Pub Date : 2025-11-03DOI: 10.1053/j.ajkd.2025.08.014
Insa M. Schmidt , Eugene P. Rhee
{"title":"Interpreting Metabolomics and Proteomics in Kidney Disease: A Practical Guide","authors":"Insa M. Schmidt , Eugene P. Rhee","doi":"10.1053/j.ajkd.2025.08.014","DOIUrl":"10.1053/j.ajkd.2025.08.014","url":null,"abstract":"","PeriodicalId":7419,"journal":{"name":"American Journal of Kidney Diseases","volume":"87 1","pages":"Pages 129-133"},"PeriodicalIF":8.2,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145450596","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 : 2025-10-31DOI: 10.1053/j.ajkd.2025.10.004
Greco B Malijan,Rebecca J Sardell,Natalie Staplin,Olivier Devuyst,Daniel Chapman,Michael Hill,Nadine Nägele,Stewart Moffat,Dilushi Wijayaratne,Killian Donovan,Doreen Zhu,Dominik Steubl,Sibylle J Hauske,Michaela Petrini,Sarah Y A Ng,Roberto Pontremoli,David Z I Cherney,Katherine R Tuttle,Martin J Landray,Christoph Wanner,Colin Baigent,Michael G Shlipak,Richard Haynes,Parminder K Judge,Joachim H Ix,William G Herrington,
RATIONALE & OBJECTIVESodium-glucose co-transporter 2 (SGLT2) inhibitors substantially slow progression of chronic kidney disease and reduce risk of acute kidney injury, but their effects on kidney physiology are incompletely understood. This study sought to assess the effects of empagliflozin on a comprehensive set of urine tubular and glomerular biomarkers.STUDY DESIGNRandomized controlled trial.SETTING & PARTICIPANTS2,752 participants from EMPA-KIDNEY.EXPOSUREEmpagliflozin 10mg daily versus placebo.OUTCOMEUrine biomarkers indexed to urine creatinine and averaged across on-study timepoints. Urine biomarkers included markers of glomerular disease (albumin, total protein); proximal tubular reabsorption (alpha-1 microglobulin [α1M]); functional tubular reserve (epidermal growth factor [EGF], uromodulin [UMOD]); tubular injury/inflammation (kidney injury molecule-1 [KIM-1], neutrophil gelatinase-associated lipocalin [NGAL]) and; tubular ischemia/stress (dickkopf-3 [DKK-3], monocyte chemoattractant protein-1 [MCP-1]).ANALYTICAL APPROACHMixed model repeated measures.RESULTSAllocation to empagliflozin reduced urine albumin by 19% (95% CI; -24,-14%), total protein by 7% (-11, -2%), and UMOD by 63% (-65, -61%). It increased α1M by 29% (25, 34%), DKK-3 by 22% (16, 29%), and NGAL by 7% (0, 13%). Overall, there were no significant effects on EGF (1%, -1,4%), KIM-1 (2%, -1,6%), and MCP-1 (0%, -4,3%). The magnitude of effects on biomarker levels was generally similar at 2 and 18 months of follow-up. The large reductions in UMOD were evident regardless of baseline diabetes status, primary cause of kidney disease, and level of eGFR and or albuminuria. Exploratory mediation analyses suggest that reductions in albuminuria and UMOD accounted for 32% (15-52%) of the beneficial effect of empagliflozin on chronic eGFR slope.LIMITATIONSThe mediation analyses cannot be used to formally confirm that UMOD reduction is a causal mediator for the kidney benefits of SGLT2 inhibitors.CONCLUSIONSGLT2 inhibition imparts a large and sustained reduction in urine UMOD, and also increases some biomarkers partially reabsorbed by proximal tubules, without consistently affecting markers of tubular injury. These effects deserve further detailed experimental exploration, particularly the effect on thick ascending limb-derived UMOD, which could represent a novel mechanism of kidney protection.
{"title":"Effects of Empagliflozin on Urine Biomarkers in EMPA-KIDNEY.","authors":"Greco B Malijan,Rebecca J Sardell,Natalie Staplin,Olivier Devuyst,Daniel Chapman,Michael Hill,Nadine Nägele,Stewart Moffat,Dilushi Wijayaratne,Killian Donovan,Doreen Zhu,Dominik Steubl,Sibylle J Hauske,Michaela Petrini,Sarah Y A Ng,Roberto Pontremoli,David Z I Cherney,Katherine R Tuttle,Martin J Landray,Christoph Wanner,Colin Baigent,Michael G Shlipak,Richard Haynes,Parminder K Judge,Joachim H Ix,William G Herrington, ","doi":"10.1053/j.ajkd.2025.10.004","DOIUrl":"https://doi.org/10.1053/j.ajkd.2025.10.004","url":null,"abstract":"RATIONALE & OBJECTIVESodium-glucose co-transporter 2 (SGLT2) inhibitors substantially slow progression of chronic kidney disease and reduce risk of acute kidney injury, but their effects on kidney physiology are incompletely understood. This study sought to assess the effects of empagliflozin on a comprehensive set of urine tubular and glomerular biomarkers.STUDY DESIGNRandomized controlled trial.SETTING & PARTICIPANTS2,752 participants from EMPA-KIDNEY.EXPOSUREEmpagliflozin 10mg daily versus placebo.OUTCOMEUrine biomarkers indexed to urine creatinine and averaged across on-study timepoints. Urine biomarkers included markers of glomerular disease (albumin, total protein); proximal tubular reabsorption (alpha-1 microglobulin [α1M]); functional tubular reserve (epidermal growth factor [EGF], uromodulin [UMOD]); tubular injury/inflammation (kidney injury molecule-1 [KIM-1], neutrophil gelatinase-associated lipocalin [NGAL]) and; tubular ischemia/stress (dickkopf-3 [DKK-3], monocyte chemoattractant protein-1 [MCP-1]).ANALYTICAL APPROACHMixed model repeated measures.RESULTSAllocation to empagliflozin reduced urine albumin by 19% (95% CI; -24,-14%), total protein by 7% (-11, -2%), and UMOD by 63% (-65, -61%). It increased α1M by 29% (25, 34%), DKK-3 by 22% (16, 29%), and NGAL by 7% (0, 13%). Overall, there were no significant effects on EGF (1%, -1,4%), KIM-1 (2%, -1,6%), and MCP-1 (0%, -4,3%). The magnitude of effects on biomarker levels was generally similar at 2 and 18 months of follow-up. The large reductions in UMOD were evident regardless of baseline diabetes status, primary cause of kidney disease, and level of eGFR and or albuminuria. Exploratory mediation analyses suggest that reductions in albuminuria and UMOD accounted for 32% (15-52%) of the beneficial effect of empagliflozin on chronic eGFR slope.LIMITATIONSThe mediation analyses cannot be used to formally confirm that UMOD reduction is a causal mediator for the kidney benefits of SGLT2 inhibitors.CONCLUSIONSGLT2 inhibition imparts a large and sustained reduction in urine UMOD, and also increases some biomarkers partially reabsorbed by proximal tubules, without consistently affecting markers of tubular injury. These effects deserve further detailed experimental exploration, particularly the effect on thick ascending limb-derived UMOD, which could represent a novel mechanism of kidney protection.","PeriodicalId":7419,"journal":{"name":"American Journal of Kidney Diseases","volume":"166 1","pages":""},"PeriodicalIF":13.2,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427886","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 : 2025-10-27DOI: 10.1053/j.ajkd.2025.07.010
Leigh-Anne Dale , Jason A. Freed
Hematologic abnormalities including both cytopenias and cytoses are the rule rather than the exception in the first year after kidney transplantation, yet many are benign reflections of immunosuppression, infection prophylaxis, or residual chronic kidney disease. In this Core Curriculum, we distill the evidence on the disorders clinicians encounter most often, cytopenias including neutropenia, lymphopenia, anemia, thrombocytopenia, and thrombotic microangiopathy, as well as cytoses including eosinophilia, lymphocytosis, erythrocytosis, and thrombocytosis. Each is framed by typical timing, dominant mechanisms, key drugs or pathogens, and decision making checkpoints. Five real-world cases illustrate how to differentiate harmless findings from conditions that mandate prompt action, such as drug-induced marrow suppression, cytomegalovirus reactivation, antibiotic-triggered eosinophilia, posttransplant erythrocytosis, and thrombotic microangiopathy. Adopting this problem-oriented approach can reduce unnecessary drug interruptions, target hematology referrals, and preserve both patient safety and allograft longevity.
{"title":"Hematologic Considerations in Kidney Transplantation: Core Curriculum 2025","authors":"Leigh-Anne Dale , Jason A. Freed","doi":"10.1053/j.ajkd.2025.07.010","DOIUrl":"10.1053/j.ajkd.2025.07.010","url":null,"abstract":"<div><div>Hematologic abnormalities including both cytopenias and cytoses are the rule rather than the exception in the first year after kidney transplantation, yet many are benign reflections of immunosuppression, infection prophylaxis, or residual chronic kidney disease. In this Core Curriculum, we distill the evidence on the disorders clinicians encounter most often, cytopenias including neutropenia, lymphopenia, anemia, thrombocytopenia, and thrombotic microangiopathy, as well as cytoses including eosinophilia, lymphocytosis, erythrocytosis, and thrombocytosis. Each is framed by typical timing, dominant mechanisms, key drugs or pathogens, and decision making checkpoints. Five real-world cases illustrate how to differentiate harmless findings from conditions that mandate prompt action, such as drug-induced marrow suppression, cytomegalovirus reactivation, antibiotic-triggered eosinophilia, posttransplant erythrocytosis, and thrombotic microangiopathy. Adopting this problem-oriented approach can reduce unnecessary drug interruptions, target hematology referrals, and preserve both patient safety and allograft longevity.</div></div>","PeriodicalId":7419,"journal":{"name":"American Journal of Kidney Diseases","volume":"86 6","pages":"Pages 828-842"},"PeriodicalIF":8.2,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145374040","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 : 2025-10-25DOI: 10.1053/j.ajkd.2025.10.002
Nivetha Subramanian, Shuchi Anand
{"title":"Vaccines as a Core Conversation in Nephrology","authors":"Nivetha Subramanian, Shuchi Anand","doi":"10.1053/j.ajkd.2025.10.002","DOIUrl":"10.1053/j.ajkd.2025.10.002","url":null,"abstract":"","PeriodicalId":7419,"journal":{"name":"American Journal of Kidney Diseases","volume":"86 6","pages":"Pages 724-726"},"PeriodicalIF":8.2,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145370631","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 : 2025-10-22DOI: 10.1053/j.ajkd.2025.07.016
Vishnu S. Potluri , Jeremy Rubin , Jarcy Zee , Sarah J. Ratcliffe , Michael O. Harhay , Peter L. Abt , Emily A. Vail , Chirag R. Parikh , Roy D. Bloom , Alessandro Gasparini , Michael Crowther , David S. Goldberg , Peter P. Reese
Rationale & Objective
The Kidney Donor Risk Index (KDRI) is widely used to rank the quality of deceased-donor kidneys and is integrated into the U.S. kidney allograft allocation system. However, the KDRI has modest predictive accuracy for allograft survival, and recent revisions to the KDRI, which removed donor race and hepatitis C virus status, also revealed model calibration problems. This study aimed to evaluate novel approaches for predicting posttransplant allograft survival.
Study Design
Retrospective cohort study using Organ Procurement and Transplantation Network data from May 1, 2007, through December 31, 2021.
Predictors
(1) Donor demographic and clinical variables (established predictors); (2) longitudinal laboratory data from the donor’s terminal hospitalization, such as serum creatinine (new predictors); and (3) recipient clinical variables (new predictors).
Setting & Participants
75,867 adult kidney recipients at U.S. centers.
Outcomes
The primary outcome was time to all-cause allograft failure over 3 years. A secondary outcome was delayed graft function, defined as dialysis in the first week after the transplant.
Analytical Approach
We implemented and compared machine-learning statistical models versus traditional modeling approaches (ie, proportional hazards for the primary outcome and logistic regression of the secondary outcome) that incorporated various combinations of predictors. The performance metrics used to assess discrimination were the integrated (time-dependent) area under the curve (AUC) for allograft survival and the AUC for delayed graft function. To assess calibration, we calculated Brier scores and visually compared the predicted outcomes with the observed ones. Predictive performance was assessed in a 20% testing data split.
Results
Neither machine-learning models nor the addition of longitudinal laboratory data from the donor hospitalization to traditional models improved discrimination. For the primary outcome, the final model (named the Kidney Allograft Survival Index) used a proportional hazards modeling approach. Adding recipient variables improved model discrimination (integrated AUC, 0.68) and achieved excellent calibration for the overall cohort and subgroups. The final model for delayed allograft function used logistic regression, included recipient variables, and had an AUC of 0.75 with acceptable calibration.
Limitations
No external validation.
Conclusions
Improving the discrimination and calibration of kidney allograft survival prediction models is achievable by including recipient characteristics. These enhanced models have potential to improve the system of kidney allocation.
{"title":"Assessing Deceased-Donor Kidneys Through Posttransplant Survival Prediction Algorithms","authors":"Vishnu S. Potluri , Jeremy Rubin , Jarcy Zee , Sarah J. Ratcliffe , Michael O. Harhay , Peter L. Abt , Emily A. Vail , Chirag R. Parikh , Roy D. Bloom , Alessandro Gasparini , Michael Crowther , David S. Goldberg , Peter P. Reese","doi":"10.1053/j.ajkd.2025.07.016","DOIUrl":"10.1053/j.ajkd.2025.07.016","url":null,"abstract":"<div><h3>Rationale & Objective</h3><div>The Kidney Donor Risk Index (KDRI) is widely used to rank the quality of deceased-donor kidneys and is integrated into the U.S. kidney allograft allocation system. However, the KDRI has modest predictive accuracy for allograft survival, and recent revisions to the KDRI, which removed donor race and hepatitis C virus status, also revealed model calibration problems. This study aimed to evaluate novel approaches for predicting posttransplant allograft survival.</div></div><div><h3>Study Design</h3><div>Retrospective cohort study using Organ Procurement and Transplantation Network data from May 1, 2007, through December 31, 2021.</div></div><div><h3>Predictors</h3><div>(1) Donor demographic and clinical variables (established predictors); (2) longitudinal laboratory data from the donor’s terminal hospitalization, such as serum creatinine (new predictors); and (3) recipient clinical variables (new predictors).</div></div><div><h3>Setting & Participants</h3><div>75,867 adult kidney recipients at U.S. centers.</div></div><div><h3>Outcomes</h3><div>The primary outcome was time to all-cause allograft failure over 3 years. A secondary outcome was delayed graft function, defined as dialysis in the first week after the transplant.</div></div><div><h3>Analytical Approach</h3><div>We implemented and compared machine-learning statistical models versus traditional modeling approaches (ie, proportional hazards for the primary outcome and logistic regression of the secondary outcome) that incorporated various combinations of predictors. The performance metrics used to assess discrimination were the integrated (time-dependent) area under the curve (AUC) for allograft survival and the AUC for delayed graft function. To assess calibration, we calculated Brier scores and visually compared the predicted outcomes with the observed ones. Predictive performance was assessed in a 20% testing data split.</div></div><div><h3>Results</h3><div>Neither machine-learning models nor the addition of longitudinal laboratory data from the donor hospitalization to traditional models improved discrimination. For the primary outcome, the final model (named the Kidney Allograft Survival Index) used a proportional hazards modeling approach. Adding recipient variables improved model discrimination (integrated AUC, 0.68) and achieved excellent calibration for the overall cohort and subgroups. The final model for delayed allograft function used logistic regression, included recipient variables, and had an AUC of 0.75 with acceptable calibration.</div></div><div><h3>Limitations</h3><div>No external validation.</div></div><div><h3>Conclusions</h3><div>Improving the discrimination and calibration of kidney allograft survival prediction models is achievable by including recipient characteristics. These enhanced models have potential to improve the system of kidney allocation.</div></div>","PeriodicalId":7419,"journal":{"name":"American Journal of Kidney Diseases","volume":"87 1","pages":"Pages 18-30"},"PeriodicalIF":8.2,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145357748","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 : 2025-10-20DOI: 10.1053/j.ajkd.2025.08.004
Katherine A. Barraclough , Jane Waugh
{"title":"Reducing the Environmental Footprint of Hemodialysis: The Case for Centralized Acid Delivery","authors":"Katherine A. Barraclough , Jane Waugh","doi":"10.1053/j.ajkd.2025.08.004","DOIUrl":"10.1053/j.ajkd.2025.08.004","url":null,"abstract":"","PeriodicalId":7419,"journal":{"name":"American Journal of Kidney Diseases","volume":"86 5","pages":"Pages 585-587"},"PeriodicalIF":8.2,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320639","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 : 2025-10-20DOI: 10.1053/j.ajkd.2025.08.005
Alvin H. Moss , Christine M. Corbett , Dale E. Lupu
{"title":"Supportive Care for Patients Receiving Maintenance Hemodialysis: Why Nephrologists Should Care and What They Can Do","authors":"Alvin H. Moss , Christine M. Corbett , Dale E. Lupu","doi":"10.1053/j.ajkd.2025.08.005","DOIUrl":"10.1053/j.ajkd.2025.08.005","url":null,"abstract":"","PeriodicalId":7419,"journal":{"name":"American Journal of Kidney Diseases","volume":"86 5","pages":"Pages 579-581"},"PeriodicalIF":8.2,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320635","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 : 2025-10-20DOI: 10.1053/j.ajkd.2025.09.002
Benjamin Catanese , Semra Ozdemir , Rasheeda K. Hall
{"title":"Choices Matter: Expanding the Quality of Shared Decision-Making for Older Adults With Advanced CKD","authors":"Benjamin Catanese , Semra Ozdemir , Rasheeda K. Hall","doi":"10.1053/j.ajkd.2025.09.002","DOIUrl":"10.1053/j.ajkd.2025.09.002","url":null,"abstract":"","PeriodicalId":7419,"journal":{"name":"American Journal of Kidney Diseases","volume":"86 5","pages":"Pages 588-590"},"PeriodicalIF":8.2,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320640","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 : 2025-10-20DOI: 10.1053/j.ajkd.2025.09.003
Pascale Khairallah , Elizabeth C. Lorenz , Puneet Sood
{"title":"Rethinking Transplant Care Through a Sex- and Gender-Based Lens","authors":"Pascale Khairallah , Elizabeth C. Lorenz , Puneet Sood","doi":"10.1053/j.ajkd.2025.09.003","DOIUrl":"10.1053/j.ajkd.2025.09.003","url":null,"abstract":"","PeriodicalId":7419,"journal":{"name":"American Journal of Kidney Diseases","volume":"86 5","pages":"Pages 582-584"},"PeriodicalIF":8.2,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320638","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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