TY - JOUR
T1 - Use of urine biomarker-derived clusters to predict the risk of chronic kidney disease and all-cause mortality in HIV-infected women
AU - Scherzer, Rebecca
AU - Lin, Haiqun
AU - Abraham, Alison
AU - Thiessen-Philbrook, Heather
AU - Parikh, Chirag R.
AU - Bennett, Michael
AU - Cohen, Mardge H.
AU - Nowicki, Marek
AU - Gustafson, Deborah R.
AU - Sharma, Anjali
AU - Young, Mary
AU - Tien, Phyllis
AU - Jotwani, Vasantha
AU - Shlipak, Michael G.
N1 - Funding Information:
The WIHS Kidney Aging Study is funded by grant 1 R01 AG034853-01A2 (PI, M.G.S.), which was administered by the Northern California Institute for Research and Education, and with resources of the Veterans Affairs Medical Center, San Francisco, California. M.G.S. and R.S. were also supported by the Connie Wofsy Women's HIV Study (U01-AI-034989. The WIHS is funded primarily by the National Institute of Allergy and Infectious Diseases (NIAID), with additional co-funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), the National Cancer Institute (NCI), the National Institute on Drug Abuse (NIDA) and the National Institute on Mental Health (NIMH). Targeted supplemental funding for specific projects is also provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute on Alcohol Abuse and Alcoholism (NIAAA), the National Institute on Deafness and other Communication Disorders (NIDCD) and the NIH Office of Research on Women's Health. WIHS data collection is also supported by UL1-TR000004 (UCSF CTSA) and UL1- TR000454 (Atlanta CTSA).
Publisher Copyright:
© 2016 Published by Oxford University Press on behalf of ERA-EDTA.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - 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 m2 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.
AB - 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 m2 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.
KW - HIV
KW - biomarker
KW - chronic kidney disease
KW - cluster analysis
KW - risk discrimination
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U2 - 10.1093/ndt/gfv426
DO - 10.1093/ndt/gfv426
M3 - Article
C2 - 26754833
AN - SCOPUS:84994840966
SN - 0931-0509
VL - 31
SP - 1478
EP - 1485
JO - Nephrology Dialysis Transplantation
JF - Nephrology Dialysis Transplantation
IS - 9
ER -
