The last decade has seen an explosion in clinical research focusing on the use of noninvasive biomarkers in kidney transplantation. Much of the published literature focuses on donor-derived cell-free DNA (dd-cfDNA). Although initially studied as a noninvasive means of identifying acute rejection, it is now clear that dd-cfDNA is more appropriately described as a marker of severe injury and irrespective of the etiology, elevated dd-cfDNA ≥0.5% portends worse graft outcomes. Blood gene expression profiling is also commercially available and has mostly been studied in the context of early identification of subclinical rejection, although additional data is needed to validate these findings. Torque teno virus, a ubiquitous DNA virus, has emerged as a biomarker of immunosuppression exposure as peripheral blood Torque teno virus copy numbers might mirror the intensity of host immunosuppression. Urinary chemokine tests including C-X-C motif chemokine ligand 9 and C-X-C motif chemokine ligand 10 have recently been assessed in large clinical trials and hold promising potential for early diagnosis of both subclinical and acute rejection, as well as, for long-term prognosis. Urinary cellular messenger RNA and exosome vesicular RNA based studies require additional validation. Although current data does not lend itself to conclusion, future studies on multimodality testing may reveal the utility of serial surveillance for individualization of immunosuppression and identify windows of opportunity to intervene early and before the irreversible allograft injury sets in.

Background: Kidney transplantation is a life-saving treatment for end-stage kidney disease, but allograft rejection remains a critical challenge, requiring accurate and timely diagnosis. The study aims to evaluate the integration of Fourier Transform Infrared (FTIR) spectroscopy and machine learning algorithms as a minimally invasive method to detect kidney allograft rejection and differentiate between T Cell-Mediated Rejection (TCMR) and Antibody-Mediated Rejection (AMR). Additionally, the goal is to discriminate these rejection types aiming to develop a reliable decision-making support tool. Methods: This retrospective study included 41 kidney transplant recipients and analyzed 81 serum samples matched to corresponding allograft biopsies. FTIR spectroscopy was applied to pre-biopsy serum samples, and Naïve Bayes classification models were developed to distinguish rejection from non-rejection and classify rejection types. Data preprocessing involved, e.g., atmospheric compensation, second derivative, and feature selection using Fast Correlation-Based Filter for spectral regions 600-1900 cm-1 and 2800-3400 cm-1. Model performance was assessed via area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and accuracy. Results: The Naïve Bayes model achieved an AUC-ROC of 0.945 in classifying rejection versus non-rejection and AUC-ROC of 0.989 in distinguishing TCMR from AMR. Feature selection significantly improved model performance, identifying key spectral wavenumbers associated with rejection mechanisms. This approach demonstrated high sensitivity and specificity for both classification tasks. Conclusions: The integration of FTIR spectroscopy with machine learning may provide a promising, minimally invasive method for early detection and precise classification of kidney allograft rejection. Further validation in larger, more diverse populations is needed to confirm these findings' reliability.

Currently in the United States, there are more than 250,000 patients with a functioning kidney allograft and over 100,000 waitlisted patients awaiting kidney transplant, with a burgeoning number added to the kidney transplant wait list every year. Although early post-transplant care is delivered at the transplant center, the increasing number of kidney transplant recipients requires general nephrologists to actively participate in the long-term care of these patients. Serum creatinine and proteinuria are imperfect traditional biomarkers of allograft dysfunction and lag behind subclinical allograft injury. This manuscript reviews the various clinically available biomarkers in the field of kidney transplantation for a general nephrologist with a focus on the utility of donor-derived cell-free DNA, as a marker of early allograft injury. Blood gene expression profiling, initially studied in the context of early identification of subclinical rejection, awaits validation in larger multicentric trials. Urinary cellular messenger ribonucleic acid and chemokine CXCL10 hold promising potential for early diagnosis of both subclinical and acute rejection. Torque tenovirus, a ubiquitous DNA virus is emerging as a biomarker of immunosuppression exposure as peripheral blood torque tenovirus copy numbers might mirror the intensity of host immunosuppression. Although high-quality evidence is still being generated, evidence and recommendations are provided to aid the general nephrologist in implementation of novel biomarkers in their clinical practice.

We aimed to analyze the roles of M1 and M2 macrophage infiltration in post-renal transplant antibody-mediated rejection (AMR).

A total of 102 recipients who underwent renal allotransplant from January 2020 to February 2023 were divided into an immune tolerance group (n = 56) and a rejection group (n = 46). The transplant renal biopsy specimens were harvested by ultrasound-guided puncture. The M1 and M2 macrophages in renal tissues were counted, and the M1/M2 ratio was calculated. The numbers of M1 and M2 macrophages and M1/M2 ratios in patients with different severities of interstitial fibrosis/tubular atrophy (IF/TA) and different degrees of tubulointerstitial inflammatory cell infiltration were compared. The predictive values of M1 and M2 macrophages and M1/M2 ratio for post-renal transplant AMR were clarified.

The rejection group had significantly more M1 and M2 macrophages and higher M1/M2 ratio than those of the immune tolerance group (P < 0.05). In the rejection group, infiltrating macrophages were mainly distributed in the glomerular and interstitial capillaries, with M1 macrophages being the predominant type. With increasing severity of IF/TA, the numbers of M1 and M2 macrophages and M1/M2 ratio rose in patients with post-renal transplant AMR (P < 0.05). The numbers and ratio had significant positive correlations with the levels of blood urea nitrogen and serum creatinine (P < 0.05). The areas under the curves (AUCs) of numbers and M1 and M2 macrophages and M1/M2 ratio for predicting post-renal transplant AMR were 0.856, 0.839 and 0.887, respectively. The combined detection had AUC of 0.911 (95% CI: 0.802-0.986), sensitivity of 90.43% and specificity of 83.42%.

Significant macrophage infiltration is present in the case of post-renal transplant AMR, and closely related to the severity of IF/TA and the degree of tubulointerstitial inflammatory cell infiltration.

Recent developments in intensive desensitization protocols have enabled kidney transplantation in human leukocyte antigen (HLA)-sensitized recipients. However, cases of active antibody-mediated rejection (AABMR), when they occur, are difficult to manage, graft failure being the worst-case scenario. We aimed to assess the impact of our desensitization and AABMR treatment regimen and identify risk factors for disease progression. Among 849 patients who underwent living-donor kidney transplantation between 2014 and 2021 at our institution, 59 were diagnosed with AABMR within 1 year after transplantation. All patients received combination therapy consisting of steroid pulse therapy, intravenous immunoglobulin, rituximab, and plasmapheresis. Multivariable analysis revealed unrelated donors and preformed donor-specific antibodies as independent risk factors for AABMR. Five-year death-censored graft survival rate was not significantly different between patients with and without AABMR although 27 of 59 patients with AABMR developed chronic AABMR (CABMR) during the study period. Multivariate Cox proportional hazard regression analysis revealed that a donor age greater than 59 years and microvascular inflammation (MVI) score (g + ptc) ≥4 at AABMR diagnosis were independent risk factors for CABMR. Our combination therapy ameliorated AABMR; however, further treatment options should be considered to prevent CABMR, especially in patients with old donors and severe MVI.