Evidence map›Paper›PMID 38688923›Full record

ArticleScientific reports2024

Prediction and causal inference of hyperuricemia using gut microbiota.

Yuna Miyajima, Shigehiro Karashima, Ren Mizoguchi, Masaki Kawakami, Kohei Ogura, Kazuhiro Ogai, Aoi Koshida, Yasuo Ikagawa, Yuta Ami, Qiunan Zhu and 8 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
5.4field-weighted citation impact, top 3% 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Cold-Adapted Uric Acid-DegradingMolecules (Basel, Switzerland) · 2026
    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

18 authors at 4 institutions in 1 country.

Yuna Miyajima *Department of Clinical Laboratory Science, Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan.
Shigehiro KarashimaInstitute of Liberal Arts and Science, Kanazawa University, Kakuma, Kanazawa, Ishikawa, 920-1192, Japan. skarashima@staff.kanazawa-u.ac.jp.
Ren Mizoguchi *Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan.
Masaki KawakamiSchool of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan.
Kohei OguraInstitute for Frontier Science Initiative, Kanazawa University, Kanazawa, Japan.
Kazuhiro OgaiDepartment of Bio-Engineering Nursing, Graduate School of Nursing, Ishikawa Prefectural Nursing University, Kahoku, Ishikawa, Japan.
Aoi KoshidaInstitute for Frontier Science Initiative, Kanazawa University, Kanazawa, Japan.
Yasuo IkagawaInstitute for Frontier Science Initiative, Kanazawa University, Kanazawa, Japan.
Yuta AmiFaculty of Biology-Oriented Science and Technology, Kindai University, Kinokawa, Wakayama, Japan.
Qiunan ZhuFaculty of Pharmaceutical Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan.
Hiromasa TsujiguchiDepartment of Hygiene and Public Health, Graduate School of Advanced Preventive Medical Sciences, Kanazawa University, Kanazawa, Japan.
Akinori HaraDepartment of Hygiene and Public Health, Graduate School of Advanced Preventive Medical Sciences, Kanazawa University, Kanazawa, Japan.
Shin KuriharaFaculty of Biology-Oriented Science and Technology, Kindai University, Kinokawa, Wakayama, Japan.
Hiroshi ArakawaFaculty of Pharmaceutical Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan.
Hiroyuki NakamuraDepartment of Hygiene and Public Health, Graduate School of Advanced Preventive Medical Sciences, Kanazawa University, Kanazawa, Japan.
Ikumi TamaiFaculty of Pharmaceutical Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan.
Hidetaka NamboSchool Introduction School of Entrepreneurial and Innovation Studies, College of Transdisciplinary Sciences for Innovation, Kanazawa University, Kanazawa, Japan.
Shigefumi OkamotoLaboratory of Medical Microbiology and Microbiome, Department of Clinical Laboratory and Biomedical Sciences, Division of Health Sciences, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan. sokamoto@sahs.med.osaka-u.ac.jp.
Kanazawa University · JPKindai University · JPIshikawa Prefectural Nursing University · JPOsaka University · JP

Funding

Japan Society for the Promotion of Science JP19K17956
6 · The paper itself

Abstract

Hyperuricemia (HUA) is a symptom of high blood uric acid (UA) levels, which causes disorders such as gout and renal urinary calculus. Prolonged HUA is often associated with hypertension, atherosclerosis, diabetes mellitus, and chronic kidney disease. Studies have shown that gut microbiota (GM) affect these chronic diseases. This study aimed to determine the relationship between HUA and GM. The microbiome of 224 men and 254 women aged 40 years was analyzed through next-generation sequencing and machine learning. We obtained GM data through 16S rRNA-based sequencing of the fecal samples, finding that alpha-diversity by Shannon index was significantly low in the HUA group. Linear discriminant effect size analysis detected a high abundance of the genera Collinsella and Faecalibacterium in the HUA and non-HUA groups. Based on light gradient boosting machine learning, we propose that HUA can be predicted with high AUC using four clinical characteristics and the relative abundance of nine bacterial genera, including Collinsella and Dorea. In addition, analysis of causal relationships using a direct linear non-Gaussian acyclic model indicated a positive effect of the relative abundance of the genus Collinsella on blood UA levels. Our results suggest abundant Collinsella in the gut can increase blood UA levels.

Indexed as

Gastrointestinal MicrobiomeHyperuricemiaMachine LearningRNA, Ribosomal, 16SUric AcidAdultFecesFemaleHigh-Throughput Nucleotide SequencingHumansMaleMiddle AgedRNA, Ribosomal, 16SUric Acid

Identifiers

PMID38688923
PMCPMC11061287
OpenAlexW4396496402

What Socratic 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 Socratic graph.