Evidence map›Paper›PMID 26239688›Full record

ArticleJournal of clinical medicine2015

Urinary MicroRNA Profiling Predicts the Development of Microalbuminuria in Patients with Type 1 Diabetes.

Christos Argyropoulos, Kai Wang, Jose Bernardo, Demetrius Ellis, Trevor Orchard, David Galas, John P Johnson

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 1 pooled it
8.0field-weighted citation impact, top 2% 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

53 citing papers in PubMed, 1 synthesis or guideline pooled it, 91 citations in OpenAlex.

  1. Pooled it
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  11. MicroRNAs in kidney injury and disease.Nature reviews. Nephrology · 2022
    Review
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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

7 authors at 5 institutions in 1 country.

Christos ArgyropoulosDepartment of Medicine, Division of Nephrology, University of New Mexico, 901 University Blvd SE, Albuquerque, NM 87106, USA. cargyropoulos@salud.unm.edu.
Kai WangInstitute for Systems Biology, 401 Terry Ave. North, Seattle, WA 98109, USA. kai.wang@systemsbiology.org.
Jose BernardoDepartment of Medicine, Renal and Electrolyte Division, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261, USA. jfb15@pitt.edu.
Demetrius EllisChildren's Hospital of Pittsburgh, One Children's Hospital Drive 4401 Penn Avenue, Pittsburgh, PA 15224, USA. ellisd@upmc.edu.
Trevor OrchardDepartment of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 DeSoto Street, Pittsburgh, PA 15261, USA. orchardt@edc.pitt.edu.
David GalasPacific Northwest Diabetes Research Institute, 720 Broadway, Seattle, WA 98103, USA. djgalas@gmail.com.
John P JohnsonDepartment of Medicine, Renal and Electrolyte Division, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261, USA. johnsonj@pitt.edu.
University of Pittsburgh · USChildren's Hospital of Pittsburgh · USInstitute for Systems Biology · USPacific Northwest Diabetes Research Institute · USUniversity of New Mexico · US

Funding

Epidemiology of Diabetes Complications (EDC) Phase II: renewalR37DK034818 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ORCHARD, TREVOR J. · 2009 to 2018
$6.5M
NIDDK NIH HHS R37 DK034818
6 · The paper itself

Abstract

Microalbuminuria provides the earliest clinical marker of diabetic nephropathy among patients with Type 1 diabetes, yet it lacks sensitivity and specificity for early histological manifestations of disease. In recent years microRNAs have emerged as potential mediators in the pathogenesis of diabetes complications, suggesting a possible role in the diagnosis of early stage disease. We used quantiative polymerase chain reaction (qPCR) to evaluate the expression profile of 723 unique microRNAs in the normoalbuminuric urine of patients who did not develop nephropathy (n = 10) relative to patients who subsequently developed microalbuminuria (n = 17). Eighteen microRNAs were strongly associated with the subsequent development of microalbuminuria, while 15 microRNAs exhibited gender-related differences in expression. The predicted targets of these microRNAs map to biological pathways known to be involved in the pathogenesis and progression of diabetic renal disease. A microRNA signature (miR-105-3p, miR-1972, miR-28-3p, miR-30b-3p, miR-363-3p, miR-424-5p, miR-486-5p, miR-495, miR-548o-3p and for women miR-192-5p, miR-720) achieved high internal validity (cross-validated misclassification rate of 11.1%) for the future development of microalbuminuria in this dataset. Weighting microRNA measurements by their number of kidney-relevant targets improved the prognostic performance of the miRNA signature (cross-validated misclassification rate of 7.4%). Future studies are needed to corroborate these early observations in larger cohorts.

Indexed as

gene ontologymicroalbuminuriamicroRNAsprognostic modeltarget analysisType 1 diabetes

Identifiers

PMID26239688
PMCPMC4519802
OpenAlexW1823585579

What OpenQuestion holds

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Registered trials

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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.