Evidence map›Paper›PMID 34830282›Full record

ReviewInternational journal of molecular sciences2021

Uric Acid in Inflammation and the Pathogenesis of Atherosclerosis.

Yoshitaka Kimura, Daisuke Tsukui, Hajime Kono

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 165 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
165citing papers in PubMed, 4 pooled it
26.8field-weighted citation impact, top 1% 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

165 citing papers in PubMed, 4 syntheses or guidelines pooled it, 284 citations in OpenAlex.

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105 more citing papers are in PubMed but not listed here.

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

3 authors at 1 institution in 1 country.

Yoshitaka KimuraDepartment of Internal Medicine, Faculty of Medicine, Teikyo University of Medicine, Tokyo 173-8605, Japan.ORCID 0000-0001-5430-9909
Daisuke TsukuiDepartment of Internal Medicine, Faculty of Medicine, Teikyo University of Medicine, Tokyo 173-8605, Japan.
Hajime KonoDepartment of Internal Medicine, Faculty of Medicine, Teikyo University of Medicine, Tokyo 173-8605, Japan.
Teikyo University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hyperuricemia is a common metabolic syndrome. Elevated uric acid levels are risk factors for gout, hypertension, and chronic kidney diseases. Furthermore, various epidemiological studies have also demonstrated an association between cardiovascular risks and hyperuricemia. In hyperuricemia, reactive oxygen species (ROS) are produced simultaneously with the formation of uric acid by xanthine oxidases. Intracellular uric acid has also been reported to promote the production of ROS. The ROS and the intracellular uric acid itself regulate several intracellular signaling pathways, and alterations in these pathways may result in the development of atherosclerotic lesions. In this review, we describe the effect of uric acid on various molecular signals and the potential mechanisms of atherosclerosis development in hyperuricemia. Furthermore, we discuss the efficacy of treatments for hyperuricemia to protect against the development of atherosclerosis.

Indexed as

AnimalsAtherosclerosisComorbidityDiabetes Mellitus, Type 2GoutHumansHypertensionHyperuricemiaMetabolic SyndromeOxidative StressReactive Oxygen SpeciesRenal Insufficiency, ChronicRisk FactorsUric AcidXanthine OxidaseReactive Oxygen SpeciesUric AcidXanthine Oxidaseatherosclerosishyperuricemiainflammationreactive oxygen species

Identifiers

PMID34830282
PMCPMC8624633
OpenAlexW3214382627

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.