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

Rebecca Scherzer, Haiqun Lin, Alison Abraham, Heather Thiessen-Philbrook, Chirag R. Parikh, Michael Bennett, Mardge H. Cohen, Marek Nowicki, Deborah R. Gustafson, Anjali Sharma, Mary Young, Phyllis Tien, Vasantha Jotwani, Michael G. Shlipak

Research output: Contribution to journalArticle

8 Citations (Scopus)

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 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.

Original languageEnglish (US)
Pages (from-to)1478-1485
Number of pages8
JournalNephrology Dialysis Transplantation
Volume31
Issue number9
DOIs
StatePublished - Sep 1 2016

Fingerprint

Chronic Renal Insufficiency
Biomarkers
HIV
Urine
Mortality
Hexosaminidases
Kidney
Cystatin C
Fatty Acid-Binding Proteins
Interleukin-18
Incidence
Wounds and Injuries
Kidney Diseases
Glomerular Filtration Rate
HIV Infections
Albumins
Creatinine
Glycoproteins
Outcome Assessment (Health Care)
Acids

Keywords

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

ASJC Scopus subject areas

  • Nephrology
  • Transplantation

Cite this

Scherzer, R., Lin, H., Abraham, A., Thiessen-Philbrook, H., Parikh, C. R., Bennett, M., ... Shlipak, M. G. (2016). Use of urine biomarker-derived clusters to predict the risk of chronic kidney disease and all-cause mortality in HIV-infected women. Nephrology Dialysis Transplantation, 31(9), 1478-1485. https://doi.org/10.1093/ndt/gfv426

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

In: Nephrology Dialysis Transplantation, Vol. 31, No. 9, 01.09.2016, p. 1478-1485.

Research output: Contribution to journalArticle

Scherzer, R, Lin, H, Abraham, A, Thiessen-Philbrook, H, Parikh, CR, Bennett, M, Cohen, MH, Nowicki, M, Gustafson, DR, Sharma, A, Young, M, Tien, P, Jotwani, V & Shlipak, MG 2016, 'Use of urine biomarker-derived clusters to predict the risk of chronic kidney disease and all-cause mortality in HIV-infected women', Nephrology Dialysis Transplantation, vol. 31, no. 9, pp. 1478-1485. https://doi.org/10.1093/ndt/gfv426
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. / Use of urine biomarker-derived clusters to predict the risk of chronic kidney disease and all-cause mortality in HIV-infected women. In: Nephrology Dialysis Transplantation. 2016 ; Vol. 31, No. 9. pp. 1478-1485.
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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.

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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.

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KW - chronic kidney disease

KW - cluster analysis

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