Integrated Analysis of Blood and Urine Biomarkers to Identify Acute Kidney Injury Subphenotypes and Associations With Long-term Outcomes.

Autor:	Bhatraju PK; Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington; Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington. Electronic address: bhatraju@uw.edu., Prince DK; Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington., Mansour S; Division of Nephrology, Yale University, New Haven, Connecticut., Ikizler TA; Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee., Siew ED; Division of Nephrology and Hypertension, Vanderbilt University Medical Center, Nashville, Tennessee., Chinchilli VM; Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, Pennsylvania., Garg AX; Division of Nephrology, Department of Medicine, Western University, London, Ontario, Canada., Go AS; Division of Nephrology, Department of Medicine, University of California, San Francisco, California; Department of Epidemiology and Biostatistics, University of California, San Francisco, California; Division of Research, Kaiser Permanente Northern California, Oakland, California., Kaufman JS; Division of Nephrology, School of Medicine, New York University, New York, New York; Division of Nephrology, VA New York Harbor Healthcare System, New York, New York., Kimmel PL; National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland., Coca SG; Section of Nephrology, Department of Internal Medicine, Mount Sinai School of Medicine, New York, New York., Parikh CR; Division of Nephrology, School of Medicine, Johns Hopkins University, Baltimore, Maryland., Wurfel MM; Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington; Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington., Himmelfarb J; Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington.
Jazyk:	angličtina
Zdroj:	American journal of kidney diseases : the official journal of the National Kidney Foundation [Am J Kidney Dis] 2023 Sep; Vol. 82 (3), pp. 311-321.e1. Date of Electronic Publication: 2023 Apr 10.
DOI:	10.1053/j.ajkd.2023.01.449
Abstrakt:	Rationale & Objective: Acute kidney injury (AKI) is a heterogeneous clinical syndrome with varying causes, pathophysiology, and outcomes. We incorporated plasma and urine biomarker measurements to identify AKI subgroups (subphenotypes) more tightly linked to underlying pathophysiology and long-term clinical outcomes. Study Design: Multicenter cohort study. Setting & Participants: 769 hospitalized adults with AKI matched with 769 without AKI, enrolled from December 2009 to February 2015 in the ASSESS-AKI Study. Predictors: 29 clinical, plasma, and urinary biomarker parameters used to identify AKI subphenotypes. Outcome: Composite of major adverse kidney events (MAKE) with a median follow-up period of 4.7 years. Analytical Approach: Latent class analysis (LCA) and k-means clustering were applied to 29 clinical, plasma, and urinary biomarker parameters. Associations between AKI subphenotypes and MAKE were analyzed using Kaplan-Meier curves and Cox proportional hazard models. Results: Among 769 AKI patients both LCA and k-means identified 2 distinct AKI subphenotypes (classes 1 and 2). The long-term risk for MAKE was higher with class 2 (adjusted HR, 1.41 [95% CI, 1.08-1.84]; P=0.01) compared with class 1, adjusting for demographics, hospital level factors, and KDIGO stage of AKI. The higher risk of MAKE among class 2 was explained by a higher risk of long-term chronic kidney disease progression and dialysis. The top variables that were different between classes 1 and 2 included plasma and urinary biomarkers of inflammation and epithelial cell injury; serum creatinine ranked 20th out of the 29 variables for differentiating classes. Limitations: A replication cohort with simultaneously collected blood and urine sampling in hospitalized adults with AKI and long-term outcomes was unavailable. Conclusions: We identify 2 molecularly distinct AKI subphenotypes with differing risk of long-term outcomes, independent of the current criteria to risk stratify AKI. Future identification of AKI subphenotypes may facilitate linking therapies to underlying pathophysiology to prevent long-term sequalae after AKI. Plain-Language Summary: Acute kidney injury (AKI) occurs commonly in hospitalized patients and is associated with high morbidity and mortality. The AKI definition lumps many different types of AKI together, but subgroups of AKI may be more tightly linked to the underlying biology and clinical outcomes. We used 29 different clinical, blood, and urinary biomarkers and applied 2 different statistical algorithms to identify AKI subtypes and their association with long-term outcomes. Both clustering algorithms identified 2 AKI subtypes with different risk of chronic kidney disease, independent of the serum creatinine concentrations (the current gold standard to determine severity of AKI). Identification of AKI subtypes may facilitate linking therapies to underlying biology to prevent long-term consequences after AKI. (Copyright © 2023 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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