Pub Date : 2026-01-21DOI: 10.1016/j.kint.2025.11.006
Michael S. Wiesener , Eric Olinger
The current study by Menguy et al. describes JAG1 as a novel candidate gene for the spectrum of autosomal dominant tubulointerstitial kidney disease. Patients have been shown to develop fibrosis and chronic kidney disease, when distinct pathogenic germline variants occur in JAG1. Interestingly, genetic alterations in the Jagged1/Notch2 pathway can cause the variable appearance of Alagille syndrome, which may also occur in the very same families. Haploinsufficiency is the likely pathomechanism, but future work will need to define the underlying mechanisms.
{"title":"JAG1 of all trades, master of CKD? The role of JAG1 in autosomal dominant tubulointerstitial kidney disease","authors":"Michael S. Wiesener , Eric Olinger","doi":"10.1016/j.kint.2025.11.006","DOIUrl":"10.1016/j.kint.2025.11.006","url":null,"abstract":"<div><div>The current study by Menguy <em>et al.</em> describes <em>JAG1</em> as a novel candidate gene for the spectrum of autosomal dominant tubulointerstitial kidney disease. Patients have been shown to develop fibrosis and chronic kidney disease, when distinct pathogenic germline variants occur in <em>JAG1</em>. Interestingly, genetic alterations in the Jagged1/Notch2 pathway can cause the variable appearance of Alagille syndrome, which may also occur in the very same families. Haploinsufficiency is the likely pathomechanism, but future work will need to define the underlying mechanisms.</div></div>","PeriodicalId":17801,"journal":{"name":"Kidney international","volume":"109 2","pages":"Pages 262-265"},"PeriodicalIF":12.6,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.kint.2025.09.013
Somkanya Tungsanga , Ikechi G. Okpechi , Sandrine Damster , Anushka Massand , Jo-Ann Donner , Marcello Tonelli , Adeera Levin , David W. Johnson , Aminu K. Bello
The International Society of Nephrology-Global Kidney Health Atlas (ISN-GKHA) Interactive Map is a web-based, open-access platform designed to visualize global data on kidney health care capacity across world countries and regions. The platform presents indicators from the 2019 and 2023 ISN-GKHA surveys, allowing users to compare data across countries and regions (defined across International Society of Nephrology regions and World Bank Income Groups) over time. Key features include searchable and filterable data, interactive heatmap, barcode benchmarking, trend tracking, and exportable tables and graphics. It supports diverse users—including clinicians, researchers, policymakers, and advocates—by translating complex data into easily comprehensible and actionable items, such as kidney care capacity (organization and structures for kidney care), workforce availability, and policy implementation. It fosters stakeholder engagement, peer support, and collaborative planning to address disparities in kidney care. With continuous updates, user feedback integration, and planned enhancements such as improved data granularity, the Interactive Map is poised to be a powerful tool for driving evidence-informed policy, research, and advocacy to advance equitable kidney care globally.
{"title":"Bridging global kidney care gap through data: introducing the International Society of Nephrology–Global Kidney Health Atlas (ISN-GKHA) Interactive Map","authors":"Somkanya Tungsanga , Ikechi G. Okpechi , Sandrine Damster , Anushka Massand , Jo-Ann Donner , Marcello Tonelli , Adeera Levin , David W. Johnson , Aminu K. Bello","doi":"10.1016/j.kint.2025.09.013","DOIUrl":"10.1016/j.kint.2025.09.013","url":null,"abstract":"<div><div>The International Society of Nephrology-Global Kidney Health Atlas (ISN-GKHA) Interactive Map is a web-based, open-access platform designed to visualize global data on kidney health care capacity across world countries and regions. The platform presents indicators from the 2019 and 2023 ISN-GKHA surveys, allowing users to compare data across countries and regions (defined across International Society of Nephrology regions and World Bank Income Groups) over time. Key features include searchable and filterable data, interactive heatmap, barcode benchmarking, trend tracking, and exportable tables and graphics. It supports diverse users—including clinicians, researchers, policymakers, and advocates—by translating complex data into easily comprehensible and actionable items, such as kidney care capacity (organization and structures for kidney care), workforce availability, and policy implementation. It fosters stakeholder engagement, peer support, and collaborative planning to address disparities in kidney care. With continuous updates, user feedback integration, and planned enhancements such as improved data granularity, the Interactive Map is poised to be a powerful tool for driving evidence-informed policy, research, and advocacy to advance equitable kidney care globally.</div></div>","PeriodicalId":17801,"journal":{"name":"Kidney international","volume":"109 2","pages":"Pages 242-247"},"PeriodicalIF":12.6,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.kint.2025.11.026
Constantin Kupsch,Sarah Tsou,Friederike Martin,Rosalie Wolff von Gudenberg,Stefan Fichtner-Feigl,Reza Abdi,Stefan G Tullius
Kidney transplantation remains the gold standard treatment for end-stage renal disease. The rising prevalence of this condition, at the same time, contributes significantly to the increasing discrepancy between organ supply and demand, impacting the opportunities for treatment while creating challenges for providers and healthcare systems worldwide. Driven by demographic shifts, the most significant growth in transplant candidates is observed in the older population. Here, we detail characteristics of older recipients who may benefit most from transplantation, with an emphasis on data-driven assessment for candidate selection while also considering the impact of donor organ quality, donation type, and wait times. Distinguishing between patients with end-stage renal disease who will benefit from transplantation and those who will derive less or no benefit is essential for providing optimal care to older patients while ensuring responsible stewardship of the limited organ supply. We highlight the advantages of preemptive or expedited transplantation to minimize dialysis exposure and address specific considerations for the peri- and postoperative period. While living donation remains ideal and will shorten the wait-time, kidneys from older or high kidney donor profile index donors show satisfactory outcomes, particularly for older recipients.
{"title":"Eligibility, Timing and Organ Quality: Indications and Outcomes of Kidney Transplantation in Older Patients.","authors":"Constantin Kupsch,Sarah Tsou,Friederike Martin,Rosalie Wolff von Gudenberg,Stefan Fichtner-Feigl,Reza Abdi,Stefan G Tullius","doi":"10.1016/j.kint.2025.11.026","DOIUrl":"https://doi.org/10.1016/j.kint.2025.11.026","url":null,"abstract":"Kidney transplantation remains the gold standard treatment for end-stage renal disease. The rising prevalence of this condition, at the same time, contributes significantly to the increasing discrepancy between organ supply and demand, impacting the opportunities for treatment while creating challenges for providers and healthcare systems worldwide. Driven by demographic shifts, the most significant growth in transplant candidates is observed in the older population. Here, we detail characteristics of older recipients who may benefit most from transplantation, with an emphasis on data-driven assessment for candidate selection while also considering the impact of donor organ quality, donation type, and wait times. Distinguishing between patients with end-stage renal disease who will benefit from transplantation and those who will derive less or no benefit is essential for providing optimal care to older patients while ensuring responsible stewardship of the limited organ supply. We highlight the advantages of preemptive or expedited transplantation to minimize dialysis exposure and address specific considerations for the peri- and postoperative period. While living donation remains ideal and will shorten the wait-time, kidneys from older or high kidney donor profile index donors show satisfactory outcomes, particularly for older recipients.","PeriodicalId":17801,"journal":{"name":"Kidney international","volume":"96 1","pages":""},"PeriodicalIF":19.6,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.kint.2025.12.003
Cristian Pattaro , Belen Ponte
To characterize the genetic basis of chronic kidney disease, genome-wide association studies focused on chronic kidney disease–defining traits that were most readily measurable in large population samples. Today, convolutional neural network analysis of magnetic resonance imaging allows for accurate, large-scale kidney morphology assessment, enhancing research into chronic kidney disease determinants at population scale. Applying convolutional neural networks to UK Biobank data, Monteiro-Martins et al. generated genomic maps of kidney cortex, medulla, and sinus volumes, enabling more detailed research into kidney structure.
{"title":"Genomics of kidney volumes: one size does not fit all","authors":"Cristian Pattaro , Belen Ponte","doi":"10.1016/j.kint.2025.12.003","DOIUrl":"10.1016/j.kint.2025.12.003","url":null,"abstract":"<div><div>To characterize the genetic basis of chronic kidney disease, genome-wide association studies focused on chronic kidney disease–defining traits that were most readily measurable in large population samples. Today, convolutional neural network analysis of magnetic resonance imaging allows for accurate, large-scale kidney morphology assessment, enhancing research into chronic kidney disease determinants at population scale. Applying convolutional neural networks to UK Biobank data, Monteiro-Martins <em>et al.</em> generated genomic maps of kidney cortex, medulla, and sinus volumes, enabling more detailed research into kidney structure.</div></div>","PeriodicalId":17801,"journal":{"name":"Kidney international","volume":"109 2","pages":"Pages 266-268"},"PeriodicalIF":12.6,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.kint.2025.07.033
Jet Milders,Roemer J Janse,Willem Jan W Bos,Fergus J Caskey,Claudia Torino,Antonio Vilasi,Maciej Szymczak,Christiane Drechsler,Christoph Wanner,Maria Pippias,Kitty J Jager,Nicholas C Chesnaye,Marie Evans,Friedo W Dekker,Merel van Diepen,
INTRODUCTIONPrognostic models for mortality in patients receiving dialysis primarily use clinical predictors like age, comorbidities and laboratory markers. Studies in other fields suggest that patient-reported outcomes (PROs), like pain and fatigue, can be predictors of survival. Therefore, we aimed to assess the added value of PROs to predict mortality in incident dialysis patients.METHODSData from NECOSAD (1956 individuals) were used, and analyses were replicated in the EQUAL (415 individuals) and NECOSAD 65+ (862 individuals) studies. A base model for two-year mortality containing clinical predictors was extended using PROs (mental component score, physical component score, general health perception, depressive symptoms, number of symptoms, symptom burden, fatigue and pain). Logistic regression was used, and the added predictive performance of the PROs was evaluated using the area under the curve (AUC), measures of calibration, Brier score, likelihood ratio tests, reclassification tables, net reclassification indices, integrated discrimination improvements, and decision curve analyses. We also examined different combinations of predictors, and each PRO individually.RESULTSWithin two years, mortality rates were 22.9%, 24.3%, and 37.1% in NECOSAD, EQUAL, and NECOSAD 65+, respectively. The base model yielded optimism-corrected AUCs of 0.806, 0.781 and 0.699, which improved to 0.826, 0.878 and 0.746 after adding the PROs. Improvement of the calibration, Brier scores, and comparative measures confirmed their predictive value. The mental and physical component score, and symptom burden had the most consistent strong performance across all cohorts.CONCLUSIONSPROs improved prognostic models for mortality of patients receiving incident dialysis, even when added to an already well-performing model of clinical predictors.
{"title":"A predictor finding study found patient-reported outcomes improve the prediction of mortality of incident dialysis patients.","authors":"Jet Milders,Roemer J Janse,Willem Jan W Bos,Fergus J Caskey,Claudia Torino,Antonio Vilasi,Maciej Szymczak,Christiane Drechsler,Christoph Wanner,Maria Pippias,Kitty J Jager,Nicholas C Chesnaye,Marie Evans,Friedo W Dekker,Merel van Diepen, ","doi":"10.1016/j.kint.2025.07.033","DOIUrl":"https://doi.org/10.1016/j.kint.2025.07.033","url":null,"abstract":"INTRODUCTIONPrognostic models for mortality in patients receiving dialysis primarily use clinical predictors like age, comorbidities and laboratory markers. Studies in other fields suggest that patient-reported outcomes (PROs), like pain and fatigue, can be predictors of survival. Therefore, we aimed to assess the added value of PROs to predict mortality in incident dialysis patients.METHODSData from NECOSAD (1956 individuals) were used, and analyses were replicated in the EQUAL (415 individuals) and NECOSAD 65+ (862 individuals) studies. A base model for two-year mortality containing clinical predictors was extended using PROs (mental component score, physical component score, general health perception, depressive symptoms, number of symptoms, symptom burden, fatigue and pain). Logistic regression was used, and the added predictive performance of the PROs was evaluated using the area under the curve (AUC), measures of calibration, Brier score, likelihood ratio tests, reclassification tables, net reclassification indices, integrated discrimination improvements, and decision curve analyses. We also examined different combinations of predictors, and each PRO individually.RESULTSWithin two years, mortality rates were 22.9%, 24.3%, and 37.1% in NECOSAD, EQUAL, and NECOSAD 65+, respectively. The base model yielded optimism-corrected AUCs of 0.806, 0.781 and 0.699, which improved to 0.826, 0.878 and 0.746 after adding the PROs. Improvement of the calibration, Brier scores, and comparative measures confirmed their predictive value. The mental and physical component score, and symptom burden had the most consistent strong performance across all cohorts.CONCLUSIONSPROs improved prognostic models for mortality of patients receiving incident dialysis, even when added to an already well-performing model of clinical predictors.","PeriodicalId":17801,"journal":{"name":"Kidney international","volume":"39 1","pages":""},"PeriodicalIF":19.6,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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