Abdominal Waist Circumference and Subcutaneous Adipose Tissue Thickness Predicts Development of Post-Transplant Diabetes Mellitus

Abdominal Waist Circumference and Subcutaneous Adipose Tissue Thickness Predicts Development of Post-Transplant Diabetes Mellitus | 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 Abdominal Waist Circumference and Subcutaneous Adipose Tissue Thickness Predicts Development of Post-Transplant Diabetes Mellitus Mehmet Kanbay, Feyyaz Yagmur, Bilge Karadeniz, Hande Ozen Atalay, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8543193/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 Post-transplant diabetes mellitus (PTDM) is a major comorbidity affecting 10–40% of kidney transplant recipients with significant clinical consequences including diabetic microvascular and macrovascular complications, infectious complications, allograft loss, and mortality. Although multiple modifiable and non-modifiable risk factors have been identified for PTDM development, there is a strong need for higher quality determinants of PTDM risk for early identification of high-risk patients. We hereby aim to evaluate the efficacy of abdominal waist circumference and subcutaneous adipose tissue thickness as predictors of PTDM among kidney transplant recipients. Methods We have performed a single-centered retrospective clinical study involving non-diabetic kidney transplant recipients between December 2018 and January 2025. Baseline demographic and clinical data, laboratory workup and pre-transplant abdominal computed tomography (CT) had been utilized. Abdominal waist circumference and subcutaneous adipose tissue thickness have been obtained from abdominal CT scan. The diagnosis of PTDM was based upon the criteria established by the American Diabetes Association. Results We have included a total of 478 adult kidney transplant recipients with a mean age of 41.1 with slight female predominance (57.1%). Patients developing PTDM were more likely to be at elderly age, have higher body-mass index, higher abdominal subcutaneous adipose tissue thickness, higher abdominal waist circumference and higher baseline serum glucose and triglyceride levels compared to patients not developing PTDM (p-value < 0.001 for all). The pairwise comparison of the ROC curve data for such variables has revealed the superiority of higher abdominal subcutaneous adipose tissue thickness and abdominal waist circumference in predicting PTDM risk over body-mass index among kidney transplant recipients. Conclusions We have identified two independent risk factors novel for PTDM development as abdominal waist circumference and abdominal subcutaneous adipose tissue thickness. Post-transplant diabetes mellitus Subcutaneous adipose tissue Waist circumference Kidney transplantation Figures Figure 1 Figure 2 Key learning points What was known Post-transplant diabetes mellitus is a major comorbidity affecting 10–40% of kidney transplant recipients with significant clinical consequences including diabetic microvascular and macrovascular complications, infectious complications, allograft loss, and mortality. This study adds Abdominal waist circumference and subcutaneous adipose tissue thickness, measured by abdominal CT scan, predict the development of post-transplant diabetes mellitus better than body-mass index. Potential impact Baseline measurement of abdominal waist circumference and subcutaneous adipose tissue thickness may help to predict development of posttransplant diabetes mellitus. This may also facilitate early management of these patients. Data are expressed as median with interquartile range or number and percent frequency, as appropriate. Bold values are statistically significant. Abbreviations: ALT, alanine aminotransferase; ALP, alkaline phosphatase; AST, aspartate aminotransferase; BMI, body mass index; CMV, cytomegalovirus; eGFR, estimated glomerular filtration rate; HDL high density lipoprotein; LDL, low density lipoprotein; TSH, thyroid-stimulating hormone. Introduction Kidney transplantation is the gold standard therapeutic approach for the management of end-stage kidney disease (ESKD) [ 1 ]. Diabetes mellitus is the most common underlying etiology of end-stage kidney disease. For those without diabetes at the time of transplantation, a major complication is post-transplant diabetes mellitus (PTDM), defined as newly diagnosed diabetes after transplantation. However, diagnosis of PTDM within the first 45 days post-transplant is discouraged due to transient factors such as infections, induction immunosuppression, or acute rejection treatments [ 2 , 3 ]. Currently, PTDM affects 10–20% of kidney transplant recipients with a strong association with premature major cardiovascular events, allograft loss and mortality [ 4 , 5 ]. Multiple transplant-related risk factors including older age, obesity and metabolic syndrome, hepatitis C (HCV) or cytomegalovirus (CMV) infections, calcineurin inhibitor or mammalian target of rapamycin inhibitor therapy, corticosteroid therapy, acute rejection episodes, higher pre-transplant hepatic or pancreatic steatosis status have been identified acting through either pancreatic beta-cell dysfunction or peripheral insulin resistance [ 6 , 7 ]. Even though obesity and high body-mass index (BMI) have been identified as risk factors for PTDM [ 8 ], BMI lacks specificity as it may not differentiate between different body compartments such as skeletal muscle and adipose tissue, distribution of adipose tissue or hypervolemia. As PTDM is a prevalent complication with devastating clinical consequences in the solid organ transplant population, the screening and pre-transplant risk stratification and/or potential preventive measures are at most importance [ 9 ]. We hereby aim to evaluate the efficacy of pre-transplant anthropometric measures including abdominal waist circumference and abdominal subcutaneous adipose tissue thickness in predicting PTDM risk among non-diabetic kidney transplant recipients. Materials & Methods Study Design: We conducted a single-center retrospective cohort study involving kidney transplant recipients at the Koç University Hospital Solid Organ Transplant Center between December 2018 and January 2025. The inclusion criteria are as follows: (a) Being a kidney transplant recipient at or over age 18; (b) Availability of abdominal computed tomography (CT) scan within six months prior to transplantation; (c) Presence of post-transplant follow-up data for at least six months. Exclusion criteria include diagnosis of diabetes mellitus prior to kidney transplantation. All patients included in this retrospective study received rabbit anti-thymocyte globulin as part of an induction immunosuppression regimen with dose titration relative to CD3 cell count and a triple maintenance immunosuppressive regimen including calcineurin inhibitor (tacrolimus), anti-metabolite agent (mycophenolate mofetil) and low-dose prednisolone. Maintenance immunosuppressive regimens may be switched to include the mammalian target of rapamycin inhibitors (everolimus) or other anti-metabolite agents (azathioprine) in accordance with the adverse effects, individual patient risk assessment for rejection or other considerations including pregnancy or malignancies. Ethical approval for this study was obtained from the institutional board of ethics (Ethics Board Approval 2023.137.IRB1.048). The diagnosis of PTDM was based upon the criteria established by the American Diabetes Association: (a) oral Glucose Tolerance Test: 2-h plasma glucose levels ≥ 200 mg/dL; (b) fasting plasma glucose ≥ 126 mg/dL; (c) random plasma glucose levels ≥ 200 mg/dL with the presence of diabetes symptoms; (d) hemoglobin A1c level > 6.5% measured methods with NGSP certified and standardized to the DCCT assay [ 10 ]. We utilized random plasma glucose with diabetes symptoms, hemoglobin A1C measurements and fasting plasma glucose levels in combinations of two to conclude on the diagnosis of PTDM, we did not use OGTT to diagnose any patients for PTDM. Transplant recipients’ blood samples are obtained 8.00-9.00AM in the morning with at least 8 hours of overnight fasting. Kidney transplant recipients meeting the mentioned diagnostic criteria and/or patients taking any anti-hyperglycemic medications at least after post-transplant 120 days were categorized as having PTDM. Metabolic Parameters: The baseline medical history and laboratory workup of all kidney transplant recipients have been extensively evaluated. The medical records included in our analysis include chronic kidney disease etiology, comorbidities including hypertension and heart failure, smoking history and acute rejection episodes. The laboratory tests included in our analysis include complete blood count, kidney function tests including serum creatinine and estimated glomerular filtration rate (eGFR), serum lipid profile, liver function tests, plasma glucose, thyroid function tests, pre-transplant (a day before surgery) magnesium, post-transplant (a week later) magnesium, serological workup of donor and recipients anti-hepatitis B core antibody, anti-hepatitis C antibody, hepatitis B surface antigen, anti-cytomegalovirus (CMV) antibodies of donor and recipients. Estimated glomerular filtration rate (eGFR) had been calculated via the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula considering age, race, gender, and serum creatinine level [ 11 ]. Radiological Evaluation: Pre-transplant evaluation was performed via abdominal CT with intravenous iodinated contrast material. Abdominal CT scans were conducted using a 256-slice multidetector scanner (Somatom Definition AS and Flash; Siemens Healthcare, Germany) with parameters of 120 kVp, 1.5-mm slice thickness, 1.5-mm reconstruction intervals, and a 512 × 512 matrix. Two experienced radiologists analyzed non-contrast abdominal CT images on consensus. They were unaware of the patients' clinical information during the assessment. Abdominal circumference and subcutaneous adipose tissue thickness were calculated. The abdominal circumference was accepted as the outer contour length of the abdominal skin and calculated on the axial slice located at the midpoint between the inferior margin of the lowest rib and the superior margin of the iliac crest [ 12 ]. Abdominal subcutaneous fat thickness was assessed at the umbilical level [ 13 ]. Bilateral measurements were taken from the inner skin edge to the outer margin of the abdominal muscle, and their average was used in the analysis. Statistical Analyses: Data are presented as median with interquartile range or number and percent frequency, as appropriate. The comparison between groups was performed using the Chi-square or Fisher test for categorical variables, Mann–Whitney test for non-normally distributed variables and by independent T test for normally distributed variables. We used the Shapiro–Wilk test for assessing the normality of the distribution. A univariable logistic regression analysis was used to identify factors associated with the development of posttransplant diabetes. All the variables associated with the outcome (P < .05) were included into a stepwise multivariable logistic regression analysis. We considered a P value of less than 0.05 to be significant. All analyses were performed using Statistical Package for the Social Sciences (SPSS) version 29.0. Results We included 478 adult kidney transplant recipients (mean age 41.2 years, range 18–71; 57.1% female) in our analysis. The details of the participant selection process are outlined in Supplementary Fig. 1. The median follow-up period was 3.65 years. Baseline demographic, laboratory and imaging outcomes of included participants are summarized at Table 1 . A total of 80 patients (16.7%) have developed PTDM during the follow-up period. None of the patients had received corticosteroid-free maintenance immunosuppressive regimen. All of the patients were diagnosed with PTDM either with ongoing need for glucose lowering treatment after 120 days postoperatively or having high fasting plasma glucose and hemoglobin A1c levels > 6.5%. None of the patients were diagnosed with OGTT. Patients developing PTDM were older with higher BMI, abdominal subcutaneous adipose tissue thickness, and waist circumference and higher baseline serum glucose and triglyceride levels than patients not developing PTDM (Table 1 ). No differences were recorded in terms of gender, baseline comorbidity status, eGFR or serological parameters in-between groups. Table 1 The baseline demographic, laboratory and imaging findings of participants. Total, N = 478 Post transplant DM (+), N = 80 Post transplant DM (-), N = 398 p value Demographic and Clinical Characteristics of Recipients Age, year 42 (18) 49 (18) 40.5 (18) < 0.001 Male, n (%) 205 (42.9) 30 (37.5) 175 (44) 0.287 Smoking, n (%) 111(23.2) 22 (27.5) 89 (22.4) 0.322 Hypertension, n (%) 361 (75.5) 67 (83.8) 294 (75.8) 0.122 Heart Failure, n (%) 27 (5.6) 3 (3.8) 24 (6.2) 0.396 Radiologic and Anthropomorphic Features BMI, kg/m2 24.98 (7.2) 28.28 (6) 24.34 (6) < 0.001 Abdominal subcutaneous fat thickness, mm 21.5 (13.5) 30.25 (7.61) 19.90 (13) < 0.001 Abdomen circumference, mm 886.5 (222.8) 997.50 (143.3) 858.50 (199.3) < 0.001 Liver Tests AST, U/L 13 (8) 13 (10) 13 (8) 0.392 ALT, U/L 13 (8) 13 (7) 13 (9) 0.250 ALP, U/L 75 (38) 75 (54) 75 (37) 0.469 Total Bilirubin, mg/dL 0.31 (0.2) 0.30 (0.1) 0.31 (0.2) 0.156 Albumin, g/dL 4.31 (0.6) 4.30 (0.5) 4.31 (0.6) 0.857 Complete Blood Count Parameters Hemoglobin, g/dL 10.3 (2.3) 9.9 (2.2) 10.3 (2.3) 0.304 Platelets, 103/mm3 203 (85) 198 (75) 205 (87) 0.567 Kidney Tests and Basic Metabolic Panel Creatinine, mg/dL 7.1 (3) 7.0 (3.1) 7.1 (3.1) 0.911 Blood Urea Nitrogen, mg/dL 61.0 (30.3) 61.5 (36.8) 61.0 (29) 0.682 eGFR, mL/min/1.73m2 8 (4) 8 (4) 8 (4) 0.369 Baseline serum glucose, mg/dL 91 (11) 95 (12) 90 (11) < 0.001 Baseline uric acid, mg/dL 6.4 (2.7) 6.2 (2.9) 6.4 (2.6) 0.872 Pre-transplant magnesium, mg/dL 2.22 (0.58) 2.21 (0.62) 2.22 (0.58) 0.945 Post-transplant magnesium, mg/dL 1.82 (0.32) 1.84 (0.35) 1.82 (0.31 0.787 TSH, mIU/L 2.1 (1.7) 1.96 (2.7) 2.11 (1.6) 0.971 Lipid Profile Triglycerides, mg/dL 138.5 (94) 157.5 (132) 135.5 (87) < 0.001 Total cholesterol, mg/dL 191 (63) 195 (69) 189.5 (62) 0.821 HDL cholesterol, mg/dL 45 (20) 41.5 (17) 46 (21) 0.275 LDL cholesterol, mg/dL 121.5 (55) 121 (45) 121.5 (57) 0.416 Infection Serology and Graft Rejection Status Recipient anti CMV antibodies, n (%) 457 (95.6) 80 (100) 377 (95.2) 0.046 Donor anti CMV antibodies, n (%) 446 (93.3) 69 (95.8) 377 (99.2) 0.022 Kidney graft rejection, n (%) 42 (8.8) 8 (10) 34 (8.5) 0.675 Data are expressed as median with interquartile range or number and percent frequency, as appropriate. Bold values are statistically significant. Abbreviations: ALT, alanine aminotransferase; ALP, alkaline phosphatase; AST, aspartate aminotransferase; BMI, body mass index; CMV, cytomegalovirus; eGFR, estimated glomerular filtration rate; HDL high density lipoprotein; LDL, low density lipoprotein; TSH, thyroid-stimulating hormone. Abbreviations: ALT, alanine aminotransferase; ALP, alkaline phosphatase; AST, aspartate aminotransferase; BMI, body mass index; CD, cluster of differentiation; CI, confidence interval; CMV, cytomegalovirus; CT, computed tomography; eGFR, estimated glomerular filtration rate; ESKD, end-stage kidney disease; HDL high density lipoprotein; IQR, interquartile range; LDL, low density lipoprotein; mTOR, mammalian target of rapamycin; OR, odds ratio; PTDM, post-transplant diabetes mellitus; TSH, thyroid-stimulating hormone. In the univariable analysis, age, BMI, abdominal subcutaneous adipose tissue thickness, abdominal waist circumference, baseline serum glucose and triglyceride levels associated with PTDM development (Table 2 ). After performing multivariable logistic regression analysis, age, abdominal subcutaneous adipose tissue thickness and abdominal waist circumference were statistically significant. There is also a strong positive correlation in-between those parameters were illustrated via scatterplots (Fig. 1 A, 1 B, 1 C). Table 2 Univariable and multivariable predictors of post-transplant diabetes among kidney transplant recipients. Univariable Multivariable OR CI %95 p -value OR CI %95 p -value Age, year 1.058 1.036–1.081 < .001 1.052 1.025–1.052 < .001 Body mass index, kg/m2 1.131 1.078–1.187 < .001 0.879 0.792–0.975 0.015 Abdominal subcutaneous fat thickness, mm 1.117 1.084–1.151 < .001 1.112 1.064–1.162 < .001 Abdomen circumference, mm 1.007 1.005–1.009 < .001 1.004 1.000–1.008 0.033 Baseline serum glucose, mg/dL 1.040 1.017–1.063 < .001 1.023 0.997–1.051 0.088 Triglycerides, mg/dL 1.004 1.002–1.006 < .001 1.001 0.999–1.004 0.292 The ROC curve analysis of data for BMI has suggested 24.87 kg/m 2 as a cutoff with area under curve (AUC) of 0.693 while similar analysis for abdominal subcutaneous adipose tissue thickness has illustrated 24.75 mm as a cutoff with AUC of 0.811. Moreover, the ROC curve analysis for abdominal waist circumference illustrated 937.5 mm as a cutoff with AUC of 0.778 (Fig. 2 ). The pairwise comparison of the ROC curves for BMI, abdominal waist circumference and abdominal subcutaneous adipose tissue thickness has revealed the superiority of abdominal waist circumference and abdominal subcutaneous adipose tissue thickness over BMI in predicting PTDM risk (p-value < 0.001) among kidney transplant recipients (Supplementary Table 1). On the other hand, there were no statistically significant inter-difference between abdominal waist circumference and abdominal subcutaneous adipose tissue thickness (p-value = 0.17). Discussion Our retrospective cohort study has demonstrated that higher pre-transplant age, BMI, abdominal waist circumference and subcutaneous adipose tissue thickness were significant predictors for PTDM among kidney transplant recipients. Furthermore, abdominal waist circumference and subcutaneous adipose tissue thickness were superior to BMI in terms of PTDM risk prediction. Even though the association of such parameters with diabetes mellitus risk in general population have recently been established [ 14 ], application of such context to transplant recipients is a novel field of research. The prevalence of PTDM in our study population was 20.4% with a mean follow up time of 3.65 years. A large-scale retrospective study has revealed that PTDM prevalence of 7%, 10%, 13% and 21% among a total of 2.078 non-diabetic renal allograft recipients at post-transplant year 1, 3, 5 and 10, respectively [ 15 ]. Other large-scale clinical studies have revealed PTDM prevalence between 10% and 40% depending on age, ethnicity, immunosuppressive regimens, and acute rejection episodes [ 16 , 17 ]. The potential risk factors for PTDM include older age, higher BMI (> 25 kg/m 2 ), pre-transplant diabetes status (ie. Impaired fasting plasma glucose or impaired glucose tolerance), family history, ethnicity, certain genetic risk factors (ie. Variation in genes coding for IL-4, IL-17R, IL-7R, IL-2, KCNJ11), immunosuppressive regimens, CMV and HCV infection, deceased donor, and acute rejection episodes [ 18 ]. Kidney transplant recipients over age 45 have more than double the risk of PTDM with an even higher risk with increasing age [ 19 ] which was well correlated with our cohort. Higher pre-transplant BMI has shown to be a strong risk factor for PTDM development in multiple clinical studies. A retrospective analysis of 204 adult kidney transplant recipients has revealed that PTDM risk at months 3, 6 and 12 has increased by a factor of 1.11 (95% CI 1.0-1.23), 1.13 (95% CI: 1.03–1.24), and 1.15 (95% CI: 1.05–1.27), respectively, per unit increase in pre-transplantation BMI [ 20 ]. Our previous study involving 373 kidney transplant recipients has also illustrated a statistically significant association between higher BMI (OR = 1.13, 95% CI = 1.07–1.19 for each kg/m2 increase; p < 0.001) and age (odds ratio (OR) = 1.06; 95% confidence interval (CI) = 1.03–1.08 for each year increase; p < 0.001) and PTDM risk [ 7 ]. We have identified two novel risk factors for PTDM risk as increased abdominal waist circumference and subcutaneous adipose tissue thickness. Increased waist circumference has long been considered as a major risk factor for type 2 diabetes mellitus, though, our study is, to the best of our knowledge, the first clinical study to evaluate such an association among kidney transplant recipients. A large-scale prospective cohort study, referred as the European Prospective Investigation into Cancer and Nutrition (EPIC) study involving a total of 9.753 males and 15.491 females, has demonstrated a statistically significant association between type 2 diabetes mellitus risk and higher baseline waist circumference over a mean follow-up period of 8 years as evident from approximately 8% increase in relative risk with every 1 cm increase in waist circumference [ 21 ]. A similar pattern has been reported in multiple other clinical trials, as well [ 22 – 24 ]. Moreover, there are controversial data regarding the association between abdominal subcutaneous adipose tissue thickness and type 2 diabetes mellitus risk. Even though higher adiposity may hypothetically lead to hyperinsulinemia and peripheral insulin resistance through multiple mechanisms, including the release of free fatty acids entering portal circulation and inducing gluconeogenesis and hyperlipidemia, inhibition of skeletal muscle glucose uptake [ 25 ] and release of pro-inflammatory and/or pro-fibrotic cytokines [ 26 ], the clinical scenario is not that straightforward. Despite the early reports indicating higher risk for type 2 diabetes among patients with higher abdominal subcutaneous fat deposition [ 27 – 29 ], a large-scale clinical study involving a total of 12.137 participants in which adipose tissue accumulation was evaluated via magnetic resonance imaging has identified subcutaneous fat deposition as a protective factor for new onset diabetes in contrast to visceral fat accumulation [ 30 ]. Similarly, the protective effects of subcutaneous adipose tissue accumulation at the extremities have been validated in other studies [ 31 , 32 ]. Such controversy may be attributable to lower angiogenic capacity of subcutaneous adipose tissue compared to visceral adipose tissue [ 33 ], though, there is a clear need for future clinical studies on such an issue. Our study provides the first clinical evidence of a strong association between abdominal subcutaneous adipose tissue accumulation and PTDM risk. Moreover, our study has provided the first clinical evidence supporting the superiority of such two measures over BMI in a statistically significant manner in assessing PTDM risk. The clinical significance of waist circumference was evident especially at patients with low BMI as a few studies indicate equally greater risk for diabetes among patients of low to normal BMI (< 25 kg/m2) with a large waist circumference and overweight patients (BMI: 25-29.9 kg/m2) with a small waist circumference [ 21 ]. Such superiority may be attributable to the inability of BMI to discriminate between various different body components despite being correlated with adipose tissue percentage [ 34 ] and inability to differentiate between adipose tissue distribution or hypervolemia [ 35 ], though, evaluation of such measures are challenging and not as straightforward as BMI. Furthermore, the clinical implications of such measures over BMI in the prediction of PTDM risk are unclear despite providing more statistically significant predictive power. Patients with higher risk for PTDM might be detected, followed, and educated regarding their risk for diabetes, even during the pretransplant evaluation. The risk categorization could be renewed during routine follow-up evaluations after the transplant, simply with waist circumference. This information might be used for patients to remind self-measurements of waist circumference at home alongside with tracking of weight. The patients with higher risk for PTDM with pretransplant tomography evaluations and developing risk with waist circumference track, might be managed with exercise, lifestyle modifications and consulted with dietitian to prevent PTDM. The primary limitations of our study include; (a) retrospective and single-center design limiting the generalizability of our findings without any data enabling external validation of our findings; (b) lack of heterogeneity in terms of CMV and HCV serological status among transplant recipients preventing meaningful statistical analysis on serological data; (c) high percentage of patients with unknown etiology for ESKD which may or may not alter PTDM risk; (d) diagnosis of PTDM based upon fasting or random plasma glucose levels with no patients undergoing oral glucose tolerance testing which may lead to potential underestimation of true PTDM incidence; (e) lack of routine pre-transplant OGTT screening which may lead to missing the patients with pretransplant undiagnosed prediabetes and overestimation of PTDM incidence; (f) risk factors that previously established in literature, the family history of diabetes and potential effect of uricosuric agents was not included in the analysis due to retrospective design; (g) use of standardized maintenance immunosuppressive regimens preventing statistical analysis regarding the association between PTDM and calcineurin inhibitors or mTOR inhibitors without any comparison evaluating the effect of immunosuppressive medications’ trough level differences; (h) inability to perform time-to-event analysis using Cox regression due to difficulty in accurately determining the time from transplantation to the diagnosis of PTDM. Furthermore, clinical utility of our parameters, namely CT-derived abdominal waist circumference and subcutaneous adipose tissue thickness, may be limited outside of transplant centers as routine abdominal cross-sectional imaging modalities are not commonly employed for such purpose. However, the knowledge regarding substitution of CT-derived measures with anthropometric measurement is yet scarce. On the other hand, standardized evaluation of participants with abdominal computed tomography with regard to waist circumference and subcutaneous adipose tissue thickness and the inclusion of a high number of kidney transplant recipients are the major strengths of our study that enhance the quality of our research. Even though we have identified multiple significant risk factors for PTDM development, there is a clear need for future large-scale multicentered clinical trials to evaluate pre-transplant risk factors and to identify high-risk patients along with trials to evaluate therapeutic approaches such as change in lifestyle, using glucagon-like peptide-1 analogues. Conclusion PTDM is associated with poor clinical outcomes, including allograft function and/or survival, major adverse cardiovascular events and all-cause mortality. We have performed a retrospective cohort study of 478 non-diabetic adult kidney transplant recipients aiming to evaluate potential determinants of PTDM risk. We found that older age and BMI, along with higher abdominal subcutaneous adipose tissue thickness and waist circumference were associated with PTDM independently. Furthermore, our study demonstrated superiority of abdominal waist circumference and abdominal subcutaneous adipose tissue thickness over BMI to predict PTDM. Large-scale prospective clinical studies are needed to evaluate additional risk factors and to identify high-risk solid organ transplant recipients. Declarations Conflict of interest: The authors declare that they have no conflict of interest. Financial Disclosure: None Ethical approval: Ethical approval was obtained from the institutional ethics board (Ethics Board Approval 2023.137.IRB1.048). Informed consent to participate: This is a retrospective study and could not be able to get informed consent. Competing Interest: Dr Rossing reports grants to Steno Diabetes Center Copenhagen from Bayer, Astra Zeneca, and Novo Nordisk and honoraria for education or consulting from AstraZeneca, Abbott, Bayer, Boehringer Ingelheim, Daiichi Sankyo, Novartis, Gilead, Novo Nordisk, and Sanofi (all honoraria to Steno Diabetes Center Copenhagen). K.R.T. is supported by NIH research grants R01MD014712, U2CDK114886, UL1TR002319, U54DK083912, U01DK100846, OT2HL161847 and UM1AI109568, OT2OD032581, and CDC project numbers 75D301-21-P-12254 and 75D301-23-C-18264. She has also received investigator-initiated grant support from Travere, Bayer and the Doris Duke Foundation outside of the submitted work. She reports consultancy fees from Boehringer Ingelheim, Eli Lilly, Novo Nordisk, Bayer, AstraZeneca, ProKidney, Travere, Mineralys and Pfizer; and speaker fees from Novo Nordisk, Bayer and AstraZeneca. Other authors declare no relevant financial interests. 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Schulze MB, Heidemann C, Schienkiewitz A, Bergmann MM, Hoffmann K, Boeing H: Comparison of anthropometric characteristics in predicting the incidence of type 2 diabetes in the EPIC-Potsdam study. Diabetes Care 2006, 29(8):1921–1923. Schulze MB, Hoffmann K, Boeing H, Linseisen J, Rohrmann S, Möhlig M, Pfeiffer AF, Spranger J, Thamer C, Häring HU et al : An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care 2007, 30(3):510–515. Abe M, Fujii H, Funakoshi S, Satoh A, Kawazoe M, Maeda T, Tada K, Yokota S, Yamanokuchi T, Yoshimura C et al : Comparison of Body Mass Index and Waist Circumference in the Prediction of Diabetes: A Retrospective Longitudinal Study. Diabetes Ther 2021, 12(10):2663–2676. Matsuzawa Y, Shimomura I, Nakamura T, Keno Y, Kotani K, Tokunaga K: Pathophysiology and pathogenesis of visceral fat obesity. Obes Res 1995, 3 Suppl 2:187s-194s. Patel P, Abate N: Role of subcutaneous adipose tissue in the pathogenesis of insulin resistance. J Obes 2013, 2013:489187. Abate N, Garg A, Peshock RM, Stray-Gundersen J, Grundy SM: Relationships of generalized and regional adiposity to insulin sensitivity in men. J Clin Invest 1995, 96(1):88–98. Abate N, Garg A, Peshock RM, Stray-Gundersen J, Adams-Huet B, Grundy SM: Relationship of generalized and regional adiposity to insulin sensitivity in men with NIDDM. Diabetes 1996, 45(12):1684–1693. Goodpaster BH, Thaete FL, Simoneau JA, Kelley DE: Subcutaneous abdominal fat and thigh muscle composition predict insulin sensitivity independently of visceral fat. Diabetes 1997, 46(10):1579–1585. Chen P, Hou X, Hu G, Wei L, Jiao L, Wang H, Chen S, Wu J, Bao Y, Jia W: Abdominal subcutaneous adipose tissue: a favorable adipose depot for diabetes? Cardiovascular Diabetology 2018, 17(1):93. Snijder MB, Visser M, Dekker JM, Goodpaster BH, Harris TB, Kritchevsky SB, De Rekeneire N, Kanaya AM, Newman AB, Tylavsky FA et al : Low subcutaneous thigh fat is a risk factor for unfavourable glucose and lipid levels, independently of high abdominal fat. The Health ABC Study. Diabetologia 2005, 48(2):301–308. Xu F, Earp JE, Riebe D, Delmonico MJ, Lofgren IE, Greene GW: The relationship between fat distribution and diabetes in US adults by race/ethnicity. Front Public Health 2024, 12:1373544. Gealekman O, Guseva N, Hartigan C, Apotheker S, Gorgoglione M, Gurav K, Tran KV, Straubhaar J, Nicoloro S, Czech MP et al : Depot-specific differences and insufficient subcutaneous adipose tissue angiogenesis in human obesity. Circulation 2011, 123(2):186–194. Smalley KJ, Knerr AN, Kendrick ZV, Colliver JA, Owen OE: Reassessment of body mass indices. Am J Clin Nutr 1990, 52(3):405–408. Kruschitz R, Wallner-Liebmann SJ, Hamlin MJ, Moser M, Ludvik B, Schnedl WJ, Tafeit E: Detecting body fat-A weighty problem BMI versus subcutaneous fat patterns in athletes and non-athletes. PLoS One 2013, 8(8):e72002. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.png Supplementary Figure 1: The flow chart for participant selection process in our retrospective cohort study along with reasons for exclusion. SupplementaryTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8543193","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614193794,"identity":"1bf96570-32fa-4315-b460-c8a712a0a1ef","order_by":0,"name":"Mehmet Kanbay","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDACHgYGZiAlJwHlEK/FGKZFgmgtiTOI1qLbc/jZ44Kaw+kzZyQwPnjbxlBn3kBAi9nZNnPjGccO586WSGA2nNvGICFzgJCW8wxm0jxsh3PnSSSwSfMCtRB0mdl59m/SPP8Op8tJJLD/Jk7L2R4zoOGHE6SBtjATp+XMmTLpmX3phjN7HjZLzjknITmDsJb0bdIF36zlJY4nH/zwpsyGn4iIAYNmIGZsYCAqJqGgjmiVo2AUjIJRMAIBAC2fN8LJRn65AAAAAElFTkSuQmCC","orcid":"","institution":"Koç University","correspondingAuthor":true,"prefix":"","firstName":"Mehmet","middleName":"","lastName":"Kanbay","suffix":""},{"id":614193795,"identity":"9c5a68f6-8879-4aea-aea0-8904e64c982e","order_by":1,"name":"Feyyaz Yagmur","email":"","orcid":"","institution":"Koç University","correspondingAuthor":false,"prefix":"","firstName":"Feyyaz","middleName":"","lastName":"Yagmur","suffix":""},{"id":614193800,"identity":"10450a5b-1373-454d-98fa-8761ab35b5ea","order_by":2,"name":"Bilge Karadeniz","email":"","orcid":"","institution":"Koç University","correspondingAuthor":false,"prefix":"","firstName":"Bilge","middleName":"","lastName":"Karadeniz","suffix":""},{"id":614193803,"identity":"47504445-48dd-40c9-8eff-95fb1d115bd2","order_by":3,"name":"Hande Ozen Atalay","email":"","orcid":"","institution":"Koç University","correspondingAuthor":false,"prefix":"","firstName":"Hande","middleName":"Ozen","lastName":"Atalay","suffix":""},{"id":614193805,"identity":"82c0d333-f7cb-4978-838c-db9ddab01fa8","order_by":4,"name":"Ahmet Ak","email":"","orcid":"","institution":"Koç University","correspondingAuthor":false,"prefix":"","firstName":"Ahmet","middleName":"","lastName":"Ak","suffix":""},{"id":614193808,"identity":"cd2a16bf-4d72-4cd8-acee-1db8c2b0210b","order_by":5,"name":"Sidar Copur","email":"","orcid":"","institution":"Koç University","correspondingAuthor":false,"prefix":"","firstName":"Sidar","middleName":"","lastName":"Copur","suffix":""},{"id":614193809,"identity":"d46bec79-6219-4bd9-b141-e855bfae36df","order_by":6,"name":"Helin Orak","email":"","orcid":"","institution":"Koç University","correspondingAuthor":false,"prefix":"","firstName":"Helin","middleName":"","lastName":"Orak","suffix":""},{"id":614193811,"identity":"cb79000d-fc18-4113-9d5a-40a6d4f3d800","order_by":7,"name":"Nur Genc","email":"","orcid":"","institution":"Koç University","correspondingAuthor":false,"prefix":"","firstName":"Nur","middleName":"","lastName":"Genc","suffix":""},{"id":614193812,"identity":"bb14b9c2-b78a-49b5-ba88-a90b930887e4","order_by":8,"name":"Nuri Hasbal","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nuri","middleName":"","lastName":"Hasbal","suffix":""},{"id":614193816,"identity":"1dc4c48f-ec6d-44a6-a910-658c544a07d7","order_by":9,"name":"Gizem Kula","email":"","orcid":"","institution":"Koç University","correspondingAuthor":false,"prefix":"","firstName":"Gizem","middleName":"","lastName":"Kula","suffix":""},{"id":614193818,"identity":"ef27f6d1-464e-4d40-a56e-ffda9d672582","order_by":10,"name":"Afak Durur Karakaya","email":"","orcid":"","institution":"Koç University","correspondingAuthor":false,"prefix":"","firstName":"Afak","middleName":"Durur","lastName":"Karakaya","suffix":""},{"id":614193820,"identity":"8f59c99b-2c2b-494f-8a0e-6f0b515a8b5e","order_by":11,"name":"Peter Rossing","email":"","orcid":"","institution":"Steno Diabetes Center","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Rossing","suffix":""},{"id":614193822,"identity":"80fe248d-74aa-4fe9-8fc4-88486d0822d3","order_by":12,"name":"Adrian Covic","email":"","orcid":"","institution":"Grigore T. Popa University of Medicine and Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Adrian","middleName":"","lastName":"Covic","suffix":""},{"id":614193830,"identity":"045e67bb-95f5-468d-8419-5e49009cf153","order_by":13,"name":"Katherine Tuttle","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Tuttle","suffix":""}],"badges":[],"createdAt":"2026-01-07 15:24:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8543193/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8543193/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106189193,"identity":"dfa6808d-691f-4cb6-a4c7-40144fa32a9a","added_by":"auto","created_at":"2026-04-05 17:09:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":400886,"visible":true,"origin":"","legend":"\u003cp\u003eThe scatterplots for the evaluation of association in-between body-mass index (BMI), abdominal waist circumference and abdominal subcutaneous adipose tissue thickness. (A) The positive correlation in-between abdominal waist circumference and abdominal subcutaneous adipose tissue thickness (R=0.53); (B) The positive correlation in-between BMI and abdominal subcutaneous adipose tissue thickness (R=0.40); (C) The positive correlation in-between BMI and abdominal waist circumference (R=0.49).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8543193/v1/2efe60d1c93001de1d873261.png"},{"id":106189267,"identity":"cb48f688-71a4-437d-a086-bc75ce5f5369","added_by":"auto","created_at":"2026-04-05 17:09:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87295,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve analysis of body mass index, abdominal subcutaneous adipose tissue thickness and abdominal waist circumference.\u003c/p\u003e\n\u003cp\u003eAUC: area under the curve\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8543193/v1/b02b1aff5f93e78ea604ff06.png"},{"id":106189604,"identity":"55148159-ee7b-48f4-85ab-99a013aa1b43","added_by":"auto","created_at":"2026-04-05 17:10:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1396301,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8543193/v1/277b0f7a-a52b-42d8-a8a9-6dda2ab32997.pdf"},{"id":106189493,"identity":"1bca2b5f-0879-45d5-b2b0-7f6fb97e666d","added_by":"auto","created_at":"2026-04-05 17:10:18","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":309077,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1:\u003c/strong\u003e The flow chart for participant selection process in our retrospective cohort study along with reasons for exclusion.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8543193/v1/5aff36733a9edf9a3acb4ac9.png"},{"id":106189225,"identity":"1b64ab9f-44dd-4d50-83d0-5af2b57d4082","added_by":"auto","created_at":"2026-04-05 17:09:22","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16886,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8543193/v1/d697ab8ce35d5ccdba8ac3b7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Abdominal Waist Circumference and Subcutaneous Adipose Tissue Thickness Predicts Development of Post-Transplant Diabetes Mellitus","fulltext":[{"header":"Key learning points","content":"\u003cp\u003e\u003cstrong\u003eWhat was known\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePost-transplant diabetes mellitus is a major comorbidity affecting 10\u0026ndash;40% of kidney transplant recipients with significant clinical consequences including diabetic microvascular and macrovascular complications, infectious complications, allograft loss, and mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis study adds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbdominal waist circumference and subcutaneous adipose tissue thickness, measured by abdominal CT scan, predict the development of post-transplant diabetes mellitus better than body-mass index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential impact\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline measurement of abdominal waist circumference and subcutaneous adipose tissue thickness may help to predict development of posttransplant diabetes mellitus. This may also facilitate early management of these patients.\u003c/p\u003e\n\u003cp\u003eData are expressed as median with interquartile range or number and percent frequency, as appropriate. Bold values are statistically significant. Abbreviations: ALT, alanine aminotransferase; ALP, alkaline phosphatase; AST, aspartate aminotransferase; BMI, body mass index; CMV, cytomegalovirus; eGFR, estimated glomerular filtration rate; HDL high density lipoprotein; LDL, low density lipoprotein; TSH, thyroid-stimulating hormone.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eKidney transplantation is the gold standard therapeutic approach for the management of end-stage kidney disease (ESKD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Diabetes mellitus is the most common underlying etiology of end-stage kidney disease. For those without diabetes at the time of transplantation, a major complication is post-transplant diabetes mellitus (PTDM), defined as newly diagnosed diabetes after transplantation. However, diagnosis of PTDM within the first 45 days post-transplant is discouraged due to transient factors such as infections, induction immunosuppression, or acute rejection treatments [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, PTDM affects 10\u0026ndash;20% of kidney transplant recipients with a strong association with premature major cardiovascular events, allograft loss and mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Multiple transplant-related risk factors including older age, obesity and metabolic syndrome, hepatitis C (HCV) or cytomegalovirus (CMV) infections, calcineurin inhibitor or mammalian target of rapamycin inhibitor therapy, corticosteroid therapy, acute rejection episodes, higher pre-transplant hepatic or pancreatic steatosis status have been identified acting through either pancreatic beta-cell dysfunction or peripheral insulin resistance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Even though obesity and high body-mass index (BMI) have been identified as risk factors for PTDM [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], BMI lacks specificity as it may not differentiate between different body compartments such as skeletal muscle and adipose tissue, distribution of adipose tissue or hypervolemia. As PTDM is a prevalent complication with devastating clinical consequences in the solid organ transplant population, the screening and pre-transplant risk stratification and/or potential preventive measures are at most importance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. We hereby aim to evaluate the efficacy of pre-transplant anthropometric measures including abdominal waist circumference and abdominal subcutaneous adipose tissue thickness in predicting PTDM risk among non-diabetic kidney transplant recipients.\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design:\u003c/h2\u003e \u003cp\u003eWe conducted a single-center retrospective cohort study involving kidney transplant recipients at the Ko\u0026ccedil; University Hospital Solid Organ Transplant Center between December 2018 and January 2025. The inclusion criteria are as follows: (a) Being a kidney transplant recipient at or over age 18; (b) Availability of abdominal computed tomography (CT) scan within six months prior to transplantation; (c) Presence of post-transplant follow-up data for at least six months. Exclusion criteria include diagnosis of diabetes mellitus prior to kidney transplantation. All patients included in this retrospective study received rabbit anti-thymocyte globulin as part of an induction immunosuppression regimen with dose titration relative to CD3 cell count and a triple maintenance immunosuppressive regimen including calcineurin inhibitor (tacrolimus), anti-metabolite agent (mycophenolate mofetil) and low-dose prednisolone. Maintenance immunosuppressive regimens may be switched to include the mammalian target of rapamycin inhibitors (everolimus) or other anti-metabolite agents (azathioprine) in accordance with the adverse effects, individual patient risk assessment for rejection or other considerations including pregnancy or malignancies. Ethical approval for this study was obtained from the institutional board of ethics (Ethics Board Approval 2023.137.IRB1.048).\u003c/p\u003e \u003cp\u003eThe diagnosis of PTDM was based upon the criteria established by the American Diabetes Association: (a) oral Glucose Tolerance Test: 2-h plasma glucose levels\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL; (b) fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL; (c) random plasma glucose levels\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL with the presence of diabetes symptoms; (d) hemoglobin A1c level\u0026thinsp;\u0026gt;\u0026thinsp;6.5% measured methods with NGSP certified and standardized to the DCCT assay [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. We utilized random plasma glucose with diabetes symptoms, hemoglobin A1C measurements and fasting plasma glucose levels in combinations of two to conclude on the diagnosis of PTDM, we did not use OGTT to diagnose any patients for PTDM. Transplant recipients\u0026rsquo; blood samples are obtained 8.00-9.00AM in the morning with at least 8 hours of overnight fasting. Kidney transplant recipients meeting the mentioned diagnostic criteria and/or patients taking any anti-hyperglycemic medications at least after post-transplant 120 days were categorized as having PTDM.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMetabolic Parameters:\u003c/h3\u003e\n\u003cp\u003eThe baseline medical history and laboratory workup of all kidney transplant recipients have been extensively evaluated. The medical records included in our analysis include chronic kidney disease etiology, comorbidities including hypertension and heart failure, smoking history and acute rejection episodes. The laboratory tests included in our analysis include complete blood count, kidney function tests including serum creatinine and estimated glomerular filtration rate (eGFR), serum lipid profile, liver function tests, plasma glucose, thyroid function tests, pre-transplant (a day before surgery) magnesium, post-transplant (a week later) magnesium, serological workup of donor and recipients anti-hepatitis B core antibody, anti-hepatitis C antibody, hepatitis B surface antigen, anti-cytomegalovirus (CMV) antibodies of donor and recipients. Estimated glomerular filtration rate (eGFR) had been calculated via the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula considering age, race, gender, and serum creatinine level [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eRadiological Evaluation:\u003c/h3\u003e\n\u003cp\u003ePre-transplant evaluation was performed via abdominal CT with intravenous iodinated contrast material. Abdominal CT scans were conducted using a 256-slice multidetector scanner (Somatom Definition AS and Flash; Siemens Healthcare, Germany) with parameters of 120 kVp, 1.5-mm slice thickness, 1.5-mm reconstruction intervals, and a 512 \u0026times; 512 matrix.\u003c/p\u003e \u003cp\u003eTwo experienced radiologists analyzed non-contrast abdominal CT images on consensus. They were unaware of the patients' clinical information during the assessment.\u003c/p\u003e \u003cp\u003eAbdominal circumference and subcutaneous adipose tissue thickness were calculated. The abdominal circumference was accepted as the outer contour length of the abdominal skin and calculated on the axial slice located at the midpoint between the inferior margin of the lowest rib and the superior margin of the iliac crest [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Abdominal subcutaneous fat thickness was assessed at the umbilical level [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Bilateral measurements were taken from the inner skin edge to the outer margin of the abdominal muscle, and their average was used in the analysis.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses:\u003c/h3\u003e\n\u003cp\u003eData are presented as median with interquartile range or number and percent frequency, as appropriate. The comparison between groups was performed using the Chi-square or Fisher test for categorical variables, Mann\u0026ndash;Whitney test for non-normally distributed variables and by independent T test for normally distributed variables. We used the Shapiro\u0026ndash;Wilk test for assessing the normality of the distribution. A univariable logistic regression analysis was used to identify factors associated with the development of posttransplant diabetes. All the variables associated with the outcome (P\u0026thinsp;\u0026lt;\u0026thinsp;.05) were included into a stepwise multivariable logistic regression analysis. We considered a P value of less than 0.05 to be significant. All analyses were performed using Statistical Package for the Social Sciences (SPSS) version 29.0.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe included 478 adult kidney transplant recipients (mean age 41.2 years, range 18\u0026ndash;71; 57.1% female) in our analysis. The details of the participant selection process are outlined in Supplementary Fig.\u0026nbsp;1. The median follow-up period was 3.65 years. Baseline demographic, laboratory and imaging outcomes of included participants are summarized at Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 80 patients (16.7%) have developed PTDM during the follow-up period. None of the patients had received corticosteroid-free maintenance immunosuppressive regimen. All of the patients were diagnosed with PTDM either with ongoing need for glucose lowering treatment after 120 days postoperatively or having high fasting plasma glucose and hemoglobin A1c levels\u0026thinsp;\u0026gt;\u0026thinsp;6.5%. None of the patients were diagnosed with OGTT. Patients developing PTDM were older with higher BMI, abdominal subcutaneous adipose tissue thickness, and waist circumference and higher baseline serum glucose and triglyceride levels than patients not developing PTDM (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No differences were recorded in terms of gender, baseline comorbidity status, eGFR or serological parameters in-between groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe baseline demographic, laboratory and imaging findings of participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;478\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost transplant DM (+), N\u0026thinsp;=\u0026thinsp;80\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost transplant DM (-), N\u0026thinsp;=\u0026thinsp;398\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDemographic and Clinical Characteristics of Recipients\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.5 (18)\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\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111(23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e361 (75.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e294 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiologic and Anthropomorphic Features\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.98 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.28 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.34 (6)\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\u003eAbdominal subcutaneous fat thickness, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.5 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.25 (7.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.90 (13)\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\u003eAbdomen circumference, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e886.5 (222.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e997.50 (143.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e858.50 (199.3)\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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver Tests\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Bilirubin, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.31 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.30 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.31 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplete Blood Count Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.3 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets, 103/mm3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203 (85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKidney Tests and Basic Metabolic Panel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.1 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood Urea Nitrogen, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.0 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.0 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mL/min/1.73m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline serum glucose, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (11)\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\u003eBaseline uric acid, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.4 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-transplant magnesium, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-transplant magnesium, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.82 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.84 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82 (0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH, mIU/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.96 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipid Profile\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\u003e138.5 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.5 (132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.5 (87)\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\u003eTotal cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189.5 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.5 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121.5 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.5 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInfection Serology and Graft Rejection Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecipient anti CMV antibodies, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e457 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e377 (95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eDonor anti CMV antibodies, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e446 (93.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (95.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e377 (99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney graft rejection, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are expressed as median with interquartile range or number and percent frequency, as appropriate. Bold values are statistically significant. Abbreviations: ALT, alanine aminotransferase; ALP, alkaline phosphatase; AST, aspartate aminotransferase; BMI, body mass index; CMV, cytomegalovirus; eGFR, estimated glomerular filtration rate; HDL high density lipoprotein; LDL, low density lipoprotein; TSH, thyroid-stimulating hormone.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eALT, alanine aminotransferase; ALP, alkaline phosphatase; AST, aspartate aminotransferase; BMI, body mass index; CD, cluster of differentiation; CI, confidence interval; CMV, cytomegalovirus; CT, computed tomography; eGFR, estimated glomerular filtration rate; ESKD, end-stage kidney disease; HDL high density lipoprotein; IQR, interquartile range; LDL, low density lipoprotein; mTOR, mammalian target of rapamycin; OR, odds ratio; PTDM, post-transplant diabetes mellitus; TSH, thyroid-stimulating hormone.\u003c/p\u003e\u003cp\u003eIn the univariable analysis, age, BMI, abdominal subcutaneous adipose tissue thickness, abdominal waist circumference, baseline serum glucose and triglyceride levels associated with PTDM development (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After performing multivariable logistic regression analysis, age, abdominal subcutaneous adipose tissue thickness and abdominal waist circumference were statistically significant. There is also a strong positive correlation in-between those parameters were illustrated via scatterplots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\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\u003eUnivariable and multivariable predictors of post-transplant diabetes among kidney transplant recipients.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariable\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\u003eCI %95\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\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\u003eCI %95\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.036\u0026ndash;1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.025\u0026ndash;1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index, kg/m2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.078\u0026ndash;1.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.792\u0026ndash;0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbdominal subcutaneous fat thickness, mm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.084\u0026ndash;1.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.064\u0026ndash;1.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbdomen circumference, mm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.005\u0026ndash;1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u0026ndash;1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline serum glucose, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.017\u0026ndash;1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.997\u0026ndash;1.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglycerides, mg/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.002\u0026ndash;1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.999\u0026ndash;1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.292\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\u003eThe ROC curve analysis of data for BMI has suggested 24.87 kg/m\u003csup\u003e2\u003c/sup\u003e as a cutoff with area under curve (AUC) of 0.693 while similar analysis for abdominal subcutaneous adipose tissue thickness has illustrated 24.75 mm as a cutoff with AUC of 0.811. Moreover, the ROC curve analysis for abdominal waist circumference illustrated 937.5 mm as a cutoff with AUC of 0.778 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The pairwise comparison of the ROC curves for BMI, abdominal waist circumference and abdominal subcutaneous adipose tissue thickness has revealed the superiority of abdominal waist circumference and abdominal subcutaneous adipose tissue thickness over BMI in predicting PTDM risk (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among kidney transplant recipients (Supplementary Table\u0026nbsp;1). On the other hand, there were no statistically significant inter-difference between abdominal waist circumference and abdominal subcutaneous adipose tissue thickness (p-value\u0026thinsp;=\u0026thinsp;0.17).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur retrospective cohort study has demonstrated that higher pre-transplant age, BMI, abdominal waist circumference and subcutaneous adipose tissue thickness were significant predictors for PTDM among kidney transplant recipients. Furthermore, abdominal waist circumference and subcutaneous adipose tissue thickness were superior to BMI in terms of PTDM risk prediction. Even though the association of such parameters with diabetes mellitus risk in general population have recently been established [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], application of such context to transplant recipients is a novel field of research.\u003c/p\u003e \u003cp\u003eThe prevalence of PTDM in our study population was 20.4% with a mean follow up time of 3.65 years. A large-scale retrospective study has revealed that PTDM prevalence of 7%, 10%, 13% and 21% among a total of 2.078 non-diabetic renal allograft recipients at post-transplant year 1, 3, 5 and 10, respectively [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Other large-scale clinical studies have revealed PTDM prevalence between 10% and 40% depending on age, ethnicity, immunosuppressive regimens, and acute rejection episodes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The potential risk factors for PTDM include older age, higher BMI (\u0026gt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e), pre-transplant diabetes status (ie. Impaired fasting plasma glucose or impaired glucose tolerance), family history, ethnicity, certain genetic risk factors (ie. Variation in genes coding for IL-4, IL-17R, IL-7R, IL-2, KCNJ11), immunosuppressive regimens, CMV and HCV infection, deceased donor, and acute rejection episodes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Kidney transplant recipients over age 45 have more than double the risk of PTDM with an even higher risk with increasing age [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] which was well correlated with our cohort. Higher pre-transplant BMI has shown to be a strong risk factor for PTDM development in multiple clinical studies. A retrospective analysis of 204 adult kidney transplant recipients has revealed that PTDM risk at months 3, 6 and 12 has increased by a factor of 1.11 (95% CI 1.0-1.23), 1.13 (95% CI: 1.03\u0026ndash;1.24), and 1.15 (95% CI: 1.05\u0026ndash;1.27), respectively, per unit increase in pre-transplantation BMI [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our previous study involving 373 kidney transplant recipients has also illustrated a statistically significant association between higher BMI (OR\u0026thinsp;=\u0026thinsp;1.13, 95% CI\u0026thinsp;=\u0026thinsp;1.07\u0026ndash;1.19 for each kg/m2 increase; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and age (odds ratio (OR)\u0026thinsp;=\u0026thinsp;1.06; 95% confidence interval (CI)\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.08 for each year increase; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PTDM risk [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe have identified two novel risk factors for PTDM risk as increased abdominal waist circumference and subcutaneous adipose tissue thickness. Increased waist circumference has long been considered as a major risk factor for type 2 diabetes mellitus, though, our study is, to the best of our knowledge, the first clinical study to evaluate such an association among kidney transplant recipients. A large-scale prospective cohort study, referred as the European Prospective Investigation into Cancer and Nutrition (EPIC) study involving a total of 9.753 males and 15.491 females, has demonstrated a statistically significant association between type 2 diabetes mellitus risk and higher baseline waist circumference over a mean follow-up period of 8 years as evident from approximately 8% increase in relative risk with every 1 cm increase in waist circumference [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A similar pattern has been reported in multiple other clinical trials, as well [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, there are controversial data regarding the association between abdominal subcutaneous adipose tissue thickness and type 2 diabetes mellitus risk. Even though higher adiposity may hypothetically lead to hyperinsulinemia and peripheral insulin resistance through multiple mechanisms, including the release of free fatty acids entering portal circulation and inducing gluconeogenesis and hyperlipidemia, inhibition of skeletal muscle glucose uptake [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and release of pro-inflammatory and/or pro-fibrotic cytokines [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the clinical scenario is not that straightforward. Despite the early reports indicating higher risk for type 2 diabetes among patients with higher abdominal subcutaneous fat deposition [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], a large-scale clinical study involving a total of 12.137 participants in which adipose tissue accumulation was evaluated via magnetic resonance imaging has identified subcutaneous fat deposition as a protective factor for new onset diabetes in contrast to visceral fat accumulation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Similarly, the protective effects of subcutaneous adipose tissue accumulation at the extremities have been validated in other studies [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Such controversy may be attributable to lower angiogenic capacity of subcutaneous adipose tissue compared to visceral adipose tissue [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], though, there is a clear need for future clinical studies on such an issue.\u003c/p\u003e \u003cp\u003eOur study provides the first clinical evidence of a strong association between abdominal subcutaneous adipose tissue accumulation and PTDM risk. Moreover, our study has provided the first clinical evidence supporting the superiority of such two measures over BMI in a statistically significant manner in assessing PTDM risk. The clinical significance of waist circumference was evident especially at patients with low BMI as a few studies indicate equally greater risk for diabetes among patients of low to normal BMI (\u0026lt;\u0026thinsp;25 kg/m2) with a large waist circumference and overweight patients (BMI: 25-29.9 kg/m2) with a small waist circumference [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Such superiority may be attributable to the inability of BMI to discriminate between various different body components despite being correlated with adipose tissue percentage [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and inability to differentiate between adipose tissue distribution or hypervolemia [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], though, evaluation of such measures are challenging and not as straightforward as BMI. Furthermore, the clinical implications of such measures over BMI in the prediction of PTDM risk are unclear despite providing more statistically significant predictive power.\u003c/p\u003e \u003cp\u003ePatients with higher risk for PTDM might be detected, followed, and educated regarding their risk for diabetes, even during the pretransplant evaluation. The risk categorization could be renewed during routine follow-up evaluations after the transplant, simply with waist circumference. This information might be used for patients to remind self-measurements of waist circumference at home alongside with tracking of weight. The patients with higher risk for PTDM with pretransplant tomography evaluations and developing risk with waist circumference track, might be managed with exercise, lifestyle modifications and consulted with dietitian to prevent PTDM.\u003c/p\u003e \u003cp\u003eThe primary limitations of our study include; (a) retrospective and single-center design limiting the generalizability of our findings without any data enabling external validation of our findings; (b) lack of heterogeneity in terms of CMV and HCV serological status among transplant recipients preventing meaningful statistical analysis on serological data; (c) high percentage of patients with unknown etiology for ESKD which may or may not alter PTDM risk; (d) diagnosis of PTDM based upon fasting or random plasma glucose levels with no patients undergoing oral glucose tolerance testing which may lead to potential underestimation of true PTDM incidence; (e) lack of routine pre-transplant OGTT screening which may lead to missing the patients with pretransplant undiagnosed prediabetes and overestimation of PTDM incidence; (f) risk factors that previously established in literature, the family history of diabetes and potential effect of uricosuric agents was not included in the analysis due to retrospective design; (g) use of standardized maintenance immunosuppressive regimens preventing statistical analysis regarding the association between PTDM and calcineurin inhibitors or mTOR inhibitors without any comparison evaluating the effect of immunosuppressive medications\u0026rsquo; trough level differences; (h) inability to perform time-to-event analysis using Cox regression due to difficulty in accurately determining the time from transplantation to the diagnosis of PTDM. Furthermore, clinical utility of our parameters, namely CT-derived abdominal waist circumference and subcutaneous adipose tissue thickness, may be limited outside of transplant centers as routine abdominal cross-sectional imaging modalities are not commonly employed for such purpose. However, the knowledge regarding substitution of CT-derived measures with anthropometric measurement is yet scarce. On the other hand, standardized evaluation of participants with abdominal computed tomography with regard to waist circumference and subcutaneous adipose tissue thickness and the inclusion of a high number of kidney transplant recipients are the major strengths of our study that enhance the quality of our research. Even though we have identified multiple significant risk factors for PTDM development, there is a clear need for future large-scale multicentered clinical trials to evaluate pre-transplant risk factors and to identify high-risk patients along with trials to evaluate therapeutic approaches such as change in lifestyle, using glucagon-like peptide-1 analogues.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePTDM is associated with poor clinical outcomes, including allograft function and/or survival, major adverse cardiovascular events and all-cause mortality. We have performed a retrospective cohort study of 478 non-diabetic adult kidney transplant recipients aiming to evaluate potential determinants of PTDM risk. We found that older age and BMI, along with higher abdominal subcutaneous adipose tissue thickness and waist circumference were associated with PTDM independently. Furthermore, our study demonstrated superiority of abdominal waist circumference and abdominal subcutaneous adipose tissue thickness over BMI to predict PTDM. Large-scale prospective clinical studies are needed to evaluate additional risk factors and to identify high-risk solid organ transplant recipients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Disclosure:\u003c/strong\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e Ethical approval was obtained from the institutional ethics board (Ethics Board Approval 2023.137.IRB1.048).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent to participate:\u0026nbsp;\u003c/strong\u003eThis is a retrospective study and could not be able to get informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest:\u0026nbsp;\u003c/strong\u003eDr Rossing reports grants to Steno Diabetes Center Copenhagen from Bayer, Astra Zeneca, and Novo Nordisk and honoraria for education or consulting from AstraZeneca, Abbott, Bayer, Boehringer Ingelheim, Daiichi Sankyo, Novartis, Gilead, Novo Nordisk, and Sanofi (all honoraria to Steno Diabetes Center Copenhagen).\u0026nbsp;K.R.T. is supported by NIH research grants R01MD014712, U2CDK114886, UL1TR002319, U54DK083912, U01DK100846, OT2HL161847 and UM1AI109568, OT2OD032581, and CDC project numbers 75D301-21-P-12254 and 75D301-23-C-18264. She has also received investigator-initiated grant support from Travere, Bayer and the Doris Duke Foundation outside of the submitted work. She reports consultancy fees from Boehringer Ingelheim, Eli Lilly, Novo Nordisk, Bayer, AstraZeneca, ProKidney, Travere, Mineralys and Pfizer; and speaker fees from Novo Nordisk, Bayer and AstraZeneca. Other authors declare no relevant financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding acknowledgement:\u003c/strong\u003e This study was not funded by any grant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e Data can be shared if requested.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKovesdy CP: Epidemiology of chronic kidney disease: an update 2022. \u003cem\u003eKidney Int Suppl (2011)\u003c/em\u003e 2022, 12(1):7\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharif A, Hecking M, de Vries AP, Porrini E, Hornum M, Rasoul-Rockenschaub S, Berlakovich G, Krebs M, Kautzky-Willer A, Schernthaner G \u003cem\u003eet al\u003c/em\u003e: Proceedings from an international consensus meeting on posttransplantation diabetes mellitus: recommendations and future directions. \u003cem\u003eAm J Transplant\u003c/em\u003e 2014, 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Although multiple modifiable and non-modifiable risk factors have been identified for PTDM development, there is a strong need for higher quality determinants of PTDM risk for early identification of high-risk patients. We hereby aim to evaluate the efficacy of abdominal waist circumference and subcutaneous adipose tissue thickness as predictors of PTDM among kidney transplant recipients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe have performed a single-centered retrospective clinical study involving non-diabetic kidney transplant recipients between December 2018 and January 2025. Baseline demographic and clinical data, laboratory workup and pre-transplant abdominal computed tomography (CT) had been utilized. Abdominal waist circumference and subcutaneous adipose tissue thickness have been obtained from abdominal CT scan. The diagnosis of PTDM was based upon the criteria established by the American Diabetes Association.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe have included a total of 478 adult kidney transplant recipients with a mean age of 41.1 with slight female predominance (57.1%). Patients developing PTDM were more likely to be at elderly age, have higher body-mass index, higher abdominal subcutaneous adipose tissue thickness, higher abdominal waist circumference and higher baseline serum glucose and triglyceride levels compared to patients not developing PTDM (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all). The pairwise comparison of the ROC curve data for such variables has revealed the superiority of higher abdominal subcutaneous adipose tissue thickness and abdominal waist circumference in predicting PTDM risk over body-mass index among kidney transplant recipients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe have identified two independent risk factors novel for PTDM development as abdominal waist circumference and abdominal subcutaneous adipose tissue thickness.\u003c/p\u003e","manuscriptTitle":"Abdominal Waist Circumference and Subcutaneous Adipose Tissue Thickness Predicts Development of Post-Transplant Diabetes Mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-05 17:08:33","doi":"10.21203/rs.3.rs-8543193/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":"268cbf99-aaaf-489c-bfbe-13e0959e1789","owner":[],"postedDate":"April 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T17:23:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-05 17:08:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8543193","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8543193","identity":"rs-8543193","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}