Post-transplant diabetes mellitus (PTDM) is one of the most common metabolic complications following kidney transplantation, affecting approximately 10%–30% of recipients depending on the study population and diagnostic criteria [1–4]. PTDM is diagnosed according to international consensus recommendations after excluding transient post-transplant hyperglycemia and is associated with increased cardiovascular events, infectious complications, reduced graft survival, and premature mortality [1–5]. Despite advances in immunosuppressive therapy and perioperative management, PTDM remains an important determinant of long-term patient and allograft outcomes.

PTDM results from a complex interaction between preexisting metabolic susceptibility and transplant-related factors. Established risk factors include older age, obesity, family history of diabetes, hypertension, dyslipidemia, hepatitis C infection, and the diabetogenic effects of calcineurin inhibitors and corticosteroids [1, 2, 6–8]. Although these clinical characteristics identify patients at increased risk, accurately predicting PTDM before transplantation remains challenging. Moreover, body mass index (BMI), the most commonly used measure of obesity, does not adequately characterize body fat distribution or distinguish metabolically active visceral fat from lean tissue [9].

Accumulating evidence indicates that visceral adiposity is more strongly associated with insulin resistance and metabolic dysfunction than generalized obesity [10, 11]. Computed tomography (CT), routinely obtained during the evaluation of kidney transplant candidates at many transplant centers, provides an objective and reproducible method for quantifying regional adiposity without additional imaging or radiation exposure beyond standard pre-transplant assessment. Initial studies using CT-based analytic morphomics demonstrated that body composition is associated with the subsequent development of PTDM after kidney transplantation [12]. More recently, automated CT-derived body composition analysis has further improved the prediction of PTDM beyond conventional clinical variables, supporting the growing role of CT-based body composition analysis in pre-transplant metabolic risk assessment [13, 14]. However, these investigations have primarily focused on global visceral adiposity or automated body composition metrics rather than individual perirenal adiposity measurements.

To our knowledge, no study has comprehensively evaluated multiple CT-derived perirenal adiposity measurements and their independent association with PTDM after kidney transplantation. Therefore, we aimed to evaluate the association between multiple pre-transplant CT-derived perirenal adiposity measurements and the development of PTDM after kidney transplantation. We hypothesized that increased perirenal fat thickness would be independently associated with PTDM after adjustment for age, sex, and body mass index, and that it could serve as a simple, readily available imaging biomarker for pre-transplant metabolic risk stratification.

A total of 202 kidney transplant recipients met the study inclusion criteria. Among these, 49 patients (24.3%) developed post-transplant diabetes mellitus (PTDM), whereas 153 (75.7%) remained free of PTDM during follow-up (Figure 1). The mean follow-up duration was 68.8 ± 39.3 months for the overall cohort. Mean follow-up was similar between recipients who developed PTDM and those who did not (71.7 ± 37.3 vs. 67.9 ± 39.9 months, P = 0.54).

In this single-center retrospective cohort study, we evaluated the association between pre-transplant CT-derived adiposity measurements and the development of post-transplant diabetes mellitus (PTDM) after kidney transplantation. Our principal finding was that right anterior perirenal fat thickness demonstrated the strongest independent association with PTDM after adjustment for recipient age, sex, and body mass index (BMI). The right maximum perirenal fat thickness and anterior subcutaneous fat thickness also remained independently associated with PTDM after multivariable adjustment, although right anterior perirenal fat thickness demonstrated the greatest discriminatory ability for PTDM. Furthermore, incorporation of right anterior perirenal fat thickness into a clinical prediction model improved discrimination, increasing the area under the receiver operating characteristic curve (AUC) from 0.632 to 0.720. Collectively, these findings suggest that a simple measurement obtained from routine pre-transplant CT imaging may improve identification of kidney transplant recipients at increased risk for PTDM beyond conventional clinical risk factors.

These findings are clinically important because they suggest that regional fat distribution assessed on routine pre-transplant CT captures aspects of metabolic risk that are not adequately reflected by conventional clinical characteristics. Although established risk factors such as advanced age, obesity, impaired glucose metabolism, and diabetogenic immunosuppressive agents are routinely considered during transplant evaluation, they incompletely explain why some recipients develop PTDM, whereas others with similar clinical profiles do not. This highlights the need for complementary approaches that better characterize an individual’s metabolic risk before transplantation. Our findings suggest that simple CT-derived adiposity measurements may provide clinically relevant information beyond traditional risk factors and improve pre-transplant metabolic risk stratification [2, 3, 6, 7].

Our findings build upon a growing body of literature demonstrating the value of CT-based body composition analysis for metabolic risk assessment in kidney transplantation. Early work by Cron et al. demonstrated that pre-transplant CT-derived morphometric measurements were associated with the subsequent development of PTDM after kidney transplantation. More recently, Kim et al. showed that automated deep learning-based quantification of visceral adiposity improved prediction beyond conventional clinical variables, while Budhiraja et al. further demonstrated that integrating CT-derived body composition features with clinical characteristics enhanced predictive performance [12–14]. Collectively, these studies support the concept that body composition analysis provides clinically relevant information beyond traditional anthropometric measurements and underscore the potential role of CT in improving pre-transplant metabolic risk assessment.

However, our study differs from previous investigations in several important respects. Whereas previous studies have primarily evaluated global visceral adiposity or relied on automated body composition analysis, we comprehensively assessed multiple manually measured perirenal fat parameters obtained from routine pre-transplant CT examinations. Right anterior perirenal fat thickness emerged as the most informative parameter, demonstrating the strongest and most consistent independent association with PTDM after adjustment for recipient age, sex, and BMI. More importantly, these measurements can be obtained from routinely acquired pre-transplant CT scans without specialized software, automated segmentation, or artificial intelligence-based image analysis, making this approach practical and applicable in everyday clinical practice.

An important observation emerging from our analyses was the limited predictive value of BMI. Although obesity is a well-established risk factor for PTDM and BMI has consistently been identified as an independent predictor in previous studies, BMI was not significantly associated with PTDM in our cohort, whereas several CT-derived adiposity measurements demonstrated significant associations. One possible explanation is that recipients with pre-existing diabetes were excluded and kidney transplant candidates undergo careful pre-transplant evaluation, resulting in a relatively narrow BMI distribution. Consequently, BMI may have had limited ability to distinguish recipients at different levels of metabolic risk despite substantial variation in regional fat distribution.

Beyond the relatively narrow BMI distribution observed in our cohort, BMI itself may be an imperfect surrogate for metabolic risk in kidney transplant candidates. As a global anthropometric measure, BMI does not distinguish lean body mass from adipose tissue or characterize regional fat distribution. In contrast, perirenal fat measurements obtained from routine pre-transplant CT imaging directly quantify ectopic adipose tissue, which may more accurately reflect the metabolic abnormalities underlying PTDM. Consistent with this concept, right anterior perirenal fat thickness demonstrated better discrimination than BMI alone (AUC 0.699 vs. 0.573) and further improved the predictive performance of a clinical model incorporating age, sex, and BMI (AUC increased from 0.632 to 0.720). Together, these findings suggest that assessment of regional fat distribution may identify kidney transplant recipients at increased metabolic risk despite similar BMI values.

Having established the predictive value of right anterior perirenal fat thickness, it is worth considering the biological basis for this association. Perirenal adipose tissue is increasingly recognized as a metabolically active ectopic fat depot that secretes adipokines, inflammatory cytokines, and other bioactive mediators implicated in insulin resistance, chronic inflammation, and glucose dysregulation. Consequently, increased perirenal fat accumulation may reflect an adverse metabolic milieu that predisposes kidney transplant recipients to the development of PTDM. Although the predominance of right-sided measurements observed in our study remains unexplained, it may reflect anatomical differences, variability in fat distribution, or measurement-related factors. Further mechanistic and imaging studies are needed to clarify the biological basis of this laterality and determine whether it represents a true physiological phenomenon or a reproducible imaging characteristic [10, 11].

These findings have several important clinical implications. Because abdominal CT imaging is routinely performed during the evaluation of many kidney transplant candidates, assessment of right anterior perirenal fat thickness can be incorporated into the pre-transplant evaluation without additional imaging, radiation exposure, or cost. The measurement is straightforward, requires minimal additional effort, and can be readily obtained from standard CT images. Identification of recipients at increased risk for PTDM before transplantation may facilitate individualized patient counseling, closer post-transplant glucose surveillance, early lifestyle interventions, and consideration of strategies to minimize diabetogenic immunosuppression when clinically appropriate. If validated in larger prospective multicenter studies, incorporation of this simple measurement into routine pre-transplant assessment may improve individualized metabolic risk stratification and help guide preventive strategies following kidney transplantation. Additionally, future studies comparing perirenal fat with traditional anthropometric measures, such as waist and hip circumference, may further refine their individual or combined predictive value for PTDM.

Despite the use of standardized CT measurements and a carefully defined cohort of kidney transplant recipients, our findings should be interpreted in light of several limitations. First, this was a retrospective single-center study, which may limit the generalizability of our findings. Second the modest sample size and 49 PTDM events limit the precision and robustness of the multivariable estimates. Model discrimination was evaluated in the development cohort and may therefore be optimistic. Independent validation is required before clinical implementation. Third, although our models adjusted for important clinical confounders, residual confounding from unmeasured variables cannot be excluded. Finally, the predictive performance of our findings requires external validation in larger prospective multicenter cohorts before routine clinical implementation.

Right anterior perirenal fat thickness was independently associated with the development of PTDM after kidney transplantation and improved the prediction of PTDM beyond conventional clinical risk factors. Because this simple measurement can be readily obtained from routine pre-transplant CT imaging without additional imaging, radiation exposure, cost, or specialized software, it represents a practical approach to enhancing pre-transplant metabolic risk assessment. Prospective multicenter studies are warranted to externally validate these findings and determine whether the incorporation of right anterior perirenal fat thickness into clinical risk assessment improves individualized risk stratification and supports targeted preventive strategies for kidney transplant recipients.