Đenić, Aleksandar

Background: Hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), and preterm delivery are associated with higher long-term cardiovascular, metabolic, and kidney disease risk. Their implications for living kidney donation remain uncertain. Methods: We conducted a retrospective cohort study of 498 female living kidney donors (2000–2017) from the Mayo Clinic Living Donor Program with documented pregnancy histories, pre-donation CT angiography, implantation kidney biopsy, and measured GFR. Donors with prior HDP (n=45), GDM (n=18), or preterm delivery (n=65) were compared with donors without adverse pregnancy outcomes (APO; n=385). Kidney morphometry, histology, and function were analyzed with adjustment for age at donation and first pregnancy, parity, and hypertension. Post-donation kidney outcomes were assessed using surveys and medical record review. Results: Donors with prior HDP and GDM had higher BMI at donation (+7.9% and +8.9%; p=0.006 and 0.04), whereas donors with preterm delivery had lower BMI (−4.8%; p=0.03) compared with donors with no APO. After adjustment, donors with HDP showed no differences in kidney volumes, biopsy features, or kidney function at donation or during follow-up. Donors with GDM had larger total and cortical kidney volumes (9.5–10%), less arterial luminal stenosis, and higher 5-year post-donation eGFR (+15%; p=0.03). Donors with preterm delivery had a lower risk of eGFR <45 mL/min/1.73 m 2 (HR 0.28; p=0.03). Conclusions: Prior adverse pregnancy outcomes were not associated with adverse kidney structure at donation or accelerated kidney function decline post-donation. These findings support that APO history alone should not preclude living kidney donation when other eligibility criteria are met.

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.

Obesity is increasingly prevalent among living kidney donor candidates, presenting complex clinical, ethical, and logistical challenges. Although obesity is linked to increased post-donation hypertension, diabetes, proteinuria, and chronic kidney disease, the overall absolute risks are modest and may differ depending on metabolic health and patterns of fat distribution. Current reliance on body mass index alone fails to capture the full spectrum of obesity-related risks, leading to inconsistent donor selection practices and potential inequities. This review examines the pathophysiological mechanisms of obesity-related kidney injury and available evidence on comorbidities and postdonation outcomes related to obesity. We discuss tools for better risk stratification, such as imaging-based adiposity assessment and personalized weight management strategies, and note that some risks may remain after weight loss. Barriers such as rigid body mass index cutoffs, limited access to treatment, and lack of long-term follow-up exacerbate disparities in access to donation. We argue for a shift toward individualized, risk-based evaluation supported by multidisciplinary care. We underscore the importance of donor counseling and advocate for a comprehensive, rather than paternalistic approach to living kidney donor selection. Advancing safe and equitable living donation in the context of rising obesity rates will necessitate revised guidelines, improved access to treatment, and an ongoing commitment to donor well-being.

Key Points Increased protein expression of tissue inhibitor of metalloproteinase-3, glypican 6, synuclein γ , apolipoprotein A-4, and Ecto-5′-nucleotidase was observed in old adults compared with young adults. In old adults, overexpressed proteins were enriched in mitochondrial processes while under expressed proteins enriched in RNA metabolism. Validated differentially expressed proteins were not significantly correlated with kidney pathology after adjusting for age. Background Kidney aging is characterized by a loss of glomeruli, predominantly in the superficial cortex, with a resultant decline in GFR and an increased risk of various kidney-related diseases. The early molecular alterations in glomeruli associated with the aging process are not well studied. Methods We combined laser capture microdissection and mass spectrometry–based unbiased proteomic analysis of nonsclerosed, nonischemic glomeruli in the superficial cortex from young and old adults who underwent a radical nephrectomy for a tumor to understand the age-related molecular changes in glomeruli. Twenty-four young and 30 old adults were used for the discovery dataset, and the significant differentially expressed proteins were further validated using an independent set comprising six young and eight old adults. Results Kidneys from old adults had lower eGFR, less kidney parenchyma on computed tomography imaging, and more glomerulosclerosis and arteriosclerosis on histology. Quantitative proteomic analysis of nonsclerosed, nonischemic glomeruli identified increased expression of tissue inhibitor of metalloproteinase-3, glypican 6, synuclein γ , apolipoprotein A-4, and Ecto-5′-nucleotidase in old adults that were further validated in an independent set. Pathway analysis indicated that proteins with increased expression in old adults were enriched in mitochondrial translational processes, aerobic respiration, and TCA cycle, whereas proteins with decreased expression in old adults were enriched in mRNA splicing, mRNA processing, and nonsense mediated decay. Furthermore, validated differentially expressed proteins did not show any significant correlation with the kidney pathology independent of age group. Conclusions Overall, this study identified proteins that are specifically associated with the aging process in otherwise normal-appearing glomeruli.

Key Points Automated cyst segmentation in autosomal dominant polycystic kidney disease could transform care by enabling precise monitoring of cyst progression with minimal manual effort. This technology can facilitate more consistent assessments of cyst burden across patients. Translating these segmentation models from magnetic resonance imaging to computed tomography is crucial for providing comprehensive, modality-independent evaluations. Background Total kidney and liver volumes are key image-based biomarkers to predict the severity of kidney and liver phenotype in autosomal dominant polycystic kidney disease (ADPKD). However, magnetic resonance (MR) imaging-based advanced biomarkers like total cyst number and cyst parenchyma surface area have been shown to more accurately assess cyst burden and improve the prediction of disease progression. The main aim of this study was to extend the calculation of advanced biomarkers to other imaging modalities; thus, we propose a fully automated model to segment kidney and liver cysts in computed tomography (CT) images. Methods Abdominal CTs of patients with ADPKD were gathered retrospectively between 2001 and 2018. A three-dimensional deep learning method using the nnU-Net architecture was trained to learn cyst edges-cores and the noncystic kidney/liver parenchyma. Separate segmentation models were trained for kidney cysts in contrast-enhanced CTs and liver cysts in noncontrast CTs using an active learning approach. Two experienced research fellows manually generated the reference standard segmentation, which were reviewed by an expert radiologist for accuracy. Results Two-hundred CT scans from 148 patients (mean age, 51.2±14.1 years; 48% male) were used for model training (80%) and testing (20%). In the test set, both models showed good agreement with the reference standard segmentations, similar to the agreement between two independent human readers (model versus reader: TCN kidney/liver r =0.96/0.97 and CPSA kidney r =0.98, inter-reader: TCN kidney/liver r =0.96/0.98 and CPSA kidney r =0.99). Conclusions Our study demonstrates that automated models can segment kidney and liver cysts accurately in CT scans of patients with ADPKD.

Artificial neural networks are now used across many fields, from medicine to chatbots. However, they are often complicated and give “black-box” predictions. To better understand these models, we outline some of the terminology, provide example applications in solid organ transplantation, and offer recommendations for those interested in starting to use such models. Examples and definitions are sourced from PubMed searches as well as a review of several applied texts in machine and deep learning. Neural networks have been applied in transplantation since the 1990s, but more recent applications involve their use to process nontabular data, such as images and text. Although interpretations of model results should take context into account, the power of these models holds great potential for the future in transplantation.