentration values for a specific patient were plotted on a time-dependent graph, and the AUC of the tacrolimus level was calculated using the Wagner–Nelson equation. [ 12 ] Steroid therapy commenced with an intravenous injection of 500 mg on the day of surgery and was subsequently gradually tapered to 5 mg of oral prednisolone over 4 weeks. Steroids were administered after breakfast to alleviate gastrointestinal discomfort, and this was continued for 6 months. Mycophenolate mofetil (500 mg) or an equivalent dose of mycophenolic acid was administered twice daily. In cases where adverse effects such as leukopenia or elevated liver enzyme levels were observed, the medication was either discontinued or adjusted based on the clinical severity and progression of side effects. For postoperative glycemic control, the regular insulin (RI) sliding scale protocol was initiated if the CBGM exceeded 250mg/dL immediately after surgery. Humulin R (100 IU) was mixed with 100 mL of normal saline and started at 1 cc/h, with the dose adjusted according to the established protocol. Once the RI was tapered, intermittent subcutaneous injections of Humulin R were administered according to a consistent protocol (Supplementary Table 1). Statistical analyses Categorical variables are expressed as percentages within the respective derived groups and were assessed using Pearson’s chi-square and Fisher’s exact tests. Continuous variables are presented as means ± standard deviations and were evaluated using a Student’s t -test following a normality test. Univariate and multivariate logistic regression analyses were conducted to identify factors that were independently and significantly associated with the onset of PTDM. Before conducting multivariate logistic regression, a multicollinearity test was performed using the variance inflation factor (VIF) among independent variables. If the VIF value exceeded 10, it was used as the criterion for variable removal from the model, and variables were removed in consideration of clinical relevance to the study. Variables with a P -value of less than 0.05 in the univariate analyses were subsequently included in the multivariate logistic regression using a backward elimination method. We established two models, the CBGM and CGM model, with postoperative blood glucose data (postoperative CGM). Additionally, receiver operating characteristic (ROC) curves were generated to compare the AUC of different glucose monitoring models (postoperative CBGM vs. postoperative CGM) for PTDM prediction. DeLong’s test was conducted to compare the AUCs of both models. All analyses were performed using IBM SPSS version 26.0 (IBM Corp., Armonk, NY, USA) and R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). A P-value of less than 0.05 was considered significant. Ethical approval This study was conducted in accordance with the principles of the Declaration of Helsinki. All study procedures were approved by the independent Institutional Review Board (IRB) of Seoul National University Hospital (IRB number: 2102-083-1197). All participants provided written informed consent. RESULTS Patient characteristics During the enrollment period, 264 patients underwent KT at our center, of which, 100 consented to participate in the study. In the cohort of 100 patients, 15 were diagnosed with preoperative DM, four had undergone deceased-donor transplantation without preoperative CGM data, 18 had insufficient glucose monitoring data, and three had insufficient diabetes-related data and were thus excluded from the study. Finally, 60 patients were included in the final analysis. Among them, 14 (23.3%) developed PTDM during the study period. Patient and donor demographics and transplant characteristics are summarized in Table 1. The PTDM group was significantly older (56.4±9.8 years) than the non-PTDM group (46.2±13.0 years, P =0.009). In addition, male patients were more prevalent in the PTDM group (85.7%) than in the non-PTDM group (45.7%, P =0.008). No differences were observed among donors with respect to human leukocyte antigen incompatibility, age, sex, body mass index (BMI), or their relationship with the recipient between the two groups. All study patients exhibited a normal preoperative glucose profile, with a mean C-peptide level of 5.9±3.5 ng/mL. Indices of insulin secretion and resistance, represented by HOMA-B (226.8±251.8) and HOMA-IR (2.3±1.1), respectively, were within the normal range. Although enrolled patients showed a normal range of HbA1c levels (5.2±0.5%), patients in the PTDM group exhibited significantly higher preoperative HbA1c levels (5.5±0.4% vs. 5.2±0.5%, respectively; P =0.023). Those in the PTDM group also had lower HDL levels than those in the non-PTDM group (37.7±11.8 mg/dL vs. 51.3±16.3 mg/dL, respectively; P =0.006). Transplant outcomes All transplantations were performed successfully without immediate postoperative complications or rejections. Regarding immunosuppressive agents, there were no significant differences in terms of induction agents, steroid pulse treatment during follow-up, or mean steroid dose during hospitalization. However, the PTDM group exhibited significantly higher tacrolimus trough concentrations on the day of discharge than the non-PTDM group (11.4±2.2 ng/mL vs. 9.8±1.9 ng/mL, respectively; P =0.014), along with higher mean tacrolimus concentrations during the index admission (11.3±2.2 ng/mL vs. 9.6±1.7 ng/mL, respectively; P =0.003). Furthermore, the 3-month CET was significantly greater in the PTDM group than in the non-PTDM group (898.3±54.4 ng/mL vs. 804.7±90.0 ng/mL, respectively; P =0.001) (Table 1). No significant difference was seen in the occurrence of transplant rejection demonstrated by indication biopsy and postoperative 10-days protocol biopsy between the non-PTDM (37.0%) and PTDM (21.4%) groups ( P =0.281). No graft failure occurred during follow-up in either group. The estimated glomerular filtration rate levels at discharge and 1, 2, 3, and 6 months postoperatively showed no significant differences between the two groups (Table 2). Changes in perioperative glucose profile Figure 1 illustrates the preoperative and postoperative changes in glucose variation relative to PTDM development. An increase in daily glucose levels and variation were seen in the postoperative period compared with values in the preoperative period in the PTDM group. Figure 2 presents a visualized graph of changes in glucose levels before and after KT for the entire cohort, and Figure 3 further shows significant increases in average, peak, and nadir glucose levels that occurred postoperatively in both groups. In the preoperative period, CGM showed that the PTDM group had significantly higher preoperative mean glucose levels (107.4±13.2 mg/dL vs. 95.5±13.4 mg/dL, respectively; P =0.006), GMI (5.8±0.3% vs. 5.6±0.3%, respectively; P =0.016), and mean peak glucose levels (175.5±19.6 mg/dL vs. 150.0±31.5 mg/dL, respectively; P =0.006) than the non-PTDM group. In the postoperative period, the CGM values of the mean glucose levels (141.6±21.4 mg/dL vs. 120.2±18.0 mg/dL, respectively; P <0.001), TAR of 180 mg/dL during the day (21.1±12.3% vs. 9.0±8.8%, respectively; P <0.001), and mean daily peak glucose levels (221.5±22.3 vs. 189.1±31.2 mg/dL, respectively; P =0.002) were significantly higher in the PTDM group than those in the non-PTDM group (Table 3). Similarly, CBGM also showed that the PTDM group exhibited higher average glucose levels postoperatively (164.6±25.3 mg/dL vs. 151.9±18.9 mg/dL, respectively; P =0.027) and daily peak glucose values (208.7±31.7 mg/dL vs. 189.6±26.2 mg/dL, respectively; P =0.048) than the non-PTDM group. Postoperative changes in metabolic indices during the follow-up period There were no significant differences in BMI changes between the two groups at 1, 2, 3, and 6 months postoperatively. However, the PTDM group consistently demonstrated significantly higher of HbA1c levels than the non-PTDM group at all follow-up periods. There were no significant differences in total cholesterol and LDL levels between the two groups during the 6-month follow-up period. However, the PTDM group showed significantly lower HDL levels at 2 and 3 months postoperatively. The HOMA-IR values at 1, 2, and 3 months after KT did not show any significant differences between the two groups. However, the PTDM group showed an increasing trend of HOMA-IR after transplantation and exhibited a significantly higher value of HOMA-IR (7.0±9.5) at 6 months, whereas patients in the non-PTDM group had a lower value (3.1±1.5, P =0.001). There were no significant differences between the two groups in terms of HOMA-B during the follow-up period (Figure 4). Risk factors and prediction models for PTDM In the univariate analysis, baseline characteristics such as age, male sex, and preoperative HbA1c and HDL levels were associated with the development of PTDM. The 3-month CET was identified as an immunosuppression-related factor for PTDM development. Among postoperative glucose profiles, mean daily peak glucose by CBGM, mean glucose level, TAR of 180 mg/dL, and mean daily peak glucose levels by CGM were statistically significant. A multicollinearity assessment was conducted on factors exhibiting statistical significance in the univariate analysis (Table 4). Confirming no evidence of multicollinearity, we performed the multivariate logistic regression analysis using baseline characteristics and postoperative indices. Male sex (odds ratio [OR]: 17.45; 95% confidence interval [CI]: 1.79-70.01; P =0.014) and a CGM-detected postoperative TAR of 180 mg/dL (OR: 1.17; 95% CI: 1.06-1.29; P =0.002) were found to be independent risk factors associated with the occurrence of PTDM (Table 5). To compare the predictive abilities of the CBGM and CGM models for PTDM, we plotted the ROC curves for the CBGM model and the postoperative CGM model. Each model was constructed by combining variables with a P -value of less than 0.05, identified through univariate regression analysis. The AUCs of CBGM and postoperative CGM were 0.865 and 0.916, respectively (Figure 5). In DeLong’s test, the difference in AUCs between the CBGM model and the postoperative CGM model resulted in a P -value of 0.12. DISCUSSION Previous studies have demonstrated the accuracy, reliability, and feasibility of CGM in patients with type 1 DM, simultaneous pancreas-KT, and critical care settings. [13-16] Furthermore, some transplant centers have adopted CGM as part of their standard protocol. However, only a limited number of studies have analyzed postoperative glucose dynamics through CGM in KT recipients, specifically examining its association with the onset of PTDM. Additionally, monitoring in previous studies was limited to the short stress period within 5 days immediately postoperatively, making it difficult to secure accurate predictive power. [17,18] We conducted a comprehensive analysis to investigate factors influencing the incidence of PTDM using CGM. Blood glucose levels were monitored using CGM for 14 days, before and after surgery, in addition to CBGM during hospitalization. KT recipients showed elevated mean glucose levels, GMI, TAR of 180 mg/dL, and mean daily peak glucose levels post-KT compared with pre-KT values. Among the patients included in the study, 23.3% developed PTDM. Our findings identified major risk factors associated with PTDM, including male sex and elevated postoperative TAR of 180 mg/dL, detected using CGM. Preoperative impaired glucose tolerance (IGT) and impaired fasting glucose (IFG) represent significant risk factors for developing PTDM. [19] Previous studies have indicated a 15% prevalence of pre-transplantation IGT or IFG, with a subsequent 35% progression to PTDM. [20,21] Although all values remained within the normal range, preoperative CGM indicated that patients in the PTDM group had significantly higher mean glucose levels, GMI, and mean daily peak glucose levels prior to transplantation than those in the non-PTDM group. Similar to DM, IGT or IFG is characterized by significant glucose variability, which can be assessed through CGM.19 Without overt DM prior to transplantation, the preemptive use of antihyperglycemic agents or insulin may not be warranted. However, lifestyle modifications and dietary adjustments alone have been reported to prevent the progression to DM in patients with impaired glucose metabolism. [22,23] Furthermore, unlike invasive CBGM, CGM can be implemented in an outpatient setting before surgery and provide comprehensive 24-h data, offering more sensitive glucose readings. It also allows patients to independently access their glucose data and initiate management strategies prior to surgery. Therefore, CGM not only improves the accuracy of predicting PTDM, but also has the potential to reduce its incidence through early detection and correction. Regarding postoperative glycemic control, the multivariate logistic regression analysis identified a postoperative TAR of 180mg/dL using CGM as a significant risk factor for PTDM. However, values obtained through CBGM were not statistically significant. The CBGM follows a standard protocol that involves measuring glucose levels upon waking up, before lunch and dinner, and before bedtime. Patients with KT experience various physiological changes along with stress hormone secretion due to surgery immediately after transplantation, administration of high-dose immunosuppressants, and the dawn phenomenon. Administration of prednisolone after breakfast contributes to prolonged elevation of glucose levels or an extended period of prolonged elevation before levels return to baseline. However, intermittent monitoring with CBGM leads to imprecision in average values and precludes the capture of glucose dynamics, [24,25] including peak values or TAR of 180 mg/dL. Consequently, CGM showed higher predictive power for PTDM occurrence compared with CBGM. This study has a few limitations. First, it was based on a single-center sample with a relatively small number of patients. Therefore, our results may not be readily generalizable, and further validation is warranted through extensive multicenter investigations. Second, an oral glucose tolerance test (OGTT) was not conducted in our center. However, ADA guidelines emphasize the equivalency of FPG, 2-h plasma glucose OGTT, and HbA1c for diagnostic screening. Furthermore, it is underscored that utilizing FPG and HbA1c tests in screening can effectively reduce the overall requirement for OGTTs. [4] Finally, the carbohydrate intake and physical activity of the cohort were not controlled. Therefore, we cannot exclude the possibility that our findings could be explained by variations in the patients’ diet or physical activity levels. CONCLUSION In conclusion, we found that male patients with a higher postoperative TAR of 180 mg/dL have an increased risk of PTDM. Moreover, CGM provides a reliable method for glucose monitoring and offers superior predictive performance for detecting the occurrence of PTDM compared with CBGM. The utilization of CGM facilitates the identification of individuals at risk of developing PTDM and could support the implementation of more rigorous glycemic control in at-risk patients. Further investigation is warranted to substantiate these results, including cost-effectiveness considerations. Declarations Data availability statement The source data for all figures included in the manuscript are stored in Mendeley Data (doi: 10.17632/r5n5zt4xs9.1). If permissible, the dataset generated and/or analyzed during the current research will be made available upon request from the corresponding author. Limited access to certain clinical data generated in the current study is restricted due to the absence of prior authorization for external sharing of data from research subjects without explicit consent. Acknowledgements None Author contributions JY : Data curation, Formal analysis, Investigation, Writing—original draft, Writing—review & editing. EA : Conceptualization, Data curation, Methodology, Project administration. HY, AR, and MH : Investigation, Methodology, Project administration. SW : Software, Validation, Visualization, Formal analysis. AR and JW : Investigation, Resources, Supervision. SI : Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing—review & editing. Competing interests statement The authors declare that they have no competing intersests References Song, J. L. et al. Higher tacrolimus blood concentration is related to increased risk of post-transplantation diabetes mellitus after living donor liver transplantation. 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Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N. Engl. J. Med. 344, 1343–1350 (2001). Li, G. et al. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 371, 1783–1789 (2008). Rodríguez, L. M., Knight, R. J. & Heptulla, R. A. Continuous glucose monitoring in subjects after simultaneous pancreas-kidney and kidney-alone transplantation. Diabetes Technol. Ther. 12, 347–351 (2010). Clemens, K. K. et al. Reducing hyperglycaemia post-kidney and liver transplant: a quality improvement initiative. BMJ Open Qual. 11, e001796 (2022). Tables Table 1. Patient and transplantation characteristics of the non-PTDM and PTDM groups Characteristics Total (n=60) Non-PTDM (n=46) PTDM (n=14) P -value Baseline characteristics Age (years) 48.6±12.5 46.2±13.0 56.4±9.8 0.009 Male sex 33 (55.0%) 21 (45.7%) 12 (85.7%) 0.008 BMI (kg/m 2 ) 23.0±3.5 22.7±3.6 24.2±2.9 0.150 Waist-to-hip ratio 0.9±0.1 0.9±0.1 0.9±0.1 0.460 Fat ratio (%) 29.8±10.4 31.2±10.3 25.7±9.9 0.098 Muscle ratio (%) 35.9±8.8 33.7±10.6 39.6±8.0 0.070 Donor characteristics Preoperative desensitization 16 (26.7%) 13 (28.3%) 3 (21.4%) 0.740 Donor age 49.6±12.3 51.2±11.7 44.5±13.2 0.073 Donor male sex 27 (45.0%) 21 (45.7%) 6 (42.9%) 0.854 Donor BMI 23.3±4.1 23.3±4.2 23.2±3.8 0.959 Related donor 35 (58.3%) 26 (56.5%) 9 (64.3%) 0.606 Underlying disease Hypertension 42 (70.0%) 31 (67.4%) 11 (78.6%) 0.520 Dyslipidemia 6 (10.0%) 5 (10.9%) 1 (7.1%) 1.000 Liver disease 5 (8.3%) 4 (9.3%) 1 (7.1%) 1.000 Coronary artery disease 7 (11.7%) 5 (10.9%) 2 (14.3%) 0.660 Family history of DM 3 (5%) 3 (6.5%) 0 (0%) 1.000 ESRD cause Glomerulonephritis 27 (45.0%) 21(45.7%) 6 (42.9%) 1.000 Unknown 11 (18.3%) 9 (19.6%) 2 (14.3%) 1.000 Polycystic disease 9 (15.0%) 6 (13.0%) 3 (21.4%) 0.423 Hypertension 6 (10.0%) 4 (8.7%) 2 (14.3%) 0.617 Other 1 (1.7%) 1 (2.2%) 0 (0%) 1.000 Dialysis state Preemptive 26 (43.3%) 18 (39.1%) 8 (57.1%) 0.234 Baseline laboratory results Fasting plasma glucose (mg/dL) 83.6±12.5 83.4±13.5 84.5±8.8 0.767 HbA1c (%) 5.2±0.5 5.2±0.5 5.5±0.4 0.023 Fasting insulin (μIU/mL) 11.1±4.8 10.8±4.7 12.0±5.0 0.428 C-peptide (ng/mL) 5.9±3.5 5.7±2.9 6.4±4.9 0.558 HOMA-IR 2.3±1.1 2.2±1.1 2.5±1.0 0.445 HOMA-B 226.8±251.8 218.9±269.5 251.5±192.2 0.678 Total cholesterol (mg/dL) 150.0±33.4 153.2±33.4 139.7±32.6 0.190 Triglyceride (mg/dL) 99.0±44.8 98.22±47.5 101.5±36.0 0.813 High-density lipids (mg/dL) 48.1±16.4 51.3±16.3 37.7±11.8 0.006 Low-density lipids (mg/dL) 79.5±26.8 81.6±26.4 72.8±28.0 0.284 Immunosuppression Mean tacrolimus C0 level during hospitalization (ng/mL) 10.0±1.9 9.6±1.7 11.3±2.2 0.003 Tacrolimus C0 level at discharge (ng/mL) 10.2±2.1 9.8±1.9 11.4±2.2 0.014 3-Month cumulative exposure of tacrolimus (ng/mL) 826.5±91.8 804.7±90.0 898.3±54.4 0.001 Anti-thymoglobulin induction 5 (8.3%) 4 (8.7%) 1 (7.1%) 1.000 Steroid pulse during admission 4 (6.7%) 3 (6.5%) 1 (7.1%) 1.000 Mean steroids dose during hospitalization 143.1±56.6 144.1±60.5 139.9±42.7 0.811 Renal function Baseline eGFR (CKD-EPI) (mL/min/1.73 m 2 ) 7.3±3.0 7.3±3.1 7.4±2.7 0.876 Cold ischemia time (min) 59.06±20.5 60.23±21.6 55.0±16.8 0.317 Total hospital stay (days) 17.0±9.1 17.9±10.0 14.2±4.1 0.051 Values are presented as means ± standard deviations or numbers (%). Liver disease is defined as chronic hepatitis B virus or hepatitis C virus infection or liver cirrhosis. BMI, body mass index; DM, diabetes mellitus; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; eGFR, estimated glomerular filtration rate; ESRD, end-stage renal disease; HbA1c, hemoglobin A1c; HOMA-B, homeostasis model assessment of beta-cell function; HOMA-IR, homeostasis model assessment of insulin resistance; PTDM, post-transplantation diabetes mellitus. Table 2. Transplantation outcomes. Outcomes Non-PTDM (n=46) PTDM (n=14) P -value Rejection 17 (37.0) 3 (21.4) 0.281 Graft failure 0 0 eGFR (CKD-EPI) Discharge 68.3±21.4 54.5±15.0 0.544 1 Month 63.4±18.4 61.3±16.2 0.682 2 Months 64.3±17.3 63.8±16.6 0.919 3 Months 61.1±16.8 60.8±12.8 0.957 6 Months 60.3±18.3 61.7±12.2 0.746 Values are presented as means ± standard deviations or numbers (%). Rejection is defined by evidence of either an indication biopsy or a postoperative 10-day protocol biopsy, with pathologic evaluation according to the Banff score 2022 criteria. CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; eGFR, estimated glomerular filtration rate; PTDM, post-transplantation diabetes mellitus. Table 3. Perioperative glucose profiles of the non-PTDM and PTDM groups Profiles Total (n=60) Non-PTDM (n=46) PTDM (n=14) P -value Postoperative CBGM Mean glucose (mg/dL) 154.9±21.0 151.9±18.9 164.6±25.3 0.027 Mean daily peak glucose (mg/dL) 194.0±28.5 189.6±26.2 208.7±31.7 0.048 Preoperative CGM Mean glucose (mg/dL) 98.5±14.2 95.5±13.4 107.4±13.2 0.006 Glucose management index (%) 5.7±0.3 5.6±0.3 5.8±0.3 0.016 Coefficient of variation (%) 28.6±29.8 29.4±34.3 26.0±4.8 0.713 Time in the range of 70–180 mg/dL (%) 87.3±14.5 86.0±15.9 91.4±8.1 0.223 Time above the range of 180 mg/dL (%) 3.2±9.5 3.3±10.8 3.0±3.5 0.976 Time below the range of 70 mg/dL (%) 9.3±13.0 10.6±14.1 5.6±8.5 0.214 Mean daily peak glucose (mg/dL) 156.4±30.9 150.0±31.5 175.5±19.6 0.006 Mean daily nadir glucose (mg/dL) 68.9±10.9 67.7±10.7 72.4±11.4 0.975 Postoperative CGM Mean glucose (mg/dL) 125.3±20.8 120.2±18.0 141.6±21.4 <0.001 Glucose management index (%) 6.6±2.3 6.6±2.6 6.7±0.5 0.962 Coefficient of variation (%) 31.2±7.8 30.9±8.5 32.3±4.8 0.549 Time in the range of 70–180 mg/dL (%) 82.1±15.2 84.2±15.7 75.6±11.5 0.066 Time above the range of 180 mg/dL (%) 11.9±10.9 9.0±8.8 21.1±12.3 <0.001 Time below the range of 70 mg/dL (%) 4.6±6.0 5.1±6.4 3.2±4.4 0.314 Mean daily peak glucose (mg/dL) 196.5±32.3 189.1±31.2 221.5±22.3 0.002 Mean daily nadir glucose (mg/dL) 88.0±15.3 85.8±15.1 95.3±14.5 0.185 Values are presented as means ± standard deviations. CBGM, capillary blood glucose monitoring; CGM, continuous glucose monitoring; PTDM, post-transplantation diabetes mellitus. Table 4. Multicollinearity analysis results for different combinations of variables associated with PTDM. Variable combinations VIF value Baseline characteristics Age 1.281 Male sex 1.363 HbA1c 1.312 High-density lipids 1.576 3-Month tacrolimus AUC (ng/mL) 1.517 Postoperative CBGM Mean daily peak glucose (mg/dL) 2.117 Postoperative CGM Mean glucose (mg/dL) 5.476 Time above the range of 180 mg/dL (%) 6.145 Mean daily peak glucose (mg/dL) 3.833 AUC, area under the receiver operating characteristic curve; CBGM, capillary blood glucose monitoring; CGM, continuous glucose monitoring; HbA1c, hemoglobin A1c; PTDM, post-transplantation diabetes mellitus; VIF, variance inflation factor. Table 5. Univariate and multivariate logistic regression analyses of risk factors associated with PTDM occurrence. Univariate analysis Multivariate analysis OR (95% CI) P -value OR (95% CI) P -value Baseline characteristics Age 1.08 (1.02–1.16) 0.016 Male sex 7.14 (1.43–35.57) 0.016 17.45 (1.79–70.01) 0.014 Baseline laboratory results HbA1c 4.78 (1.17–19.52) 0.030 High-density lipids 0.93 (0.89–0.98) 0.010 Immunosuppression 3-Month tacrolimus AUC (ng/mL) 1.01 (1.01–1.02) 0.003 Postoperative CBGM Mean glucose (mg/dL) 1.03 (0.99–1.06) 0.056 Mean daily peak glucose(mg/dL) 1.03 (1.00–1.05) 0.034 Postoperative CGM Mean glucose (mg/dL) 1.06 (1.02–1.11) 0.003 Time above range of 180 mg/dL (%) 1.11 (1.04–1.19) 0.002 1.17 (1.06–1.29) 0.002 Mean daily peak glucose (mg/dL) 1.04 (1.01–1.06) 0.004 AUC, area under the receiver operating characteristic curve; CBGM, capillary blood glucose monitoring; CGM, continuous glucose monitoring; CI, confidence interval; HbA1c, hemoglobin A1c; OR, odds ratio; PTDM, post-transplantation diabetes mellitus. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.docx Cite Share Download PDF Status: Published Journal Publication published 11 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Aug, 2024 Reviews received at journal 31 Jul, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviews received at journal 12 Jul, 2024 Reviewers agreed at journal 26 Jun, 2024 Reviewers invited by journal 24 Jun, 2024 Editor assigned by journal 24 Jun, 2024 Editor invited by journal 21 Jun, 2024 Submission checks completed at journal 17 Jun, 2024 First submitted to journal 16 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4589321","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":320683227,"identity":"bcb6b3aa-6ec8-473b-b1c6-5f651e916720","order_by":0,"name":"Jiyoung Shin","email":"","orcid":"","institution":"Seoul National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiyoung","middleName":"","lastName":"Shin","suffix":""},{"id":320683228,"identity":"bdb7ddb7-b757-464f-98e1-b8d23fa1388c","order_by":1,"name":"Eun-Ah Jo","email":"","orcid":"","institution":"Chung-Ang University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Eun-Ah","middleName":"","lastName":"Jo","suffix":""},{"id":320683229,"identity":"513ab55a-749d-42a8-b7d1-10eba03c8a90","order_by":2,"name":"Ara Cho","email":"","orcid":"","institution":"Seoul National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ara","middleName":"","lastName":"Cho","suffix":""},{"id":320683231,"identity":"b7703f51-f6f1-4b4d-a873-b31160e8217a","order_by":3,"name":"Myeonghyeon Ko","email":"","orcid":"","institution":"Seoul National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Myeonghyeon","middleName":"","lastName":"Ko","suffix":""},{"id":320683233,"identity":"8f7eff64-b88a-4564-b081-ab7f2bd71c34","order_by":4,"name":"Sangwan Kim","email":"","orcid":"","institution":"Seoul National University Medical Research Center","correspondingAuthor":false,"prefix":"","firstName":"Sangwan","middleName":"","lastName":"Kim","suffix":""},{"id":320683234,"identity":"d4ecf045-d7b3-4401-997c-205b75ebacf8","order_by":5,"name":"Ahram Han","email":"","orcid":"","institution":"Seoul National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ahram","middleName":"","lastName":"Han","suffix":""},{"id":320683235,"identity":"31785702-22ec-43f2-9b1b-8abe47161aa4","order_by":6,"name":"Jongwon Ha","email":"","orcid":"","institution":"Seoul National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jongwon","middleName":"","lastName":"Ha","suffix":""},{"id":320683236,"identity":"47a53454-7faf-4ba9-ada9-d9c8a5611e67","order_by":7,"name":"Sangil Min","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFADZuYDQFJChhQtbAkgLTykWMNjACYJqpOfkfv4xYeKO9Hy7TyfX92oseBhYD98dAM+LQY30s0sZ5x5lrvhMO8265xjQIfxpKXdwKtFIo3NmLftcO4GZt5txjlsQC0SPGZ4tcjPAGr5C9Qyv5nnmXHOPyK0MNxIY37MCNTScJiH+XFuGxFaDM48Y2PsOQN02GE2M+bcPgkeNkJ+kW9PY/7wowLosP7Djz/nfKuT42c/fAy/wxgY2CRQGGwElIMA8wd0xigYBaNgFIwCFAAAUmNHTzwumq8AAAAASUVORK5CYII=","orcid":"","institution":"Seoul National University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Sangil","middleName":"","lastName":"Min","suffix":""},{"id":320683237,"identity":"d862a684-6f97-46fa-bd10-a2e6b9658b4b","order_by":8,"name":"Hye Young Woo","email":"","orcid":"","institution":"Seoul National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hye","middleName":"Young","lastName":"Woo","suffix":""}],"badges":[],"createdAt":"2024-06-16 10:21:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4589321/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4589321/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-72025-7","type":"published","date":"2024-09-11T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60353135,"identity":"b14453b3-012c-42f7-ba20-c4fe19549d87","added_by":"auto","created_at":"2024-07-15 23:39:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2616870,"visible":true,"origin":"","legend":"\u003cp\u003eDaily glucose variation in the preoperative and postoperative non-PTDM and PTDM groups was assessed using CGM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Preoperative daily glucose variation in the non-PTDM group.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Postoperative daily glucose variation in the non-PTDM group.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Preoperative daily glucose variation in the PTDM group.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Postoperative daily glucose variation in the PTDM group.\u003c/p\u003e\n\u003cp\u003eCGM, continuous glucose monitoring; PTDM, post-transplantation diabetes mellitus.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4589321/v1/2e9b0765a00e222f6a14348a.png"},{"id":60353929,"identity":"736aa88b-9cad-48f8-81af-017b929ef235","added_by":"auto","created_at":"2024-07-15 23:47:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1668180,"visible":true,"origin":"","legend":"\u003cp\u003ePerioperative daily variation of glucose levels in the entire patient cohort was detected using a continuous glucose monitoring device in the preoperative 2-week period (A) and postoperative 2-week period (B).\u003c/p\u003e\n\u003cp\u003eThe solid black lines in each graph represent the mean glucose levels of the cohort.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4589321/v1/bc71875a14cf7716f29f084d.png"},{"id":60353930,"identity":"fe2d58bc-abbd-4160-a5cb-2da2dade02f2","added_by":"auto","created_at":"2024-07-15 23:47:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1157986,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of preoperative and postoperative glucose levels in the PTDM and non-PTDM groups as assessed using continuous glucose monitoring.\u003c/p\u003e\n\u003cp\u003e(A) Preoperative and postoperative mean daily peak glucose levels in the non-PTDM and PTDM groups.\u003c/p\u003e\n\u003cp\u003e(B) Preoperative and postoperative mean daily glucose levels in the non-PTDM and PTDM groups.\u003c/p\u003e\n\u003cp\u003e(C) Preoperative and postoperative mean daily nadir glucose levels in the non-PTDM and PTDM groups.\u003c/p\u003e\n\u003cp\u003ePre-op, preoperative; post-op, postoperative; PTDM, post-transplantation diabetes mellitus.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4589321/v1/76737e62ef2e0e0f6a7d53e7.png"},{"id":60353139,"identity":"36818c8c-40af-47ff-a176-a428f6840e2e","added_by":"auto","created_at":"2024-07-15 23:39:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":752966,"visible":true,"origin":"","legend":"\u003cp\u003ePostoperative changes in the metabolic indices HbA1c, HDL, HOMA-IR, and HOMA-B during the follow-up period.\u003c/p\u003e\n\u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e\n\u003cp\u003eCGM, continuous glucose monitoring; HOMA-B, homeostasis model assessment of beta-cell function; HOMA-IR, homeostasis model assessment of insulin resistance; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; PTDM, post-transplantation diabetes mellitus.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4589321/v1/b4f57993b309f14e7b44734c.png"},{"id":60355021,"identity":"7b8a7453-6038-4aa4-ab81-b1b4aa5072c4","added_by":"auto","created_at":"2024-07-15 23:55:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":320015,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristics curve analysis for evaluation of the predictive performance of post-transplantation diabetes mellitus occurrence using postoperative CBGM and CGM models.\u003c/p\u003e\n\u003cp\u003eAUC, area under the curve; CBGM, capillary blood glucose monitoring; CGM, continuous glucose monitoring.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4589321/v1/545e58d6f2144209744d324c.png"},{"id":64618973,"identity":"b01e9044-e15c-495c-a483-0c2781d54f9d","added_by":"auto","created_at":"2024-09-16 16:09:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8950566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4589321/v1/8b76a8cf-3e39-4657-97a2-3d0aef5fc94c.pdf"},{"id":60353136,"identity":"c4454dfb-9a14-4e9d-aae5-73b6bf93cda1","added_by":"auto","created_at":"2024-07-15 23:39:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14134,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4589321/v1/865ccd2a7d330034349d6022.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Changes in blood glucose profile before and after kidney transplantation: a prospective cohort study using continuous glucose monitoring","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePost-transplantation diabetes mellitus (PTDM) refers to the development of diabetes mellitus (DM) in individuals without DM prior to organ transplantation. The reported occurrence rate of PTDM ranges from 2\u0026ndash;52%, and the highest prevalence occurs in cases of kidney transplantation (KT). [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] The development of PTDM amplifies the risk of cardiovascular disease and infections, diminishes quality of life, and ultimately leads to lower overall patient and graft survival. Risk factors for PTDM overlap with conventional risk factors for type 2 DM, encompassing age, ethnicity, obesity, family history, genetic predisposition, and metabolic syndromes. Additionally, transplant-related factors such as hepatitis C, immunosuppressive medications, and cytomegalovirus infection contribute to the onset of PTDM. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Physicians strive to manage these risk factors through various strategies, including adjustments to immunosuppressive medication and the implementation of rigorous glucose monitoring and regulation. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eHowever, reducing the use of calcineurin inhibitors and glucocorticoids, which serve as essential immunosuppressive agents for preventing rejection but are also diabetogenic, remains challenging. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Therefore, the best effective modifiable strategy is an early prediction of PTDM, which facilitates the reduction of complications through stringent blood glucose management and ensures long-term patient and graft survival after KT. The commonly used method, skin-prick capillary blood glucose monitoring (CBGM) is invasive and makes frequent monitoring difficult. Consequently, it is not sensitive enough to adequately monitor for dysglycemia. However, continuous glucose monitoring (CGM) in the form of a wearable device is non-invasive and provides complete 24-h data along with detailed glucose profiles, offering insights into changes in patients\u0026rsquo; glucose levels. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Recent study have reported that incorporating CGM into standard diagnostic methods allows for earlier identification of individuals with diabetes or pre-diabetes from healthy individuals. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] This study aimed to identify changes in glucose levels pre- and post-KT using CGM and investigate the risk factors associated with the incidence of PTDM. Furthermore, we compared the predictive efficacy of CBGM and postoperative CGM in relation to PTDM occurrence.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis was a single-center, prospective, observational study (ClinicalTrials.gov NCT) conducted from June 2021 to September 2022. All patients scheduled for living-donor KT were identified as potentially eligible participants. The exclusion criteria included: patients younger than 18 years, prior renal transplant recipients, those undergoing deceased-donor or multi-organ transplantation, and those diagnosed with DM prior to transplantation. All potential candidates provided informed consent prior to enrollment. Enrolled patients who completed glucose monitoring and assessment up to 6 months post-transplantation were included in the final analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eOnce consent was obtained, preoperative glucose data were collected 14 days before the scheduled surgery using a CGM sensor (Freestyle Libre 1; Abbott Diabetes Care Ltd., Maidenhead, UK). The device was placed on the patient\u0026rsquo;s upper arm according to the manufacturer\u0026rsquo;s instructions. The patients installed the corresponding application on their smartphones and were instructed to scan the sensors using their smartphones at least once every 8 h. The sensor collected glucose data every min and automatically stored a reading every 15 min. In addition to the daily glucose levels, the CGM system provided calculated parameters based on these daily levels, including glucose management index (GMI) (%), coefficient of variation (%), time within the range of 70\u0026ndash;180 mg/dL (%), time above the range (TAR) of 180 mg/dL (%), time below the range of 70 mg/dL (%), and nadir and peak glucose levels.\u003c/p\u003e \u003cp\u003eTo collect postoperative glucose data, a new sensor was applied and used for an additional 14 days following surgery. Throughout the hospitalization period for transplantation, patients adhered to the established protocol for CBGM, which was performed daily before each meal and bedtime. Interventions for abnormal glucose levels were based on the CBGM rather than CGM readings.\u003c/p\u003e \u003cp\u003ePreoperative waist-to-hip circumference ratio and fat-to-muscle ratio measurements using bioimpedance analysis (InBody970; InBody Co., Ltd., Seoul, Korea) were also collected in the enrolled patients. Further laboratory examinations were conducted for fasting plasma glucose (FPG), insulin, hemoglobin A1c (HbA1c), C-peptide, and lipid profiles (including total cholesterol, high-density lipoprotein [HDL], low-density lipoprotein [LDL], and triglycerides) at baseline and 1, 2, 3, and 6 months postoperatively. The homeostasis model assessment of insulin resistance (HOMA-IR) and beta-cell function (HOMA-B) were used to estimate insulin resistance and secretion by analyzing insulin levels. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of PTDM\u003c/h2\u003e \u003cp\u003ePTDM was defined based on the American Diabetes Association (ADA) diagnostic criteria at 6 months post-transplantation in patients without a preoperative diagnosis of DM. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Study patients who developed PTDM were categorized as the PTDM group. Those who did not develop PTDM during the study period were defined as the non-PTDM group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eImmunosuppressive regimen and postoperative glucose control protocol\u003c/h2\u003e \u003cp\u003eThe immunosuppressive regimen consisted of induction therapy and triple maintenance agents. Induction therapy included basiliximab or rabbit anti-human thymocyte immunoglobulin. The maintenance regimen consisted of tacrolimus, antimetabolites (mycophenolate mofetil and mycophenolic acid), and corticosteroids. Tacrolimus was administered twice a day. During the first 3 months post-transplantation, trough concentration levels were maintained within the range of 8\u0026ndash;12 ng/mL, followed by a target range of 6\u0026ndash;10 ng/mL from 3 to 6 months. Cumulative exposure to tacrolimus (CET) for 3 months was calculated as the area under the concentration\u0026ndash;time curve (AUC) based on trough concentrations. All measured tacrolimus concentration values for a specific patient were plotted on a time-dependent graph, and the AUC of the tacrolimus level was calculated using the Wagner\u0026ndash;Nelson equation. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Steroid therapy commenced with an intravenous injection of 500 mg on the day of surgery and was subsequently gradually tapered to 5 mg of oral prednisolone over 4 weeks. Steroids were administered after breakfast to alleviate gastrointestinal discomfort, and this was continued for 6 months. Mycophenolate mofetil (500 mg) or an equivalent dose of mycophenolic acid was administered twice daily. In cases where adverse effects such as leukopenia or elevated liver enzyme levels were observed, the medication was either discontinued or adjusted based on the clinical severity and progression of side effects.\u003c/p\u003e \u003cp\u003eFor postoperative glycemic control, the regular insulin (RI) sliding scale protocol was initiated if the CBGM exceeded 250mg/dL immediately after surgery. Humulin R (100 IU) was mixed with 100 mL of normal saline and started at 1 cc/h, with the dose adjusted according to the established protocol. Once the RI was tapered, intermittent subcutaneous injections of Humulin R were administered according to a consistent protocol (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eCategorical variables are expressed as percentages within the respective derived groups and were assessed using Pearson\u0026rsquo;s chi-square and Fisher\u0026rsquo;s exact tests. Continuous variables are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations and were evaluated using a Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test following a normality test.\u003c/p\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses were conducted to identify factors that were independently and significantly associated with the onset of PTDM. Before conducting multivariate logistic regression, a multicollinearity test was performed using the variance inflation factor (VIF) among independent variables. If the VIF value exceeded 10, it was used as the criterion for variable removal from the model, and variables were removed in consideration of clinical relevance to the study. Variables with a \u003cem\u003eP\u003c/em\u003e-value of less than 0.05 in the univariate analyses were subsequently included in the multivariate logistic regression using a backward elimination method.\u003c/p\u003e \u003cp\u003eWe established two models, the CBGM and CGM model, with postoperative blood glucose data (postoperative CGM). Additionally, receiver operating characteristic (ROC) curves were generated to compare the AUC of different glucose monitoring models (postoperative CBGM vs. postoperative CGM) for PTDM prediction. DeLong\u0026rsquo;s test was conducted to compare the AUCs of both models. All analyses were performed using IBM SPSS version 26.0 (IBM Corp., Armonk, NY, USA) and R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). A P-value of less than 0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. \u0026nbsp; All study procedures were approved by the independent Institutional Review Board (IRB) of Seoul National University Hospital (IRB number: 2102-083-1197). All participants provided written informed consent.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003ePatient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the enrollment period, 264 patients underwent KT at our center, of which, 100 consented to participate in the study. In the cohort of 100 patients, 15 were diagnosed with preoperative DM, four had undergone deceased-donor transplantation without preoperative CGM data, 18 had insufficient glucose monitoring data, and three had insufficient diabetes-related data and were thus excluded from the study. Finally, 60 patients were included in the final analysis. Among them, 14 (23.3%) developed PTDM during the study period. Patient and donor demographics and transplant characteristics are summarized in Table 1. The PTDM group was significantly older (56.4\u0026plusmn;9.8 years) than the non-PTDM group (46.2\u0026plusmn;13.0 years, \u003cem\u003eP\u003c/em\u003e=0.009). In addition, male patients were more prevalent in the PTDM group (85.7%) than in the non-PTDM group (45.7%, \u003cem\u003eP\u003c/em\u003e=0.008). No differences were observed among donors with respect to human leukocyte antigen incompatibility, age, sex, body mass index (BMI), or their relationship with the recipient between the two groups. All study patients exhibited a normal preoperative glucose profile, with a mean C-peptide level of 5.9\u0026plusmn;3.5 ng/mL. Indices of insulin secretion and resistance, represented by HOMA-B (226.8\u0026plusmn;251.8) and HOMA-IR (2.3\u0026plusmn;1.1), respectively, were within the normal range. Although enrolled patients showed a normal range of HbA1c levels (5.2\u0026plusmn;0.5%), patients in the PTDM group exhibited significantly higher preoperative HbA1c levels (5.5\u0026plusmn;0.4% vs. 5.2\u0026plusmn;0.5%, respectively; \u003cem\u003eP\u003c/em\u003e=0.023). Those in the PTDM group also had lower HDL levels than those in the non-PTDM group (37.7\u0026plusmn;11.8 mg/dL vs. 51.3\u0026plusmn;16.3 mg/dL, respectively; \u003cem\u003eP\u003c/em\u003e=0.006).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransplant outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll transplantations were performed successfully without immediate postoperative complications or rejections. Regarding immunosuppressive agents, there were no significant differences in terms of induction agents, steroid pulse treatment during follow-up, or mean steroid dose during hospitalization. However, the PTDM group exhibited significantly higher tacrolimus trough concentrations on the day of discharge than the non-PTDM group (11.4\u0026plusmn;2.2 ng/mL vs. 9.8\u0026plusmn;1.9 ng/mL, respectively; \u003cem\u003eP\u003c/em\u003e=0.014), along with higher mean tacrolimus concentrations during the index admission (11.3\u0026plusmn;2.2 ng/mL vs. 9.6\u0026plusmn;1.7 ng/mL, respectively; \u003cem\u003eP\u003c/em\u003e=0.003). Furthermore, the 3-month CET was significantly greater in the PTDM group than in the non-PTDM group (898.3\u0026plusmn;54.4 ng/mL vs. 804.7\u0026plusmn;90.0 ng/mL, respectively; \u003cem\u003eP\u003c/em\u003e=0.001) (Table 1).\u003c/p\u003e\n\u003cp\u003eNo significant difference was seen in the occurrence of transplant rejection demonstrated by indication biopsy and postoperative 10-days protocol biopsy between the non-PTDM (37.0%) and PTDM (21.4%) groups (\u003cem\u003eP\u003c/em\u003e=0.281). No graft failure occurred during follow-up in either group. The estimated glomerular filtration rate levels at discharge and 1, 2, 3, and 6 months postoperatively showed no significant differences between the two groups (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChanges in perioperative glucose profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 illustrates the preoperative and postoperative changes in glucose variation relative to PTDM development. An increase in daily glucose levels and variation were seen in the postoperative period compared with values in the preoperative period in the PTDM group. Figure 2 presents a visualized graph of changes in glucose levels before and after KT for the entire cohort, and Figure 3 further shows significant increases in average, peak, and nadir glucose levels that occurred postoperatively in both groups.\u003c/p\u003e\n\u003cp\u003eIn the preoperative period, CGM showed that the PTDM group had significantly higher preoperative mean glucose levels (107.4\u0026plusmn;13.2 mg/dL vs. 95.5\u0026plusmn;13.4 mg/dL, respectively; \u003cem\u003eP\u003c/em\u003e=0.006), GMI (5.8\u0026plusmn;0.3% vs. 5.6\u0026plusmn;0.3%, respectively; \u003cem\u003eP\u003c/em\u003e=0.016), and mean peak glucose levels (175.5\u0026plusmn;19.6 mg/dL vs. 150.0\u0026plusmn;31.5 mg/dL, respectively; \u003cem\u003eP\u003c/em\u003e=0.006) than the non-PTDM group. In the postoperative period, the CGM values of the mean glucose levels (141.6\u0026plusmn;21.4 mg/dL vs. 120.2\u0026plusmn;18.0 mg/dL, respectively; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), TAR of 180 mg/dL during the day (21.1\u0026plusmn;12.3% vs. 9.0\u0026plusmn;8.8%, respectively; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), and mean daily peak glucose levels (221.5\u0026plusmn;22.3 vs. 189.1\u0026plusmn;31.2 mg/dL, respectively; \u003cem\u003eP\u003c/em\u003e=0.002) were significantly higher in the PTDM group than those in the non-PTDM group (Table 3).\u003c/p\u003e\n\u003cp\u003eSimilarly, CBGM also showed that the PTDM group exhibited higher average glucose levels postoperatively (164.6\u0026plusmn;25.3 mg/dL vs. 151.9\u0026plusmn;18.9 mg/dL, respectively; \u003cem\u003eP\u003c/em\u003e=0.027) and daily peak glucose values (208.7\u0026plusmn;31.7 mg/dL vs. 189.6\u0026plusmn;26.2 mg/dL, respectively; \u003cem\u003eP\u003c/em\u003e=0.048) than the non-PTDM group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePostoperative changes in metabolic indices during the follow-up period\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no significant differences in BMI changes between the two groups at 1, 2, 3, and 6 months postoperatively. However, the PTDM group consistently demonstrated significantly higher of HbA1c levels than the non-PTDM group at all follow-up periods. There were no significant differences in total cholesterol and LDL levels between the two groups during the 6-month follow-up period. However, the PTDM group showed significantly lower HDL levels at 2 and 3 months postoperatively. The HOMA-IR values at 1, 2, and 3 months after KT did not show any significant differences between the two groups. However, the PTDM group showed an increasing trend of HOMA-IR after transplantation and exhibited a significantly higher value of HOMA-IR (7.0\u0026plusmn;9.5) at 6 months, whereas patients in the non-PTDM group had a lower value (3.1\u0026plusmn;1.5, \u003cem\u003eP\u003c/em\u003e=0.001). There were no significant differences between the two groups in terms of HOMA-B during the follow-up period (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk factors and prediction models for PTDM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the univariate analysis, baseline characteristics such as age, male sex, and preoperative HbA1c and HDL levels were associated with the development of PTDM. The 3-month CET was identified as an immunosuppression-related factor for PTDM development. Among postoperative glucose profiles, mean daily peak glucose by CBGM, mean glucose level, TAR of 180 mg/dL, and mean daily peak glucose levels by CGM were statistically significant. A multicollinearity assessment was conducted on factors exhibiting statistical significance in the univariate analysis (Table 4). Confirming no evidence of multicollinearity, we performed the multivariate logistic regression analysis using baseline characteristics and postoperative indices. Male sex (odds ratio [OR]: 17.45; 95% confidence interval [CI]: 1.79-70.01; \u003cem\u003eP\u003c/em\u003e=0.014) and a CGM-detected postoperative TAR of 180 mg/dL (OR: 1.17; 95% CI: 1.06-1.29; \u003cem\u003eP\u003c/em\u003e=0.002) were found to be independent risk factors associated with the occurrence of PTDM (Table 5).\u003c/p\u003e\n\u003cp\u003eTo compare the predictive abilities of the CBGM and CGM models for PTDM, we plotted the ROC curves for the CBGM model and the postoperative CGM model. Each model was constructed by combining variables with a \u003cem\u003eP\u003c/em\u003e-value\u003cem\u003e\u0026nbsp;\u003c/em\u003eof less than 0.05, identified through univariate regression analysis. The AUCs of CBGM and postoperative CGM were 0.865 and 0.916, respectively (Figure 5). In DeLong\u0026rsquo;s test, the difference in AUCs between the CBGM model and the postoperative CGM model resulted in a \u003cem\u003eP\u003c/em\u003e-value of 0.12.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003ePrevious studies have demonstrated the accuracy, reliability, and feasibility of CGM in patients with type 1 DM, simultaneous pancreas-KT, and critical care settings. [13-16] Furthermore, some transplant centers have adopted CGM as part of their standard protocol. However, only a limited number of studies have analyzed postoperative glucose dynamics through CGM in KT recipients, specifically examining its association with the onset of PTDM. Additionally, monitoring in previous studies was limited to the short stress period within 5 days immediately postoperatively, making it difficult to secure accurate predictive power. [17,18]\u003c/p\u003e\n\u003cp\u003eWe conducted a comprehensive analysis to investigate factors influencing the incidence of PTDM using CGM. Blood glucose levels were monitored using CGM for 14 days, before and after surgery, in addition to CBGM during hospitalization. KT recipients showed elevated mean glucose levels, GMI, TAR of 180 mg/dL, and mean daily peak glucose levels post-KT compared with pre-KT values. Among the patients included in the study, 23.3% developed PTDM. Our findings identified major risk factors associated with PTDM, including male sex and elevated postoperative TAR of 180 mg/dL, detected using CGM.\u003c/p\u003e\n\u003cp\u003ePreoperative impaired glucose tolerance (IGT) and impaired fasting glucose (IFG) represent significant risk factors for developing PTDM. [19] Previous studies have indicated a 15% prevalence of pre-transplantation IGT or IFG, with a subsequent 35% progression to PTDM. [20,21] Although all values remained within the normal range, preoperative CGM indicated that patients in the PTDM group had significantly higher mean glucose levels, GMI, and mean daily peak glucose levels prior to transplantation than those in the non-PTDM group. Similar to DM, IGT or IFG is characterized by significant glucose variability, which can be assessed through CGM.19 Without overt DM prior to transplantation, the preemptive use of antihyperglycemic agents or insulin may not be warranted. However, lifestyle modifications and dietary adjustments alone have been reported to prevent the progression to DM in patients with impaired glucose metabolism. [22,23] Furthermore, unlike invasive CBGM, CGM can be implemented in an outpatient setting before surgery and provide comprehensive 24-h data, offering more sensitive glucose readings. It also allows patients to independently access their glucose data and initiate management strategies prior to surgery. Therefore, CGM not only improves the accuracy of predicting PTDM, but also has the potential to reduce its incidence through early detection and correction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding postoperative glycemic control, the multivariate logistic regression analysis identified a postoperative TAR of 180mg/dL using CGM as a significant risk factor for PTDM. However, values obtained through CBGM were not statistically significant. The CBGM follows a standard protocol that involves measuring glucose levels upon waking up, before lunch and dinner, and before bedtime. Patients with KT experience various physiological changes along with stress hormone secretion due to surgery immediately after transplantation, administration of high-dose immunosuppressants, and the dawn phenomenon. Administration of prednisolone after breakfast contributes to prolonged elevation of glucose levels or an extended period of prolonged elevation before levels return to baseline. However, intermittent monitoring with CBGM leads to imprecision in average values and precludes the capture of glucose dynamics, [24,25] including peak values or TAR of 180 mg/dL.\u0026nbsp;Consequently, CGM showed higher predictive power for PTDM occurrence compared with CBGM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has a few limitations. First, it was based on a single-center sample with a relatively small number of patients. Therefore, our results may not be readily generalizable, and further validation is warranted through extensive multicenter investigations. Second, an oral glucose tolerance test (OGTT) was not conducted in our center. However, ADA guidelines emphasize the equivalency of FPG, 2-h plasma glucose OGTT, and HbA1c for diagnostic screening. Furthermore, it is underscored that utilizing FPG and HbA1c tests in screening can effectively reduce the overall requirement for OGTTs. [4] Finally, the carbohydrate intake and physical activity of the cohort were not controlled. Therefore, we cannot exclude the possibility that our findings could be explained by variations in the patients\u0026rsquo; diet or physical activity levels.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, we found that male patients with a higher postoperative TAR of 180 mg/dL have an increased risk of PTDM. Moreover, CGM provides a reliable method for glucose monitoring and offers superior predictive performance for detecting the occurrence of PTDM compared with CBGM. The utilization of CGM facilitates the identification of individuals at risk of developing PTDM and could support the implementation of more rigorous glycemic control in at-risk patients. Further investigation is warranted to substantiate these results, including cost-effectiveness considerations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe source data for all figures included in the manuscript are stored in Mendeley Data (doi: 10.17632/r5n5zt4xs9.1). If permissible, the dataset generated and/or analyzed during the current research will be made available upon request from the corresponding author. Limited access to certain clinical data generated in the current study is restricted due to the absence of prior authorization for external sharing of data from research subjects without explicit consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJY\u003c/strong\u003e: Data curation, Formal analysis, Investigation, Writing\u0026mdash;original draft, Writing\u0026mdash;review \u0026amp; editing.\u003cstrong\u003e\u0026nbsp;EA\u003c/strong\u003e: Conceptualization, Data curation, Methodology, Project administration. \u003cstrong\u003eHY, AR,\u003c/strong\u003e and \u003cstrong\u003eMH\u003c/strong\u003e: Investigation, Methodology, Project administration. \u003cstrong\u003eSW\u003c/strong\u003e: Software, Validation, Visualization, Formal analysis. \u003cstrong\u003eAR\u003c/strong\u003e and \u003cstrong\u003eJW\u003c/strong\u003e: Investigation, Resources, Supervision. \u003cstrong\u003eSI\u003c/strong\u003e: Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing\u0026mdash;review \u0026amp; editing.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing intersests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSong, J. L. \u003cem\u003eet al.\u003c/em\u003e Higher tacrolimus blood concentration is related to increased risk of post-transplantation diabetes mellitus after living donor liver transplantation. Int. J. Surg. 51, 17\u0026ndash;23 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, L., \u003cem\u003eet al.\u003c/em\u003e Postoperative fasting plasma glucose and family history diabetes mellitus can predict post-transplantation diabetes mellitus in kidney transplant recipients. Endocrine. 81, 58\u0026ndash;66 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZielińska, K. \u003cem\u003eet al.\u003c/em\u003e Prevalence and risk factors of new-onset diabetes after transplantation (NODAT). Ann. Transplant. 25, e926556 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmyrli, M., Smyrlis, A., Tsouka, G., Apostolou, T. \u0026amp; Vougas, V. Risk factors of the development of diabetes mellitus after kidney transplantation. \u003cem\u003eTransplant. Proc.\u003c/em\u003e 53, 2782\u0026ndash;2785 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHecking, M., Sharif, A., Eller, K. \u0026amp; Jenssen, T. Management of post-transplant diabetes: immunosuppression, early prevention, and novel antidiabetics. Transpl. Int. 34, 27\u0026ndash;48 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDziedziejko, V. \u003cem\u003eet al.\u003c/em\u003e Leptin receptor gene polymorphisms in kidney transplant patients with post-transplant diabetes mellitus treated with tacrolimus. Int. Immunopharmacol. 124, 110989 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, Y. \u003cem\u003eet al.\u003c/em\u003e Impact of varied immunosuppressive agents and posttransplant diabetes mellitus on prognosis among diverse transplant recipients (experimental studies). Int. J. Surg. 110, 2007\u0026ndash;2024 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartens, T. \u003cem\u003eet al.\u003c/em\u003e Effect of continuous glucose monitoring on glycemic control in patients with type 2 diabetes treated with basal insulin: a randomized clinical trial. Jama. 325, 2262\u0026ndash;2272 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGottfried, S., Pontiggia, L., Newberg, A., Laynor, G. \u0026amp; Monti, D. Continuous glucose monitoring metrics for earlier identification of pre-diabetes: protocol for a systematic review and meta-analysis. BMJ Open. 12, e061756 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatthews, D. R. et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 28, 412\u0026ndash;419 (1985).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Diabetes Association Professional Practice Committee; 2. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes\u0026mdash;2022. \u003cem\u003eDiabetes Care.\u003c/em\u003e 45, S17-S38 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-Per\u0026aacute;lvarez, M. \u003cem\u003eet al.\u003c/em\u003e Cumulative exposure to tacrolimus and incidence of cancer after liver transplantation. Am. J. Transplant. 22, 1671\u0026ndash;1682 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Hayek, A. A., Robert, A. A. \u0026amp; Al Dawish, M. A. Evaluation of FreeStyle libre flash glucose monitoring system on glycemic control, health-related quality of life, and fear of hypoglycemia in patients with type 1 diabetes. Clin. Med. Insights Endocrinol. Diabetes. 10, 1179551417746957 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDmitriev, I. V. \u003cem\u003eet al.\u003c/em\u003e Continuous glucose monitoring in patients following simultaneous pancreas-kidney transplantation: time in range and glucose variability. Diagnostics. 13, 1606 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDadlani, V. \u003cem\u003eet al.\u003c/em\u003e Continuous glucose monitoring to assess glycemic control in the first 6 weeks after pancreas transplantation. Clin. Transplant. 33, e13719 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerez-Guzman, M. C., Shang, T., Zhang, J. Y., Jornsay, D. \u0026amp; Klonoff, D. C. Continuous glucose monitoring in the hospital. Endocrinol Metab. 36, 240\u0026ndash;255 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJandovitz, N. \u003cem\u003eet al.\u003c/em\u003e A randomized trial of continuous glucose monitoring to improve post-transplant glycemic control. Clin. Transplant. 37, e15139 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWojtusciszyn, A., Mourad, G., Bringer, J. \u0026amp; Renard, E. Continuous glucose monitoring after kidney transplantation in non-diabetic patients: early hyperglycaemia is frequent and may herald post-transplantation diabetes mellitus and graft failure. Diabetes Metab. 39, 404\u0026ndash;410 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMittal, S. \u003cem\u003eet al.\u003c/em\u003e Early postoperative continuous glucose monitoring in pancreas transplant recipients. Transpl. Int. 28, 604\u0026ndash;609 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaillard, S. \u003cem\u003eet al.\u003c/em\u003e Incidence and risk factors of glucose metabolism disorders in kidney transplant recipients: role of systematic screening by oral glucose tolerance test. Transplantation. 91, 757\u0026ndash;764 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuthoff, M. \u003cem\u003eet al.\u003c/em\u003e Diabetes mellitus and prediabetes on kidney transplant waiting list- prevalence, metabolic phenotyping and risk stratification approach. PLoS One. 10, e0134971 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuomilehto, J. \u003cem\u003eet al.\u003c/em\u003e Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N. Engl. J. Med. 344, 1343\u0026ndash;1350 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, G. \u003cem\u003eet al.\u003c/em\u003e The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 371, 1783\u0026ndash;1789 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez, L. M., Knight, R. J. \u0026amp; Heptulla, R. A. Continuous glucose monitoring in subjects after simultaneous pancreas-kidney and kidney-alone transplantation. Diabetes Technol. Ther. 12, 347\u0026ndash;351 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClemens, K. K. et al. Reducing hyperglycaemia post-kidney and liver transplant: a quality improvement initiative. BMJ Open Qual. 11, e001796 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Patient and transplantation characteristics of the non-PTDM and PTDM groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-PTDM (n=46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTDM (n=14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eBaseline characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e48.6\u0026plusmn;12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e46.2\u0026plusmn;13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e56.4\u0026plusmn;9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e33 (55.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e21 (45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e12 (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e23.0\u0026plusmn;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e22.7\u0026plusmn;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e24.2\u0026plusmn;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eWaist-to-hip ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e0.9\u0026plusmn;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e0.9\u0026plusmn;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e0.9\u0026plusmn;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eFat ratio (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e29.8\u0026plusmn;10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e31.2\u0026plusmn;10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e25.7\u0026plusmn;9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eMuscle ratio (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e35.9\u0026plusmn;8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e33.7\u0026plusmn;10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e39.6\u0026plusmn;8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eDonor characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003ePreoperative desensitization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e16 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e13 (28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e3 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eDonor age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e49.6\u0026plusmn;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e51.2\u0026plusmn;11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e44.5\u0026plusmn;13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eDonor male sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e27 (45.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e21 (45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e6 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eDonor BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e23.3\u0026plusmn;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e23.3\u0026plusmn;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e23.2\u0026plusmn;3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eRelated donor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e35 (58.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e26 (56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e9 (64.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eUnderlying disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e42 (70.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e31 (67.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e11 (78.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e6 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e5 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e1 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eLiver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e5 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e4 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e1 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eCoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e7 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e5 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e2 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eFamily history of DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e3 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e3 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eESRD cause\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eGlomerulonephritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e27 (45.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e21(45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e6 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e11 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e9 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e2 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003ePolycystic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e9 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e6 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e3 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e6 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e4 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e2 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e1 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eDialysis state\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003ePreemptive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e26 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e18 (39.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e8 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eBaseline laboratory results\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eFasting plasma glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e83.6\u0026plusmn;12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e83.4\u0026plusmn;13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e84.5\u0026plusmn;8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e5.2\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e5.2\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e5.5\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eFasting insulin (\u0026mu;IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e11.1\u0026plusmn;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e10.8\u0026plusmn;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e12.0\u0026plusmn;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eC-peptide (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e5.9\u0026plusmn;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e5.7\u0026plusmn;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e6.4\u0026plusmn;4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e2.3\u0026plusmn;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e2.2\u0026plusmn;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e2.5\u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eHOMA-B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e226.8\u0026plusmn;251.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e218.9\u0026plusmn;269.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e251.5\u0026plusmn;192.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e150.0\u0026plusmn;33.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e153.2\u0026plusmn;33.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e139.7\u0026plusmn;32.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e99.0\u0026plusmn;44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e98.22\u0026plusmn;47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e101.5\u0026plusmn;36.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eHigh-density lipids (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e48.1\u0026plusmn;16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e51.3\u0026plusmn;16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e37.7\u0026plusmn;11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eLow-density lipids (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e79.5\u0026plusmn;26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e81.6\u0026plusmn;26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e72.8\u0026plusmn;28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eMean tacrolimus C0 level during hospitalization (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e10.0\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e9.6\u0026plusmn;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e11.3\u0026plusmn;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eTacrolimus C0 level at discharge (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e10.2\u0026plusmn;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e9.8\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e11.4\u0026plusmn;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e3-Month cumulative exposure of tacrolimus (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e826.5\u0026plusmn;91.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e804.7\u0026plusmn;90.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e898.3\u0026plusmn;54.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eAnti-thymoglobulin induction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e5 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e4 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e1 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eSteroid pulse during admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e4 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e3 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e1 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eMean steroids dose during hospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e143.1\u0026plusmn;56.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e144.1\u0026plusmn;60.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e139.9\u0026plusmn;42.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eRenal function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eBaseline eGFR (CKD-EPI) (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e7.3\u0026plusmn;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e7.3\u0026plusmn;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e7.4\u0026plusmn;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eCold ischemia time (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e59.06\u0026plusmn;20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e60.23\u0026plusmn;21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e55.0\u0026plusmn;16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003eTotal hospital stay (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e17.0\u0026plusmn;9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e17.9\u0026plusmn;10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.171717171717173%\" valign=\"top\"\u003e\n \u003cp\u003e14.2\u0026plusmn;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as means \u0026plusmn; standard deviations or numbers (%).\u003c/p\u003e\n\u003cp\u003eLiver disease is defined as chronic hepatitis B virus or hepatitis C virus infection or liver cirrhosis.\u003c/p\u003e\n\u003cp\u003eBMI, body mass index; DM, diabetes mellitus; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; eGFR, estimated glomerular filtration rate; ESRD, end-stage renal disease; HbA1c, hemoglobin A1c; HOMA-B, homeostasis model assessment of beta-cell function; HOMA-IR, homeostasis model assessment of insulin resistance; PTDM, post-transplantation diabetes mellitus.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Transplantation outcomes.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.56410256410256%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34188034188034%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-PTDM (n=46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.316239316239315%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTDM (n=14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.56410256410256%\" valign=\"top\"\u003e\n \u003cp\u003eRejection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34188034188034%\" valign=\"top\"\u003e\n \u003cp\u003e17 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.316239316239315%\" valign=\"top\"\u003e\n \u003cp\u003e3 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.56410256410256%\" valign=\"top\"\u003e\n \u003cp\u003eGraft failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34188034188034%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.316239316239315%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.56410256410256%\" valign=\"top\"\u003e\n \u003cp\u003eeGFR\u0026nbsp;(CKD-EPI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34188034188034%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.316239316239315%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.56410256410256%\" valign=\"top\"\u003e\n \u003cp\u003eDischarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34188034188034%\" valign=\"top\"\u003e\n \u003cp\u003e68.3\u0026plusmn;21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.316239316239315%\" valign=\"top\"\u003e\n \u003cp\u003e54.5\u0026plusmn;15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.56410256410256%\" valign=\"top\"\u003e\n \u003cp\u003e1 Month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34188034188034%\" valign=\"top\"\u003e\n \u003cp\u003e63.4\u0026plusmn;18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.316239316239315%\" valign=\"top\"\u003e\n \u003cp\u003e61.3\u0026plusmn;16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.56410256410256%\" valign=\"top\"\u003e\n \u003cp\u003e2 Months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34188034188034%\" valign=\"top\"\u003e\n \u003cp\u003e64.3\u0026plusmn;17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.316239316239315%\" valign=\"top\"\u003e\n \u003cp\u003e63.8\u0026plusmn;16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.56410256410256%\" valign=\"top\"\u003e\n \u003cp\u003e3 Months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34188034188034%\" valign=\"top\"\u003e\n \u003cp\u003e61.1\u0026plusmn;16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.316239316239315%\" valign=\"top\"\u003e\n \u003cp\u003e60.8\u0026plusmn;12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.56410256410256%\" valign=\"top\"\u003e\n \u003cp\u003e6 Months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.34188034188034%\" valign=\"top\"\u003e\n \u003cp\u003e60.3\u0026plusmn;18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.316239316239315%\" valign=\"top\"\u003e\n \u003cp\u003e61.7\u0026plusmn;12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77777777777778%\" valign=\"top\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as means \u0026plusmn; standard deviations or numbers (%).\u003c/p\u003e\n\u003cp\u003eRejection is defined by evidence of either an indication biopsy or a postoperative 10-day protocol biopsy, with pathologic evaluation according to the Banff score 2022 criteria.\u003c/p\u003e\n\u003cp\u003eCKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; eGFR, estimated glomerular filtration rate; PTDM, post-transplantation diabetes mellitus.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Perioperative glucose profiles of the non-PTDM and PTDM groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfiles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n=60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-PTDM (n=46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePTDM (n=14)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003ePostoperative CBGM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eMean glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e154.9\u0026plusmn;21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e151.9\u0026plusmn;18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e164.6\u0026plusmn;25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eMean daily peak glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e194.0\u0026plusmn;28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e189.6\u0026plusmn;26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e208.7\u0026plusmn;31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003ePreoperative CGM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eMean glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e98.5\u0026plusmn;14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e95.5\u0026plusmn;13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e107.4\u0026plusmn;13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eGlucose management index (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e5.7\u0026plusmn;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e5.6\u0026plusmn;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e5.8\u0026plusmn;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eCoefficient of variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e28.6\u0026plusmn;29.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e29.4\u0026plusmn;34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e26.0\u0026plusmn;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eTime in the range of 70\u0026ndash;180 mg/dL (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e87.3\u0026plusmn;14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e86.0\u0026plusmn;15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e91.4\u0026plusmn;8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eTime above the range of 180 mg/dL\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e3.2\u0026plusmn;9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e3.3\u0026plusmn;10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e3.0\u0026plusmn;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eTime below the range of 70 mg/dL (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e9.3\u0026plusmn;13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e10.6\u0026plusmn;14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e5.6\u0026plusmn;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eMean daily peak glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e156.4\u0026plusmn;30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e150.0\u0026plusmn;31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e175.5\u0026plusmn;19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eMean daily nadir glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e68.9\u0026plusmn;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e67.7\u0026plusmn;10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e72.4\u0026plusmn;11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003ePostoperative CGM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eMean glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e125.3\u0026plusmn;20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e120.2\u0026plusmn;18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e141.6\u0026plusmn;21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eGlucose management index (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e6.6\u0026plusmn;2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e6.6\u0026plusmn;2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e6.7\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eCoefficient of variation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e31.2\u0026plusmn;7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e30.9\u0026plusmn;8.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e32.3\u0026plusmn;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eTime in the range of 70\u0026ndash;180 mg/dL (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e82.1\u0026plusmn;15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e84.2\u0026plusmn;15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e75.6\u0026plusmn;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eTime above the range of 180 mg/dL (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e11.9\u0026plusmn;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e9.0\u0026plusmn;8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e21.1\u0026plusmn;12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eTime below the range of 70 mg/dL (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e4.6\u0026plusmn;6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e5.1\u0026plusmn;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e3.2\u0026plusmn;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eMean daily peak glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e196.5\u0026plusmn;32.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e189.1\u0026plusmn;31.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e221.5\u0026plusmn;22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.267886855241265%\" valign=\"top\"\u003e\n \u003cp\u003eMean daily nadir glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e88.0\u0026plusmn;15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003e85.8\u0026plusmn;15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.806988352745424%\" valign=\"top\"\u003e\n \u003cp\u003e95.3\u0026plusmn;14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.813643926788686%\" valign=\"top\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as means \u0026plusmn; standard deviations.\u003c/p\u003e\n\u003cp\u003eCBGM, capillary blood glucose monitoring; CGM, continuous glucose monitoring; PTDM, post-transplantation diabetes mellitus.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Multicollinearity analysis results for different combinations of variables associated with PTDM.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.6969696969697%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable combinations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBaseline characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.6969696969697%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\" valign=\"top\"\u003e\n \u003cp\u003e1.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.6969696969697%\" valign=\"top\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\" valign=\"top\"\u003e\n \u003cp\u003e1.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.6969696969697%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\" valign=\"top\"\u003e\n \u003cp\u003e1.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.6969696969697%\" valign=\"top\"\u003e\n \u003cp\u003eHigh-density lipids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\" valign=\"top\"\u003e\n \u003cp\u003e1.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.6969696969697%\" valign=\"top\"\u003e\n \u003cp\u003e3-Month tacrolimus AUC (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\" valign=\"top\"\u003e\n \u003cp\u003e1.517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePostoperative CBGM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.6969696969697%\" valign=\"top\"\u003e\n \u003cp\u003eMean daily peak glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\" valign=\"top\"\u003e\n \u003cp\u003e2.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePostoperative CGM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.6969696969697%\" valign=\"top\"\u003e\n \u003cp\u003eMean glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\" valign=\"top\"\u003e\n \u003cp\u003e5.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.6969696969697%\" valign=\"top\"\u003e\n \u003cp\u003eTime above the range of 180 mg/dL\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\" valign=\"top\"\u003e\n \u003cp\u003e6.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.6969696969697%\" valign=\"top\"\u003e\n \u003cp\u003eMean daily peak glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\" valign=\"top\"\u003e\n \u003cp\u003e3.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAUC, area under the receiver operating characteristic curve; CBGM, capillary blood glucose monitoring; CGM, continuous glucose monitoring;\u0026nbsp;HbA1c, hemoglobin A1c; PTDM, post-transplantation diabetes mellitus; VIF, variance inflation factor.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 5.\u003c/strong\u003e Univariate and multivariate logistic regression analyses of risk factors associated with PTDM occurrence.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.286189683860233%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.10648918469218%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.607321131447584%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.870370370370374%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.59259259259259%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.9537037037037%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eBaseline characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.588039867109636%\" valign=\"top\"\u003e\n \u003cp\u003e1.08 (1.02\u0026ndash;1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.624584717607974%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.083056478405314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.465116279069768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.588039867109636%\" valign=\"top\"\u003e\n \u003cp\u003e7.14 (1.43\u0026ndash;35.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.624584717607974%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.083056478405314%\" valign=\"top\"\u003e\n \u003cp\u003e17.45 (1.79\u0026ndash;70.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.465116279069768%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eBaseline laboratory results\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.588039867109636%\" valign=\"top\"\u003e\n \u003cp\u003e4.78 (1.17\u0026ndash;19.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.624584717607974%\" valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.083056478405314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.465116279069768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003eHigh-density lipids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.588039867109636%\" valign=\"top\"\u003e\n \u003cp\u003e0.93 (0.89\u0026ndash;0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.624584717607974%\" valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.083056478405314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.465116279069768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eImmunosuppression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003e3-Month tacrolimus AUC (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.588039867109636%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.624584717607974%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.083056478405314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.465116279069768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003ePostoperative CBGM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003eMean glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.588039867109636%\" valign=\"top\"\u003e\n \u003cp\u003e1.03 (0.99\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.624584717607974%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.083056478405314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.465116279069768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003eMean daily peak glucose(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.588039867109636%\" valign=\"top\"\u003e\n \u003cp\u003e1.03 (1.00\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.624584717607974%\" valign=\"top\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.083056478405314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.465116279069768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003ePostoperative CGM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003eMean glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.588039867109636%\" valign=\"top\"\u003e\n \u003cp\u003e1.06 (1.02\u0026ndash;1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.624584717607974%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.083056478405314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.465116279069768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003eTime above range of 180 mg/dL (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.588039867109636%\" valign=\"top\"\u003e\n \u003cp\u003e1.11 (1.04\u0026ndash;1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.624584717607974%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.083056478405314%\" valign=\"top\"\u003e\n \u003cp\u003e1.17 (1.06\u0026ndash;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.465116279069768%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.239202657807308%\" valign=\"top\"\u003e\n \u003cp\u003eMean daily peak glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.588039867109636%\" valign=\"top\"\u003e\n \u003cp\u003e1.04 (1.01\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.624584717607974%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.083056478405314%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.465116279069768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAUC, area under the receiver operating characteristic curve; CBGM, capillary blood glucose monitoring; CGM, continuous glucose monitoring; CI, confidence interval; HbA1c, hemoglobin A1c; OR, odds ratio; PTDM, post-transplantation diabetes mellitus.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"kidney transplantation, diabetes mellitus, continuous glucose monitoring, blood glucose, postoperative care, follow up","lastPublishedDoi":"10.21203/rs.3.rs-4589321/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4589321/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePost-transplantation diabetes mellitus (PTDM) negatively affects graft and patient survival after kidney transplantation (KT). This prospective study used continuous glucose monitoring (CGM) to evaluate perioperative blood glucose dynamics, identify PTDM risk factors, and compare predictive accuracy with capillary blood glucose monitoring (CBGM) in 60 non-diabetic living-donor KT recipients. Patients underwent 2-week pre- and postoperative CGM, including routine CBGM during their in-hospital stays. PTDM-related risk factors and glucose profiles were analyzed with postoperative CGM and CBG. PTDM developed in 14 (23.3%) patients and was associated with older age, male sex, higher baseline HbA1c, high-density lipoprotein cholesterol, and 3-month cumulative tacrolimus exposure levels. Male sex and postoperative time above the range (TAR) of 180 mg/dL by CGM were PTDM-related risk factors in the multivariate analysis. For predictive power, the CGM model with postoperative glucose profiles exhibited higher accuracy compared with the CBGM model (areas under the curves of 0.916, and 0.865 respectively). Therefore, we found that male patients with a higher postoperative TAR of 180 mg/dL have an increased risk of PTDM. Postoperative CGM provides detailed glucose dynamics and demonstrates superior predictive potential for PTDM than CBGM.\u003c/p\u003e","manuscriptTitle":"Changes in blood glucose profile before and after kidney transplantation: a prospective cohort study using continuous glucose monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 23:39:51","doi":"10.21203/rs.3.rs-4589321/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-01T11:04:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-31T23:53:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8173249348853155301732143947121124177","date":"2024-07-13T13:36:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-12T17:22:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182082611811285923315565438808797776716","date":"2024-06-26T08:23:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-25T02:21:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-24T15:23:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-21T17:23:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-17T12:31:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-16T10:18:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"852c9b90-5ab7-4a59-aa95-16459f2285cb","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33904327,"name":"Health sciences/Endocrinology"},{"id":33904328,"name":"Health sciences/Medical research"},{"id":33904329,"name":"Health sciences/Nephrology"}],"tags":[],"updatedAt":"2024-09-16T15:59:51+00:00","versionOfRecord":{"articleIdentity":"rs-4589321","link":"https://doi.org/10.1038/s41598-024-72025-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-09-11 15:57:07","publishedOnDateReadable":"September 11th, 2024"},"versionCreatedAt":"2024-07-15 23:39:51","video":"","vorDoi":"10.1038/s41598-024-72025-7","vorDoiUrl":"https://doi.org/10.1038/s41598-024-72025-7","workflowStages":[]},"version":"v1","identity":"rs-4589321","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4589321","identity":"rs-4589321","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}