Use of urine biomarker-derived clusters to predict the risk of chronic kidney disease and all-cause mortality in HIV-infected women

作者：Scherzer Rebecca; Lin Haiqun; Abraham Alison; Thiessen Philbrook Heather; Parikh Chirag R; Bennett Michael; Cohen Mardge H; Nowicki Marek; Gustafson Deborah R; Sharma Anjali; Young Mary; Tien Phyllis; Jotwani Vasantha; Shlipak Michael G^*

来源：Nephrology Dialysis Transplantation, 2016, 31(9): 1478-1485.

DOI：10.1093/ndt/gfv426

• biomarker
• chronic kidney disease
• cluster analysis
• HIV
• risk discrimination

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.
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-beta-d-glucosaminidase (NAG), kidney injury molecule-1 (KIM-1), alpha-1 microglobulin (alpha 1m), interleukin 18, neutrophil gelatinase-associated lipocalin, albumin-to-creatinine ratio, liver fatty acid-binding protein and alpha-1-acid-glycoprotein. A group-based cluster method classified participants into three distinct clusters using the three most distinguishing biomarkers (NAG, KIM-1 and alpha 1m), 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.
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.
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.

• 出版日期2016-9