Evidence map›Paper›PMID 28261998›Full record

ArticleProteomics. Clinical applications2017

Use of a Targeted Urine Proteome Assay (TUPA) to identify protein biomarkers of delayed recovery after kidney transplant.

Kenneth R Williams, Christopher M Colangelo, Lin Hou, Lisa Chung, Justin M Belcher, Thomas Abbott, Isaac E Hall, Hongyu Zhao, Lloyd G Cantley, Chirag R Parikh

Abstract read
In one paragraph

Article in Proteomics. Clinical applications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.2field-weighted citation impact, top 49% of its field
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

4 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 3 countries.

Kenneth R WilliamsW.M. Keck Foundation Biotechnology Laboratory, Yale University School of Medicine, New Haven, USA.
Christopher M ColangeloPrimary Ion, Old Lyme, USA.
Lin HouCenter for Statistical Science, Tsinghua University, Beijing, China.
Lisa ChungW.M. Keck Foundation Biotechnology Laboratory, Yale University School of Medicine, New Haven, USA.
Justin M BelcherInternal Medicine, Yale University School of Medicine, New Haven, USA.
Thomas AbbottW.M. Keck Foundation Biotechnology Laboratory, Yale University School of Medicine, New Haven, USA.
Isaac E HallDivision of Nephrology, Hypertension & Renal Transplantation, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, USA.
Hongyu ZhaoEpidemiology & Public Health, Yale University School of Medicine, New Haven, USA.
Lloyd G CantleyInternal Medicine, Yale University School of Medicine, New Haven, USA.
Chirag R ParikhInternal Medicine, Yale University School of Medicine, New Haven, USA.
Yale University · USW. M. Keck Foundation · USPrimary Health Care · QATsinghua University · CNUniversity of Utah · US

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
YALE UNIVERSITY CLINICAL AND TRANSLATIONAL SCIENCE AWARD PROGRAMUL1RR024139 · NCRR · YALE UNIVERSITY · PI SHERWIN, ROBERT S · 2006 to 2011
$56.8M
Renal Physiology and Phenotyping CoreP30DK079310 · NIDDK · YALE UNIVERSITY · PI ARONSON, PETER S. · 2008 to 2022
$16.8M
PATHOPHYSIOLOGY OF ACUTE AND CHRONIC RENAL DISEASET32DK007276 · NIDDK · YALE UNIVERSITY · PI ARONSON, PETER S., CANTLEY, LLOYD G · 1986 to 2022
$9.3M
Center for Polycystic Kidney Disease Research at YaleP30DK090744 · NIDDK · YALE UNIVERSITY · PI CAPLAN, MICHAEL J. · 2010 to 2014
$5.6M
Mentoring Program for Translational and Patient Oriented Research in AKIK24DK090203 · NIDDK · YALE UNIVERSITY · PI PARIKH, CHIRAG R · 2012 to 2016
$896k
5500 QTrap Mass Spectrometer for Yale University, Keck LaboratoryS10RR026707 · NCRR · YALE UNIVERSITY · PI COLANGELO, CHRISTOPHER MICHAEL · 2010 to 2010
$492k
NCATS NIH HHS UL1 TR001863NCRR NIH HHS S10 RR026707NCRR NIH HHS UL1 RR024139NIDDK NIH HHS K24 DK090203NIDDK NIH HHS P30 DK079310NIDDK NIH HHS P30 DK090744NIDDK NIH HHS T32 DK007276
6 · The paper itself

Abstract

purposeDevelopment of delayed graft function (DGF) following kidney transplant is associated with poor outcomes. An ability to rapidly identify patients with DGF versus those with immediate graft function (IGF) may facilitate the treatment of DGF and the research needed to improve prognosis. The purpose of this study was to use a Targeted Urine Proteome Assay to identify protein biomarkers of delayed recovery from kidney transplant. EXPERIMENTAL

designPotential biomarkers were identified using the Targeted Urine Proteome (MRM) Assay to interrogate the relative DGF/IGF levels of expression of 167 proteins in urine taken 12-18 h after kidney implantation from 21 DGF, 15 SGF (slow graft function), and 16 IGF patients. An iterative Random Forest analysis approach evaluated the relative importance of each biomarker, which was then used to identify an optimum biomarker panel that provided the maximum sensitivity and specificity with the least number of biomarkers. CONCLUSIONS AND CLINICAL RELEVANCE: Four proteins were identified that together distinguished DGF with a sensitivity of 77.4%, specificity of 82.6%, and AUC of 0.891. This panel represents an important step toward identifying DGF at an early stage so that more effective treatments can be developed to improve long-term graft outcomes.

Indexed as

ProteomicsUrinalysisBiomarkersDelayed Graft FunctionGene Expression RegulationHumansKidney TransplantationBiomarkersDisease biomarkersKidney transplantTargeted proteomicsUrine

Identifiers

PMID28261998
PMCPMC5549272
OpenAlexW2592665476

What OpenQuestion holds

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LicenceTDM
Read underepoch 390

Registered trials

None linked

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.