Evidence map›Paper›PMID 26754833›Full record

ArticleNephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association2016

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 and 4 more

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Article in Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Effects of Intensive Blood Pressure Lowering on Kidney Tubule Injury in CKD: A Longitudinal Subgroup Analysis in SPRINT.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2019
    Trial
  2. Review
  3. Article
  4. Biomarkers of kidney tubule injury and dysfunction and risk of incident hypertension in community-living individuals: results from the multi-ethnic study of atherosclerosis.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2023
    Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Rebecca ScherzerUniversity of California and Veterans Affairs Medical Center, San Francisco, CA, USA.
Haiqun LinDepartment of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA.
Alison AbrahamDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Heather Thiessen-PhilbrookDivision of Nephrology, London Health Sciences Centre, Western University, London, ON, Canada.
Chirag R ParikhSection of Nephrology, Department of Medicine, Program of Applied Translational Research, Yale University, New Haven, CT, USA.
Michael BennettDivision of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Mardge H CohenDepartment of Medicine, Stroger Hospital and Rush University, Chicago, IL, USA.
Marek NowickiDepartment of Medicine, University of Southern California, Los Angeles, CA, USA.
Deborah R GustafsonDepartment of Neurology, State University of New York-Downstate Medical Center, Brooklyn, NY, USA.
Anjali SharmaDepartment of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA.
Mary YoungDivision of Infectious Diseases and Travel Medicine, Department of Medicine, Georgetown University Medical Center, Washington, DC, USA.
Phyllis TienUniversity of California and Veterans Affairs Medical Center, San Francisco, CA, USA.
Vasantha JotwaniUniversity of California and Veterans Affairs Medical Center, San Francisco, CA, USA.
Michael G ShlipakUniversity of California and Veterans Affairs Medical Center, San Francisco, CA, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Engaging University of California Stakeholders for Biorespository ResearchUL1TR000004 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRANDIS, JENNIFER RUBIN · 2012 to 2015
$78.0M
Women's Interagency HIV Study (WIHS-V)U01AI035004 · NIAID · MONTEFIORE MEDICAL CENTER (BRONX, NY) · PI ANASTOS, KATHRYN M., SHARMA, ANJALI · 1993 to 2018
$65.7M
WOMEN'S INTERAGENCY HIV STUDY ORGANIZATION, MANAGEMENT AND COMMUNICATIONSU01AI034989 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI AOUIZERAT, BRADLEY E, GREENBLATT, RUTH MARTHA · 1993 to 2018
$65.6M
Women's Interagency HIV Study (WIHS) IIIU01AI031834 · NIAID · SUNY DOWNSTATE MEDICAL CENTER · PI GUSTAFSON, DEBORAH R., MINKOFF, HOWARD · 1998 to 2018
$53.5M
WOMEN'S INTERAGENCY HIV STUDY (WIHS) ORGANIZATION AND MANAGEMENTU01AI034994 · NIAID · GEORGETOWN UNIVERSITY · PI KASSAYE, SEBLE G · 1993 to 2018
$52.0M
Women's Interagency HIV Study (WIHS) IV, Chicago ConsortiumU01AI034993 · NIAID · HEKTOEN INSTITUTE FOR MEDICAL RESEARCH · PI COHEN, MARDGE H, FRENCH, AUDREY · 1994 to 2018
$51.0M
WOMENS INTERAGENCY HIV STUDY (WIHS)U01HD032632 · NICHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MILAM, JOEL E · 1994 to 2012
$42.0M
WIHS DATA MANAGEMENT AND ANALYSIS CENTER(WDMAC)U01AI042590 · NIAID · JOHNS HOPKINS UNIVERSITY · PI GANGE, STEPHEN J, GOLUB, ELIZABETH T · 1998 to 2018
$38.4M
Atlanta Clinical and Translational Science Institute (ACTSI) RenewalUL1TR000454 · NCATS · EMORY UNIVERSITY · PI STEPHENS, DAVID S · 2012 to 2016
$25.8M
Renal Physiology and Phenotyping CoreP30DK079310 · NIDDK · YALE UNIVERSITY · PI SOMLO, STEFAN · 2008 to 2022
$16.8M
Emory WIHS CRS - Longitudinal CohortU01AI103408 · NIAID · EMORY UNIVERSITY · PI OFOTOKUN, IGHOVWERHA, WINGOOD, GINA MARIA · 2013 to 2018
$14.1M
NCATS NIH HHS UL1 TR000004NCATS NIH HHS UL1 TR000454NCATS NIH HHS UL1 TR001863NIAID NIH HHS U01 AI031834NIAID NIH HHS U01 AI034989NIAID NIH HHS U01 AI034993NIAID NIH HHS U01 AI034994NIAID NIH HHS U01 AI035004NIAID NIH HHS U01 AI042590NIAID NIH HHS U01 AI103390NIAID NIH HHS U01 AI103397NIAID NIH HHS U01 AI103401NIAID NIH HHS U01 AI103408NIAMS NIH HHS K23 AR061993NIA NIH HHS R01 AG034853NICHD NIH HHS U01 HD032632NIDDK NIH HHS P30 DK079310
6 · The paper itself

Abstract

backgroundAlthough 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.

methodsWe 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.

resultsOver 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.

conclusionsFor 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.

Indexed as

AcetylglucosaminidaseAdultAlpha-GlobulinsBiomarkersCreatinineCystatin CFatty Acid-Binding ProteinsFemaleHepatitis A Virus Cellular Receptor 1HIV-1HIV InfectionsHumansInterleukin-18Lipocalin-2Middle AgedPredictive Value of TestsAcetylglucosaminidasealpha-1-microglobulinAlpha-GlobulinsBiomarkersCreatinineCystatin CFABP1 protein, humanFatty Acid-Binding ProteinsHepatitis A Virus Cellular Receptor 1Interleukin-18Lipocalin-2biomarkerchronic kidney diseasecluster analysisHIVrisk discrimination

Identifiers

PMID26754833
PMCPMC5009288

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.