Park, Walter D.

Abstract Introduction: Surveillance biopsies after kidney transplantation provide an opportunity to assess early renal disease processes, including diabetic nephropathy, before clinical manifestation. This study aimed to identify proteomic alterations in renal allografts at 5 years post-transplant (5yrPT) and their associations with histologic features in diabetic/obese (DM/OB) versus non-diabetic/non-obese (nDM/nOB) recipients. Methods: We studied protocol biopsies from 17 DM/OB and 14 nDM/nOB kidney transplant recipients at Mayo Clinic (2000–2012). Laser capture microdissection followed by mass spectrometry was used for proteomic profiling. Histologic assessment included Banff and Tervaert scoring and morphometric analysis. Statistical comparisons were performed using ANOVA with Tukey post-hoc testing. Results: DM/OB recipients demonstrated greater histologic and metabolic injury at 5yrPT. Proteomic analysis identified 101 glomerular and 86 non-glomerular proteins differentially expressed between groups. Significant alterations were observed in proteins associated with mitochondrial dysfunction, including VDAC1 (fold change [FC] 2.6, p = 0.03), NDUFB9 (FC 0.15, p = 0.009), and NDUFA9 (FC 1.72, p = 0.01). Extracellular matrix remodeling proteins COL6A1, COL6A2, and COL6A3 were upregulated (FC 1.6–1.8, p < 0.05). Immune and inflammatory markers C9 (FC 1.6, p < 0.03) and TGFBI (FC 1.7, p < 0.001), as well as oxidative stress–related CLU, were increased. GGACT showed the greatest decrease (FC 0.30, p < 0.001), implicating altered energy metabolism. Conclusion: Distinct proteomic signatures at 5yrPT in DM/OB recipients highlight early mitochondrial, metabolic, immune, and extracellular matrix pathways involved in diabetic kidney disease. These changes precede clinical disease and identify candidate biomarkers and therapeutic targets to improve graft longevity.

ABSTRACT Predicting delayed graft function (DGF) relies on donor, recipient, and perioperative factors. Despite the growing recognition that DGF duration strongly influences patient outcomes, no models currently address or predict its impact—highlighting an important gap in current research and clinical practice. This study aimed to develop an ensemble‐based machine learning model using perioperative data to predict DGF occurrence and duration. The gradient‐boosted decision trees model was trained and validated on 2725 patients, with k‐fold cross‐validation in an external cohort of 284 patients. Model performance was evaluated based on accuracy, ROC‐AUC, and other metrics using R and Python libraries. The binary DGF prediction model achieved a ROC‐AUC of 0.77, while the DGF duration classification model had an accuracy of 79.2%. DGF duration AUC values by time interval were 0.76 for 0–1 weeks, 0.81 for >1–2 weeks, 0.80 for >2–3 weeks, and 0.87 for >3 weeks; the overall macro‐averaged AUC was 0.81. The mean Brier score for multi‐class predictions was 0.14. External validation showed a 78% accuracy for DGF duration prediction. Acute kidney injury (AKI) and donor donation after circulatory death (DCD) status were key DGF predictors. The use of gradient‐boosted decision trees (GBDT) improves the prediction of both the likelihood and duration of DGF, addressing a current gap in kidney transplant patient care. By facilitating personalized transplant care, this model supports more effective perioperative planning and timely interventions, which may contribute to better patient outcomes.

Background. Acute cellular rejection (ACR) remains a major cause of morbidity after heart transplantation despite advances in immunosuppression. Whole genome transcriptomic profiling offers a systems-based, unbiased approach to elucidate the molecular mechanisms underlying ACR. However, noninvasive, longitudinal biomarker assessments capable of capturing the temporal dynamics of rejection biology remain scarce. Methods. RNA sequencing of peripheral blood from heart transplant recipients before, during, and after ACR was compared with nonrejection controls. Pathway analysis was conducted using differentially expressed genes (DEGs), and a machine learning approach was applied to assess gene-based prediction of ACR. Results. A total of 235 rejection-specific significant DEGs and 863 postrejection DEGs (false discovery rate < 0.05) were identified. During ACR, DEGs were enriched for T-cell activation/differentiation, apoptosis, and B-cell receptor signaling pathways. By combining the 2 sets of DEGs, a panel of 71 common genes was identified that reflected the significant, longitudinal transcriptomic dynamics of ACR. In an elastic net machine learning–based classifier, DYNLL1 and SERF2 were identified as ACR predictive genes, and achieved a cross-validated area under the receiver operating characteristic curve of 0.63. Conclusions. Peripheral blood transcriptomics identify dynamic temporal responses in ACR including T- and B-cell pathways with potential ACR predictive genes that warrant further investigation.

ABSTRACTIntroductionThe incidence of mortality late in the pandemic, particularly after widespread vaccine availability, is not well understood. Herein, we elucidate the effect of this impact of the COVID pandemic as well as risk factors for mortality during it.MethodsThe primary end point was death with a functioning graft with secondary endpoints of mortality rates in subgroups and at different time intervals during the pandemic.ResultsDespite widespread vaccination, mortality rates for kidney transplant (KTx) recipients almost doubled during the COVID‐19 era (6.40 deaths per 100 person years vs. 3.54 pre‐COVID). Mortality increased in all racial/ethnic groups but increased more in Native Americans, Hispanics, and African Americans compared to non‐Hispanic Caucasians. The highest mortality rate occurred during the Delta and Omicron time frames. In contrast to the general population, mortality was more evenly spread across age groups in KTx recipients.ConclusionsMortality rates during the COVID‐19 era were extremely high, more than doubling in some racial/ethnic groups. We conclude that the KTx population was a particularly vulnerable group during the COVID‐19 era and suggests the need for further research into the management of variants in the future.

Key Points Morphometry allows for a more prognostic multidimensional quantification of interstitial fibrosis and tubular atrophy (IFTA) in kidneys than does visual inspection. The density of IFTA foci is determined by dividing the number of contiguous IFTA patches in the kidney cortex by the area of cortex. Higher density of IFTA foci significantly predicted renal allograft failure beyond %IFTA and other biopsy and clinical characteristics. Background Contraction of interstitial fibrosis and tubular atrophy (IFTA) may cause %IFTA to under-represent the severity of nephron loss. Higher density of IFTA foci is an important predictor of progressive CKD in native kidneys independent of %IFTA. Methods We studied kidney transplant recipients transplanted between 2000 and 2013 who had a 5-year surveillance kidney biopsy and subsequent follow-up. Banff ci score (interstitial fibrosis) was obtained from the pathology reports. After digitizing the biopsies, we traced cortex area and each distinct IFTA focus on a single trichrome-stained section. Percent IFTA area and IFTA foci density (count of IFTA foci/cortex area) were calculated. Cox models assessed the risk of death-censored graft failure after the 5-year biopsy with Banff ci score, morphometric %IFTA, and IFTA foci density. Results There were 58 death-censored allograft failures among 835 kidney recipients during the 5 years of follow-up. Biopsies from grafts that failed had higher mean Banff ci score (1.5 versus 0.7, P < 0.0001), %IFTA (22.6% versus 7.0%, P < 0.0001), and IFTA foci density (1.3/mm 2 versus 0.4/mm 2 , P < 0.0001). After adjusting for other Banff scores or clinical variables, Banff ci did not correlate with allograft failure, but both higher %IFTA (hazard ratio = 1.56, P < 0.0001) and higher IFTA foci density (hazard ratio = 2.34, P < 0.0001) did. All but four allograft failures by 10 years had biopsies in the top quartile of either %IFTA or IFTA foci density at 5 years. A model using just these two morphometric measures without clinical characteristics resulted in a c-statistic of 0.891 with respect to allograft failure. Conclusions Morphometric characterization of IFTA foci density is a strong predictor of death-censored allograft failure not captured in current Banff classification for grading of kidney fibrosis.

Background. Chronic systemic inflammation is associated with mortality in patients with chronic kidney disease, cardiovascular disease, and diabetes. The goal of this study was to examine the relationship between pretransplant inflammatory biomarkers (growth differentiation factor-15 [GDF-15], interleukin-6 [IL-6], soluble tumor necrosis factor receptor-1, monokine induced by gamma interferon/chemokine [C-X-C motif] ligand 9 [MIG/CXCL9], monocyte chemoattractant protein-1, soluble FAS, tumor necrosis factor-α, interleukin-15, and interleukin-1β) and death with function (DWF) after kidney transplantation (KT). Methods. We retrospectively measured inflammatory biomarker levels in serum collected up to 1 y before KT (time from blood draw to KT was 130 ± 110 d) in recipients transplanted between January 2006 and December 2018. Kaplan-Meier estimation, Cox regression, and Gradient Boosting Machine modeling were used to examine the relationship between inflammatory biomarkers and DWF. Results. Our cohort consisted of 1595 KT recipients, of whom 62.9% were male and 83.2% were non-Hispanic White. Over a mean follow-up of 7.4 ± 3.9 y, 21.2% of patients (n = 338) experienced DWF. Patients with the highest quartile levels of GDF-15 (>4766 pg/mL), IL-6 (>6.11 pg/mL), and MIG/CXCL9 (> 5835 pg/mL) had increased rates of DWF, and each predicted mortality independently of the others. When adjusted for clinical factors (age, diabetes, etc), the highest quartile levels of GDF-15 and IL-6 remained independently associated with DWF. Adding inflammatory markers to a clinical Cox model improved the C-statistic for DWF from 0.727 to 0.762 using a Gradient Boosting Machine modeling approach. Conclusions. These findings suggest that pre-KT serum concentrations of GDF-15, IL-6, and MIG/CXCL9 may help to risk stratify and manage patients undergoing KT and suggests that chronic inflammation may play a role in mortality in KT recipients.

Background. Mesangial expansion (ME) is an understudied histologic lesion in renal allografts. The current Banff mm score is not reproducible and may miss important ME features. The study aimed to improve the quantification of ME using morphometry, assess changes over time, and determine its association with allograft loss. Methods. We studied ME in 1-y and 5-y surveillance biopsies in 835 kidney transplants performed between January 2000 and December 2013. ME was assessed using the Banff mm score by a central pathologist and by morphometry. We derived 3 different morphometric measures: (1) %ME mm (%glomeruli with ME in ≥2 lobules, like Banff mm); (2) %MEany (%glomeruli with any ME lesion); and (3) %ME area (sum of all ME areas/all glomerular tuft areas). Unadjusted and adjusted Cox models assessed the risk of death-censored allograft loss. Results. From 1- to 5-y biopsies, the mean Banff mm score increased from 0.18 to 0.34, whereas %ME mm increased from 2.5% to 13.3%. Banff mm score had modest correlations with morphometric ME measures. Moderate-severe %ME mm was present in 20.1% of 5-y biopsies, whereas only 6.6% of Banff mm scores were. In general, higher ME on both 1- and 5-y biopsies was associated with a deceased donor, older recipient age, recipient diabetes/obesity (present in >50% of severely affected biopsies), higher hemoglobin A1c at 5 y posttransplant, and recurrent kidney disease. Higher ME on 5-y biopsies was associated with delayed graft function. A higher Banff mm score at 1-y biopsy and morphometry ME measures at 5-y biopsy were associated with rejection during the first year posttransplant. Morphometric ME measures were associated with allograft loss independent of Banff scores and all clinical characteristics, including kidney function and recurrent disease. The model with %MEany had the highest c-statistic (0.872). Conclusions. Banff mm score underestimates the pervasiveness of ME in 5-y biopsies. ME is common and associated with alloimmune and nonalloimmune causes of graft loss.

Background. Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. Methods. We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms’ outputs based on a single preoperative ECG were correlated with patient mortality data. Results. Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00–9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). Conclusions. The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.