Rebecca Scherzer1, Haiqun Lin2, Alison Abraham3, Heather Thiessen-Philbrook4, Chirag R Parikh5, Michael Bennett6, Mardge H Cohen7, Marek Nowicki8, Deborah R Gustafson9, Anjali Sharma10, Mary Young11, Phyllis Tien1, Vasantha Jotwani1, Michael G Shlipak1. 1. University of California and Veterans Affairs Medical Center, San Francisco, CA, USA. 2. Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA. 3. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 4. Division of Nephrology, London Health Sciences Centre, Western University, London, ON, Canada. 5. Section of Nephrology, Department of Medicine, Program of Applied Translational Research, Yale University, New Haven, CT, USA. 6. Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. 7. Department of Medicine, Stroger Hospital and Rush University, Chicago, IL, USA. 8. Department of Medicine, University of Southern California, Los Angeles, CA, USA. 9. Department of Neurology, State University of New York-Downstate Medical Center, Brooklyn, NY, USA. 10. Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA. 11. Division of Infectious Diseases and Travel Medicine, Department of Medicine, Georgetown University Medical Center, Washington, DC, USA.
Abstract
BACKGROUND: Although individual urine biomarkers are associated with chronic kidney disease (CKD) incidence and all-cause mortality in the setting of HIV infection, their combined utility for prediction remains unknown. METHODS: We measured eight urine biomarkers shown previously to be associated with incident CKD and mortality risk among 902 HIV-infected women in the Women's Interagency HIV Study: N-acetyl-β-d-glucosaminidase (NAG), kidney injury molecule-1 (KIM-1), alpha-1 microglobulin (α1m), interleukin 18, neutrophil gelatinase-associated lipocalin, albumin-to-creatinine ratio, liver fatty acid-binding protein and α-1-acid-glycoprotein. A group-based cluster method classified participants into three distinct clusters using the three most distinguishing biomarkers (NAG, KIM-1 and α1m), independent of the study outcomes. We then evaluated associations of each cluster with incident CKD (estimated glomerular filtration rate <60 mL/min/1.73 m(2) by cystatin C) and all-cause mortality, adjusting for traditional and HIV-related risk factors. RESULTS: Over 8 years of follow-up, 177 CKD events and 128 deaths occurred. The first set of clusters partitioned women into three groups, containing 301 (Cluster 1), 470 (Cluster 2) and 131 (Cluster 3) participants. The rate of CKD incidence was 13, 21 and 50% across the three clusters; mortality rates were 7.3, 13 and 34%. After multivariable adjustment, Cluster 3 remained associated with a nearly 3-fold increased risk of both CKD and mortality, relative to Cluster 1 (both P < 0.001). The addition of the multi-biomarker cluster to the multivariable model improved discrimination for CKD (c-statistic = 0.72-0.76, P = 0.0029), but only modestly for mortality (c = 0.79-0.80, P = 0.099). Clusters derived with all eight markers were no better for discrimination than the three-biomarker clusters. CONCLUSIONS: For predicting incident CKD in HIV-infected women, clusters developed from three urine-based kidney disease biomarkers were as effective as an eight-marker panel in improving risk discrimination. Published by Oxford University Press on behalf of ERA-EDTA 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.
BACKGROUND: Although individual urine biomarkers are associated with chronic kidney disease (CKD) incidence and all-cause mortality in the setting of HIV infection, their combined utility for prediction remains unknown. METHODS: We measured eight urine biomarkers shown previously to be associated with incident CKD and mortality risk among 902 HIV-infectedwomen in the Women's Interagency HIV Study: N-acetyl-β-d-glucosaminidase (NAG), kidney injury molecule-1 (KIM-1), alpha-1 microglobulin (α1m), interleukin 18, neutrophil gelatinase-associated lipocalin, albumin-to-creatinine ratio, liver fatty acid-binding protein and α-1-acid-glycoprotein. A group-based cluster method classified participants into three distinct clusters using the three most distinguishing biomarkers (NAG, KIM-1 and α1m), independent of the study outcomes. We then evaluated associations of each cluster with incident CKD (estimated glomerular filtration rate <60 mL/min/1.73 m(2) by cystatin C) and all-cause mortality, adjusting for traditional and HIV-related risk factors. RESULTS: Over 8 years of follow-up, 177 CKD events and 128 deaths occurred. The first set of clusters partitioned women into three groups, containing 301 (Cluster 1), 470 (Cluster 2) and 131 (Cluster 3) participants. The rate of CKD incidence was 13, 21 and 50% across the three clusters; mortality rates were 7.3, 13 and 34%. After multivariable adjustment, Cluster 3 remained associated with a nearly 3-fold increased risk of both CKD and mortality, relative to Cluster 1 (both P < 0.001). The addition of the multi-biomarker cluster to the multivariable model improved discrimination for CKD (c-statistic = 0.72-0.76, P = 0.0029), but only modestly for mortality (c = 0.79-0.80, P = 0.099). Clusters derived with all eight markers were no better for discrimination than the three-biomarker clusters. CONCLUSIONS: For predicting incident CKD in HIV-infectedwomen, clusters developed from three urine-based kidney disease biomarkers were as effective as an eight-marker panel in improving risk discrimination. Published by Oxford University Press on behalf of ERA-EDTA 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.
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