Risk Factors of the Development of Post-transplant Diabetes Mellitus After Kidney Transplantation, and Comparison Between Older and Younger Kidney Transplant Recipients with Post-Transplant Diabetes Mellitus - Single-Center Study

Risk Factors of the Development of Post-transplant Diabetes Mellitus After Kidney Transplantation, and Comparison Between Older and Younger Kidney Transplant Recipients with Post-Transplant Diabetes Mellitus - Single-Center Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Risk Factors of the Development of Post-transplant Diabetes Mellitus After Kidney Transplantation, and Comparison Between Older and Younger Kidney Transplant Recipients with Post-Transplant Diabetes Mellitus - Single-Center Study Aleksandra Barbachowska-Kubik, Jolanta Gozdowska, Magdalena Durlik This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6284246/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Diabetes mellitus after kidney transplantation (post-transplant diabetes mellitus PTDM) is a commonly observed metabolic complication. Its occurrence ranges from 4% to 25%. The aim of this study was to analyse potential risk factors associated with PTDM in renal transplant recipients. Additionally, the study focused on determining differences between older, and younger patients with PTDM. Methods: In this retrospective study, we screened 375 patients who received a kidney transplant between January 2021 and February 2024. PTDM was defined based on the 2013 International Consensus Meeting on Post-transplant Diabetes Mellitus. Kidney transplant recipients who developed PTDM were compared with patients without PTDM, and then patients with PTDM were divided into two subgroups based on age (≥60 years, and <60 years), and compared. Results: The data of 218 kidney transplant recipients were analyzed. Of those, fifty-five patients (25%) developed PTDM. Age (p<0.001), elevated body mass index (p<0.001), hypomagnesemia( p<0.013), hipertriglyceridemia (p<0.001), and hypercholesterolemia (p<0.001) were significant risk factors for PTDM occurrence. A comparison between older and younger patients with PTDM did not reveal significant differences. Conclusions: PTDM is a common complication after kidney transplantation. Older age showed the strongest association with PTDM. Patients who are at high risk should be carefully monitored, and treated aggressively if the diabetes develops. More research comparing older and younger patients with PTDM is needed, so that a better, and more individualized approach can be implemented. kidney transplantation post-transplant diabetes mellitus older kidney recipients metabolic complications Figures Figure 1 Figure 2 Introduction Kidney transplantation is considered the most effective treatment for end-stage chronic kidney disease (ESKD) [ 1 ], however various complications can occur after this procedure. Post-Transplant Diabetes Mellitus (PTDM) is one of these complications, and is a commonly observed metabolic disorder [ 2 ]. PTDM is defined as newly diagnosed diabetes mellitus in the post-transplant setting, irrespective of whether it was present but undetected before transplantation [ 3 ]. Diabetes mellitus after transplantation was first described in 1964 among kidney transplant recipients [ 4 ], and since then the nomenclature of this disease has changed many times. In 2014, the International Expert Panel consisting of transplant nephrologists, diabetologists, and clinical scientists recommended changing the terminology from New-Onset Diabetes After Transplantation (NODAT) to post-transplant diabetes mellitus (PTDM). This change was due to the high prevalence of undiagnosed pre-transplant diabetes mellitus [ 4 ]. The incidence of PTDM ranges from 4–25% [ 2 ], however a higher incidence has also been reported (up to 40%) [ 6 ]. Several modifiable, and non-modifiable risk factors for PTDM have been reported. Some of them are the same as risk factors for type 2 diabetes mellitus (DM), and include African American and Hispanic ethnicity, age, elevated BMI, family history of diabetes, and male sex. Other reported risk factors are specific to solid organ transplantation and include hypomagnesemia, a history of biopsy proven acute rejection (BPAR), use of steroids and calcineurin inhibitors (CNI), cytomegalovirus (CMV) infection, hepatitis C, and certain human leukocyte antigen (HLA) types [ 7 ]. Moreover genetic and epigenetic polymorphisms have also been also associated with PTDM [ 8 ]. The occurrence of PTDM has a significant impact on quality of life and mortality. Diabetes after transplantation has been associated with worse patient and graft survival [ 9 , 10 ]. Moreover, it can also promote other transplant complications such as cardiovascular diseases, infections, and impaired wound healing [ 11 , 12 ]. Although our knowledge about risk factors leading to PTDM occurrence has improved, there is still a need for further research, to achieve a better understanding in this area. Thus, the aim of this single center study was to analyse potential risk factors associated with PTDM in renal transplant recipients. Additionally, we focused on a comparison between older and younger patients with PTDM, seeking to determine differences between these groups, and assess the need for a dedicated approach. Methods Study population In this retrospective, observational study we analyzed patients who underwent kidney transplant (KT) between January 2021 and February 2024. A total of 375 KT were performed. Patients who received single-organ kidney transplant, without prior history of diabetes mellitus, were included in further research. The exclusion criteria included multi-organ transplantation, pre-existing diabetes mellitus (DM), conversion from cyclosporine to tacrolimus, transferring to a different center during the follow-up period. All information was accessed through medical records and laboratory test results. Our data included sex, age, BMI prior to KT, type of donor, type of dialysis, type of induction therapy (if applicable), treatment of biopsy-proven acute rejection (if applicable). The follow-up period was 6 months. Definitions Post-transplant diabetes mellitus was diagnosed based on the 2013 International Consensus Meeting on Post-transplant Diabetes Mellitus, and included symptoms of hyperglycemia (polydipsia, polyuria, and unintentional weight loss) with random blood glucose ≥ 200 mg/dl, fasting plasma glucose ≥ 126 mg/dl, or two-hour plasma glucose ≥ 200 mg/dl during oral glucose tolerance test (OGTT) [ 5 ]. The diagnosis of PTDM was made only after the patient had been on maintenance immunosuppression treatment (3 months post transplantation). Biopsy proven acute rejection was treated with 500 mg of methylprednisolone for three consecutive days. Older patients were defined as those aged ≥ 60 years, based on United Nation definition. Statistical analysis Continuous variables were summarized as mean ± standard deviation (SD) or median and interquartile range (IQR), while categorical variables were presented as n (%). Normality was evaluated with the Shapiro-Wilk test, supported by assessments of skewness and kurtosis. Levene's test was applied to assess homogeneity of variances. Comparisons between groups were made with the Student's t-test, Mann-Whitney U test, Pearson's chi-square test, or Fisher's exact test, as appropriate. Two-step logistic regression analysis was performed to identify risk factors for post-transplant diabetes mellitus. Variables for the multivariate model were selected based on p-value threshold of < 0.25 [ 13 ], and a stepwise approach was used for final variables selection. Model fit was assessed using Nagelkerke R2 and Hosmer and Lemeshow Goodness of Fit (GOF) test. Variance inflation factors (VIF) were calculated to verify multicollinearity. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the prognostic performance of the selected variables for PTDM. Optimal cut-offs were indicated with the Youden method. A significance level (alpha) of 0.05 was used for statistical significance. All analyses were performed using R software (R4.1.2). Results Characteristics of study groups and comparison of study groups Of the 375 KT procedures, 218 patients were included in the study. Among them 131 (60.09%) were male, and 87 (39.91%) were female. Of those 189 were single-organ kidney, while 29 were repeat kidney. The mean age was 45.5 years. Fifty-five patients developed PTDM (26 women and 29 men; 47.3% vs. 52.7%). Of these, 24 patients were aged ≥ 60 years (12 women and 12 men). All patients received a standard immunosuppression protocol consisting of steroids, calcineurin inhibitors (CNI) - tacrolimus, and mycophenolate acid. Seventy-five patients received induction therapy of thymoglobulin (ATG), and 24 patients received basiliximab prior to transplantation. The group without PTDM was compared with the group with PTDM in terms of clinical and laboratory data. Subsequently, patients who developed PTDM were divided into two subgroups according to age (< 60 years and ≥ 60 years), and comparisons between them were conducted. Patients with PTDM were significantly older, MD = 11.11, CI95 [7.19;15.03], p < 0.001, and had significantly higher BMI( MD = 2.46, CI95 [1.20;3.71], p < 0.001). The proportion of patients with age above or equal 60 years was significantly higher among those with PTDM (43.6% vs 12.9%, p < 0.001). A comparison between patients with PTDM and patients without PTDM is presented in Table 1 . Table 1 Characteristics and comparison of study groups. Variable Patients with PTDM Patients without PTDM MD (95% CI) p N 55 (100.0) 163 (100.0) - - Sex, female, n (%) 26 (47.3) 61 (37.4) - 0.258 Age, years, mean ± SD 54.04 ± 12.38 42.93 ± 12.88 11.11 (7.19;15.03) < 0.001 1 Age ≥ 60 years, n (%) 24 (43.6) 21 (12.9) - < 0.001 Thymoglobulin, n (%) 15 (27.3) 60 (36.8) - 0.261 Basiliximab, n (%) 7 (12.7) 17 (10.4) - 0.825 GKS pulses, n (%) 10 (18.2) 16 (9.8) - 0.157 Month of PTDM, mean ± SD 2.73 ± 1.80 - - - Insulin-based therapy, n (%) 15 (27.3) 0 (0.0) - < 0.001 3 Medicines, n (%) 43 (78.2) 1 (0.6) - < 0.001 BMI, kg/m 2 , mean ± SD 26.49 ± 4.56 24.04 ± 3.90 2.46 (1.20;3.71) 0.999 3 PD, n (%) 4 (7.3) 18 (11.0) - 0.587 Duration of dialysis, years, median (IQR) 2.00 (1.50;4.00) 2.00 (1.00;4.00) 0.00 (-1.00;0.00) 0.735 2 DDKT, n (%) 44 (80.0) 127 (77.9) - 0.892 LDKT, n (%) 11 (20.0) 36 (22.1) - 0.892 Infection within 6 months, n (%) 30 (54.5) 73 (44.8) - 0.272 TG, mg/dl, median (IQR) 203.00 (150.50;296.00) 141.00 (108.50;197.00) 62.00 (32.00;85.00) < 0.001 2 Hypertriglyceridemia, n (%) 51 (92.7) 106 (65.0) - < 0.001 Lipid-lowering treatment, n (%) 28 (50.9) 57 (35.0) - 0.053 Cholesterol, mg/dl, mean ± SD 208.64 ± 54.40 182.47 ± 45.48 26.16 (11.45;40.88) 0.001 1 Hypercholesterolaemia, n (%) 45 (81.8) 101 (62.0) - 0.011 Tacrolimus ng/ml, mean ± SD 11.83 ± 2.04 11.79 ± 1.83 0.04 (-0.54;0.62) 0.885 1 Hyperglycaemia, n (%) 1 (1.8) 48 (29.4) - < 0.001 Uric acid, mg/dl, median (IQR) 6.80 (6.00;8.40) 6.60 (5.60;7.60) 0.20 (-0.10;0.90) 0.117 2 Treatment for hyperuricemia, n (%) 18 (32.7) 43 (26.4) - 0.464 Hyperuricemia, n (%) 34 (61.8) 94 (57.7) - 0.702 Magnesium, mg/dl, median (IQR) 1.70 (1.50;1.80) 1.90 (1.70;2.10) -0.20 (-0.30;-0.10) < 0.001 2 Hypomagnesemia, n (%) 20 (36.4) 32 (19.6) - 0.020 Creatinine mg/dl, median (IQR) 1.50 (1.15;2.00) 1.50 (1.19;1.85) 0.00 (-0.10;0.20) 0.671 2 BPAR, n (%) 10 (18.2) 16 (9.8) - 0.157 CMV, n (%) 8 (14.5) 17 (10.4) - 0.559 BKV, n (%) 8 (14.5) 10 (6.1) - 0.094 TG - triglycerides; DDKT- deceased donor kidney transplantation; LDKT -living donor kidney transplantation; PD - peritoneal dialysis, HD-hemodialysis; BMI- body mass index; GKS pulses - methylprednisolone pulses; SD – standard deviation, IQR – interquartile range, MD – mean or median difference (with PTDM vs without PTDM), CI – confidence interval. Groups compared with t-Student test 1 , Mann-Whitney U test 2 , Pearson’s chi-square test or Fisher’s exact test 3 , as appropriate. Risk factors of PTDM – logistic regression analysis In the univariate analysis, advanced age significantly increased the odds of PTDM (OR = 1.07, CI95 [1.04;1.10], p < 0.001). Additionally, patients aged 60 years or above had fivefold higher odds of PTDM than patients below 60 years (OR = 5.24, CI95 [2.60;10.68], p < 0.001). Other risk factors associated with PTDM occurrence were: higher BMI (OR = 1.15, CI95 [1.07;1.25], p < 0.001), hypertriglyceridemia (OR = 1.01, CI95 [1.00;1.01], p < 0.001), hypercholesterolaemia (OR = 1.01, CI95 [1.00;1.02], p < 0.001), hypomagnesemia (OR = 2.34, CI95 [1.19;4.57], p = 0.013). No correlation between sex, type of donor, type of dialysis, hyperuricemia, induction therapy, presence and treatment of acute rejection, and mean tacrolimus level during the first three months was observed. Figure 1 presents boxplot charts illustrating the distribution of variables significantly different between patients with PTDM and those without PTDM. Multivariate logistic regression model confirmed that age had a significant impact on the odds of PTDM. Each additional year increased odds of PTDM by 8%, OR = 1.08, CI95 [1.04;1.11], p < 0.001. Odds of PTDM were 12% higher for each 1 kg/m 2 increase in BMI, OR = 1.12, CI95 [1.02;1.23], p = 0.026. Concentration of triglycerides slightly influenced the odds of PTDM, OR = 1.00, CI95 [1.00;1.01], p = 0.046. Hypertriglyceridemia increased the odds of PTDM fourfold, OR = 3.54, CI95 [1.12;13.93], p = 0.045. Higher magnesium concentrations reduced the odds of PTDM by 88%, OR = 0.12, CI95 [0.03;0.46], p = 0.003. Table 2 presents the outcomes of logistic regression analysis for PTDM. Table 2 Outcome of logistic regression analysis for PTDM Variable Univariate models Multivariate model OR 95% CI p OR 95% CI p Sex, female (vs male) 1.50 0.81–2.78 0.198 - - - Age, years 1.07 1.04–1.10 < 0.001 1.08 1.04–1.11 < 0.001 Age ≥ 60 years (vs < 60 years) 5.24 2.60-10.68 < 0.001 - - - Thymoglobulin 0.64 0.32–1.24 0.200 - - - Basiliximab 1.25 0.46–3.09 0.638 - - - Methylprednisolone pulses 2.04 0.84–4.76 0.103 - - - BMI, kg/m 2 1.15 1.07–1.25 < 0.001 1.12 1.02–1.23 0.026 Peritoneal dialysis 0.63 0.18–1.79 0.426 - - - LDKT (vs DDKT) 0.88 0.40–1.84 0.745 - - - Triglycerides, mg/dl 1.01 1.00-1.01 < 0.001 1.00 1.00-1.01 0.046 Hypertriglyceridemia 6.86 2.63–23.51 < 0.001 3.54 1.12–13.93 0.045 Cholesterol, mg/dl 1.01 1.00-1.02 < 0.001 1.01 1.00-1.01 0.111 Hypercholesterolaemia 2.76 1.34–6.16 0.008 - - - Mean tacrolimus levels during first 3 months 1.01 0.86–1.19 0.884 - - - Uric acid, mg/dl 1.17 0.98–1.41 0.091 - - - Hyperuricemia 1.19 0.64–2.25 0.589 - - - Magnesium, mg/dl 0.10 0.03–0.32 < 0.001 0.12 0.03–0.46 0.003 Hypomagnesemia 2.34 1.19–4.57 0.013 - - - BPAR 2.04 0.84–4.76 0.103 - - - OR – odds ratio, CI – confidence interval, LDKT - living donor kidney transplantation, DDKT - deceased donor kidney transplantation, BPAR - biopsy-proven acute rejection Receiver Operating Characteristics (ROC) analysis Receiver Operating Characteristics (ROC) analysis was conducted to evaluate the predictive ability of selected parameters for PTDM. The highest AUC (Area Under the Curve), which referred to best prognostic properties, was found for age (AUC = 0.733, CI95 [0.658;0.809]) with a cut-off of 48.5 years. Patients above the cut-off were prognosed to develop PTDM with a sensitivity of 71% and a specificity of 69%. AUC values for other variables ranged from 0.638 (presence of hypertriglyceridemia) to 0.693 (concentration of triglycerides) indicating moderate prognostic properties. The results are summarized in Table 3 , and Fig. 2 visualizes the ROC curves for selected parameters. Table 3 Outcome of Receiver Operating Characteristics (ROC) assessing quality of selected parameters to predict PTDM Variable Cut-off* AUC (95% CI) Sensitivity Specificity PPV NPV Accuracy p Age, years 48.50 0.733 (0.658;0.811) 0.71 0.69 0.44 0.88 0.70 < 0.001 BMI, kg/m 2 27.48 0.662 (0.580;0.744) 0.45 0.82 0.45 0.82 0.72 < 0.001 Triglycerides, mg/dl 175.50 0.693 (0.602;0.773) 0.65 0.69 0.41 0.85 0.68 < 0.001 Hypertriglyceridemia - 0.638 (0.587;0.687) 0.93 0.35 0.32 0.93 0.50 < 0.001 Magnesium, mg/dl 1.89 0.684 (0.606;0.763) 0.78 0.52 0.36 0.88 0.59 < 0.001 AUC – area under curve, CI – confidence interval, PPV – positive predictive value, NPV – negative predictive value. * Only for continuous parameters. Comparison of patients with PTDM aged ≥ 60 years and patients with PTDM aged < 60 years Younger group was compared with older group in terms of induction therapy, type of PTDM treatment (insulin vs oral medications), infection occurrence, cytomegalovirus (CMV) replication, and polyomavirus (BKV) replication, creatinine level after 6 months, presence of biopsy proven acute rejection, level of cholesterol and triglycerides. No significant difference was confirmed between patients with PTDM aged ≥ 60 years and patients with PTDM aged 0.05). Results are presented in Table 4 . Table 4 Comparison of patients aged ≥ 60 years and patients aged < 60 years with PTDM Variable Patients with PTDM aged ≥ 60 years Patients with PTDM aged 0.999 3 GKS, n (%) 5 (20.8) 5 (16.1) - 0.733 3 Insulin-based therapy, n (%) 6 (25.0) 9 (29.0) - 0.978 Medicines, n (%) 19 (79.2) 24 (77.4) - > 0.999 Infection within 6 months, n (%) 16 (66.7) 14 (45.2) - 0.188 Triglycerides, mg/dl, median (IQR) 188.00 (151.25;238.50) 245.00 (152.50;339.00) -57.00 (-111.00;17.00) 0.169 2 Cholesterol, mg/dl, mean ± SD 219.83 ± 51.50 199.97 ± 55.82 19.87 (-9.58;49.31) 0.182 1 Creatinine mg/dl, median (IQR) 1.60 (1.40;2.02) 1.45 (1.00;1.85) 0.15 (-0.10;0.60) 0.137 2 BPAR, n (%) 5 (20.8) 5 (16.1) - 0.733 3 CMV, n (%) 4 (16.7) 4 (12.9) - 0.718 3 BKV, n (%) 2 (8.3) 6 (19.4) - 0.443 3 CMV - cytomegalovirus replication, BKV- polyomavirus replication, BPAR -biopsy-proven acute rejection; GKS - additional methylprednisolone pulses; SD – standard deviation, IQR – interquartile range, MD – mean or median difference (≥ 60 years vs < 60 years), CI – confidence interval. Groups compared with t-Student test 1 , Mann-Whitney U test 2 , Pearson’s chi-square test or Fisher’s exact test 3 , as appropriate. Discussion Posttransplant diabetes mellitus is a common complication after kidney transplantation. In our study 25% developed this metabolic disorder, which is quite a high percentage, however, it corresponds to other studies [ 11 , 14 ]. As mentioned above, the incidence of PTDM ranges from 10 to 25% [ 2 ].The reason for such wide variation of its occurrence, may result from lack of standard definition of PTDM, duration of the follow-up in studies, and also presence of modifiable and nonmodifiable risk factors in kidney transplant recipients - homogenous cohorts. In our study, risk factors such as advanced age, higher BMI, hipertrigliceridemia, hypercholesterolaemia, and hypomagnesemia have been associated with PTDM. Since age is a well known non-modifiable risk factor of type 2 diabetes mellitus [ 15 ], there was no surprise that in our study, it also increased risk of PTDM for KT patients. Furthermore, patient age was found to be the strongest risk factor, with a cut-off of 48,5 years of age. Additionally, patients aged 60 years or above had five times higher odds of PTDM than patients below 60 years (p < 0.001). Comparable conclusions were pointed out in other studies [ 16 , 17 , 18 ]. Consistent with trends in the general population, elevated BMI was a significant risk factor for PTDM in our study. Mechanisms responsible for insulin resistance in obesity (BMI > 30 kg/m2), and overweight (BMI > 25 kg/m2) are not fully understood. However, it may be the consequence of a chronic inflammatory state caused by excessive fat tissue, which stimulates macrophage recruitment to adipocytes, and the release of proinflammatory adipokines leading to the downregulation of insulin signaling [ 19 ]. Furthermore adipose tissue produces tumor necrosis factor-alpha (TNF-α), which activation is associated with insulin resistance due to reduced expression of insulin-sensitive transporters [ 20 ]. Some studies suggest that post transplant weight gain is also a risk factor of PTDM occurrence [ 18 , 21 ]. Another important aspect might be the body fat distribution. Cron et al demonstrated that PTDM was strongly associated with central obesity [ 22 ]. In a study performed by von Düring et al, visceral fat tissue was correlated to PTDM occurrence, and hyperglycemia early after transplantation [ 23 ]. Thus it might be essential to monitor not only BMI, but also waist circumference in KT recipients. Our study also showed that elevated triglyceride levels were associated with PTDM. The reason for that might be due to association between hypertriglyceridemia and insulin resistance, which can then lead to future diabetes [ 24 ]. Hypomagnesemia has been related to increased risk of PTDM, although the underlying mechanism of that remains unclear [ 11 , 25 ]. Lower magnesium level impacts insulin signaling [ 26 ], however it also might be the effect of calcineurin inhibitor treatment which is considered to be a risk factor of developing PTDM [ 27 ]. Moreover, Augusto et al presented that pretransplant, rather than post-transplant, hypomagnesemia was an independent risk factor of PTDM [ 28 ]. The same results were shown by Xu et al [ 29 ]. In our study posttransplant hypomagnesemia was an independent risk factor of development of PTDM, though pretransplant serum magnesium level should also be taken into consideration in further research. Our study revealed that hypercholesterolaemia was associated with PTDM. Sinangil et al, also revealed positive correlation between elevated total cholesterol level and LDL-C (low-density lipoprotein cholesterol) in patients with PTDM [ 30 ]. On the contrary some studies found out that the rise of TG/HDL-C (triglyceride/ high-density lipoprotein cholesterol) ratio and lower HDL-C were increasing risk of diabetes mellitus in KT recipients [ 19 , 31 ]. The impact of cholesterol and its fractions on PTDM might be due to the fact that excess cholesterol accumulation leads to β-cell dysfunction, thus impairing glucose tolerance, and affecting insulin secretion. Moreover, islet cholesterol deposition can cause increased islet amyloid polypeptide aggregation, and increased islet amyloid formation, thus further deteriorating β-cell function and affecting glucose homeostasis [ 23 , 32 , 33 ]. Numerous studies suggest that immunosuppression therapy, particularly calcineurin inhibitors, and steroids, may contribute to PTDM development in a dose-dependent manner [ 27 , 29 , 34 ]. However, in our study, no correlation was found between mean tacrolimus levels during the first three months post-transplantation, additional steroid doses (used to treat BPAC), and PTDM. This may be due to the short follow-up period, and the low incidence of BPAR, which has limited statistical significance. In the final stage of the study we divided the group who developed PTDM into 2 subgroups based on age (≥ 60 years of age, and < 60 years of age), and then compared them. There was no significant difference in regard to BPAR, CMV infection, BKV infection, or type of PTDM treatment (insulin vs oral medications). Mean creatinine level at the end of follow-up period was 1,6 mg/dl for older patients, and 1,4 mg/dl for younger patients, which in our opinion is similar, and acceptable outcome. Revanur et al, in a retrospective study, revealed that survival of patients over the age of 55 years with PTDM was similar to the control group. On the contrary, KT recipients under 55 years of age with PTDM were associated with a much higher risk of death. No difference in regard to graft survival, and acute rejection was found [ 9 ]. Comparison between older and younger KT recipients with PTDM has not been extensively studied. More research in regard to differences between PTDM patients is needed, thus an adequate approach can be performed. This study has a number of limitations. Firstly, it is a single center study with only 218 participants, from which 55 developed PTDM, which limits extrapolation of the results to other populations. Secondly, it is a retrospective study of database analysis,therefore the reliability of available data, or lack of them, limits the scope of the results. Moreover, the follow-up period was relatively short compared to other studies, thus some factors, for example immunosuppression, might have long-term prodiabetogenic effects which weren't observed in this study. Lastly, a relatively small group of older and younger patients with PTDM (24 vs 31 recipients). Conclusions Advanced age had the strongest association with PTDM. Elevated BMI, hypomagnesemia, and hipercholesterolemia also increased risk of PTDM. No significant differences in terms of serum creatinine level, CMV infection, BKV infection, BPAR, type of PTDM treatment (insulin vs oral medications) were detected in younger, and older recipients with PTDM. PTDM influences patient and graft survival, and increases risk of cardiovascular diseases, more research is necessary to establish modifiable risk factors, thus PTDM can be prevented [ 9 , 10 , 35 ]. Additionally KT recipients with nonmodifiable risk factors should be regularly screened for PTDM, and aggressive treatment should be implied if they develop diabetes, to minimise the risk of complications. More research comparing older and younger patients with PTDM is needed, thus better, and an individualized approach can be performed. Declarations Ethical approval Ethical approval was waived by the local Ethics Committee of University A in view of the retrospective nature of the study and all the procedures being performed were part of the routine care. Consent for publications Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding No funding was received for conducting this study. Author’s contributions Research idea and study design: ABK, JG, data acquisition: ABK; supervision or mentorship: JG,MD; statistical analysis: ABK. Each author contributed important intellectual content during manuscript drafting or revision and accepted accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. 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Clin Transplant. 2001;15(2):89–94. 10.1034/j.1399-0012.2001.150202.x . PMID: 11264633. Siraj ES, Abacan C, Chinnappa P, Wojtowicz J, Braun W. Risk factors and outcomes associated with posttransplant diabetes mellitus in kidney transplant recipients. Transplant Proc. 2010;42(5):1685-9. 10.1016/j.transproceed.2009.12.062 . PMID: 20620501. Mizrahi N, Braun M, Ben Gal T, Rosengarten D, Kramer MR, Grossman A. Post-transplant diabetes mellitus: incidence, predicting factors and outcomes. Endocrine. 2020;69(2):303–9. 10.1007/s12020-020-02339-9 . Epub 2020 May 16. PMID: 32418071. David W. Hosmer, Stanley Lemeshow, Applied Logistic Regression, 2000. Malik RF, Jia Y, Mansour SG, Reese PP, Hall IE, Alasfar S, Doshi MD, Akalin E, Bromberg JS, Harhay MN, Mohan S, Muthukumar T, Schröppel B, Singh P, Weng FL, Thiessen Philbrook HR, Parikh CR. Post-transplant Diabetes Mellitus in Kidney Transplant Recipients: A Multicenter Study. Kidney360. 2021;2(8):1296–307. PMID: 35369651; PMCID: PMC8676388. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2011;34(1):S62–9. 10.2337/dc11-S062 . PMID: 21193628; PMCID: PMC3006051. Cosio FG, Pesavento TE, Osei K, Henry ML, Ferguson RM. Post-transplant diabetes mellitus: increasing incidence in renal allograft recipients transplanted in recent years. Kidney Int. 2001;59(2):732-7. 10.1046/j.1523-1755.2001.059002732.x . PMID: 11168956. Sharif A, Baboolal K. Risk factors for new-onset diabetes after kidney transplantation. Nat Rev Nephrol. 2010;6(7):415–23. 10.1038/nrneph.2010.66 . Epub 2010 May 25. PMID: 20498675. Araki M, Flechner SM, Ismail HR, Flechner LM, Zhou L, Derweesh IH, Goldfarb D, Modlin C, Novick AC, Faiman C. Posttransplant diabetes mellitus in kidney transplant recipients receiving calcineurin or mTOR inhibitor drugs. Transplantation. 2006;81(3):335 – 41. 10.1097/01.tp.0000195770.31960.18 . PMID: 16477217. Cai R, Wu M, Xing Y. Pretransplant metabolic syndrome and its components predict post-transplantation diabetes mellitus in Chinese patients receiving a first renal transplant. Ther Clin Risk Manag. 2019;15:497–503. PMID: 30936711; PMCID: PMC6422405. Rodrigo E, Fernández-Fresnedo G, Valero R, Ruiz JC, Piñera C, Palomar R, González-Cotorruelo J, Gómez-Alamillo C, Arias M. New-onset diabetes after kidney transplantation: Risk factors. J Am Soc Nephrol. 2006;17(Suppl 3):S291–5. 10.1681/ASN.2006080929 . Parikh CR, Klem P, Wong C, Yalavarthy R, Chan L. Obesity as an independent predictor of posttransplant diabetes mellitus. Transplant Proc. 2003;35(8):2922-6. 10.1016/j.transproceed.2003.10.074 . PMID: 14697939. Cron DC, Noon KA, Cote DR, Terjimanian MN, Augustine JJ, Wang SC, Englesbe MJ, Woodside KJ. Using analytic morphomics to describe body composition associated with post-kidney transplantation diabetes mellitus. Clin Transpl. 2017;31(9). 10.1111/ctr.13040 . Epub 2017 Jul 20. PMID: 28640481. Ye Y, Gao J, Liang J, Yang Y, Lv C, Chen M, Wang J, Zhu D, Rong R, Xu M, Zhu T, Yu M. Association between preoperative lipid profiles and new-onset diabetes after transplantation in Chinese kidney transplant recipients: a retrospective cohort study. J Clin Lab Anal. 2021;35(8):23867. 10.1002/jcla.23867 . Benhamou PY, Penfornis A. Natural history, prognosis, and management of transplantation-induced diabetes mellitus. Diabetes Metab. 2002;28(3):166–75. PMID: 12149596. Smyrli M, Smyrlis A, Tsouka G, Apostolou T, Vougas V. Risk Factors of the Development of Diabetes Mellitus After Kidney Transplantation. Transplant Proc. 2021;53(9):2782–2785. 10.1016/j.transproceed.2021.09.001 . Epub 2021 Oct 21. PMID: 34690002. Pham PC, Pham PM, Pham SV, Miller JM, Pham PT. Hypomagnesemia in patients with type 2 diabetes. Clin J Am Soc Nephrol. 2007;2(2):366 – 73. 10.2215/CJN.02960906 . Epub 2007 Jan 3. PMID: 17699436. Okumi M, Unagami K, Hirai T, Shimizu T, Ishida H, Tanabe K, Japan Academic Consortium of Kidney Transplantation (JACK). Diabetes mellitus after kidney transplantation in Japanese patients: The Japan Academic Consortium of Kidney Transplantation study. Int J Urol. 2017;24(3):197–204. 10.1111/iju.13253 . Epub 2016 Nov 11. PMID: 27862344. Augusto JF, Subra JF, Duveau A, Rakotonjanahary J, Dussaussoy C, Picquet J, et al. Relation between pretransplant magnesemia and the risk of new onset diabetes after transplantation within the first year of kidney transplantation. Transplantation. 2014;97:1155e60. Xu J, Xu L, Wei X, Li X, Cai M. Incidence and Risk Factors of Posttransplantation Diabetes Mellitus in Living Donor Kidney Transplantation: A Single-Center Retrospective Study in China. Transplant Proc. 2018;50(10):3381–3385. doi: 10.1016/j.transproceed.2018.08.007. Epub 2018 Aug 9. PMID: 30471834. Sinangil A, Celik V, Barlas S, Koc Y, Basturk T, Sakaci T, Akin EB, Ecder T. The incidence of new onset diabetes after transplantation and related factors: Single center experience. Nefrologia. 2017 Mar-Apr;37(2):181–8. Epub 2017 Mar 2. PMID: 28262264. Le Ha K, Nguyen Van D, Do Manh H, Tran Thi D, Nguyen Trung K, Le Viet T, Nguyen Thi Thu H. Elevated Plasma High Sensitive C-Reactive Protein and Triglyceride/High-Density Lipoprotein Cholesterol Ratio are Risks Factors of Diabetes Progression in Prediabetes Patients After Kidney Transplant: A 3-Year Single-Center Study in Vietnam. Int J Gen Med. 2024;17:5095–103. 10.2147/IJGM.S490561 . PMID: 39526064; PMCID: PMC11550698. Peng J, Zhao F, Yang X, Pan X, Xin J, Wu M, Peng YG. Association between dyslipidemia and risk of type 2 diabetes mellitus in middle-aged and older Chinese adults: a secondary analysis of a nationwide cohort. BMJ Open. 2021;11(5):e042821. 10.1136/bmjopen-2020-042821 . Bai Z, Zhang DS, Zhang R, Yin C, Wang RN, Huang WY, Ding J, Yang JL, Huang PY, Liu N, Wang YF, Cheng N, Bai YN. A nested case-control study on relationship of traditional and combined lipid metabolism indexes with incidence of diabetes. Zhonghua Liu Xing Bing Xue Za Zhi. 2021;42(4):656–61. 10.3760/cma.j.cn112338-20200401-00490 . Hjelmesaeth J, Hartmann A, Kofstad J, Stenstrøm J, Leivestad T, Egeland T, Fauchald P. Glucose intolerance after renal transplantation depends upon prednisolone dose and recipient age. Transplantation. 1997;64(7):979 – 83. 10.1097/00007890-199710150-00008 . PMID: 9381545. Seoane-Pillado MT, Pita-Fernández S, Valdés-Cañedo F, Seijo-Bestilleiro R, Pértega-Díaz S, Fernández-Rivera C, Alonso-Hernández Á, González-Martín C, Balboa-Barreiro V. Incidence of cardiovascular events and associated risk factors in kidney transplant patients: a competing risks survival analysis. BMC Cardiovasc Disord. 2017;17(1):72. 10.1186/s12872-017-0505-6 . PMID: 28270107; PMCID: PMC5341360. Additional Declarations No competing interests reported. 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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-6284246","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444992166,"identity":"04e3d1b9-7652-44eb-934f-7a8b5aa55546","order_by":0,"name":"Aleksandra Barbachowska-Kubik","email":"","orcid":"","institution":"Medical University of Warsaw","correspondingAuthor":false,"prefix":"","firstName":"Aleksandra","middleName":"","lastName":"Barbachowska-Kubik","suffix":""},{"id":444992167,"identity":"aa4a2a3b-f0e2-40f9-af94-38b46fb62a90","order_by":1,"name":"Jolanta Gozdowska","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACxgYgwQPEEgzMByBCIOoBcVrYEhBaEghZBdHCY0CcFub+xYc/vKk4bC/ZfuabNG8OgxzfjQS2B/i0MM54lmA458zhxNk8udukebcxGEveSGA3wK/ljEEyb9vtBDmG3G23gVoSNwBtkSCk5TBQi70c/5tnIC31hLX09xg2A7UwzpbIYQNpSTAgbAtbMuOcM/8TZ854Zv5z7jYJw5lnHrbj9Yth/2FQiKXZS5xPfmzwdpuNPN/x5GMPPuDTMgPVQAmQzW14NDAwyPMfwBRkw6tlFIyCUTAKRhwAAEFPU2vOX0IFAAAAAElFTkSuQmCC","orcid":"","institution":"Medical University of Warsaw","correspondingAuthor":true,"prefix":"","firstName":"Jolanta","middleName":"","lastName":"Gozdowska","suffix":""},{"id":444992169,"identity":"0013d2da-1d26-4ac0-8555-401005909a05","order_by":2,"name":"Magdalena Durlik","email":"","orcid":"","institution":"Medical University of Warsaw","correspondingAuthor":false,"prefix":"","firstName":"Magdalena","middleName":"","lastName":"Durlik","suffix":""}],"badges":[],"createdAt":"2025-03-22 14:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6284246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6284246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82053130,"identity":"ccb3c383-ada0-4f1e-a653-00ee8ab64032","added_by":"auto","created_at":"2025-05-06 10:09:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59167,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplot charts presenting distribution of continuous variables which proved to differ significantly between patients with PTDM and patients without PTDM\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6284246/v1/111ece204ad92ab48c1f0b98.jpg"},{"id":82053936,"identity":"62d2e895-1de9-4296-9a7e-c73df7181d40","added_by":"auto","created_at":"2025-05-06 10:17:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves visualizing prognostic properties of selected parameters for PTDM.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTG- triglycerides, BMI- body mass index\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6284246/v1/3a2f94bc75e6052be5f57d4e.jpg"},{"id":82205916,"identity":"21a37893-fab0-49c3-b2f7-54fba226b472","added_by":"auto","created_at":"2025-05-07 17:23:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1170978,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6284246/v1/f135d788-b0f9-4804-ad5d-d91d51c007ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Factors of the Development of Post-transplant Diabetes Mellitus After Kidney Transplantation, and Comparison Between Older and Younger Kidney Transplant Recipients with Post-Transplant Diabetes Mellitus - Single-Center Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eKidney transplantation is considered the most effective treatment for end-stage chronic kidney disease (ESKD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], however various complications can occur after this procedure.\u003c/p\u003e \u003cp\u003ePost-Transplant Diabetes Mellitus (PTDM) is one of these complications, and is a commonly observed metabolic disorder [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePTDM is defined as newly diagnosed diabetes mellitus in the post-transplant setting, irrespective of whether it was present but undetected before transplantation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Diabetes mellitus after transplantation was first described in 1964 among kidney transplant recipients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and since then the nomenclature of this disease has changed many times. In 2014, the International Expert Panel consisting of transplant nephrologists, diabetologists, and clinical scientists recommended changing the terminology from New-Onset Diabetes After Transplantation (NODAT) to post-transplant diabetes mellitus (PTDM). This change was due to the high prevalence of undiagnosed pre-transplant diabetes mellitus [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe incidence of PTDM ranges from 4–25% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], however a higher incidence has also been reported (up to 40%) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral modifiable, and non-modifiable risk factors for PTDM have been reported. Some of them are the same as risk factors for type 2 diabetes mellitus (DM), and include African American and Hispanic ethnicity, age, elevated BMI, family history of diabetes, and male sex. Other reported risk factors are specific to solid organ transplantation and include hypomagnesemia, a history of biopsy proven acute rejection (BPAR), use of steroids and calcineurin inhibitors (CNI), cytomegalovirus (CMV) infection, hepatitis C, and certain human leukocyte antigen (HLA) types [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover genetic and epigenetic polymorphisms have also been also associated with PTDM [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe occurrence of PTDM has a significant impact on quality of life and mortality. Diabetes after transplantation has been associated with worse patient and graft survival [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, it can also promote other transplant complications such as cardiovascular diseases, infections, and impaired wound healing [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough our knowledge about risk factors leading to PTDM occurrence has improved, there is still a need for further research, to achieve a better understanding in this area.\u003c/p\u003e \u003cp\u003eThus, the aim of this single center study was to analyse potential risk factors associated with PTDM in renal transplant recipients. Additionally, we focused on a comparison between older and younger patients with PTDM, seeking to determine differences between these groups, and assess the need for a dedicated approach.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eStudy population\u003c/p\u003e\u003cp\u003eIn this retrospective, observational study we analyzed patients who underwent kidney transplant (KT) between January 2021 and February 2024. A total of 375 KT were performed. Patients who received single-organ kidney transplant, without prior history of diabetes mellitus, were included in further research. The exclusion criteria included multi-organ transplantation, pre-existing diabetes mellitus (DM), conversion from cyclosporine to tacrolimus, transferring to a different center during the follow-up period.\u003c/p\u003e\u003cp\u003eAll information was accessed through medical records and laboratory test results. Our data included sex, age, BMI prior to KT, type of donor, type of dialysis, type of induction therapy (if applicable), treatment of biopsy-proven acute rejection (if applicable). The follow-up period was 6 months.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eDefinitions\u003c/strong\u003e \u003c/p\u003e\u003cp\u003ePost-transplant diabetes mellitus was diagnosed based on the 2013 International Consensus Meeting on Post-transplant Diabetes Mellitus, and included symptoms of hyperglycemia (polydipsia, polyuria, and unintentional weight loss) with random blood glucose ≥ 200 mg/dl, fasting plasma glucose ≥ 126 mg/dl, or two-hour plasma glucose ≥ 200 mg/dl during oral glucose tolerance test (OGTT) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The diagnosis of PTDM was made only after the patient had been on maintenance immunosuppression treatment (3 months post transplantation).\u003c/p\u003e\u003cp\u003eBiopsy proven acute rejection was treated with 500 mg of methylprednisolone for three consecutive days.\u003c/p\u003e\u003cp\u003eOlder patients were defined as those aged ≥ 60 years, based on United Nation definition.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were summarized as mean ± standard deviation (SD) or median and interquartile range (IQR), while categorical variables were presented as n (%). Normality was evaluated with the Shapiro-Wilk test, supported by assessments of skewness and kurtosis. Levene's test was applied to assess homogeneity of variances. Comparisons between groups were made with the Student's t-test, Mann-Whitney U test, Pearson's chi-square test, or Fisher's exact test, as appropriate. Two-step logistic regression analysis was performed to identify risk factors for post-transplant diabetes mellitus. Variables for the multivariate model were selected based on p-value threshold of \u0026lt; 0.25 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and a stepwise approach was used for final variables selection. Model fit was assessed using Nagelkerke R2 and Hosmer and Lemeshow Goodness of Fit (GOF) test. Variance inflation factors (VIF) were calculated to verify multicollinearity. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the prognostic performance of the selected variables for PTDM. Optimal cut-offs were indicated with the Youden method. A significance level (alpha) of 0.05 was used for statistical significance. All analyses were performed using R software (R4.1.2).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eCharacteristics of study groups and comparison of study groups\u003c/p\u003e \u003cp\u003eOf the 375 KT procedures, 218 patients were included in the study. Among them 131 (60.09%) were male, and 87 (39.91%) were female. Of those 189 were single-organ kidney, while 29 were repeat kidney. The mean age was 45.5 years. Fifty-five patients developed PTDM (26 women and 29 men; 47.3% vs. 52.7%). Of these, 24 patients were aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (12 women and 12 men).\u003c/p\u003e \u003cp\u003eAll patients received a standard immunosuppression protocol consisting of steroids, calcineurin inhibitors (CNI) - tacrolimus, and mycophenolate acid. Seventy-five patients received induction therapy of thymoglobulin (ATG), and 24 patients received basiliximab prior to transplantation.\u003c/p\u003e \u003cp\u003eThe group without PTDM was compared with the group with PTDM in terms of clinical and laboratory data. Subsequently, patients who developed PTDM were divided into two subgroups according to age (\u0026lt;\u0026thinsp;60 years and \u0026ge;\u0026thinsp;60 years), and comparisons between them were conducted.\u003c/p\u003e \u003cp\u003ePatients with PTDM were significantly older, MD\u0026thinsp;=\u0026thinsp;11.11, CI95 [7.19;15.03], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and had significantly higher BMI( MD\u0026thinsp;=\u0026thinsp;2.46, CI95 [1.20;3.71], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe proportion of patients with age above or equal 60 years was significantly higher among those with PTDM (43.6% vs 12.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eA comparison between patients with PTDM and patients without PTDM is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics and comparison of study groups.\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=\"char\" char=\".\" 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 \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients with PTDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients without PTDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMD\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, female, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.04\u0026thinsp;\u0026plusmn;\u0026thinsp;12.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.93\u0026thinsp;\u0026plusmn;\u0026thinsp;12.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.11 (7.19;15.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60 years, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThymoglobulin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasiliximab, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGKS pulses, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth of PTDM, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin-based therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicines, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43 (78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.49\u0026thinsp;\u0026plusmn;\u0026thinsp;4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.04\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46 (1.20;3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (94.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (93.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of dialysis, years, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.00 (1.50;4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 (1.00;4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (-1.00;0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.735\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDKT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDKT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection within 6 months, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mg/dl, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e203.00 (150.50;296.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.00 (108.50;197.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.00 (32.00;85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertriglyceridemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51 (92.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering treatment, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol, mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e208.64\u0026thinsp;\u0026plusmn;\u0026thinsp;54.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182.47\u0026thinsp;\u0026plusmn;\u0026thinsp;45.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.16 (11.45;40.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypercholesterolaemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTacrolimus ng/ml, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04 (-0.54;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.885\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperglycaemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, mg/dl, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.80 (6.00;8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.60 (5.60;7.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20 (-0.10;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment for hyperuricemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperuricemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium, mg/dl, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.70 (1.50;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90 (1.70;2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.20 (-0.30;-0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypomagnesemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine mg/dl, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.50 (1.15;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 (1.19;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (-0.10;0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPAR, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMV, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBKV, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eTG - triglycerides; DDKT- deceased donor kidney transplantation; LDKT -living donor kidney transplantation; PD - peritoneal dialysis, HD-hemodialysis; BMI- body mass index; GKS pulses - methylprednisolone pulses; SD \u0026ndash; standard deviation, IQR \u0026ndash; interquartile range, MD \u0026ndash; mean or median difference (with PTDM vs without PTDM), CI \u0026ndash; confidence interval. Groups compared with t-Student test\u003csup\u003e1\u003c/sup\u003e, Mann-Whitney U test\u003csup\u003e2\u003c/sup\u003e, Pearson\u0026rsquo;s chi-square test or Fisher\u0026rsquo;s exact test\u003csup\u003e3\u003c/sup\u003e, as appropriate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRisk factors of PTDM \u0026ndash; logistic regression analysis\u003c/p\u003e \u003cp\u003eIn the univariate analysis, advanced age significantly increased the odds of PTDM (OR\u0026thinsp;=\u0026thinsp;1.07, CI95 [1.04;1.10], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, patients aged 60 years or above had fivefold higher odds of PTDM than patients below 60 years (OR\u0026thinsp;=\u0026thinsp;5.24, CI95 [2.60;10.68], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other risk factors associated with PTDM occurrence were: higher BMI (OR\u0026thinsp;=\u0026thinsp;1.15, CI95 [1.07;1.25], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hypertriglyceridemia (OR\u0026thinsp;=\u0026thinsp;1.01, CI95 [1.00;1.01], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hypercholesterolaemia (OR\u0026thinsp;=\u0026thinsp;1.01, CI95 [1.00;1.02], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hypomagnesemia (OR\u0026thinsp;=\u0026thinsp;2.34, CI95 [1.19;4.57], p\u0026thinsp;=\u0026thinsp;0.013). No correlation between sex, type of donor, type of dialysis, hyperuricemia, induction therapy, presence and treatment of acute rejection, and mean tacrolimus level during the first three months was observed. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents boxplot charts illustrating the distribution of variables significantly different between patients with PTDM and those without PTDM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMultivariate logistic regression model confirmed that age had a significant impact on the odds of PTDM. Each additional year increased odds of PTDM by 8%, OR\u0026thinsp;=\u0026thinsp;1.08, CI95 [1.04;1.11], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Odds of PTDM were 12% higher for each 1 kg/m\u003csup\u003e2\u003c/sup\u003e increase in BMI, OR\u0026thinsp;=\u0026thinsp;1.12, CI95 [1.02;1.23], p\u0026thinsp;=\u0026thinsp;0.026. Concentration of triglycerides slightly influenced the odds of PTDM, OR\u0026thinsp;=\u0026thinsp;1.00, CI95 [1.00;1.01], p\u0026thinsp;=\u0026thinsp;0.046. Hypertriglyceridemia increased the odds of PTDM fourfold, OR\u0026thinsp;=\u0026thinsp;3.54, CI95 [1.12;13.93], p\u0026thinsp;=\u0026thinsp;0.045. Higher magnesium concentrations reduced the odds of PTDM by 88%, OR\u0026thinsp;=\u0026thinsp;0.12, CI95 [0.03;0.46], p\u0026thinsp;=\u0026thinsp;0.003.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the outcomes of logistic regression analysis for PTDM.\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\u003eOutcome of logistic regression analysis for PTDM\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate models\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\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\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\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\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, female (vs male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026ndash;2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026ndash;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u0026ndash;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60 years (vs\u0026thinsp;\u0026lt;\u0026thinsp;60 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.60-10.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThymoglobulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u0026ndash;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasiliximab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u0026ndash;3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethylprednisolone pulses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026ndash;4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07\u0026ndash;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeritoneal dialysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026ndash;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDKT (vs DDKT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40\u0026ndash;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertriglyceridemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.63\u0026ndash;23.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u0026ndash;13.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00-1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypercholesterolaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.34\u0026ndash;6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean tacrolimus levels during first 3 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u0026ndash;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperuricemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u0026ndash;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u0026ndash;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u0026ndash;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypomagnesemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19\u0026ndash;4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026ndash;4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOR \u0026ndash; odds ratio, CI \u0026ndash; confidence interval, LDKT - living donor kidney transplantation, DDKT - deceased donor kidney transplantation, BPAR - biopsy-proven acute rejection\u003c/p\u003e \u003cp\u003eReceiver Operating Characteristics (ROC) analysis\u003c/p\u003e \u003cp\u003eReceiver Operating Characteristics (ROC) analysis was conducted to evaluate the predictive ability of selected parameters for PTDM. The highest AUC (Area Under the Curve), which referred to best prognostic properties, was found for age (AUC\u0026thinsp;=\u0026thinsp;0.733, CI95 [0.658;0.809]) with a cut-off of 48.5 years. Patients above the cut-off were prognosed to develop PTDM with a sensitivity of 71% and a specificity of 69%. AUC values for other variables ranged from 0.638 (presence of hypertriglyceridemia) to 0.693 (concentration of triglycerides) indicating moderate prognostic properties. The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visualizes the ROC curves for selected parameters.\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\u003eOutcome of Receiver Operating Characteristics (ROC) assessing quality of selected parameters to predict PTDM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCut-off*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.733 (0.658;0.811)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.662 (0.580;0.744)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.693 (0.602;0.773)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertriglyceridemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.638 (0.587;0.687)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.684 (0.606;0.763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAUC \u0026ndash; area under curve, CI \u0026ndash; confidence interval, PPV \u0026ndash; positive predictive value, NPV \u0026ndash; negative predictive value.\u003c/p\u003e \u003cp\u003e* Only for continuous parameters.\u003c/p\u003e \u003cp\u003eComparison of patients with PTDM aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years and patients with PTDM aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years\u003c/p\u003e \u003cp\u003eYounger group was compared with older group in terms of induction therapy, type of PTDM treatment (insulin vs oral medications), infection occurrence, cytomegalovirus (CMV) replication, and polyomavirus (BKV) replication, creatinine level after 6 months, presence of biopsy proven acute rejection, level of cholesterol and triglycerides. No significant difference was confirmed between patients with PTDM aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years and patients with PTDM aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Results are presented in Table \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\u003eComparison of patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years and patients aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years with PTDM\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients with PTDM aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients with PTDM aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMD\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThymoglobulin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasiliximab, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGKS, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin-based therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicines, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection within 6 months, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mg/dl, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188.00 (151.25;238.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245.00 (152.50;339.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-57.00 (-111.00;17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol, mg/dl, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e219.83\u0026thinsp;\u0026plusmn;\u0026thinsp;51.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199.97\u0026thinsp;\u0026plusmn;\u0026thinsp;55.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.87 (-9.58;49.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine mg/dl, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.60 (1.40;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.45 (1.00;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15 (-0.10;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPAR, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMV, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBKV, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.443\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCMV - cytomegalovirus replication, BKV- polyomavirus replication, BPAR -biopsy-proven acute rejection; GKS - additional methylprednisolone pulses; SD \u0026ndash; standard deviation, IQR \u0026ndash; interquartile range, MD \u0026ndash; mean or median difference (\u0026ge;\u0026thinsp;60 years vs\u0026thinsp;\u0026lt;\u0026thinsp;60 years), CI \u0026ndash; confidence interval. Groups compared with t-Student test\u003csup\u003e1\u003c/sup\u003e, Mann-Whitney U test\u003csup\u003e2\u003c/sup\u003e, Pearson\u0026rsquo;s chi-square test or Fisher\u0026rsquo;s exact test\u003csup\u003e3\u003c/sup\u003e, as appropriate.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePosttransplant diabetes mellitus is a common complication after kidney transplantation. In our study 25% developed this metabolic disorder, which is quite a high percentage, however, it corresponds to other studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As mentioned above, the incidence of PTDM ranges from 10 to 25% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].The reason for such wide variation of its occurrence, may result from lack of standard definition of PTDM, duration of the follow-up in studies, and also presence of modifiable and nonmodifiable risk factors in kidney transplant recipients - homogenous cohorts.\u003c/p\u003e \u003cp\u003eIn our study, risk factors such as advanced age, higher BMI, hipertrigliceridemia, hypercholesterolaemia, and hypomagnesemia have been associated with PTDM.\u003c/p\u003e \u003cp\u003eSince age is a well known non-modifiable risk factor of type 2 diabetes mellitus [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], there was no surprise that in our study, it also increased risk of PTDM for KT patients. Furthermore, patient age was found to be the strongest risk factor, with a cut-off of 48,5 years of age. Additionally, patients aged 60 years or above had five times higher odds of PTDM than patients below 60 years (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Comparable conclusions were pointed out in other studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsistent with trends in the general population, elevated BMI was a significant risk factor for PTDM in our study. Mechanisms responsible for insulin resistance in obesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m2), and overweight (BMI\u0026thinsp;\u0026gt;\u0026thinsp;25 kg/m2) are not fully understood. However, it may be the consequence of a chronic inflammatory state caused by excessive fat tissue, which stimulates macrophage recruitment to adipocytes, and the release of proinflammatory adipokines leading to the downregulation of insulin signaling [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore adipose tissue produces tumor necrosis factor-alpha (TNF-α), which activation is associated with insulin resistance due to reduced expression of insulin-sensitive transporters [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome studies suggest that post transplant weight gain is also a risk factor of PTDM occurrence [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Another important aspect might be the body fat distribution. Cron et al demonstrated that PTDM was strongly associated with central obesity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In a study performed by von D\u0026uuml;ring et al, visceral fat tissue was correlated to PTDM occurrence, and hyperglycemia early after transplantation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus it might be essential to monitor not only BMI, but also waist circumference in KT recipients.\u003c/p\u003e \u003cp\u003eOur study also showed that elevated triglyceride levels were associated with PTDM. The reason for that might be due to association between hypertriglyceridemia and insulin resistance, which can then lead to future diabetes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHypomagnesemia has been related to increased risk of PTDM, although the underlying mechanism of that remains unclear [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Lower magnesium level impacts insulin signaling [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], however it also might be the effect of calcineurin inhibitor treatment which is considered to be a risk factor of developing PTDM [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, Augusto et al presented that pretransplant, rather than post-transplant, hypomagnesemia was an independent risk factor of PTDM [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The same results were shown by Xu et al [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our study posttransplant hypomagnesemia was an independent risk factor of development of PTDM, though pretransplant serum magnesium level should also be taken into consideration in further research.\u003c/p\u003e \u003cp\u003eOur study revealed that hypercholesterolaemia was associated with PTDM. Sinangil et al, also revealed positive correlation between elevated total cholesterol level and LDL-C (low-density lipoprotein cholesterol) in patients with PTDM [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. On the contrary some studies found out that the rise of TG/HDL-C (triglyceride/ high-density lipoprotein cholesterol) ratio and lower HDL-C were increasing risk of diabetes mellitus in KT recipients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe impact of cholesterol and its fractions on PTDM might be due to the fact that excess cholesterol accumulation leads to β-cell dysfunction, thus impairing glucose tolerance, and affecting insulin secretion. Moreover, islet cholesterol deposition can cause increased islet amyloid polypeptide aggregation, and increased islet amyloid formation, thus further deteriorating β-cell function and affecting glucose homeostasis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNumerous studies suggest that immunosuppression therapy, particularly calcineurin inhibitors, and steroids, may contribute to PTDM development in a dose-dependent manner [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, in our study, no correlation was found between mean tacrolimus levels during the first three months post-transplantation, additional steroid doses (used to treat BPAC), and PTDM. This may be due to the short follow-up period, and the low incidence of BPAR, which has limited statistical significance.\u003c/p\u003e \u003cp\u003eIn the final stage of the study we divided the group who developed PTDM into 2 subgroups based on age (\u0026ge;\u0026thinsp;60 years of age, and \u0026lt;\u0026thinsp;60 years of age), and then compared them.\u003c/p\u003e \u003cp\u003eThere was no significant difference in regard to BPAR, CMV infection, BKV infection, or type of PTDM treatment (insulin vs oral medications). Mean creatinine level at the end of follow-up period was 1,6 mg/dl for older patients, and 1,4 mg/dl for younger patients, which in our opinion is similar, and acceptable outcome. Revanur et al, in a retrospective study, revealed that survival of patients over the age of 55 years with PTDM was similar to the control group. On the contrary, KT recipients under 55 years of age with PTDM were associated with a much higher risk of death. No difference in regard to graft survival, and acute rejection was found [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Comparison between older and younger KT recipients with PTDM has not been extensively studied. More research in regard to differences between PTDM patients is needed, thus an adequate approach can be performed.\u003c/p\u003e \u003cp\u003eThis study has a number of limitations. Firstly, it is a single center study with only 218 participants, from which 55 developed PTDM, which limits extrapolation of the results to other populations. Secondly, it is a retrospective study of database analysis,therefore the reliability of available data, or lack of them, limits the scope of the results.\u003c/p\u003e \u003cp\u003eMoreover, the follow-up period was relatively short compared to other studies, thus some factors, for example immunosuppression, might have long-term prodiabetogenic effects which weren't observed in this study. Lastly, a relatively small group of older and younger patients with PTDM (24 vs 31 recipients).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAdvanced age had the strongest association with PTDM. Elevated BMI, hypomagnesemia, and hipercholesterolemia also increased risk of PTDM. No significant differences in terms of serum creatinine level, CMV infection, BKV infection, BPAR, type of PTDM treatment (insulin vs oral medications) were detected in younger, and older recipients with PTDM. PTDM influences patient and graft survival, and increases risk of cardiovascular diseases, more research is necessary to establish modifiable risk factors, thus PTDM can be prevented [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally KT recipients with nonmodifiable risk factors should be regularly screened for PTDM, and aggressive treatment should be implied if they develop diabetes, to minimise the risk of complications. More research comparing older and younger patients with PTDM is needed, thus better, and an individualized approach can be performed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was waived by the local Ethics Committee of University A in view of the retrospective nature of the study and all the procedures being performed were part of the routine care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Funding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch idea and study design: ABK, JG, data acquisition: ABK; supervision or mentorship: JG,MD; statistical analysis: ABK. \u0026nbsp;Each author contributed important intellectual content during manuscript drafting or revision and accepted accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Marta Nowak for her assistance with statistical analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWolfe RA, Ashby VB, Milford EL, Ojo AO, Ettenger RE, Agodoa LY, Held PJ, Port. 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PMID: 28270107; PMCID: PMC5341360.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"kidney transplantation, post-transplant diabetes mellitus, older kidney recipients, metabolic complications","lastPublishedDoi":"10.21203/rs.3.rs-6284246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6284246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Diabetes mellitus after kidney transplantation (post-transplant diabetes mellitus PTDM) is a commonly observed metabolic complication. Its occurrence ranges from 4% to 25%. The aim of this study was to analyse potential risk factors associated with PTDM in renal transplant recipients. Additionally, the study focused on determining differences between older, and younger patients with PTDM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e In \u0026nbsp;this retrospective study, we screened 375 patients who received a kidney \u0026nbsp;transplant between January 2021 and February 2024. PTDM was defined based on \u0026nbsp;the 2013 International Consensus Meeting on Post-transplant Diabetes Mellitus. \u0026nbsp;Kidney transplant recipients who developed PTDM were compared with patients \u0026nbsp;without PTDM, and then patients with PTDM were divided into two subgroups \u0026nbsp;based on age (≥60 years, and \u0026lt;60 years), and compared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe data of 218 kidney transplant recipients were analyzed. Of those, fifty-five patients (25%) developed PTDM. Age (p\u0026lt;0.001), elevated body mass index (p\u0026lt;0.001), hypomagnesemia( p\u0026lt;0.013), hipertriglyceridemia (p\u0026lt;0.001), and hypercholesterolemia (p\u0026lt;0.001) were significant risk factors for PTDM occurrence. A comparison between older and younger patients with PTDM did not reveal significant differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003ePTDM is a common complication after kidney transplantation. Older age showed the strongest association with PTDM. Patients who are at high risk should be carefully monitored, and treated aggressively if the diabetes develops.\u003c/p\u003e\n\u003cp\u003eMore research comparing older and younger patients with PTDM is needed, so that a better, and more individualized approach can be implemented.\u003c/p\u003e","manuscriptTitle":"Risk Factors of the Development of Post-transplant Diabetes Mellitus After Kidney Transplantation, and Comparison Between Older and Younger Kidney Transplant Recipients with Post-Transplant Diabetes Mellitus - Single-Center Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 10:09:39","doi":"10.21203/rs.3.rs-6284246/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24564c41-b5c1-4f9d-b690-0227482cb0d6","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T17:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 10:09:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6284246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6284246","identity":"rs-6284246","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}