Incidence and Risk Factors of Post-Transplant Diabetes Mellitus in Kidney Transplant Recipients: A Retrospective Study from a Tertiary Center in Saudi Arabia

Incidence and Risk Factors of Post-Transplant Diabetes Mellitus in Kidney Transplant Recipients: A Retrospective Study from a Tertiary Center in Saudi Arabia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Incidence and Risk Factors of Post-Transplant Diabetes Mellitus in Kidney Transplant Recipients: A Retrospective Study from a Tertiary Center in Saudi Arabia Mutlaq Alotaibi, Najlaa Almalki, Majed Alosaimi, Monther Alazwari, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6316096/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Aug, 2025 Read the published version in BMC Nephrology → Version 1 posted 6 You are reading this latest preprint version Abstract Background Post-transplant diabetes mellitus (PTDM) is a common metabolic complication following kidney transplantation, adversely affecting graft and patient outcomes. This study aims to identify the prevalence, risk factors, and clinical implications of PTDM among kidney transplant recipients at Alhada Armed Forces Hospital, Taif, Saudi Arabia. Methods We conducted a retrospective cohort study including adult kidney transplant recipients from January 1984 to December 2023, excluding patients with pre-existing diabetes. Data were extracted from electronic medical records, encompassing demographics, clinical characteristics, transplantation details, and laboratory parameters. PTDM was diagnosed based on the American Diabetes Association criteria. Statistical analyses included t-tests, multivariate logistic regression, chi-square tests, Mann-Whitney U test, and Fisher’s exact tests. Receiver operating characteristic (ROC) curve analysis determined optimal cutoff values for predictive variables. Results Of 228 kidney transplant recipients (64% males, mean age 47.2 ± 14.6 years), 54 (23.7%) developed PTDM. PTDM patients were significantly older (53.1 ± 12.9 vs. 45.4 ± 14.6 years, p < 0.001) and higher BMI (27.0 ± 4.7 vs. 25.2 ± 5.4 kg/m², p = 0.023). Hypertension was a more frequent cause of ESRD in the PTDM group (24.1% vs. 6.3%, p = 0.006). Tacrolimus levels ≥ 7 ng/mL were associated with higher PTDM incidence (70% vs. 52%, p = 0.032). ROC analysis indicated that age and BMI were significant predictors of PTDM (AUC = 0.72 and 0.68, respectively). Multivariate logistic regression identified age, BMI, and tacrolimus levels as independent PTDM predictors (p < 0.05). Conclusions PTDM affects a substantial proportion of kidney transplant recipients, with older age, higher BMI, and elevated tacrolimus levels emerging as key risk factors. Close monitoring and individualized immunosuppressive strategies may mitigate PTDM risk and improve post-transplant outcomes. Trial Registration Not applicable. Post-transplant diabetes mellitus Kidney transplant Risk factors Incidence Retrospective study Saudi Arabia Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Kidney transplantation (KT) is the most effective treatment for patients with end-stage renal disease (ESRD), offering improved survival and quality of life. Post-transplant diabetes mellitus (PTDM), also referred to as new-onset diabetes after transplantation (NODAT), emerges as a significant metabolic complication in kidney transplant recipients (KTRs). 1,2 PTDM not only jeopardizes graft survival but also increases cardiovascular risk and overall mortality. Despite advancements in immunosuppressive therapy and post-transplant care, PTDM remains a prevalent challenge, affecting approximately 10–20% of KTRs, with incidence rates varying based on population characteristics and treatment regimens. 3,4 Rossi et al. ( 2024 ) stated that the pathophysiology of PTDM is multifactorial, combining traditional diabetes risk factors with transplant-specific contributors. Classical risk factors such as obesity, age, family history of diabetes, and metabolic syndrome are compounded by the diabetogenic effects of immunosuppressive therapy, particularly corticosteroids, calcineurin inhibitors (CNI), and mammalian target of rapamycin (mTOR) inhibitors. 5 These agents impair pancreatic beta-cell function and induce insulin resistance, accelerating the onset of diabetes in susceptible individuals. Additional transplant-specific factors, including hypomagnesemia, cytomegalovirus (CMV) infection, and graft rejection episodes, further exacerbate glucose metabolism disorders, leading to an increased PTDM incidence. 3 Beyond its metabolic consequences, PTDM significantly impacts long-term patient and graft outcomes. It serves as an independent risk factor for cardiovascular disease (CVD), a leading cause of mortality in KTRs. Additionally, the presence of PTDM is associated with heightened infection rates, impaired wound healing, and an increased likelihood of graft dysfunction. Therefore, early identification of at-risk individuals, preemptive interventions, and tailored post-transplant management strategies are crucial to mitigating PTDM’s burden. 3 This retrospective study aims to determine the incidence and risk factors of post-transplant diabetes mellitus (PTDM) in kidney transplant recipients at a tertiary center in Saudi Arabia through a retrospective cohort analysis. Methods Study Design and Population This retrospective cohort study analyzed kidney transplant recipients at Alhada Armed Forces Hospital, Taif, Saudi Arabia, from January 1984 to December 2023. It examined PTDM predictors and characteristics. Eligible participants were adults with at least one year of post-transplant follow-up. Patients with pre-existing diabetes or conditions affecting outcomes were excluded. The study received IRB approval. Data Collection and Variables Electronic medical records provided data on demographics, clinical characteristics, transplantation details, lab results, and outcomes. PTDM was diagnosed per ADA criteria. Graft outcomes, including delayed graft function (DGF) and acute rejection (TCMR, ABMR), were assessed. Statistical Analysis Analyses were conducted using IBM SPSS 29. Normality was checked via the Shapiro-Wilk test and Q-Q plots. Normally distributed data were reported as mean ± SD; non-normal data as median (IQR). Categorical variables were summarized as percentages. Comparisons between PTDM and non-PTDM groups used t-tests (normal data), Mann-Whitney U tests (non-normal data), and chi-squared/Fisher’s exact tests for categorical variables. Paired t-tests analyzed pre- and post-transplant changes. ROC analysis determined optimal age and ACR cutoff values for PTDM prediction, with AUC, sensitivity, and specificity calculations. Multivariate logistic regression identified independent PTDM predictors, including significant univariate variables (p < 0.05). A p-value < 0.05 was considered statistically significant. Results Baseline Demographics and Patient Characteristics A total of 228 kidney transplant recipients met the inclusion criteria and were analyzed. The cohort included 146 males (64%) and 82 females (36%), with a mean transplantation age of 47.2 ± 14.6 years (Table 1 ). During follow-up, 54 patients (23.7%) developed post-transplant diabetes mellitus (PTDM). Those who developed PTDM were significantly older at the time of transplantation than those who did not (53.1 ± 12.9 years vs. 45.4 ± 14.6 years, p < 0.001). PTDM patients also had significantly higher baseline body weight, height, and BMI compared to non-PTDM patients (27.0 ± 4.7 kg/m² vs. 25.2 ± 5.4 kg/m², p = 0.023). Figures 1 and 2 illustrate the positive correlation between PTDM incidence and both increasing age and higher BMI. The underlying causes of end-stage renal disease (ESRD) differed significantly between PTDM and non-PTDM patients (p = 0.006). Hypertension was a more frequent cause of ESRD in PTDM patients compared to non-PTDM patients (24.1% vs. 6.3%). While the majority of patients in both groups had an unknown ESRD etiology, this was less common in PTDM patients (63% vs. 76.4%). Table 1 Baseline Demographics and Clinical Characteristics Total No PTDM PTDM P value N 228 174 (76.3%) 54 (23.7%) Age 47.2 ± 14.6 45.4 ± 14.6 53.1 ± 12.9 < 0.001 Gender Female 82 (36%) 67 (38.5%) 15 (27.8%) 0.203 Male 146 (64%) 107 (61.5%) 39 (72.2%) Weight 68.3 ± 16.4 66.5 ± 16.3 75.5 ± 15.1 0.002 Height 162.9 ± 9.5 161.9 ± 9.7 165.6 ± 8.6 0.026 BMI 25.5 ± 5.3 25.2 ± 5.4 27 ± 4.7 0.023 Etiology Unknown 167 (73.2%) 133 (76.4%) 34 (63%) 0.006 HTN 24 (10.5%) 11 (6.3%) 13 (24.1%) GN 30 (13.2%) 24 (13.8%) 6 (11.1%) APKD 3 (1.3%) 2 (1.1%) 1 (1.9%) Reflex 4 (1.8%) 4 (2.3%) 0 (0%) Transplantation Details and Immunosuppression There was no significant difference in the type of kidney transplantation between PTDM and non-PTDM patients (p = 0.259). Overall, 11.4% received kidneys from deceased donors, 61% from living-related donors, and 27.6% from living non-related donors. The use of induction immunosuppression agents was similar between groups, with 30.3% receiving ATG and 33.3% receiving basiliximab (p = 0.923). Maintenance immunosuppression was predominantly tacrolimus-based (86.8%), with the remaining patients on cyclosporine-based regimens (Table 2 ). A greater proportion of PTDM patients had tacrolimus levels ≥ 7 ng/mL compared to non-PTDM patients (70% vs. 52%, p = 0.032). While higher tacrolimus levels showed a trend toward increased PTDM incidence (Fig. 3 ), the difference was not statistically significant when comparing levels of 5–7 ng/mL, 7–10 ng/mL, and > 10 ng/mL (p = 0.075). Graft outcomes, including delayed graft function (DGF) and acute rejection episodes (TCMR and ABMR), were comparable between the two groups (p = 0.427). Serum creatinine levels at the last follow-up were similar (median 100 µmol/L in both groups, p = 0.540), as were eGFR values (median 75 mL/min/1.73 m², p = 0.505). Table 2 Transplantation Details and Immunosuppression Regimens Total No PTDM PTDM P value Tx Type Deceased 26 (11.4%) 21 (12.1%) 5 (9.3%) 0.259 Living-related 139 (61%) 101 (58%) 38 (70.4%) Living non-related 63 (27.6%) 52 (29.9%) 11 (20.4%) Induction Thymoglobulin 69 (30.3%) 53 (30.5%) 16 (29.6%) 0.923 Basilixmab 76 (33.3%) 59 (33.9%) 17 (31.5%) Unknown 83 (36.4%) 62 (35.6%) 21 (38.9%) Maintenance Tacrolimus + MMF + prednisolone 187 (82%) 140 (80.5%) 47 (87%) 0.330 Cyclosporine + MMF + Prednisolone 30 (13.2%) 26 (14.9%) 4 (7.4%) Tacrolimus + Azathiuoprine + prednisolone 11 (4.8%) 8 (4.6%) 3 (5.6%) Tacrolimus 198 (86.8%) 148 (85.1%) 50 (92.6%) 0.174 Cyclosporine 30 (13.2%) 26 (14.9%) 4 (7.4%) FK level 5–7 86 (43.4%) 71 (48%) 15 (30%) 0.075 7–10 94 (47.5%) 64 (43.2%) 30 (60%) > 10 18 (9.1%) 13 (8.8%) 5 (10%) FK level < 7 86 (43.4%) 71 (48%) 15 (30%) 0.032 ≥ 7 112 (56.6%) 77 (52%) 35 (70%) Graft Outcome None 206 (90.4%) 159 (91.4%) 47 (87%) 0.874 DGF 4 (1.8%) 3 (1.7%) 1 (1.9%) TCMR 15 (6.6%) 10 (5.7%) 5 (9.3%) ABMR 3 (1.3%) 2 (1.1%) 1 (1.9%) None 206 (90.4%) 159 (91.4%) 47 (87%) 0.427 Any 22 (9.6%) 15 (8.6%) 7 (13%) Laboratory Findings Table 3 presents the laboratory findings of the participants. Hypomagnesemia was significantly more prevalent among PTDM patients (75.9% vs. 29.9%, p < 0.001). PTDM patients also exhibited higher levels of proteinuria and albuminuria. The median UPCR was significantly elevated in PTDM patients compared to non-PTDM patients (98.5 mg/day [IQR: 29 to 160] vs. 21 mg/day [IQR: 10 to 34], p < 0.001). Similarly, the median ACR was higher in PTDM patients (18.5 mg/mmol [IQR: 4 to 44] vs. 4 mg/mmol [IQR: 1 to 10], p < 0.001). The prevalence of CMV infection was significantly higher in the PTDM group compared to the non-PTDM group (9.3% vs. 2.3%, p = 0.036). The rate of HCV infection did not differ significantly between the groups. Table 3 Laboratory Findings Total No PTDM PTDM P value HCV 10 (4.4%) 8 (4.6%) 2 (3.7%) 1 CMV 9 (3.9%) 4 (2.3%) 5 (9.3%) 0.036 Low Mg 93 (40.8%) 52 (29.9%) 41 (75.9%) < 0.001 Last creatinine 100 (81 to 125) 100 (80 to 129) 100 (81 to 114) 0.540 Last eGFR 75 (57 to 94) 75 (55 to 94) 75 (64 to 91) 0.505 Urine PCr (mg/day) 23 (11.5 to 71) 21 (10 to 34) 98.5 (29 to 160) < 0.001 ACR (mg/mmol) 4 (1 to 21) 4 (1 to 10) 18.5 (4 to 44) < 0.001 PTDM Characteristics and Management At the time of PTDM diagnosis, the majority of patients (68.5%) had fasting blood glucose levels between 7–10 mmol/L, while 31.5% had levels exceeding 10 mmol/L. HbA1c levels were between 6.5% and 9% in 74.1% of patients, while 25.9% had levels above 9%. The median time to PTDM onset was 2 years post-transplant (IQR: 1–4.2 years). PTDM patients experienced significant post-transplant weight gain, with an average increase of 7.1 kg (95% CI: 4.6–9.5 kg). At the time of diagnosis, the mean BMI was 28.9 ± 5.3 kg/m². Regarding treatment, metformin was the most commonly prescribed medication (68.5%). Insulin and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) were each used in 48.1% of patients. Dipeptidyl peptidase-4 inhibitors (DPP-4i) were prescribed to 33.3%, sodium-glucose co-transporter-2 inhibitors (SGLT2i) to 22.2%, and sulfonylureas to 16.7% (Table 4 ). Table 4 PTDM Characteristics and Management Total 54 FBG 7–10 37 (68.5%) > 10 17 (31.5%) HBA1C 6.5-9 40 (74.1%) > 9 14 (25.9%) Median time to devolve PTDM b 2 (1 to 4.2) Weight at DM diagnosis 79.6 ± 17 BMI at DM diagnosis 28.9 ± 5.3 Weight change 7.1 (4.6 to 9.5) Medications Metformin 37 (68.5%) DPP-4 18 (33.3%) SUs 9 (16.7%) SGLT2i 12 (22.2%) Insulin 26 (48.1%) GLP-1 agonists 26 (48.1%) Risk Factors Analysis ROC curve analysis was performed to determine the optimal cutoff values for age and albumin-to-creatinine ratio (ACR) in predicting post-transplant diabetes mellitus (PTDM). An age threshold of over 36 years demonstrated a sensitivity of 94.4% and a specificity of 31% for PTDM prediction, with an area under the curve (AUC) of 0.658 (95% CI: 0.579–0.736; p < 0.001). Similarly, an ACR of ≥ 9 mg/mmol yielded sensitivity and specificity values of 68.5% and 69.5%, respectively (AUC = 0.697; 95% CI: 0.620–0.775; p < 0.001). Univariate logistic regression analysis identified multiple factors significantly associated with an increased risk of PTDM. Each additional year of age increased the risk by 3.9% (OR: 1.039; 95% CI: 1.016–1.063; p < 0.001), while individuals older than 36 years had markedly higher odds of developing PTDM compared to younger counterparts (OR: 8.412; 95% CI: 2.497–28.343; p < 0.001). Hypertension was another significant risk factor (OR: 4.623; 95% CI: 1.904–11.223; p < 0.001), as were elevated tacrolimus trough levels (≥ 7 ng/mL), which increased the likelihood of PTDM (OR: 2.219; 95% CI: 1.095–4.494; p = 0.027). Additionally, hypomagnesemia significantly heightened the risk (OR: 7.399; 95% CI: 3.663–14.949; p < 0.001), while an ACR of ≥ 9 mg/mmol was strongly correlated with PTDM development (OR: 4.969; 95% CI: 2.571–9.602; p < 0.001). Moreover, cytomegalovirus (CMV) infection was identified as a risk factor in the univariate analysis (OR: 4.337; 95% CI: 1.121–16.773; p = 0.034). Multivariate logistic regression analysis, adjusting for potential confounders, confirmed several independent predictors of PTDM. Age over 36 years remained a strong predictor (aOR: 13.862; 95% CI: 2.293–83.788; p = 0.004), alongside hypertension (aOR: 7.925; 95% CI: 1.828–34.352; p = 0.006) and elevated tacrolimus trough levels (≥ 7 ng/mL) (aOR: 7.991; 95% CI: 1.930–33.090; p = 0.004). Hypomagnesemia also showed a significant association (aOR: 8.303; 95% CI: 2.484–27.755; p < 0.001), while an ACR of ≥ 9 mg/mmol emerged as the strongest predictor (aOR: 14.786; 95% CI: 4.036–54.172; p < 0.001). However, baseline weight and CMV infection did not retain statistical significance in the multivariate model. Figure 4 provides a visual summary of PTDM incidence and key variables identified in the multivariate analysis. Table 5 Univariate and Multivariate Analysis of PTDM Risk Factors Univariate Multivariate OR 95% CI P value aOR 95% CI P value Age (years) 1.039 1.016 to 1.063 < 0.001 ≥ 36 vs. <36 8.412 2.497 to 28.343 < 0.001 13.862 2.293 to 83.788 0.004 Males vs. Females 1.628 0.834 to 3.179 0.153 Bassline weight 1.036 1.011 to 1.062 0.005 1.022 0.982 to 1.063 0.284 Baseline height 1.044 1.005 to 1.085 0.028 Baseline BMI 1.073 0.995 to 1.157 0.066 Tx Type Deceased Reference LR 1.58 0.556 to 4.49 0.39 LNR 0.888 0.275 to 2.869 0.843 HTN 4.698 1.963 to 11.248 < 0.001 7.925 1.828 to 34.352 0.006 Etiology Unknown Reference HTN 4.623 1.904 to 11.223 < 0.001 GN 0.978 0.37 to 2.581 0.964 APKD 1.956 0.172 to 22.213 0.588 Reflex 0 0.999 Induction ATG Reference SIMU 0.954 0.439 to 2.076 0.906 Unknown 1.122 0.532 to 2.367 0.763 Maintenance FK + MMF + pr Reference CSA + MMF + Prd 0.458 0.152 to 1.381 0.166 FK + AZA + pr 1.117 0.285 to 4.385 0.874 FK level > 7 2.152 1.084 to 4.270 0.028 7.991 1.930 to 33.090 0.004 Graft Outcome None Reference DGF 1.128 0.115 to 11.096 0.918 TCMR 1.691 0.551 to 5.193 0.358 ABMR 1.691 0.15 to 19.068 0.671 HCV 0.798 0.164 to 3.877 0.78 CMV 4.337 1.121 to 16.773 0.034 1.722 0.158 to 18.764 0.656 Low Mg 7.399 3.663 to 14.949 < 0.001 8.303 2.484 to 27.755 < 0.001 Last creatinine 0.997 0.993 to 1.002 0.268 Last eGFR 1.004 0.992 to 1.016 0.511 Urine PCr (mg/day) 1.002 1.001 to 1.004 0.009 ACR (mg/mmol) 1.001 0.998 to 1.004 0.478 ≥ 9 vs. <9 4.969 2.571 to 9.602 < 0.001 14.786 4.036 to 54.172 < 0.001 Discussion The incidence of PTDM in our cohort (23.7%) aligns with previously reported rates in the literature, which range from 10–40% depending on study design, population characteristics, and follow-up duration. 7 The significantly higher mean age in PTDM patients (53.1 vs. 45.4 years, p < 0.001) underscores the well-documented association between aging and impaired glucose metabolism 8 . Older kidney transplant recipients may have reduced insulin secretion and increased insulin resistance, making them more vulnerable to PTDM development. 9 This finding is consistent with prior studies that have reported older age as a predominant risk factor for PTDM. 10,11 Obesity is another well-established risk factor for PTDM, and our study supports this association. PTDM patients had significantly higher baseline BMI (27.0 vs. 25.2 kg/m², p = 0.023), indicating that increased adiposity may contribute to insulin resistance post-transplantation. 12 The positive correlation between BMI and PTDM incidence highlights the importance of pre- and post-transplant weight management strategies to mitigate diabetes risk. 13 Hypertension was found to be a more common etiology of ESRD in PTDM patients (24.1% vs. 6.3%, p = 0.006). While the pathophysiology underlying this association is complex, chronic hypertension can induce vascular and metabolic changes that predispose individuals to insulin resistance. Additionally, hypertension is frequently treated with medications such as corticosteroids and calcineurin inhibitors, both of which have diabetogenic potential in transplant recipients. 14,15 Immunosuppressive therapy, particularly tacrolimus, plays a significant role in PTDM development as Nandula et al. ( 2022 ) stated that cyclosporine A (CYC), acrolimus (TAC), and everolimus (EVL) can increase PTDM incidence. Our study found that a higher proportion of PTDM patients had tacrolimus levels ≥ 7 ng/mL (70% vs. 52%, p = 0.032). 16 Tacrolimus is known to impair pancreatic beta-cell function and reduce insulin secretion, contributing to post-transplant hyperglycemia. Although our findings did not establish a statistically significant dose-response relationship across different tacrolimus level categories (p = 0.075), the trend suggests that maintaining lower tacrolimus trough levels may reduce PTDM risk. 17 Our laboratory findings revealed cyclosporine-based regimens were less frequently used (13.2%), and while cyclosporine also has diabetogenic effects, its impact on PTDM appears to be less pronounced compared to tacrolimus. The predominance of tacrolimus-based immunosuppression in our cohort (86.8%) reflects current transplant protocols favoring tacrolimus due to its superior graft survival benefits. However, given its association with PTDM, careful monitoring of tacrolimus levels is warranted to balance immunosuppressive efficacy with metabolic risks. 18 We identified hypomagnesemia as a significant biochemical marker associated with PTDM. The prevalence of hypomagnesemia was markedly higher in PTDM patients (75.9% vs. 29.9%, p < 0.001). Magnesium plays a crucial role in insulin signaling and glucose metabolism, and its deficiency has been linked to insulin resistance 19 . Several studies have proposed that calcineurin inhibitors contribute to magnesium wasting, thereby exacerbating PTDM risk. 20 Routine monitoring and supplementation of magnesium in kidney transplant recipients could be a potential strategy to mitigate PTDM development. Proteinuria and albuminuria were also more pronounced in PTDM patients, with significantly elevated median UPCR (98.5 vs. 21 mg/day, p < 0.001) and ACR (18.5 vs. 4 mg/mmol, p < 0.001). Rout and Jialal ( 2025 ) stated that presence of proteinuria may indicate early diabetic nephropathy or underlying endothelial dysfunction, reinforcing the need for strict glycemic control in PTDM patients to preserve long-term graft function. 21 Interestingly, PTDM patients had a significantly higher prevalence of CMV infection (9.3% vs. 2.3%, p = 0.036). CMV infection has been implicated in the pathogenesis of PTDM through direct pancreatic beta-cell damage and immune-mediated inflammation. 22 These findings suggest that transplant recipients with a history of CMV infection should be closely monitored for PTDM development. Conversely, HCV infection did not show a significant difference between groups, although some studies have suggested a potential link between HCV and PTDM via chronic liver disease and metabolic disturbances. For graft and patient’s outcomes, results revealed, despite the metabolic disturbances associated with PTDM, our study found no significant difference in graft function between PTDM and non-PTDM patients. Serum creatinine levels (100 µmol/L in both groups, p = 0.540) and eGFR values (median 75 mL/min/1.73 m², p = 0.505) were comparable at the last follow-up. Additionally, the incidence of delayed graft function, acute rejection (TCMR and ABMR), and overall graft survival did not differ significantly between groups. These findings suggest that while PTDM poses a significant metabolic risk, it may not have an immediate detrimental effect on graft function in the short to medium term. However, long-term studies are needed to assess the impact of PTDM on graft survival and cardiovascular outcomes. Clinical Implications, Limitations & Recommendations Our findings emphasize key strategies for managing PTDM in kidney transplant recipients. Given its strong link to older age and higher BMI, pre-transplant screening and lifestyle interventions are essential. Post-transplant care should integrate nutritional counseling, weight management, and structured exercise to lower PTDM risk. 23 Optimizing tacrolimus dosing to reduce diabetogenic effects while preserving immunosuppression is crucial. Lower tacrolimus trough levels may help, and magnesium supplementation, especially for hypomagnesemia, could improve insulin sensitivity. 24 CMV infection is another independent risk factor requiring close monitoring. Regular glucose screening and early intervention with metformin or insulin can improve outcomes. PTDM affected 23.7% of our cohort. While it doesn’t immediately impact graft function, proactive management is vital for long-term metabolic and cardiovascular health. Limitations include a single-center design, potential selection bias, unmeasured confounders, and the need for long-term follow-up on graft survival. Conclusion Our findings evaluated a significant burden of post-transplant diabetes mellitus (PTDM) among kidney transplant recipients. Our results revealed it is affecting nearly a quarter of the patients in our cohort. Older age, higher BMI, and elevated tacrolimus levels are key risk factors. Hypertension as a cause of ESRD may further increase susceptibility. Although graft survival and renal function remain comparable between PTDM and non-PTDM patients, PTDM raises the risk of cardiovascular disease and mortality. This highlights the need for close monitoring and early intervention. Higher tacrolimus levels strongly correlate with PTDM, emphasizing the importance of individualized immunosuppressive strategies to minimize metabolic risks while preventing rejection. Future research should focus on alternative immunosuppressive agents, early metabolic screening, and lifestyle modifications to lower PTDM risk. Tailored treatment approaches can improve long-term health outcomes for kidney transplant recipients. Declarations Acknowledgments: None Conflicts of Interest: The authors declare no conflicts of interest related to this study. Ethics Approval and Consent to Participate: This study was approved by The Research Ethics Committee of Armed Forces Hospitals. Informed consent was waived as per ethical guidelines. Funding: No external funding was received. Data and Materials availability: All data generated or analyzed during this study are included in the supplementary files. Trial Registration: Not applicable. Author Contributions Statement MA wrote the main manuscript NA reviewed the manuscript MAA reviewed the manuscript MoA wrote the manuscript MH Reviewed the manuscript FA collected and analyzed the data AA collected and analyzed the data AB collected and analyzed the data FB reviewed the manuscript BA reviewed the manuscript MEA wrote and reviewed the main manuscript References Kidney transplant - Mayo Clinic [Internet]. Mayo Clinic; 2025 [cited 2025 Mar 20]. Available from: https://www.mayoclinic.org/tests-procedures/kidney-transplant/about/pac-20384777 Alajous S, Budhiraja P. New-onset diabetes mellitus after kidney transplantation. J Clin Med. 2024;13(7):1928. doi:10.3390/jcm13071928 Sanchez-Baya M, Bolufer M, Vázquez F, Alonso N, Massó E, Paul J, et al. Diabetes mellitus in kidney transplant recipients: new horizons in treatment. J Clin Med. 2025;14(4):1048. doi:10.3390/jcm14041048 Conte C, Maggiore U, Cappelli G, Ietto G, Lai Q, Salis P, et al. Management of metabolic alterations in adult kidney transplant recipients: a joint position statement of the Italian Society of Nephrology (SIN), the Italian Society for Organ Transplantation (SITO) and the Italian Diabetes Society (SID). Nutr Metab Cardiovasc Dis. 2020;30(9):1427–41. doi:10.1016/j.numecd.2020.05.004 Rossi MR, Mazzali M, De Sousa MV. Post-transplant diabetes mellitus: risk factors and outcomes in a 5-year follow-up. Front Clin Diabetes Healthc. 2024;5. doi:10.3389/fcdhc.2024.1336896 Popović L, Bulum T. New-onset diabetes after organ transplantation: risk factors, treatment, and consequences. Diagnostics. 2025;15(3):284. doi:10.3390/diagnostics15030284 Chowdhury TA. Post-transplant diabetes mellitus. Clin Med. 2019;19(5):392–5. doi:10.7861/clinmed.2019-0195 Alanazi NF, Almutairi M, Aldohayan L, AlShareef A, Ghallab B, Altamimi A. The incidence and risk factors of post-transplant diabetes mellitus in living donor kidney transplantation patients: a retrospective study. BMC Nephrol. 2024;25(1). doi:10.1186/s12882-024-03816-3 Cantarin MPM. Diabetes in kidney transplantation. Adv Chronic Kidney Dis. 2021;28(6):596–605. doi:10.1053/j.ackd.2021.10.004 Cheng C, Feng Y, Wang H. Incidence and relative risk factors in posttransplant diabetes mellitus patients: a retrospective cohort study. Korean J Transplant. 2020;34(4):231–7. doi:10.4285/kjt.20.0026 Balakrishnan M, Jayam J, Srinivasaprasad N, S S, Fernando M. Prevalence and risk factors for posttransplant diabetes mellitus: data from government tertiary care center. Indian J Transplant. 2018;12(2):119. doi:10.4103/ijot.ijot_14_18 Malik RF, Jia Y, Mansour SG, Reese PP, Hall IE, Alasfar S, et al. Post-transplant diabetes mellitus in kidney transplant recipients: a multicenter study. Kidney360. 2021;2(8):1296–307. doi:10.34067/kid.0000862021 Martin-Moreno PL, Shin H, Chandraker A. Obesity and post-transplant diabetes mellitus in kidney transplantation. J Clin Med. 2021;10(11):2497. doi:10.3390/jcm10112497 Jia G, Sowers JR. Hypertension in diabetes: an update of basic mechanisms and clinical disease. Hypertension. 2021;78(5):1197–205. doi:10.1161/hypertensionaha.121.17981 Kanbay M, Guldan M, Ozbek L, Copur S, Covic AS, Covic A. Exploring the nexus: the place of kidney diseases within the cardiovascular-kidney-metabolic syndrome spectrum. Eur J Intern Med. 2024;127:1–14. doi:10.1016/j.ejim.2024.07.014 Nandula SA, Boddepalli CS, Gutlapalli SD, Lavu VK, Abdelwahab RaM, Huang R, et al. New-onset diabetes mellitus in post-renal transplant patients on tacrolimus and mycophenolate: a systematic review. Cureus. 2022. doi:10.7759/cureus.31482 Triñanes J, Rodriguez-Rodriguez A, Brito-Casillas Y, Wagner A, De Vries A, Cuesto G, et al. Deciphering tacrolimus-induced toxicity in pancreatic β cells. Am J Transplant. 2017;17(11):2829–40. doi:10.1111/ajt.14323 Tacrolimus-based immunosuppression [Internet]. PubMed; 2004 Dec 1 [cited 2025 Mar 20]. Available from: https://pubmed.ncbi.nlm.nih.gov/15599882/ Akimbekov NS, Coban SO, Atfi A, Razzaque MS. The role of magnesium in pancreatic beta-cell function and homeostasis. Front Nutr. 2024;11. doi:10.3389/fnut.2024.1458700 Pham P, Sarkar M, Pham P, Pham P. Diabetes mellitus after solid organ transplantation. Endotext - NCBI Bookshelf [Internet]. 2022 Jul 13 [cited 2025 Mar 20]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK378977/ Rout P, Jialal I. Diabetic nephropathy. StatPearls - NCBI Bookshelf [Internet]. 2025 Jan 9 [cited 2025 Mar 20]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK534200/ Varani S, Landini M. Cytomegalovirus-induced immunopathology and its clinical consequences. Herpesviridae. 2011;2(1):6. doi:10.1186/2042-4280-2-6 Laghrib Y, Hilbrands L, Oniscu GC, Crespo M, Gandolfini I, Mariat C, et al. Current practices in prevention, screening, and treatment of diabetes in kidney transplant recipients: European survey highlights from the ERA DESCARTES Working Group. Clin Kidney J. 2024;18(1). doi:10.1093/ckj/sfae367 Chua JCM, Mount PF, Lee D. Lower versus higher starting tacrolimus dosing in kidney transplant recipients. Clin Transplant. 2022;36(6). doi:10.1111/ctr.14606 Additional Declarations No competing interests reported. Supplementary Files AnalysisPTDM.docx PTDM.xlsx Cite Share Download PDF Status: Published Journal Publication published 13 Aug, 2025 Read the published version in BMC Nephrology → Version 1 posted Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 06 May, 2025 Editor assigned by journal 29 Apr, 2025 Editor invited by journal 07 Apr, 2025 Submission checks completed at journal 04 Apr, 2025 First submitted to journal 04 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6316096","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453360255,"identity":"88876a0c-fa24-412a-9618-f0a187bc895d","order_by":0,"name":"Mutlaq Alotaibi","email":"data:image/png;base64,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","orcid":"","institution":"Department of Nephrology and Kidney Transplantation, AlHada Armed Forces Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mutlaq","middleName":"","lastName":"Alotaibi","suffix":""},{"id":453360256,"identity":"6658cf8b-1506-45c7-99b5-f2878a31973a","order_by":1,"name":"Najlaa Almalki","email":"","orcid":"","institution":"Department of Nephrology and Kidney Transplantation, AlHada Armed Forces Hospital","correspondingAuthor":false,"prefix":"","firstName":"Najlaa","middleName":"","lastName":"Almalki","suffix":""},{"id":453360257,"identity":"a02f8c90-dde6-4ce7-b2c5-c6b7dbfab2ea","order_by":2,"name":"Majed Alosaimi","email":"","orcid":"","institution":"Department of Nephrology and Kidney Transplantation, AlHada Armed Forces Hospital","correspondingAuthor":false,"prefix":"","firstName":"Majed","middleName":"","lastName":"Alosaimi","suffix":""},{"id":453360258,"identity":"a70c905b-e985-4a79-96d9-58a805eca7c0","order_by":3,"name":"Monther Alazwari","email":"","orcid":"","institution":"Department of Nephrology and Kidney Transplantation, AlHada Armed Forces Hospital","correspondingAuthor":false,"prefix":"","firstName":"Monther","middleName":"","lastName":"Alazwari","suffix":""},{"id":453360260,"identity":"2b52e8dc-b7d4-486d-b3ac-a5beda3ec9e8","order_by":4,"name":"Mohamed Hussein","email":"","orcid":"","institution":"Department of Nephrology and Kidney Transplantation, AlHada Armed Forces Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Hussein","suffix":""},{"id":453360263,"identity":"3509025e-2d16-451f-95d4-ff939261064c","order_by":5,"name":"Faisal Alhomayani","email":"","orcid":"","institution":"Department of Medicine, College of Medicine, Taif University","correspondingAuthor":false,"prefix":"","firstName":"Faisal","middleName":"","lastName":"Alhomayani","suffix":""},{"id":453360265,"identity":"2a53f904-fec8-451b-b082-6140a13fcbad","order_by":6,"name":"Abdulmajeed Alotaibi","email":"","orcid":"","institution":"Department of Nephrology and Kidney Transplantation, AlHada Armed Forces Hospital","correspondingAuthor":false,"prefix":"","firstName":"Abdulmajeed","middleName":"","lastName":"Alotaibi","suffix":""},{"id":453360266,"identity":"edf3720e-e136-4cc3-9cb7-68be25acb3ec","order_by":7,"name":"Ameerah Bajaber","email":"","orcid":"","institution":"Alhada Armed Forces Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ameerah","middleName":"","lastName":"Bajaber","suffix":""},{"id":453360267,"identity":"e1492e2c-62b9-458c-876f-a8152b9cfd48","order_by":8,"name":"Fahad Bhutto","email":"","orcid":"","institution":"Department of Internal Medicine, Division of Nephrology, Security Forces hospital","correspondingAuthor":false,"prefix":"","firstName":"Fahad","middleName":"","lastName":"Bhutto","suffix":""},{"id":453360268,"identity":"b1250444-540b-49c2-b8ee-2baff9f14f9b","order_by":9,"name":"Abdulmajeed Algethami","email":"","orcid":"","institution":"Department of Medicine, College of Medicine, Taif University","correspondingAuthor":false,"prefix":"","firstName":"Abdulmajeed","middleName":"","lastName":"Algethami","suffix":""},{"id":453360269,"identity":"41248b9f-a351-4754-b5d0-ceaf35ae5fa4","order_by":10,"name":"Bassem A. Almalki","email":"","orcid":"","institution":"Department of Pharmacy Practice, College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University","correspondingAuthor":false,"prefix":"","firstName":"Bassem","middleName":"A.","lastName":"Almalki","suffix":""},{"id":453360270,"identity":"37eb46ae-79f6-48ed-842c-135a5884eb9c","order_by":11,"name":"Manal E. Alotaibi","email":"","orcid":"","institution":"Department of Medicine, Medical College, Umm Al-Qura University (UQU)","correspondingAuthor":false,"prefix":"","firstName":"Manal","middleName":"E.","lastName":"Alotaibi","suffix":""}],"badges":[],"createdAt":"2025-03-27 02:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6316096/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6316096/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12882-025-04375-x","type":"published","date":"2025-08-13T15:57:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82360992,"identity":"5cf10555-04ac-44f7-8cef-433a1c249a28","added_by":"auto","created_at":"2025-05-09 11:41:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122211,"visible":true,"origin":"","legend":"\u003cp\u003eThe Incidence of PTDM by Age Quartile (p-value \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6316096/v1/0c83d80662c9142ff8e3d56f.png"},{"id":82358693,"identity":"cdf1cbfe-db2f-4d20-a820-527586243049","added_by":"auto","created_at":"2025-05-09 11:25:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156123,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship Between BMI and PTDM Development\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6316096/v1/6d7502e45a34f9e49016dff5.png"},{"id":82359803,"identity":"5e638959-2982-4146-b32e-a4fa65034b5c","added_by":"auto","created_at":"2025-05-09 11:33:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130967,"visible":true,"origin":"","legend":"\u003cp\u003eTacrolimus Levels and PTDM Incidence\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6316096/v1/a8c2af9dd9e908b72f6ae81e.png"},{"id":82358697,"identity":"fdfef2f7-40e5-42a7-bd6f-7ea754406a9b","added_by":"auto","created_at":"2025-05-09 11:25:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209401,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of Independent Risk Factors for PTDM\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6316096/v1/69b171098fa691b744ad2cb2.png"},{"id":89310655,"identity":"8b5123cd-5c26-43bd-9d02-27b31f7f9d05","added_by":"auto","created_at":"2025-08-18 16:09:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1834607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6316096/v1/eb7a3622-9d21-4c43-8172-4e9cc83ea060.pdf"},{"id":82359805,"identity":"e05e8324-abca-4ae0-87d0-884e55046eea","added_by":"auto","created_at":"2025-05-09 11:33:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":148694,"visible":true,"origin":"","legend":"","description":"","filename":"AnalysisPTDM.docx","url":"https://assets-eu.researchsquare.com/files/rs-6316096/v1/61fda4786d491cb3a3c86a71.docx"},{"id":82358691,"identity":"d64c3626-ea73-40f4-9939-33f9b2f4ca55","added_by":"auto","created_at":"2025-05-09 11:25:12","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45686,"visible":true,"origin":"","legend":"","description":"","filename":"PTDM.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6316096/v1/d91768c8a395951cc874da97.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Incidence and Risk Factors of Post-Transplant Diabetes Mellitus in Kidney Transplant Recipients: A Retrospective Study from a Tertiary Center in Saudi Arabia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKidney transplantation (KT) is the most effective treatment for patients with end-stage renal disease (ESRD), offering improved survival and quality of life. Post-transplant diabetes mellitus (PTDM), also referred to as new-onset diabetes after transplantation (NODAT), emerges as a significant metabolic complication in kidney transplant recipients (KTRs). \u003csup\u003e1,2\u003c/sup\u003e PTDM not only jeopardizes graft survival but also increases cardiovascular risk and overall mortality. Despite advancements in immunosuppressive therapy and post-transplant care, PTDM remains a prevalent challenge, affecting approximately 10\u0026ndash;20% of KTRs, with incidence rates varying based on population characteristics and treatment regimens.\u003csup\u003e3,4\u003c/sup\u003e Rossi et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) stated that the pathophysiology of PTDM is multifactorial, combining traditional diabetes risk factors with transplant-specific contributors. Classical risk factors such as obesity, age, family history of diabetes, and metabolic syndrome are compounded by the diabetogenic effects of immunosuppressive therapy, particularly corticosteroids, calcineurin inhibitors (CNI), and mammalian target of rapamycin (mTOR) inhibitors.\u003csup\u003e5\u003c/sup\u003e These agents impair pancreatic beta-cell function and induce insulin resistance, accelerating the onset of diabetes in susceptible individuals. Additional transplant-specific factors, including hypomagnesemia, cytomegalovirus (CMV) infection, and graft rejection episodes, further exacerbate glucose metabolism disorders, leading to an increased PTDM incidence.\u003csup\u003e3\u003c/sup\u003e Beyond its metabolic consequences, PTDM significantly impacts long-term patient and graft outcomes. It serves as an independent risk factor for cardiovascular disease (CVD), a leading cause of mortality in KTRs. Additionally, the presence of PTDM is associated with heightened infection rates, impaired wound healing, and an increased likelihood of graft dysfunction. Therefore, early identification of at-risk individuals, preemptive interventions, and tailored post-transplant management strategies are crucial to mitigating PTDM\u0026rsquo;s burden.\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis retrospective study aims to determine the incidence and risk factors of post-transplant diabetes mellitus (PTDM) in kidney transplant recipients at a tertiary center in Saudi Arabia through a retrospective cohort analysis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study analyzed kidney transplant recipients at Alhada Armed Forces Hospital, Taif, Saudi Arabia, from January 1984 to December 2023. It examined PTDM predictors and characteristics. Eligible participants were adults with at least one year of post-transplant follow-up. Patients with pre-existing diabetes or conditions affecting outcomes were excluded. The study received IRB approval.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection and Variables\u003c/h3\u003e\n\u003cp\u003e Electronic medical records provided data on demographics, clinical characteristics, transplantation details, lab results, and outcomes. PTDM was diagnosed per ADA criteria. Graft outcomes, including delayed graft function (DGF) and acute rejection (TCMR, ABMR), were assessed.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAnalyses were conducted using IBM SPSS 29. Normality was checked via the Shapiro-Wilk test and Q-Q plots. Normally distributed data were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; non-normal data as median (IQR). Categorical variables were summarized as percentages.\u003c/p\u003e \u003cp\u003eComparisons between PTDM and non-PTDM groups used t-tests (normal data), Mann-Whitney U tests (non-normal data), and chi-squared/Fisher\u0026rsquo;s exact tests for categorical variables. Paired t-tests analyzed pre- and post-transplant changes.\u003c/p\u003e \u003cp\u003eROC analysis determined optimal age and ACR cutoff values for PTDM prediction, with AUC, sensitivity, and specificity calculations. Multivariate logistic regression identified independent PTDM predictors, including significant univariate variables (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Demographics and Patient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 228 kidney transplant recipients met the inclusion criteria and were analyzed. The cohort included 146 males (64%) and 82 females (36%), with a mean transplantation age of 47.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). During follow-up, 54 patients (23.7%) developed post-transplant diabetes mellitus (PTDM). Those who developed PTDM were significantly older at the time of transplantation than those who did not (53.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9 years vs. 45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). PTDM patients also had significantly higher baseline body weight, height, and BMI compared to non-PTDM patients (27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 kg/m\u0026sup2; vs. 25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 kg/m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.023). Figures\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrate the positive correlation between PTDM incidence and both increasing age and higher BMI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe underlying causes of end-stage renal disease (ESRD) differed significantly between PTDM and non-PTDM patients (p\u0026thinsp;=\u0026thinsp;0.006). Hypertension was a more frequent cause of ESRD in PTDM patients compared to non-PTDM patients (24.1% vs. 6.3%). While the majority of patients in both groups had an unknown ESRD etiology, this was less common in PTDM patients (63% vs. 76.4%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Demographics and Clinical Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo PTDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174 (76.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (72.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEtiology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (73.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133 (76.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPKD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReflex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTransplantation Details and Immunosuppression\u003c/h2\u003e \u003cp\u003eThere was no significant difference in the type of kidney transplantation between PTDM and non-PTDM patients (p\u0026thinsp;=\u0026thinsp;0.259). Overall, 11.4% received kidneys from deceased donors, 61% from living-related donors, and 27.6% from living non-related donors. The use of induction immunosuppression agents was similar between groups, with 30.3% receiving ATG and 33.3% receiving basiliximab (p\u0026thinsp;=\u0026thinsp;0.923). Maintenance immunosuppression was predominantly tacrolimus-based (86.8%), with the remaining patients on cyclosporine-based regimens (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A greater proportion of PTDM patients had tacrolimus levels\u0026thinsp;\u0026ge;\u0026thinsp;7 ng/mL compared to non-PTDM patients (70% vs. 52%, p\u0026thinsp;=\u0026thinsp;0.032). While higher tacrolimus levels showed a trend toward increased PTDM incidence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the difference was not statistically significant when comparing levels of 5\u0026ndash;7 ng/mL, 7\u0026ndash;10 ng/mL, and \u0026gt;\u0026thinsp;10 ng/mL (p\u0026thinsp;=\u0026thinsp;0.075).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGraft outcomes, including delayed graft function (DGF) and acute rejection episodes (TCMR and ABMR), were comparable between the two groups (p\u0026thinsp;=\u0026thinsp;0.427). Serum creatinine levels at the last follow-up were similar (median 100 \u0026micro;mol/L in both groups, p\u0026thinsp;=\u0026thinsp;0.540), as were eGFR values (median 75 mL/min/1.73 m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.505).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTransplantation Details and Immunosuppression Regimens\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo PTDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTx Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeceased\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (70.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving non-related\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInduction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThymoglobulin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBasilixmab\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (33.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (38.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaintenance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTacrolimus\u0026thinsp;+\u0026thinsp;MMF\u0026thinsp;+\u0026thinsp;prednisolone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (80.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCyclosporine\u0026thinsp;+\u0026thinsp;MMF\u0026thinsp;+\u0026thinsp;Prednisolone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTacrolimus\u0026thinsp;+\u0026thinsp;Azathiuoprine\u0026thinsp;+\u0026thinsp;prednisolone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTacrolimus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (86.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (85.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (92.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCyclosporine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFK level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u0026ndash;7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (43.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u0026ndash;10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (47.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFK level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (43.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 (56.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (70%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGraft Outcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206 (90.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (91.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDGF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTCMR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eABMR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206 (90.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (91.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAny\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLaboratory Findings\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the laboratory findings of the participants. Hypomagnesemia was significantly more prevalent among PTDM patients (75.9% vs. 29.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). PTDM patients also exhibited higher levels of proteinuria and albuminuria. The median UPCR was significantly elevated in PTDM patients compared to non-PTDM patients (98.5 mg/day [IQR: 29 to 160] vs. 21 mg/day [IQR: 10 to 34], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, the median ACR was higher in PTDM patients (18.5 mg/mmol [IQR: 4 to 44] vs. 4 mg/mmol [IQR: 1 to 10], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe prevalence of CMV infection was significantly higher in the PTDM group compared to the non-PTDM group (9.3% vs. 2.3%, p\u0026thinsp;=\u0026thinsp;0.036). The rate of HCV infection did not differ significantly between the groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLaboratory Findings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo PTDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCMV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow Mg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (40.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (75.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLast creatinine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (81 to 125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (80 to 129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (81 to 114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLast eGFR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (57 to 94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (55 to 94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (64 to 91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrine PCr (mg/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (11.5 to 71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (10 to 34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.5 (29 to 160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eACR (mg/mmol)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1 to 21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1 to 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.5 (4 to 44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePTDM Characteristics and Management\u003c/h3\u003e\n\u003cp\u003eAt the time of PTDM diagnosis, the majority of patients (68.5%) had fasting blood glucose levels between 7\u0026ndash;10 mmol/L, while 31.5% had levels exceeding 10 mmol/L. HbA1c levels were between 6.5% and 9% in 74.1% of patients, while 25.9% had levels above 9%. The median time to PTDM onset was 2 years post-transplant (IQR: 1\u0026ndash;4.2 years).\u003c/p\u003e \u003cp\u003ePTDM patients experienced significant post-transplant weight gain, with an average increase of 7.1 kg (95% CI: 4.6\u0026ndash;9.5 kg). At the time of diagnosis, the mean BMI was 28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3 kg/m\u0026sup2;. Regarding treatment, metformin was the most commonly prescribed medication (68.5%). Insulin and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) were each used in 48.1% of patients. Dipeptidyl peptidase-4 inhibitors (DPP-4i) were prescribed to 33.3%, sodium-glucose co-transporter-2 inhibitors (SGLT2i) to 22.2%, and sulfonylureas to 16.7% (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePTDM Characteristics and Management\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u0026ndash;10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHBA1C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6.5-9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (74.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian time to devolve PTDM\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1 to 4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight at DM diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI at DM diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.1 (4.6 to 9.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetformin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (68.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDPP-4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSUs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSGLT2i\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsulin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGLP-1 agonists\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRisk Factors Analysis\u003c/h2\u003e \u003cp\u003eROC curve analysis was performed to determine the optimal cutoff values for age and albumin-to-creatinine ratio (ACR) in predicting post-transplant diabetes mellitus (PTDM). An age threshold of over 36 years demonstrated a sensitivity of 94.4% and a specificity of 31% for PTDM prediction, with an area under the curve (AUC) of 0.658 (95% CI: 0.579\u0026ndash;0.736; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, an ACR of \u0026ge;\u0026thinsp;9 mg/mmol yielded sensitivity and specificity values of 68.5% and 69.5%, respectively (AUC\u0026thinsp;=\u0026thinsp;0.697; 95% CI: 0.620\u0026ndash;0.775; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eUnivariate logistic regression analysis identified multiple factors significantly associated with an increased risk of PTDM. Each additional year of age increased the risk by 3.9% (OR: 1.039; 95% CI: 1.016\u0026ndash;1.063; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while individuals older than 36 years had markedly higher odds of developing PTDM compared to younger counterparts (OR: 8.412; 95% CI: 2.497\u0026ndash;28.343; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hypertension was another significant risk factor (OR: 4.623; 95% CI: 1.904\u0026ndash;11.223; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as were elevated tacrolimus trough levels (\u0026ge;\u0026thinsp;7 ng/mL), which increased the likelihood of PTDM (OR: 2.219; 95% CI: 1.095\u0026ndash;4.494; p\u0026thinsp;=\u0026thinsp;0.027). Additionally, hypomagnesemia significantly heightened the risk (OR: 7.399; 95% CI: 3.663\u0026ndash;14.949; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while an ACR of \u0026ge;\u0026thinsp;9 mg/mmol was strongly correlated with PTDM development (OR: 4.969; 95% CI: 2.571\u0026ndash;9.602; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, cytomegalovirus (CMV) infection was identified as a risk factor in the univariate analysis (OR: 4.337; 95% CI: 1.121\u0026ndash;16.773; p\u0026thinsp;=\u0026thinsp;0.034).\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis, adjusting for potential confounders, confirmed several independent predictors of PTDM. Age over 36 years remained a strong predictor (aOR: 13.862; 95% CI: 2.293\u0026ndash;83.788; p\u0026thinsp;=\u0026thinsp;0.004), alongside hypertension (aOR: 7.925; 95% CI: 1.828\u0026ndash;34.352; p\u0026thinsp;=\u0026thinsp;0.006) and elevated tacrolimus trough levels (\u0026ge;\u0026thinsp;7 ng/mL) (aOR: 7.991; 95% CI: 1.930\u0026ndash;33.090; p\u0026thinsp;=\u0026thinsp;0.004). Hypomagnesemia also showed a significant association (aOR: 8.303; 95% CI: 2.484\u0026ndash;27.755; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while an ACR of \u0026ge;\u0026thinsp;9 mg/mmol emerged as the strongest predictor (aOR: 14.786; 95% CI: 4.036\u0026ndash;54.172; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, baseline weight and CMV infection did not retain statistical significance in the multivariate model. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a visual summary of PTDM incidence and key variables identified in the multivariate analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate Analysis of PTDM Risk Factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eaOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.016 to 1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;36 vs. \u0026lt;36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.497 to 28.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.293 to 83.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMales vs. Females\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.834 to 3.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBassline weight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.011 to 1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.982 to 1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline height\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.005 to 1.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.995 to 1.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTx Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeceased\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.556 to 4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLNR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.275 to 2.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.963 to 11.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.828 to 34.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEtiology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.904 to 11.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37 to 2.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPKD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172 to 22.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReflex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInduction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eATG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSIMU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.439 to 2.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.532 to 2.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaintenance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFK\u0026thinsp;+\u0026thinsp;MMF\u0026thinsp;+\u0026thinsp;pr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSA\u0026thinsp;+\u0026thinsp;MMF\u0026thinsp;+\u0026thinsp;Prd\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.152 to 1.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFK\u0026thinsp;+\u0026thinsp;AZA\u0026thinsp;+\u0026thinsp;pr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.285 to 4.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFK level\u0026thinsp;\u0026gt;\u0026thinsp;7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.084 to 4.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.930 to 33.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGraft Outcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDGF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.115 to 11.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTCMR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.551 to 5.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eABMR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15 to 19.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.164 to 3.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCMV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.121 to 16.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.158 to 18.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow Mg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.663 to 14.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.484 to 27.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLast creatinine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.993 to 1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLast eGFR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.992 to 1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrine PCr (mg/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.001 to 1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eACR (mg/mmol)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998 to 1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;9 vs. \u0026lt;9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.571 to 9.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.036 to 54.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe incidence of PTDM in our cohort (23.7%) aligns with previously reported rates in the literature, which range from 10\u0026ndash;40% depending on study design, population characteristics, and follow-up duration.\u003csup\u003e7\u003c/sup\u003e The significantly higher mean age in PTDM patients (53.1 vs. 45.4 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) underscores the well-documented association between aging and impaired glucose metabolism \u003csup\u003e8\u003c/sup\u003e. Older kidney transplant recipients may have reduced insulin secretion and increased insulin resistance, making them more vulnerable to PTDM development. \u003csup\u003e9\u003c/sup\u003e This finding is consistent with prior studies that have reported older age as a predominant risk factor for PTDM.\u003csup\u003e10,11\u003c/sup\u003e Obesity is another well-established risk factor for PTDM, and our study supports this association. PTDM patients had significantly higher baseline BMI (27.0 vs. 25.2 kg/m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.023), indicating that increased adiposity may contribute to insulin resistance post-transplantation. \u003csup\u003e12\u003c/sup\u003e The positive correlation between BMI and PTDM incidence highlights the importance of pre- and post-transplant weight management strategies to mitigate diabetes risk.\u003csup\u003e13\u003c/sup\u003e Hypertension was found to be a more common etiology of ESRD in PTDM patients (24.1% vs. 6.3%, p\u0026thinsp;=\u0026thinsp;0.006). While the pathophysiology underlying this association is complex, chronic hypertension can induce vascular and metabolic changes that predispose individuals to insulin resistance. Additionally, hypertension is frequently treated with medications such as corticosteroids and calcineurin inhibitors, both of which have diabetogenic potential in transplant recipients.\u003csup\u003e14,15\u003c/sup\u003e Immunosuppressive therapy, particularly tacrolimus, plays a significant role in PTDM development as Nandula et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) stated that cyclosporine A (CYC), acrolimus (TAC), and everolimus (EVL) can increase PTDM incidence. Our study found that a higher proportion of PTDM patients had tacrolimus levels\u0026thinsp;\u0026ge;\u0026thinsp;7 ng/mL (70% vs. 52%, p\u0026thinsp;=\u0026thinsp;0.032).\u003csup\u003e16\u003c/sup\u003e Tacrolimus is known to impair pancreatic beta-cell function and reduce insulin secretion, contributing to post-transplant hyperglycemia. Although our findings did not establish a statistically significant dose-response relationship across different tacrolimus level categories (p\u0026thinsp;=\u0026thinsp;0.075), the trend suggests that maintaining lower tacrolimus trough levels may reduce PTDM risk.\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur laboratory findings revealed cyclosporine-based regimens were less frequently used (13.2%), and while cyclosporine also has diabetogenic effects, its impact on PTDM appears to be less pronounced compared to tacrolimus. The predominance of tacrolimus-based immunosuppression in our cohort (86.8%) reflects current transplant protocols favoring tacrolimus due to its superior graft survival benefits. However, given its association with PTDM, careful monitoring of tacrolimus levels is warranted to balance immunosuppressive efficacy with metabolic risks.\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe identified hypomagnesemia as a significant biochemical marker associated with PTDM. The prevalence of hypomagnesemia was markedly higher in PTDM patients (75.9% vs. 29.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Magnesium plays a crucial role in insulin signaling and glucose metabolism, and its deficiency has been linked to insulin resistance\u003csup\u003e19\u003c/sup\u003e. Several studies have proposed that calcineurin inhibitors contribute to magnesium wasting, thereby exacerbating PTDM risk.\u003csup\u003e20\u003c/sup\u003e Routine monitoring and supplementation of magnesium in kidney transplant recipients could be a potential strategy to mitigate PTDM development. Proteinuria and albuminuria were also more pronounced in PTDM patients, with significantly elevated median UPCR (98.5 vs. 21 mg/day, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ACR (18.5 vs. 4 mg/mmol, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Rout and Jialal (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) stated that presence of proteinuria may indicate early diabetic nephropathy or underlying endothelial dysfunction, reinforcing the need for strict glycemic control in PTDM patients to preserve long-term graft function.\u003csup\u003e21\u003c/sup\u003e Interestingly, PTDM patients had a significantly higher prevalence of CMV infection (9.3% vs. 2.3%, p\u0026thinsp;=\u0026thinsp;0.036). CMV infection has been implicated in the pathogenesis of PTDM through direct pancreatic beta-cell damage and immune-mediated inflammation.\u003csup\u003e22\u003c/sup\u003e These findings suggest that transplant recipients with a history of CMV infection should be closely monitored for PTDM development. Conversely, HCV infection did not show a significant difference between groups, although some studies have suggested a potential link between HCV and PTDM via chronic liver disease and metabolic disturbances.\u003c/p\u003e \u003cp\u003eFor graft and patient\u0026rsquo;s outcomes, results revealed, despite the metabolic disturbances associated with PTDM, our study found no significant difference in graft function between PTDM and non-PTDM patients. Serum creatinine levels (100 \u0026micro;mol/L in both groups, p\u0026thinsp;=\u0026thinsp;0.540) and eGFR values (median 75 mL/min/1.73 m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.505) were comparable at the last follow-up. Additionally, the incidence of delayed graft function, acute rejection (TCMR and ABMR), and overall graft survival did not differ significantly between groups. These findings suggest that while PTDM poses a significant metabolic risk, it may not have an immediate detrimental effect on graft function in the short to medium term. However, long-term studies are needed to assess the impact of PTDM on graft survival and cardiovascular outcomes.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications, Limitations \u0026amp; Recommendations\u003c/h2\u003e \u003cp\u003eOur findings emphasize key strategies for managing PTDM in kidney transplant recipients. Given its strong link to older age and higher BMI, pre-transplant screening and lifestyle interventions are essential. Post-transplant care should integrate nutritional counseling, weight management, and structured exercise to lower PTDM risk.\u003csup\u003e23\u003c/sup\u003e Optimizing tacrolimus dosing to reduce diabetogenic effects while preserving immunosuppression is crucial. Lower tacrolimus trough levels may help, and magnesium supplementation, especially for hypomagnesemia, could improve insulin sensitivity. \u003csup\u003e24\u003c/sup\u003e CMV infection is another independent risk factor requiring close monitoring. Regular glucose screening and early intervention with metformin or insulin can improve outcomes. PTDM affected 23.7% of our cohort. While it doesn\u0026rsquo;t immediately impact graft function, proactive management is vital for long-term metabolic and cardiovascular health. Limitations include a single-center design, potential selection bias, unmeasured confounders, and the need for long-term follow-up on graft survival.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings evaluated a significant burden of post-transplant diabetes mellitus (PTDM) among kidney transplant recipients. Our results revealed it is affecting nearly a quarter of the patients in our cohort. Older age, higher BMI, and elevated tacrolimus levels are key risk factors. Hypertension as a cause of ESRD may further increase susceptibility. Although graft survival and renal function remain comparable between PTDM and non-PTDM patients, PTDM raises the risk of cardiovascular disease and mortality. This highlights the need for close monitoring and early intervention. Higher tacrolimus levels strongly correlate with PTDM, emphasizing the importance of individualized immunosuppressive strategies to minimize metabolic risks while preventing rejection. Future research should focus on alternative immunosuppressive agents, early metabolic screening, and lifestyle modifications to lower PTDM risk. Tailored treatment approaches can improve long-term health outcomes for kidney transplant recipients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare no conflicts of interest related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study was approved by The Research Ethics Committee of Armed Forces Hospitals. Informed consent was waived as per ethical guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;No external funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and Materials availability:\u0026nbsp;\u003c/strong\u003eAll data generated or analyzed during this study are included in the supplementary files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMA wrote the main manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNA reviewed the manuscript\u003c/p\u003e\n\u003cp\u003eMAA reviewed the manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoA wrote the manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMH Reviewed the manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFA collected and analyzed the data\u003c/p\u003e\n\u003cp\u003eAA collected and analyzed the data\u003c/p\u003e\n\u003cp\u003eAB collected and analyzed the data\u003c/p\u003e\n\u003cp\u003eFB reviewed the manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBA reviewed the manuscript\u003c/p\u003e\n\u003cp\u003eMEA wrote and reviewed the main manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKidney transplant - Mayo Clinic [Internet]. Mayo Clinic; 2025 [cited 2025 Mar 20]. Available from: https://www.mayoclinic.org/tests-procedures/kidney-transplant/about/pac-20384777 \u003c/li\u003e\n\u003cli\u003eAlajous S, Budhiraja P. New-onset diabetes mellitus after kidney transplantation. J Clin Med. 2024;13(7):1928. doi:10.3390/jcm13071928 \u003c/li\u003e\n\u003cli\u003eSanchez-Baya M, Bolufer M, V\u0026aacute;zquez F, Alonso N, Mass\u0026oacute; E, Paul J, et al. Diabetes mellitus in kidney transplant recipients: new horizons in treatment. J Clin Med. 2025;14(4):1048. doi:10.3390/jcm14041048 \u003c/li\u003e\n\u003cli\u003eConte C, Maggiore U, Cappelli G, Ietto G, Lai Q, Salis P, et al. Management of metabolic alterations in adult kidney transplant recipients: a joint position statement of the Italian Society of Nephrology (SIN), the Italian Society for Organ Transplantation (SITO) and the Italian Diabetes Society (SID). Nutr Metab Cardiovasc Dis. 2020;30(9):1427\u0026ndash;41. doi:10.1016/j.numecd.2020.05.004 \u003c/li\u003e\n\u003cli\u003eRossi MR, Mazzali M, De Sousa MV. Post-transplant diabetes mellitus: risk factors and outcomes in a 5-year follow-up. Front Clin Diabetes Healthc. 2024;5. doi:10.3389/fcdhc.2024.1336896 \u003c/li\u003e\n\u003cli\u003ePopović L, Bulum T. New-onset diabetes after organ transplantation: risk factors, treatment, and consequences. Diagnostics. 2025;15(3):284. doi:10.3390/diagnostics15030284 \u003c/li\u003e\n\u003cli\u003eChowdhury TA. Post-transplant diabetes mellitus. Clin Med. 2019;19(5):392\u0026ndash;5. doi:10.7861/clinmed.2019-0195 \u003c/li\u003e\n\u003cli\u003eAlanazi NF, Almutairi M, Aldohayan L, AlShareef A, Ghallab B, Altamimi A. The incidence and risk factors of post-transplant diabetes mellitus in living donor kidney transplantation patients: a retrospective study. BMC Nephrol. 2024;25(1). doi:10.1186/s12882-024-03816-3 \u003c/li\u003e\n\u003cli\u003eCantarin MPM. Diabetes in kidney transplantation. Adv Chronic Kidney Dis. 2021;28(6):596\u0026ndash;605. doi:10.1053/j.ackd.2021.10.004 \u003c/li\u003e\n\u003cli\u003eCheng C, Feng Y, Wang H. Incidence and relative risk factors in posttransplant diabetes mellitus patients: a retrospective cohort study. Korean J Transplant. 2020;34(4):231\u0026ndash;7. doi:10.4285/kjt.20.0026 \u003c/li\u003e\n\u003cli\u003eBalakrishnan M, Jayam J, Srinivasaprasad N, S S, Fernando M. Prevalence and risk factors for posttransplant diabetes mellitus: data from government tertiary care center. Indian J Transplant. 2018;12(2):119. doi:10.4103/ijot.ijot_14_18 \u003c/li\u003e\n\u003cli\u003eMalik RF, Jia Y, Mansour SG, Reese PP, Hall IE, Alasfar S, et al. Post-transplant diabetes mellitus in kidney transplant recipients: a multicenter study. Kidney360. 2021;2(8):1296\u0026ndash;307. doi:10.34067/kid.0000862021 \u003c/li\u003e\n\u003cli\u003eMartin-Moreno PL, Shin H, Chandraker A. Obesity and post-transplant diabetes mellitus in kidney transplantation. J Clin Med. 2021;10(11):2497. doi:10.3390/jcm10112497 \u003c/li\u003e\n\u003cli\u003eJia G, Sowers JR. Hypertension in diabetes: an update of basic mechanisms and clinical disease. Hypertension. 2021;78(5):1197\u0026ndash;205. doi:10.1161/hypertensionaha.121.17981 \u003c/li\u003e\n\u003cli\u003eKanbay M, Guldan M, Ozbek L, Copur S, Covic AS, Covic A. Exploring the nexus: the place of kidney diseases within the cardiovascular-kidney-metabolic syndrome spectrum. Eur J Intern Med. 2024;127:1\u0026ndash;14. doi:10.1016/j.ejim.2024.07.014 \u003c/li\u003e\n\u003cli\u003eNandula SA, Boddepalli CS, Gutlapalli SD, Lavu VK, Abdelwahab RaM, Huang R, et al. New-onset diabetes mellitus in post-renal transplant patients on tacrolimus and mycophenolate: a systematic review. Cureus. 2022. doi:10.7759/cureus.31482 \u003c/li\u003e\n\u003cli\u003eTri\u0026ntilde;anes J, Rodriguez-Rodriguez A, Brito-Casillas Y, Wagner A, De Vries A, Cuesto G, et al. Deciphering tacrolimus-induced toxicity in pancreatic \u0026beta; cells. Am J Transplant. 2017;17(11):2829\u0026ndash;40. doi:10.1111/ajt.14323 \u003c/li\u003e\n\u003cli\u003eTacrolimus-based immunosuppression [Internet]. PubMed; 2004 Dec 1 [cited 2025 Mar 20]. Available from: https://pubmed.ncbi.nlm.nih.gov/15599882/ \u003c/li\u003e\n\u003cli\u003eAkimbekov NS, Coban SO, Atfi A, Razzaque MS. The role of magnesium in pancreatic beta-cell function and homeostasis. Front Nutr. 2024;11. doi:10.3389/fnut.2024.1458700 \u003c/li\u003e\n\u003cli\u003ePham P, Sarkar M, Pham P, Pham P. Diabetes mellitus after solid organ transplantation. Endotext - NCBI Bookshelf [Internet]. 2022 Jul 13 [cited 2025 Mar 20]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK378977/ \u003c/li\u003e\n\u003cli\u003eRout P, Jialal I. Diabetic nephropathy. StatPearls - NCBI Bookshelf [Internet]. 2025 Jan 9 [cited 2025 Mar 20]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK534200/ \u003c/li\u003e\n\u003cli\u003eVarani S, Landini M. Cytomegalovirus-induced immunopathology and its clinical consequences. Herpesviridae. 2011;2(1):6. doi:10.1186/2042-4280-2-6 \u003c/li\u003e\n\u003cli\u003eLaghrib Y, Hilbrands L, Oniscu GC, Crespo M, Gandolfini I, Mariat C, et al. Current practices in prevention, screening, and treatment of diabetes in kidney transplant recipients: European survey highlights from the ERA DESCARTES Working Group. Clin Kidney J. 2024;18(1). doi:10.1093/ckj/sfae367 \u003c/li\u003e\n\u003cli\u003eChua JCM, Mount PF, Lee D. Lower versus higher starting tacrolimus dosing in kidney transplant recipients. Clin Transplant. 2022;36(6). doi:10.1111/ctr.14606\u003c/li\u003e\n\u003c/ol\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":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Post-transplant diabetes mellitus, Kidney transplant, Risk factors, Incidence, Retrospective study, Saudi Arabia","lastPublishedDoi":"10.21203/rs.3.rs-6316096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6316096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePost-transplant diabetes mellitus (PTDM) is a common metabolic complication following kidney transplantation, adversely affecting graft and patient outcomes. This study aims to identify the prevalence, risk factors, and clinical implications of PTDM among kidney transplant recipients at Alhada Armed Forces Hospital, Taif, Saudi Arabia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study including adult kidney transplant recipients from January 1984 to December 2023, excluding patients with pre-existing diabetes. Data were extracted from electronic medical records, encompassing demographics, clinical characteristics, transplantation details, and laboratory parameters. PTDM was diagnosed based on the American Diabetes Association criteria. Statistical analyses included t-tests, multivariate logistic regression, chi-square tests, Mann-Whitney U test, and Fisher\u0026rsquo;s exact tests. Receiver operating characteristic (ROC) curve analysis determined optimal cutoff values for predictive variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf 228 kidney transplant recipients (64% males, mean age 47.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6 years), 54 (23.7%) developed PTDM. PTDM patients were significantly older (53.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9 vs. 45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher BMI (27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 vs. 25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4 kg/m\u0026sup2;, p\u0026thinsp;=\u0026thinsp;0.023). Hypertension was a more frequent cause of ESRD in the PTDM group (24.1% vs. 6.3%, p\u0026thinsp;=\u0026thinsp;0.006). Tacrolimus levels\u0026thinsp;\u0026ge;\u0026thinsp;7 ng/mL were associated with higher PTDM incidence (70% vs. 52%, p\u0026thinsp;=\u0026thinsp;0.032). ROC analysis indicated that age and BMI were significant predictors of PTDM (AUC\u0026thinsp;=\u0026thinsp;0.72 and 0.68, respectively). Multivariate logistic regression identified age, BMI, and tacrolimus levels as independent PTDM predictors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePTDM affects a substantial proportion of kidney transplant recipients, with older age, higher BMI, and elevated tacrolimus levels emerging as key risk factors. Close monitoring and individualized immunosuppressive strategies may mitigate PTDM risk and improve post-transplant outcomes.\u003c/p\u003e\u003ch2\u003eTrial Registration\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Incidence and Risk Factors of Post-Transplant Diabetes Mellitus in Kidney Transplant Recipients: A Retrospective Study from a Tertiary Center in Saudi Arabia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 11:25:08","doi":"10.21203/rs.3.rs-6316096/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"254933187972347577013232734106988734035","date":"2025-05-08T06:09:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-06T05:46:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T16:25:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-07T07:39:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-04T19:25:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-04-04T19:24:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4df4a9bf-f4a2-4093-850a-0ea6d678ec62","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-18T16:05:25+00:00","versionOfRecord":{"articleIdentity":"rs-6316096","link":"https://doi.org/10.1186/s12882-025-04375-x","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2025-08-13 15:57:42","publishedOnDateReadable":"August 13th, 2025"},"versionCreatedAt":"2025-05-09 11:25:08","video":"","vorDoi":"10.1186/s12882-025-04375-x","vorDoiUrl":"https://doi.org/10.1186/s12882-025-04375-x","workflowStages":[]},"version":"v1","identity":"rs-6316096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6316096","identity":"rs-6316096","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}