Evidence map›Paper›PMID 34312814›Full record

ArticleInternational urology and nephrology2022

Analysis of profibrogenic microRNAs (miRNAs) expression in urine and serum of chronic kidney disease (CKD) stage 1-4 patients and their relationship with proteinuria and kidney function.

Rafał Donderski, Joanna Szczepanek, Natalia Naruszewicz, Renata Naruszewicz, Andrzej Tretyn, Natalia Skoczylas-Makowska, Janusz Tyloch, Grażyna Odrowąż-Sypniewska, Jacek Manitius

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In one paragraph

Article in International urology and nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 17% 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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

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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

9 authors at 1 institution in 1 country.

Rafał DonderskiDepartment of Nephrology, Hypertension and Internal Medicine, University Hospital in Bydgoszcz Nicolaus Copernicus University, Toruń, Poland. rafal.donderski@cm.umk.pl.
Joanna SzczepanekCentre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland.
Natalia NaruszewiczDepartment of Plant Physiology and Biotechnology, Nicolaus Copernicus University, Toruń, Poland.
Renata NaruszewiczHemodialysis Unit, Davita Poland, Nakło, Poland.
Andrzej TretynDepartment of Biotechnology, Institute of General and Molecular Biology, Nicolaus Copernicus University, Toruń, Poland.
Natalia Skoczylas-MakowskaDepartment of Clinical Pathology, University Hospital in Bydgoszcz, Nicolaus Copernicus University, Toruń, Poland.
Janusz TylochDepartment of Urology, University Hospital in Bydgoszcz, Nicolaus Copernicus University, Toruń, Poland.
Grażyna Odrowąż-SypniewskaDepartment of Clinical Laboratory Medicine, University Hospital in Bydgoszcz, Nicolaus Copernicus University, Toruń, Poland.
Jacek ManitiusDepartment of Nephrology, Hypertension and Internal Medicine, University Hospital in Bydgoszcz Nicolaus Copernicus University, Toruń, Poland.
Nicolaus Copernicus University · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeBesides conventional kidney diseases diagnostics, micro RNAs (miRNAs) assessment in urine and serum is considered to be a promising non-invasive method of diagnostics of renal parenchymal diseases and valuable therapeutic target also. The purpose of the study was to investigate the role of several miRNAs as a markers of kidney damage.

methodsAssessment of 45 chronic kidney disease (CKD) patients stage 1-4 and 17 healthy control. Sample of urine and blood was taken from each participant for molecular analysis using Real Time PCR method to identify such micro-RNAs as: hsa-miR-155-5p, hsa-miR-214-3p, hsa-miR-200a-5p, hsa-miR-29a-5p, hsa-miR-21-5p, hsa-miR-93-5p, and hsa-miR-196a-5p. Basic biochemical test was done. Analysis was performed in CKD patients group and subgroup with chronic glomerulonephritis (CGN) confirmed by kidney biopsy. Moreover, analysis was performed in subgroup with different estimated glomerular filtration rate (eGFR) (according to CKD-EPI equation: eGFR < 60 ml/min, eGFR > 60 ml/min) and different daily protein excretion (DPE): (DPE < 3.5 g; DPE > 3.5 g).

resultsIncreased relative expression of hsa-miR-29-5p, hsa-miR-21-5p, and hsa-miR-196a-5p and decreased expression of hsa-miR-155-5p, hsa-miR-214-5p, hsa-miR-200a-5p, and hsa-miR-93-5p was demonstrated in urine of analyzed CKD patients. In subpopulation of chronic glomerulonephritis (CGN) patients, there was higher level of expression in urine of hsa-miR-155-5p, hsa-miR 214-3p, hsa-miR-93-5p, and hsa-miR-196a-5p in CGN with DPE < 3.5 g. CGN patients with eGFR < 60 ml/min showed higher expression level of miRNAs such as hsa-miR-214-3p, hsa-miR-29-5p, hsa-miR-93-5p, and hsa-miR-196-5p in urine. There was increase in hsa-miR 155-5p, hsa-miR-214-3p, and hsa-miR-200a-5p serum expression level in CKD population and reduction of hsa-miR-29a-5p, hsa-miR-21-5p, and hsa-miR-93-5p expression. Increased level of expression of hsa-miR-155-5p; hsa-miR-214-3p, hsa-miR-200a-5p, and hsa-miR-29-5p was found in CGN patients with eGFR > 60 ml/min.

conclusionIncreased relative expression of profibrogenic miRNAs in urine or serum of CKD patients with eGFR > 60 ml/min and DPE < 3.5 g may indicate higher degree of fibrosis at early CKD stages.

Indexed as

MicroRNAsRenal Insufficiency, ChronicHumansKidneyProteinuriaReal-Time Polymerase Chain ReactionMicroRNAsMIRN214 microRNA, humanChronic kidney disease (CKD)Kidney fibrosismicroRNAs expressionTubulointerstitial compartment damage

Identifiers

PMID34312814
PMCPMC8924094
OpenAlexW3184123478

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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.