Kidney International

IntroductionThere remains considerable debate as to the cause of the epidemic of Mesoamerican Nephropathy (MeN). We have previously reported early loss of estimated glomerular filtration rate (eGFR) as a surrogate for disease onset in a population-representative cohort study of young-adults at risk of disease from Northwest Nicaragua. Using a nested case-control approach we analysed urine and serum proteins surrounding this timepoint with the aim of gaining insight into the primary disease aetiology. MethodsWe conducted label-free ultra high-performance liquid chromatography mass-spectrometry based proteomics using urine samples collected at the study visit before, and at, first observed eGFR loss amongst cases and compared results to matched controls. We then performed direct protein measurements in a discovery cohort followed by quantification of serum total immunoglobulin E (stIgE) at multiple timepoints in a replication cohort. ResultsProteomic analysis demonstrated no differences in the levels of any single protein between cases and controls (n=25 each), at either timepoint, after correction for multiple comparisons. However, functional enrichment analysis demonstrated upregulation of adaptive immune pathways amongst cases. Direct measurements in the discovery cohort using high-sensitivity PCR-based immunoassay (n=21 controls, 19 cases) demonstrated higher stIgE in cases at the study visit immediately prior to first observed eGFR loss (mean difference 810kU/L, 95% confidence interval (CI): 162-1457kU/L). In the replication cohort (n=22 cases, 21 controls) an stIgE level >500kU/L measured by electrochemiluminescence in study samples from any timepoint in the 3 years prior to the first observed loss of eGFR was independently associated with case status when compared to samples from controls at matched visits (adjusted Odds Ratio: 8.1, 95% CI: 1.4-47.8). DiscussionA high level of stIgE precedes loss of eGFR in those at risk of MeN. Understanding what leads to this rise is likely to be key to understanding the cause of the MeN epidemic. Lay SummaryMesoamerican nephropathy describes an epidemic-level chronic kidney disease impacting rural working age adults in Central America. Although a number of exposures, including occupational heat exposure, have been proposed the cause of the epidemic, there remains much debate as to the primary aetiology of the disease. In this study we interrogated urine and blood samples from individuals from affected communities at risk of disease both before and after they develop kidney dysfunction. Using two different approaches, analysis of both urine and blood samples provide evidence of upregulation of immunoglobulin-E (IgE) related pathways in the 2-3 years before individuals develop evidence of kidney disease. Infections (particularly those involving parasites) and allergic reactions, but not heat exposure, have been reported to increase IgE levels. Going forwards, understanding the cause of this increase in IgE in individuals at risk of disease is likely to provide insight into the cause of Mesoamerican Nephropathy epidemics.

BackgroundThe Kidney Precision Medicine Project (KPMP) consortium aims to redefine chronic kidney disease (CKD) by integrating clinical, pathological, and molecular tissue data from kidney biopsies. Here, we demonstrate how biopsy data in CKD can clarify disease etiology and contribute to understandings of disease pathophysiology and clinical prognosis. MethodsThe KPMP is obtaining research kidney biopsies from individuals with CKD (defined as an estimated glomerular filtration rate [eGFR] < 60 mL/min/1.73m2 and/or albuminuria [&ge;]30 mg/g creatinine) and diabetes (enrolled as diabetes and CKD or DKD) or hypertension (enrolled as hypertension and CKD or HCKD). A team of kidney pathologists and nephrologists adjudicated the primary clinico-pathological diagnosis for 258 participants with CKD. We compared pathological features and kidney transcriptional signatures between participants with a primary adjudicated diagnosis of diabetic nephropathy and those with other causes of CKD. We developed a model using clinical and biomarker data that predicted the probability of diabetic nephropathy and tested associations of the signature with CKD progression among Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes (n=229). ResultsAmong 183 participants enrolled as DKD, 102 (56%) had a primary adjudicated clinico-pathologic diagnosis of diabetic nephropathy. Among 75 participants enrolled as HCKD, 42 (56%) had a primary diagnosis of hypertension-associated kidney disease. Those with diabetic nephropathy, compared with other diagnoses, had more severe interstitial fibrosis, tubular atrophy, tubular injury, segmental sclerosis, and severe arteriolar hyalinosis, and single-nucleus and single-cell transcriptional analyses revealed upregulation of immune and inflammatory pathways and downregulation of oxidative phosphorylation. A combination of age, hemoglobin A1c, urine albumin-creatinine ratio, and serum KIM-1 and sTNFR1 predicted a clinico-pathologic diagnosis of diabetic nephropathy in the KPMP (AUC 0.82, 95% CI 0.75-0.89) and was associated with an increased risk of CKD progression among patients with diabetes enrolled in CRIC (HR 1.48 [95% CI 1.27-1.73] per 10% higher predicted probability of diabetic nephropathy). ConclusionIn common presentations of CKD, kidney biopsies may alter a priori impressions, reveal a diversity of diagnosis, structure, and function that is associated with clinical outcomes and can impact therapeutic decisions.

IntroductionPatients with type 2 diabetes mellitus (T2DM) are at increased risk of coronary artery disease and frequently undergo coronary angiography or percutaneous coronary intervention. Although risk factors for post-contrast acute kidney injury (PC-AKI) are well defined, effective preventive strategies remain limited. MethodsThis multicenter observational cohort study included 975 patients aged 18-75 years who underwent coronary angiography and/or percutaneous coronary intervention with iodinated contrast between June 2023 and June 2024. All patients received standardized intravenous hydration. Participants were grouped according to chronic sodium-glucose co-transporter-2 (SGLT2) inhibitor use ([&ge;]3 months). PC-AKI was defined as a [&ge;]25% or [&ge;]0.5 mg/dL increase in serum creatinine within 48-72 hours after contrast exposure. ResultsThe mean age was 59.2 {+/-} 11.7 years, and 70.8% were male; 16.9% were using SGLT2 inhibitors. PC-AKI occurred in 7.3% of patients, and 0.7% required renal replacement therapy. In univariate analysis, advanced age, diabetes, hypertension, heart failure, diuretic use, and elevated urea, creatinine, potassium, and uric acid levels were associated with PC-AKI. Higher eGFR, albumin, sodium levels, and SGLT2 inhibitor use were inversely associated. In multivariate analysis, age [&ge;]65.5 years (OR 4.53), diabetes (OR 2.49), and uric acid >6.75 mg/dL (OR 2.34) remained independent risk factors, while eGFR >81.5 mL/min/1.73 m2 (OR 0.38), sodium >137.5 mmol/L (OR 0.36), and SGLT2 inhibitor use (OR 0.09) were independently protective. ConclusionBeyond established cardioprotective and renoprotective effects, SGLT2 inhibitors may reduce the risk of PC-AKI in patients with T2DM, potentially through decreased renal oxygen consumption and attenuation of contrast-induced hypoxic injury.

IntroductionGenome-wide association studies (GWAS) for kidney function have mainly focused on creatinine-based glomerular filtration rate (eGFRcrea), which is affected by variation in muscle mass. Moreover, the genetic basis of the sexual dimorphism of chronic kidney disease is underexplored. MethodsWe performed a GWA meta-analysis for creatinine clearance (CrCl), a muscle mass-independent kidney function phenotype, in 58,976 individuals of European descent from the Lifelines Cohort Study. ResultsWe identified 16 independent loci with 21 genome-wide significant lead single nucleotide polymorphisms (SNPs) associated with CrCl, two of which had not been reported previously in kidney function GWASs: rs146465192, located near the RP1-249F5.3 gene (effect allele frequency (EAF) = 0.01, P = 3.38 x 10-9) and rs117014836, located near the AGPAT4 gene (EAF = 0.02, P = 5.42 x 10-9). Both SNPs were also associated with eGFRcrea in Lifelines (rs146465192: P = 1.34 x 10-8; rs117014836: P = 3.64 x 10-7), but not in previously published eGFR GWASs. In silico follow-up analyses revealed that rs146465192 was associated with plasma IGF2R ({beta} = -0.519, P = 1.40 x 10-6), while rs117014836 was associated with blood expression levels of AGPAT4 (eQTL P = 6.54 x 10-6). Furthermore, we identified two female-specific CrCl loci (t-statistic P < 0.004): rs8002366 (GPC6) and rs12908437 (IGF1R), associated with GPC6 expression in kidney (eQTL P = 8.38 x 10-10) and IGF1R expression in blood (eQTL P = 2.62 x 10-6), respectively. ConclusionThis first large-scale GWAS of CrCl revealed two new genetic variants among both sexes and two female-specific variants influencing kidney function. Lay summaryKidney function is a complex phenotype influenced by many different factors, including genetics. Earlier genetic studies often used the creatinine-based estimated glomerular filtration rate (eGFRcrea) as the measure of kidney function. However, eGFRcrea is influenced not just by kidney function but also by an individuals muscle mass, which may distort the results. Therefore, in this study we used creatinine clearance (CrCl), a measure of kidney function independent of muscle mass, to look for genes in a European-ancestry population. We identified 16 genetic regions; two of which had not been found before. We also found two additional regions that were only related to CrCl in females. This shows the added value of investigating CrCl and suggests sex-based differences in how genetics affect kidney function.

IntroductionGenomic testing is reshaping nephrology practice, yet the structure, outcomes, and implementation of kidney genetics services remain poorly characterized. MethodsWe conducted a two-part scoping study comprising (i) a literature review (JBI methodology, PRISMA-ScR compliant; OSF registration doi.org/10.17605/OSF.IO/N32VA) of English-language publications (2000-2025) describing kidney genetics services and outcomes, and (ii) an international stakeholder consultation of clinic leads to capture real-world service and implementation experiences. ResultsSixty studies were included, predominantly from North America (n=23), followed by United Kingdom/Ireland (n=5), Europe (n=17), Australia/New Zealand (n=10), and Asia (n=5). Among the 25 studies describing clinic models, four types were identified: multidisciplinary integrated (n=12), nephrologist-led (n=9), mainstreaming (n=2), and traditional genetics referral (n=2). Clinic structure varied by region. Outcome reporting focused on diagnostic yield (92%), with limited data on timeliness (16%), patient-reported outcomes (12%), or implementation outcomes (4%). Test penetration was high across regions and models, while diagnostic yield varied. Nephrologist-led clinics demonstrated comparable performance to multidisciplinary models when adequately supported. International stakeholder consultation data (n=48) revealed diversification of clinic models across regions. In Australia/New Zealand, multidisciplinary clinics predominated, supported by public funding and in-house or hybrid laboratory. United Kingdom/Ireland clinics used public funding and a national laboratory. North American clinics show greater heterogeneity, with higher prevalence of nephrologist-led models, reliance on commercial laboratories, and mixed or private funding. Asian clinics reported nephrologist-led models, with resource constraints, and hybrid testing and funding arrangements. Comprehensive sequencing with virtual panels predominated in Australia/New Zealand, United Kingdom, and Europe; phenotype-driven panels {+/-} reflex testing were more common in North America. ConclusionsKidney genetics care is expanding but remains unevenly implemented. Nephrologist-led and multidisciplinary models can be effective with appropriate support. Patient selection may influence diagnostic yield more than testing modality. Standardized outcome reporting and theory-driven implementation evaluation are essential for delivering equitable, sustainable genomic services. Lay SummaryThis study examined how kidney genetics services are delivered across the globe. We reviewed 60 studies (2000-2025) and consulted 48 clinic leaders globally. Four service models were identified--multidisciplinary integrated, nephrologist-led, mainstreaming, and traditional genetics referral--and mapped variation in care teams, test strategies, test laboratories, and funding. Most studies reported diagnostic yield, but few assessed patient experience or how well services were implemented. European programs showed the highest performance, attributed to clear referral criteria, deep phenotyping, detailed family histories, multidisciplinary review, and strong public funding. Clinics led by nephrologists performed comparably to multidisciplinary teams when adequately supported. Across all settings, patient selection was more important than the specific type of genetic test used in determining diagnostic success. Kidney genetics services are expanding but remain uneven. This study highlights the need for context-specific, theory-informed, and determinants-targeted strategies to support scalable, equitable, and sustainable genomic care worldwide.

Acute kidney injury (AKI) and chronic kidney disease (CKD) are two interconnected clinical conditions, both defined by degree of functional impairment, but with heterogeneous clinical trajectories. Using new transcriptomic technologies, recent studies have described the cellular diversity in the healthy and injured kidney at the single cell level. Here, we used single nucleus transcriptomics to investigate the molecular diversity and commonalities in kidney biopsies from over 150 participants with AKI and CKD enrolled within the Kidney Precision Medicine Project (KPMP), and did so at the patient participant level. Using an unsupervised approach, we identified two multi-cellular programs associated with clinical and histopathological features of acute injury and chronic damage, respectively. We found that these programs are expressed across patients with AKI and CKD, supporting shared, rather than distinct, underlying molecular mechanisms. These programs capture tissue-level compositional changes towards adaptive and failed-repair states in tubular epithelial cells, as well as intra-cellular molecular changes characteristic of stress in all cell types. We identified subunits of the NFkB and AP-1 complexes, as well as members of the STAT family, as putative upstream regulators of the acute and chronic programs. We were able to link these continuous molecular measures of acute injury and chronic damage with urine and plasma protein profiles obtained at time of biopsy. These non-invasive protein signatures were predictive of renal outcomes in an independent cohort of 44 thousand participants from the UK biobank. In summary, unbiased identification of cellular programs in kidney disease biopsies defined molecular programs of injury cutting across conventional disease categorisation and established a non-invasive molecular link to long term patient outcomes.

BackgroundThe effects of Banff histological diagnoses on kidney transplant outcome have been well characterized. However, repeated observation of such histological injury across multiple biopsies in kidney transplant recipients remains insufficiently explored. MethodsIn an observational cohort (N=1819 transplantations with 5736 post-transplant biopsies, recurrent event survival models quantified transitions between diagnoses of T-cell mediated rejection (TCMR), antibody-mediated rejection (AMR), DSA-negative C4d-negative microvascular inflammation (MVIDSA-/C4d-), BK polyomavirus nephropathy (BKPyVAN), borderline TCMR (bTCMR), and probable AMR (pAMR), revealing patterns in the disease trajectories. In two observational cohorts (N=1818 transplantations with 5732 biopsies, N=853 transplantations with 975 biopsies), time-dependent cumulative covariates were constructed for TCMR, AMR, MVIDSA-/C4d- and BKPyVAN, enabling estimation of associations of repeated diagnoses with graft failure using multivariable cause-specific Cox models. ResultsThe incidence rate of a diagnosis was most strongly associated with earlier diagnosis of the same type, but associations between different types of diagnoses also occurred. The hazard of kidney graft failure was significantly increased by repeated observation of TCMR in multiple biopsies (HR 7.97, 95% CI 4.94 - 12.86), as well as by repeated AMR (HR 6.19, 95% CI 3.15 - 12.17), repeated MVIDSA-/C4d- (HR 4.53, 95% CI 2.15-9.54) and repeated BKPyVAN (HR 10.90, 95% CI 5.83 - 20.35). The hazard of graft failure was increased more after repeated diagnoses in transplants than after first diagnoses. The effects of repeated TCMR and repeated AMR remained significant even when observed in protocol biopsies in the absence of graft dysfunction. Repeated observation of BKPyVAN was the most detrimental of all diagnoses when observed in indication biopsies, but it was the least harmful when observed in protocol biopsies. ConclusionIncidence of Banff histological diagnoses appears to be affected by earlier diagnoses, especially those of the same type. These repeated observations of a specific diagnosis have an additional effect on the hazard of graft failure, underscoring a critical unmet need for adequate treatment strategies for these recurrent or persistent injury processes. Lay summaryIn two observational cohorts of 1819 and 750 kidney transplant recipients, kidney transplant biopsies were taken at multiple time points after transplantation. Based on the Banff classification for transplant pathology, various post-transplant diseases were diagnosed, often at more than one time point during follow-up. We assessed patterns in the occurrence of diagnoses over time, and related these diagnoses to survival of the kidney grafts using survival models with time-dependent cumulative diagnoses. We found that repeated observation of the same diagnosis was much more common than consecutive observations of different diagnoses. Repeated diagnoses of tissue injury also decreased kidney graft survival more compared to single diagnoses. This indicates that treatment options for patients with repeated or persistent diagnoses are currently inadequate and novel strategies are needed.

BackgroundKidney disease refers to a broad range of disorders that impair renal structure and function. Among these, chronic kidney disease (CKD) is the most prevalent worldwide, affecting approximately 10% of the global adult population. While large-scale omics studies have identified numerous molecular associations with kidney function and disease, these insights often remain isolated within individual data layers, hindering a systems-level understanding of the functional interplay between genes, proteins, metabolites and clinical phenotypes. MethodsWe developed the Kidney Disease Atlas (KD Atlas) using an extended QTL-based integration strategy combined with a composite network approach. For this purpose, we leveraged results from omics studies in population-based and kidney disease-specific cohorts from the CKDGen Consortium and other large-scale initiatives and integrated them with data from knowledge databases, inferring a comprehensive network of relationships between metabolites, proteins, genes, and kidney disease-related traits. ResultsWe present the KD Atlas, an online resource (https://metabolomics.helmholtz-munich.de/kdatlas) integrating over 25 large studies providing disease-relevant information on 20,456 protein-coding genes, 1,962 proteins, 1,375 metabolites and 40 kidney disease phenotypes connected by more than 1.2 million relationships. Through an interactive web interface, researchers can dynamically construct context-specific molecular subnetworks and perform integrated analyses without requiring specialized bioinformatics expertise. Application showcases demonstrate the resources utility for providing the molecular context of KD-associated genes or metabolites and for generating novel mechanistic hypotheses. ConclusionThe KD Atlas provides a global, multi-omics network view of kidney pathophysiology through an intuitive interface, empowering researchers to formulate mechanistic hypotheses and prioritize candidate targets for subsequent experimental validation.

BackgroundIn lung transplantation, de novo immunodominant donor-specific anti-HLA antibodies recognizing HLA-DQ antigens (dn-iDSA-DQ) are predominant and can induce chronic lung allograft dysfunction (CLAD). We previously developed a method to measure the active concentration of dn-iDSA-DQ. We aimed to determine whether this new quantitative biomarker is associated with transplantation outcomes. MethodsThis retrospective multicentre cohort study included 90 lung transplant recipients (LTRs) developing dn-iDSA-DQ, evidenced through single antigen flow beads (SAFB) follow-up. We measured the active concentration of dn-iDSA-DQ at the time of their first detection (T0) for all LTRs, and within the 2 years after DSA detection, whenever possible. SAFB dn-iDSA-DQ characteristics and clinical data were retrieved up to 5 years after DSA detection. ResultsWe tested 184 sera with SPR (n=90 at T0, n=94 within the 2 years after DSA detection), among which 63 (34.4%) had a quantifiable concentration of the dn-iDSA-DQ ([&ge;]0.3 nM). The median SAFB mean fluorescence intensity (MFI) of the dn-iDSA-DQ with a concentration [&ge;]0.3 nM was higher (p<0.0001), yet the correlation between SAFB MFI and active concentration was low (r=0.758, p<0.0001). In multivariate analysis, a concentration of the dn-iDSA-DQ [&ge;]0.3 nM at T0 was independently associated with a lower 2-year CLAD-free survival (HR 2.06, p=0.02). A concentration of the dn-iDSA-DQ [&ge;]0.3 nM within the 2 years from DSA detection was associated with a lower graft survival in univariate analysis. ConclusionsActive concentration of dn-iDSA-DQ appears as a valuable biomarker to identify pathogenic DSA at their first detection because of its association with CLAD.

Introduction Glomerular filtration rate (GFR) is invasive to measure. Therefore, in clinical care, estimated GFR is derived from serum levels of endogenous filtration markers such as creatinine and cystatin C. Multiple studies from high income countries showed differences between estimated glomerular filtration rate based on cystatin C (eGFRcys) and creatinine (eGFRcr). This study aimed to assess the agreement between eGFRcys and eGFRcr in Ethiopian children and identify factors influencing higher eGFRcys and eGFRcr. Method We studied 350 Ethiopian children who were part of the iABC birth cohort study. At the recent follow-up (average age 10 years), serum cystatin C and creatinine were measured. Formulas by Berg (2015) and Hoste (2014) were used to estimate eGFRcys and eGFRcr, respectively, and Bland-Altman plots assessed their agreement. The difference in eGFR (eGFRdiff) was calculated and categorized as less than -15 mL/min/1.73 m2 (higher eGFRcr), between -15 and <15 mL/min/1.73 m2 (concordant), and greater than or equal to 15 mL/min/1.73 m2 (higher eGFRcys). Multinomial logistic regression was used to identify factors associated with higher eGFRcr and higher eGFRcys. Result Estimated glomerular filtration rate (eGFR) showed significant variation based on the estimation formula used. When using formulas by Berg (2015) and Hoste (2014), the median (IQR) eGFRcys and eGFRcr were 99.4 (90.0; 114.1), and 123.2 (110.3; 143.8) mL/min/1.73 m2, respectively. Overall, we observed a poor agreement between eGFRcys and eGFRcr, with only 94 (27.6%) children having concordant results compared to 220 (64.7%) with higher eGFRcr and 26 (7.6%) with higher eGFRcys. If the eGFRcys results are considered reliable, 27.5% of the children had eGFR below 90 mL/min/1.73 m2. Conclusion There was very marked variation in the distributions of estimated eGFRs depending on which formulas for children were used. Agreement between eGFR estimated using cystatin C and creatinine was poor among Ethiopian children. Relative to eGFRcys, kidney function may be overestimated by creatinine-based equation as up to 30ml/min in Ethiopia. Ideally, a validation study with GFR measured by gold standard methods (Inlulin clearance) among children is required. However, because of its invasive nature and financial concerns, Iohexol clearance studies are recommended.

Individuals with chronic kidney disease (CKD) have higher rates of hip fracture and post-fracture mortality. Although they may develop age-related osteoporosis similar to those without CKD, they may also exhibit CKD-related metabolic bone disease (MBD), characterized by low, high, or mixed turnover at similar levels of bone mineral density (BMD). Because BMD does not provide information about turnover status, clinical decision-making is challenging. This study evaluated the associations between circulating bone-turnover biomarkers and static histomorphometry in patients undergoing hip-fracture surgery. In this cross-sectional study, we enrolled adults with and without CKD, defined as estimated glomerular filtration rate (eGFR) [&le;]60 ml/min/1.73m{superscript 2} (CKD-EPI 2021), undergoing hip-fracture surgery. Blood samples, bone specimens from the femoral head or greater trochanter, and demographic and clinical data were collected at the time of surgery. Plasma biomarkers included -Klotho, bone alkaline phosphatase (BAP), dickkopf-related protein 1 (DKK-1), fibroblast growth factor 23 (FGF23), tartrate-resistant acid phosphatase 5b (TRAP5b), parathyroid hormone (PTH), and sclerostin. Logistic regression models, adjusted for age, gender, eGFR, and osteoporosis, assessed associations with CKD status. Tertiles of osteoblast surface (Ob.S/BS) and eroded surface (ES/BS) were defined in participants without CKD and applied to the full cohort. Multinomial and multivariable linear regression evaluated associations of biomarkers with these histomorphometry parameters. Among 97 enrolled participants (mean age 80 {+/-} 11 years; 67% female), 68% had CKD. Of 75 with complete biomarker and histomorphometry data, 96% demonstrated low bone turnover. CKD was associated with lower trabecular thickness (Tb.Th) and higher osteoid thickness (O.Th), osteoid volume (OV/BV), and osteoid surface (OS/BS), suggesting thinner, largely unmineralized trabeculae. Higher BAP (222.2% difference per doubling; 95% CI 77.2-485.8) and TRAP5b (319.3%; 95% CI 128.3-669.5) were directly associated with Ob.S/BS and ES/BS, whereas sclerostin was inversely associated with ES/BS (-28.9%; 95% CI -44.8 to -7.1). PTH was not associated with bone-turnover measures. These findings suggest that BAP, TRAP5b, and sclerostin may provide useful adjunct information alongside PTH for assessing bone turnover and guiding therapy in patients with and without CKD.

BackgroundIron metabolism disorder is highly prevalent before and after heart transplantation (HTx). The impact of pretransplant and posttransplant iron disorder on posttransplant outcomes is unclear. ObjectivePretransplant serum levels of key regulator proteins of iron metabolism (hepcidin, interleukin-6, erythroferrone) were tested for prediction of the composite outcome 1-year posttransplant all-cause mortality (ACM) or [&ge;]moderate acute cellular rejection (ACR). Furthermore, serum levels of these proteins were measured at 1-year posttransplant to explore their posttransplant course and association with ACR. ResultsIn a multicenter cohort including 276 consecutive HTx recipients, patients with or without outcome (n=118/158, respectively) did not differ for pretransplant demographics, mismatch of donor/recipient sex, mismatch of HLA epitopes, and hepcidin or interleukin-6 levels. However, pretransplant erythroferrone levels were higher (1.40 vs. 1.19 ng/mL; p=0.013) and hemoglobin levels were lower (124.5 vs. 127 g/L; p=0.004) among patients with the composite outcome. Pretransplant erythroferrone levels >2.25 ng/ml (4th-quartile) were significantly associated with the composite outcome in multivariable analysis (OR 2.17; 95% CI 1.19-3.94, p=0.011; reference: 1st-3rd quartiles). In adjusted predicted proportions analysis, the incidence of the composite outcome was higher in 4th-quartile patients when compared to 1-3rd -quartiles patients (58.0 vs. 37.7%; p=0.003). At 1-year posttransplant, 80.4% of patients with pretransplant erythroferrone levels >2.25 ng/ml remained high; 88.4% of patients with pretransplant erythroferrone levels [&le;]2.25 ng/ml had high levels posttransplant. In 1-year survivors with high erythroferrone levels and [&ge;]moderate ACR during the first postoperative year, the ratio of the opponent regulators of hepcidin gene expression, erythroferrone to interleukin-6, was higher when compared to those without ACR (1.18 vs. 0.41; p=0.016). Hepcidin levels were not different between these two subgroups indicating disproportionate ERFE increase. ConclusionHigh pretransplant erythroferrone levels predict the composite posttransplant outcome 1-year ACM and ACR. Disproportionately high posttransplant erythroferrone levels are related with [&ge;]moderate acute cellular rejection.

BackgroundRising kidney discard rates and uncertainty around accepting higher risk donor kidneys highlight the need for decision support tools that integrate donor and recipient factors and communicate risk in ways that are understandable and usable at the time of offer. Conventional indices (e.g., KDPI/KDRI) provide population level signals but do not deliver individualized, cognitively accessible information aligned with real time clinical workflows. ObjectiveTo describe how key transplant stakeholders (patients, coordinators, and providers) interpret and evaluate a prototype Kidney Risk Calculator app that generates donor-recipient specific survival projections and to identify the content, format and features, and functionality needed for clinically meaningful, patient-centered decision support. DesignQualitative study using focus groups and individual interviews. SettingUniversity of New Mexico Hospital (UNMH) Kidney Transplant Center. ParticipantsFive patients (four transplant candidates and one patient advocate), three transplant coordinators, and five transplant providers (3 attending physicians and 2 advanced practice practitioners). MethodsSemi-structured sessions (45 to 60 minutes) with 13 stakeholders (patients, coordinators, and providers) included a live app demonstration and explored usability, interpretability, contextual information needs, perceived clinical utility, and anticipated barriers/facilitators. Data were collected via one coordinator focus group, one patient focus group, and five provider interviews; sessions were recorded, transcribed, de-identified, and analyzed using inductive reflexive thematic analysis. ResultsStakeholders affirmed the value of personalized projections as an adjunct to clinical judgment, particularly for higher risk offers. Participants prioritized: 1) Content: clear education on hepatitis C virus (HCV) positive donors and Public Health Service (PHS) risk criteria; plain explanations of Calculated Panel Reactive Antibody (CPRA); and framing that makes time on dialysis and tradeoffs salient; 2) Format & Features: plain language narratives, percentages rather than decimals, simple visuals, minimized acronyms, U.S. customary units, and a stepwise (TurboTax-like) input flow preferred by patients; and 3) Functionality: attention to cognitive load and workflow alignment, given phone based time pressure and digital access constraints. Stakeholders emphasized that the value of the tool hinges on clarity, context, and workflow fit, not predictive accuracy alone. LimitationsSingle center, formative prototype study with a modest sample; findings are illustrative and may have limited transferability. Participants reacted to a demonstration rather than using the app during real time offer calls; convenience/email recruitment and Zoom only English sessions may introduce selection bias; team involvement in app development may contribute residual confirmation bias despite mitigation. ConclusionsEarly stakeholder input suggests that a kidney offer decision support tool should integrate individualized predictions with plain language explanations, contextual information that addresses common misconceptions, workflow aligned functionality, and accessible outputs. Tools designed and implemented with these features may support acceptance of medically complex kidneys and may help reduce offer bypass and organ discard. These inferences reflect stakeholder perceptions in a formative qualitative study and warrant prospective evaluation.

Background & AimsPerihilar cholangiocarcinoma is an aggressive malignancy with clinical heterogeneity and poor long-term outcomes after resection. Current prognostic assessment relies mainly on anatomical staging and pathological features, which incompletely capture the entire postoperative risk. We aimed to determine whether integrative analysis of clinical, surgical, pathological and tumor genomic data could improve time-resolved, individualized recurrence-risk prediction after curative-intent resection. MethodsWe performed a multicenter retrospective study including patients undergoing curative-intent resection for perihilar cholangiocarcinoma in ten Spanish hospitals (2003-2023). Overall and disease-free survival were analyzed using Cox models. Outcome-agnostic clinical phenotypes were derived by unsupervised clustering of clinical and surgical features. Targeted tumor sequencing of cancer-associated hotspot regions and selected genes was performed. Prognostic models integrating clinical and genomic data were trained and evaluated in independent training/test sets using penalized and latent-component Cox frameworks, with time dependent discrimination. ResultsThe final cohort comprised 142 patients, with a median follow-up of 26.4 months. Recurrence occurred in 61.3% of patients, and 53.5% died during follow-up. Classical pathological factors were strongly associated with survival and recurrence. Unsupervised outcome-agnostic clustering identified three reproducible clinical phenotypes with markedly different recurrence patterns and survival, only partially explained by anatomical staging. Integrative clinical-genomic modelling further improved recurrence-risk prediction, achieving high discrimination in independent validation (time-dependent AUC [~]0.8). Moreover, the integrative model assigned higher risk over time to patients who relapsed. Patients combining unfavorable clinical phenotype with high genomic-derived risk exhibited a high probability of early recurrence. ConclusionsIntegrated clinical phenotyping and targeted genomic profiling substantially refine recurrence-risk stratification after resection of perihilar cholangiocarcinoma beyond anatomical staging alone. This provides a pragmatic framework for risk-adapted postoperative surveillance and therapeutic decision-making. Impact and ImplicationsThis study provides a data-driven framework integrating clinical, surgical and targeted genomic information to refine prognostic stratification after resection of perihilar cholangiocarcinoma, addressing the limitations of anatomy-based staging in capturing biological heterogeneity. The results are particularly relevant for clinicians managing postoperative surveillance and adjuvant strategies, as they identify patient subgroups with markedly different risks of early recurrence despite similar conventional staging. In practical terms, the combination of unsupervised clinical phenotyping and a targeted, biologically informed genomic panel could support risk-adapted follow-up intensity, selection for adjuvant or experimental therapies, and enrolment into clinical trials. While prospective validation is required before routine implementation, this approach offers a feasible and interpretable pathway toward precision postoperative management in a highly aggressive malignancy.

BackgroundBoth hemorrhagic fever with renal syndrome (HFRS) and scrub typhus (ST) are acute zoonotic infectious diseases. There is an overlap in their epidemiological characteristics and clinical manifestations, posing challenges for early differential diagnosis. This study aims to identify predictive factors for these two diseases to provide a basis for early diagnosis. Method/FindingsA retrospective analysis was conducted on the clinical data of patients diagnosed with HFRS and ST at the First Affiliated Hospital of Dali University. Logistic regression analysis was employed to explore independent risk factors for the early differential diagnosis of these two diseases, and a nomogram model was constructed based on these risk factors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). The nomogram was utilized to visually present the predictive variables. Decision curve analysis (DCA) was performed to assess the clinical utility of the model. ResultsA total of 235 patients each with HFRS and ST were included in this study. After adjusting for confounding factors, the results of multivariate logistic regression analysis revealed that sex (male) (adjusted odds ratio [ajOR]: 2.093, 95% confidence interval [CI]: 1.107 - 3.957, P = 0.018), positive proteinuria (ajOR: 4.937, 95% CI: 2.427 - 10.042, P < 0.001), creatinine (CREA) (ajOR: 1.009, 95% CI: 1.003 - 1.015, P = 0.005), heart rate (ajOR: 0.981, 95% CI: 0.966 - 0.997, P = 0.018), and conjunctival congestion (ajOR: 16.167, 95% CI: 5.326 - 49.072, P < 0.001) were independent risk factors for differentiating HFRS from ST. The AUC of the model constructed based on these five independent risk factors was 0.856. ConclusionSex (male), positive proteinuria, elevated CREA, decreased heart rate, and conjunctival congestion are effective predictive factors.

Background and AimsCholedocholithiasis (CDL) is a common condition that can lead to serious complications, requiring effective risk stratification for timely intervention. While current guidelines use clinical predictors, imaging, and laboratory markers for risk assessment, the role of glutamate dehydrogenase (GLDH) in CDL remains poorly understood. This study aims to evaluate its potential as a clinical biomarker for identifying patients with CDL. MethodsThis single-center cohort study identified 23,103 patients who presented to the emergency department of the University Medical Center Hamburg-Eppendorf and underwent routine abdominal laboratory testing between May 2021 and December 2023. Patients were classified into CDL and other diagnoses. To assess the predictive value of age, sex and laboratory markers for CDL, we developed a random forest machine learning model, conducted a backward stepwise logistic regression and performed receiver operating characteristic (ROC) analysis. Results152 patients were diagnosed with CDL and 22,951 with other diagnoses. In the random forest machine learning model, GLDH emerged as the most significant feature for predicting CDL. ROC analysis revealed that GLDH had the highest area under the curve of 0.93 among laboratory markers. At the upper limit of normal, GLDH demonstrated the best sensitivity (92%) compared to aspartate aminotransferase (AST), alanine aminotransferase (ALT) and bilirubin. High GLDH levels exceeding 150 U/L demonstrate the highest specificity (99%) for CDL, outperforming AST, ALT and bilirubin. ConclusionGLDH outperforms AST, ALT and bilirubin as a screening and predictive marker for CDL, supporting its inclusion in clinical guidelines for risk stratification.

PurposeWhile genomic testing is integral to pediatric inborn errors of immunity (IEI) care, few studies have examined strategies to support its optimal delivery. This study aimed to characterize a pediatric IEI cohort and assess the impact of implementing a mainstream model-of-care (MoC). Materials/MethodsComprehensive chart audit was conducted for patients ([&le;]18y) who received IEI genomic testing in Queensland, Australia, from 2017-2025. Descriptive analyses captured demographic and clinical characteristics, genomic testing and results, and management outcomes. Inferential analyses assessed changes in genomic practices pre-MoC (<2021) and post-MoC ([&ge;]2021). Results322 patients met eligibility criteria (n=481 genomic test). Diagnostic yield (27.6%) varied by testing indication, with the highest rate among phagocytic defects (n=4/4;100%) and severe combined immunodeficiency (n=8/10;80%). Very-early-onset inflammatory bowel disease had the lowest diagnostic yield (n=3/68;4.4%), prompting changes to testing criteria. Molecular diagnosis resulted in management changes for 90.5% patients. Genomic testing was widely used pre-MoC (n=251 genomic tests). All outcomes significantly improved pre-and post-MoC (p<0.05): duplicate testing decreased (13.9% to 0%); variants of uncertain significance reduced (37.7% to 7.1%); informed consent documentation increased (70.5% to 88.4%); and diagnostic yield increased (16.2% to 27.4%). ConclusionTargeted interventions are needed to support delivery of genomic testing and strengthen service effectiveness.

Type 2 diabetes (T2D) affects 11.1% of the global population, underscoring the need for biomarkers that inform treatment response and glycemic outcomes. We evaluated the association between the FTO variant rs9939609-A and glycemic control in a Mexican population. A total of 174 individuals living with T2D from Merida and Sisal, Yucatan, were included, of whom 85% were receiving oral hypoglycemic agents as main treatment. Glycemic control was defined cross-sectionally as good ([&le;]130 mg/dL, n=63) or poor (>130 mg/dL, n= 111) with fasting glucose. Linear mixed models incorporating relevant covariates and a family random intercept were used. Effect size estimates were transformed to logit odds ratios. After adjustment for age, sex, BMI, years with T2D, and treatment, we observed a significant association in the additive (OR = 1.15 [1.003-1.31]) and recessive (OR = 1.51 [1.03-2.23]) models. To conclude, rs9939609-A may be associated with poorer glycemic control despite pharmacologic therapy.

BackgroundGenetic risk scores (GRS) for type 1 diabetes (T1D) have been developed primarily in European populations, limiting their generalisability across ancestries. Indians differ from Europeans in clinical characteristics of T1D and overall genetic architecture, yet systematic evaluation of T1D GRS performance in multi-regional Indian cohorts is lacking. MethodsThe study included 597 T1D patients and 3347 non-diabetic controls from different regions in India. Genotyping, imputation, quality control analysis, and construction of the 67-SNPs T1D GRS were performed using standardised pipelines. Discriminative performance was assessed using Receiver Operative Curve-Area under Curve (ROC-AUC) analysis, and optimal thresholds were derived using Youdens index. HLA-DQ diplotype frequencies were compared, and association analysis was conducted using multivariable logistic regression. FindingsT1D GRS showed consistent discriminative performance across Indian cohorts [ROC-AUC=0.84 (range=0{middle dot}78-0{middle dot}87)], supporting its comprehensive use for T1D classification in India. Notably, its performance was lower in islet cell autoantibody (IA) negative compared with IA positive T1D patients (ROC-AUC, 0{middle dot}75 vs 0{middle dot}85) and in adult-onset than in childhood-onset patients (0{middle dot}74 vs 0{middle dot}84). We observed a lower frequency of protective HLA-DQ diplotypes and a strong association of HLA-DQ81 containing diplotypes in childhood-onset T1D. Application of an India-specific T1D GRS score improved the sensitivity than the European cut-off. InterpretationT1D GRS is a valuable unified diagnostic tool in Indians, but its performance varies by islet cell autoantibody status and age at onset, likely reflecting population-specific HLA architecture. European-derived T1D GRS thresholds under-classify the genetic risk, highlighting the importance of ancestry-aware optimisation in Indians. FundingCDRC grant CDRC202111026 and CSIR Intramural Grant P50. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPrevious studies have shown that a 67-SNPs T1D genetic risk score (GRS) can distinguish T1D patients from non-diabetic controls and other forms of diabetes, but its performance varies across ancestries. Islet cell autoantibodies (IA) have important diagnostic value for classifying type 1 diabetes (T1D). However, their prevalence in India varies widely, with up to one-quarter of patients testing negative, limiting their clinical utility. Evidence supporting the use of the T1D GRS in India, combined with IA antibodies status is limited to a single cohort representing one linguistic group. The applicability of T1D GRS across multi-centric clinical settings has not been systematically evaluated. Added value of this studyThis study validates the 67-SNPs T1D GRS across multiple Indian cohorts representing major linguistic groups, supporting its use as a unified diagnostic tool. Differences in T1DGRS performance between childhood-and adult-onset T1D are linked to enrichment of protective HLA-DQ diplotypes in adult-onset disease, providing genetic insight into disease heterogeneity. The study also demonstrates that European-derived GRS thresholds systematically under-classify genetic risk in Indians and the population-specific threshold is essential. Implications of all the available evidenceThe European-derived T1D GRS can be applied across Indian clinical settings with consistent discriminative performance. However, its utility is influenced by islet cell autoantibody status and the age at onset of disease. Ancestry-aware threshold optimisation substantially improves diagnostic accuracy and is essential for equitable implementation of T1D GRS in Indians. Larger studies are needed to identify population-specific risk variants and further refine genetic tools for clinical diagnosis.

PurposeFuchs endothelial corneal dystrophy (FECD) is a common corneal disease and a leading indication for endothelial keratoplasty (EK). Although CTG18.1 repeat expansion is a major genetic risk factor, the contribution of polygenic background to disease progression remains unclear. We evaluated whether combining CTG18.1 expansion status with a FECD-specific polygenic risk score (PRS) enables genomic prediction of progression to EK. MethodsWe retrospectively analysed 589 individuals with FECD from two European centers, with replication in an independent cohort of 185 individuals. Association of CTG18.1 expansion ([&ge;]50 repeats) and PRS with time to EK were evaluated using Cox models adjusted for sex and ancestry. ResultsExpansion-positive status was associated with earlier EK (HR 2.30; 95% CI 1.62- 3.26; P<.001). Addition of PRS improved prediction (C-index 0.614 vs 0.602; P=.014). Each 1-SD increase in PRS was associated with earlier EK (HR 1.16; 95% CI 1.03-1.30; P=.015), with replication in the validation cohort (HR 1.42; 95% CI 1.15-1.75; P=.001). ConclusionIntegration of monogenic and polygenic risk enables genomic prediction of FECD progression, supporting clinical genomic risk stratification to inform individualized monitoring and timing of intervention.