Evidence map›Paper›PMID 38504855›Full record

ArticleFrontiers in nephrology2024

Alterations in urinary ceramides, sphingoid bases, and their phosphates among patients with kidney disease.

Yoshifumi Morita, Eri Sakai, Hideaki Isago, Yoshikazu Ono, Yutaka Yatomi, Makoto Kurano

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Article in Frontiers in nephrology, 2024. 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
0.7field-weighted citation impact, top 32% 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, 3 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

6 authors at 1 institution in 1 country.

Yoshifumi MoritaDepartment of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan.
Eri SakaiDepartment of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan.
Hideaki IsagoDepartment of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan.
Yoshikazu OnoDepartment of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan.
Yutaka YatomiDepartment of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan.
Makoto KuranoDepartment of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan.
University of Tokyo Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: To avoid an invasive renal biopsy, noninvasive laboratory testing for the differential diagnosis of kidney diseases is a desirable goal. As sphingolipids are demonstrated to be involved in the pathogenesis of various kidney diseases, we investigated the possible usefulness of the simultaneous measurement of urinary sphingolipids for differentiating kidney diseases. Materials and methods: Residual urine specimens were collected from patients who had been clinically diagnosed with chronic glomerulonephritis (CGN), diabetic mellitus (DM), systemic lupus erythematosus (SLE), and arterial hypertension (AH). The urinary sphingolipids-CERs C16:0, C18:0, C18:1, C20:0, C22:0, and C24:0; sphingosine [Sph]; dihydrosphingosine; sphingosine 1-phosphate [S1P]; and dihydroS1P [dhS1P]-were measured by liquid chromatography-tandem mass spectrometry. Based on the results, machine learning models were constructed to differentiate the various kidney diseases. Results: The urinary S1P was higher in patients with DM than in other participants ( Conclusion: The urinary CERs, sphingoid bases, and their phosphates show alterations among kidney diseases, suggesting their potential involvement in the development of kidney injury.

Indexed as

ceramidemachine learningsphingolipidssphingosine-1-phosphateurine

Identifiers

PMID38504855
PMCPMC10949895
OpenAlexW4392481858

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.