Evidence map›Paper›PMID 31291349›Full record

ArticlePloS one2019

Use of gas chromatography mass spectrometry to elucidate metabolites predicting the phenotypes of IgA nephropathy in hyper IgA mice.

Makoto Kurano, Yutaka Yatomi

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 37% 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

2 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
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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

2 authors at 1 institution in 1 country.

Makoto KuranoDepartment of Clinical Laboratory Medicine, The University of Tokyo, Tokyo, Japan.ORCID 0000-0002-2596-1145
Yutaka YatomiDepartment of Clinical Laboratory Medicine, The University of Tokyo, Tokyo, Japan.
The University of Tokyo · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

IgA nephropathy, a common chronic kidney disease, has various possible outcomes. Therefore, the identification of novel prognostic biomarkers is needed. To this purpose, we used gas chromatography mass spectrometry to search for metabolites capable of predicting the phenotypes of IgA nephropathy in hyper IgA (HIGA) mice, an established model mice for IgA nephropathy. We measured the plasma metabolite levels in 12- and 22-week-old mice, prior to the manifestation of IgA nephropathy phenotypes, and statistically investigated the associations between these metabolites and the phenotypes of IgA nephropathy, such as the urine protein levels and histological phenotypes of the kidney at 32 weeks. We observed that in plasma samples collected from 12- and 22-week-old HIGA mice, the urinary protein levels were significantly associated with 8 and 10 metabolites, the glomerular cellular component levels were significantly associated with 8 and 7 metabolites, and the mesangial substrate levels were significantly associated with 8 and 8 metabolites, respectively. Among the candidate metabolites associated with the phenotypes of IgA nephropathy, coniferyl alcohol levels were significantly higher in HIGA mice at all of the 12, 22, and 32 weeks of age. Since this study was an observational study, we could not elucidate the underlying mechanisms; however, we were able to identify new candidate metabolites, such as coniferyl alcohol, as being potentially involved in the pathogenesis of IgA nephropathy. These results might help to develop novel laboratory tests and therapeutic reagents for IgA nephropathy in the future.

Indexed as

MetabolomeAnimalsDisease Models, AnimalGas Chromatography-Mass SpectrometryGlomerular MesangiumGlomerulonephritis, IGAHumansImmunoglobulin AKidneyKidney GlomerulusMaleMicePhenotypeImmunoglobulin A

Identifiers

PMID31291349
PMCPMC6619804
OpenAlexW2958868212

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LicenceCC BY
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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.