Shear-wave elastography (SWE) showed the absence or presence of significant differences among stable kidney allograft function and allograft dysfunction. We evaluated the variability of kidney allograft stiffness in relation to allograft dysfunction, respectively, in terms of a correlation of stiffness with patients’ characteristics. A single-center prospective study on patients who had undergone renal transplantation was conducted between October 2017 and November 2018. Patients were clinically classified as having a stable allograft function or allograft dysfunction. SWE examinations performed by the same radiologist with a LOGIQ E9 were evaluated. Ten measurements were done for Young’s modulus (kPa) at the level of allograft cortex and another ten at the level of medulla. Eighty-three SWE examinations from 63 patients, 69 stable allografts, and 14 allografts with dysfunction were included in the analysis. The intra-examinations stiffness showed high variability, with the quantile covariation coefficient ranging from 2.21% to 45.04%. The inter-examinations stiffness showed heterogeneity (from 28.66% to 42.38%). The kidney allograft cortex stiffness showed significantly higher values in cases with dysfunction (median = 28.70 kPa, interquartile range (IQR) = (25.68–31.98) kPa) as compared to those with stable function (median = 20.99 kPa, interquartile range = (16.08–27.68) kPa; p-value = 0.0142). Allograft tissue stiffness (both cortex and medulla) was significantly negatively correlated with body mass index (−0.44, p-value < 0.0001 for allograft cortex and −0.42, p-value = 0.0001 for allograft medulla), and positively correlated with Proteinuria/Creatinuria ratio (0.33, p-value = 0.0021 for allograft cortex and 0.28, p-value = 0.0105 for allograft medulla) but remained statistically significant only in cases with stable function. The cortical tissue stiffness proved significantly higher values for patients with allograft dysfunction as compared to patients with stable function, but to evolve as an additional tool for the evaluation of patients with a kidney transplant and to change the clinical practice, more extensive studies are needed.

Tacrolimus has a narrow therapeutic window; a whole-blood trough target concentration of between 5 and 8 ng/mL is considered a safe level for stable kidney transplant recipients. Tacrolimus serum levels must be closely monitored to obtain a balance between maximizing efficacy and minimizing dose-related toxic effects. Currently, there is no specific tacrolimus toxicity biomarker except a graft biopsy. Our study aimed to identify specific serum metabolites correlated with tacrolinemia levels using serum high-precision liquid chromatography–mass spectrometry and standard laboratory evaluation. Three machine learning algorithms were used (Naïve Bayes, logistic regression, and Random Forest) in 19 patients with high tacrolinemia (8 ng/mL) and 23 patients with low tacrolinemia (5 ng/mL). Using a selected panel of five lipid metabolites (phosphatidylserine, phosphatidylglycerol, phosphatidylethanolamine, arachidyl palmitoleate, and ceramide), Mg²⁺, and uric acid, all three machine learning algorithms yielded excellent classification accuracies between the two groups. The highest classification accuracy was obtained by Naïve Bayes, with an area under the curve of 0.799 and a classification accuracy of 0.756. Our results show that using our identified five lipid metabolites combined with Mg²⁺ and uric acid serum levels may provide a novel tool for diagnosing tacrolimus toxicity in kidney transplant recipients. Further validation with targeted MS and biopsy-proven TAC toxicity is needed.

Aim: Our study aimed to assess the usefulness of contrast-enhanced ultrasound (CEUS) in the initial evaluation of the graft function.Materials and method: A cross-sectional study was conducted in the early postoperative period on patients with kidney transplantation, between September 2017 to November 2018. Two groups of patients were investigated; delayed graft function (DGF) and early graft function (EGF). All patients were examined by grey scale, Doppler ultrasound and CEUS.Results: Nineteen patients, aged from 23 to 64 years (mean age 50 years), 7 in the DGF group and 12 in the EGF group were evaluated. The resistive index (RI) show significantly higher values in the DGF group at the level of upper interlobar artery (p=0.025) and medium interlobar artery (p=0.02). The CEUS investigation shows a greater region of interest (ROI) area (p=0.02) and lower values for wash-out area under the curve (WoAUC) (p=0.047) and respectively wash-in and wash-out area under the curve (WiWoAUC) (p=0.031) for the DGF group. The quality of fit (QoF) proved lower in the DGF group either for evaluation of global graft (p=0.012), cortex (p=0.025), or medulla (p=0.009).A significant relationship among all patients was found between the glomerular filtration rate (GFR) [ml/min] and the renal artery fall time (FT) [s] (p=0.012), WoAUC [a.u.] (p=0.03), and WiWoAUC [a.u.] (p=0.024). The arterial QoF [%] was associated with the arterial ROI area (p=0.048).Conclusions: Intensity CEUS parameters WoAUC and WiWoAUC may be useful to diagnose and follow-up grafts with delayed function. Additional studies on larger cohorts are required for the recommendation of CEUS as a routine evaluation of the transplanted kidney.

(1) Background: Renal transplantation (KT) is the most efficient treatment for chronic kidney disease among pediatric patients. Antigenic matching and epitopic load should be the main criteria for choosing a renal graft in pediatric transplantation. Our study aims to compare the integration of new histocompatibility predictive algorithms with classical human leukocyte antigen (HLA) matching regarding different types of pediatric renal transplants. (2) Methods: We categorized our cohort of pediatric patients depending on their risk level, type of donor and type of transplantation, delving into discussions surrounding their mismatching values in relation to both the human leukocyte antigen Matchmaker software (versions 4.0. and 3.1.) and the most recent version of the predicted indirectly identifiable HLA epitopes (PIRCHE) II score. (3) Results: We determined that the higher the antigen mismatch, the higher the epitopic load for both algorithms. The HLAMatchmaker algorithm reveals a noticeable difference in eplet load between living and deceased donors, whereas PIRCHE II does not show the same distinction. Dialysis recipients have a higher count of eplet mismatches, which demonstrates a significant difference according to the transplantation type. Our results are similar to those of four similar studies available in the current literature. (4) Conclusions: We suggest that an integrated data approach employing PIRCHE II and HLAMatchmaker algorithms better predicts histocompatibility in KT than classical HLA matching.

This study examines the interplay between human leukocyte antigen (HLA) compatibility and killer-cell immunoglobulin-like receptor (KIR) genotypes in influencing kidney transplantation outcomes. Understanding these interactions is crucial for improving graft survival and minimizing rejection risks. We evaluated 84 kidney transplant recipients, dividing them into two groups based on post-transplant outcomes: there were 68 with stable graft function (SGF) and 16 who experienced chronic rejection (CR). Patients were selected based on specific inclusion criteria. HLA mismatches (Class I: HLA-A, -B; Class II: HLA-DR) and KIR genotypes were determined using standard genotyping techniques. Statistical analyses, including logistic regression, were performed to correlate these factors with transplant outcomes. Significant age differences were observed, with younger patients more likely to experience graft rejection, while no significant gender-based differences were noted. A significant correlation was found between Class II mismatches and increased rejection rates, highlighting the importance of HLA-DR compatibility. Further analysis revealed that certain inhibitory KIRs, such as KIR3DL1, were associated with favorable outcomes, suggesting a protective role against graft rejection. These findings were corroborated by evaluating serum creatinine levels over multiple years, serving as a biomarker for renal function post transplant. This study underscores the critical need for meticulous HLA matching and the consideration of KIR genotypes in pre-transplant evaluations to enhance graft survival and minimize rejection risks. Integrating these genetic factors into routine clinical assessments could significantly improve personalized transplant medicine strategies, ultimately enhancing patient outcomes. Further research is needed to explore the underlying mechanisms and validate these findings in larger, diverse populations.

Background: Cardiovascular disease remains a leading cause of morbidity and mortality after kidney transplantation. The relative contribution of metabolic abnormalities and inflammatory burden to cardiac remodeling and subsequent clinical outcomes in kidney transplant recipients (KTRs) remains incompletely understood. Methods: In this retrospective cohort study, 152 KTRs underwent comprehensive cardiovascular evaluation at a stable post-transplant time point (12 ± 4 months after transplantation). Metabolic phenotype was assessed using metabolic syndrome and indices of insulin resistance and atherogenic dyslipidemia (TyG index, TG/HDL ratio, and atherogenic index of plasma [AIP]). Inflammatory status was evaluated using hs-CRP and complete blood count-derived indices. Echocardiographic damage composite (EDC) was defined as the presence of left ventricular hypertrophy, diastolic dysfunction, or left atrial enlargement. Patients were followed for major adverse clinical outcome (MACO), defined as cardiovascular event, graft failure, or death, and major adverse cardiovascular and cerebrovascular events (MACCE). Results: At baseline, 78 patients (51.3%) met criteria for EDC. EDC was strongly associated with higher TyG, AIP, TG/HDL, LDL/HDL ratio, and metabolic syndrome, whereas inflammatory markers showed no association. In multivariable logistic regression adjusted for age, sex, eGFR, and proteinuria, TyG remained independently associated with EDC (OR 1.13 per 0.1 increase, 95% CI 1.05–1.21; p = 0.001), independent of hs-CRP. Similar results were observed when AIP was evaluated in place of TyG (OR 10.39, 95% CI 2.22–48.71; p = 0.003). During follow-up, 78 patients developed MACO and 49 developed MACCE. In Cox regression analysis, graft dysfunction and inflammatory markers independently predicted MACO, whereas TyG was no longer significant. In contrast, TyG remained an independent predictor of MACCE after adjustment for confounders and inflammatory markers (HR 1.10 per 0.1 increase, 95% CI 1.04–1.16; p < 0.001). Similar results were observed when AIP was tested in place of TyG (HR 10.8, 95% CI 3.06–38.11; p < 0.001). Echocardiographic damage did not independently predict outcomes after adjustment. Conclusions: In KTRs, metabolic abnormalities reflecting insulin resistance and atherogenic dyslipidemia are closely associated with cardiac remodeling one year after transplantation and remain specifically linked to subsequent cardiovascular events. In contrast, systemic inflammation and graft dysfunction are the primary determinants of overall adverse clinical outcomes. Simple metabolic indices such as TyG and AIP may provide practical tools for cardiovascular risk stratification in this population. In Cox proportional hazards models, TyG (HR 1.102, 95% CI 1.043–1.164, p = 0.001) and AIP (HR 10.8, 95% CI 3.06–38.11, p < 0.001) were independently associated with cardiovascular events during follow-up, underscoring the role of atherogenic dyslipidemia in cardiovascular risk.