Evidence map›Paper›PMID 29437585›Full record

ArticleAnnals of the rheumatic diseases2018

MR-PheWAS: exploring the causal effect of SUA level on multiple disease outcomes by using genetic instruments in UK Biobank.

Xue Li, Xiangrui Meng, Athina Spiliopoulou, Maria Timofeeva, Wei-Qi Wei, Aliya Gifford, Xia Shen, Yazhou He, Tim Varley, Paul McKeigue and 6 more

Open access · hybridAbstract readComparative Study
In one paragraph

Article in Annals of the rheumatic diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed, 3 pooled it
8.6field-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

56 citing papers in PubMed, 3 syntheses or guidelines pooled it, 101 citations in OpenAlex.

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  13. Mendelian Randomization Studies: A Metric for Quality Evaluation.Gout, urate, and crystal deposition disease · 2025
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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

16 authors at 8 institutions in 5 countries.

Xue Li *Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Xiangrui Meng *Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Athina SpiliopoulouCentre for Population Health Sciences, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Maria TimofeevaColon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Wei-Qi WeiDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Aliya GiffordDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Xia ShenCentre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Yazhou HeCentre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Tim VarleyPublic Health and Intelligence, NHS National Services Scotland, Edinburgh, UK.
Paul McKeigueCentre for Population Health Sciences, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Ioanna TzoulakiDepartment Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
Alan F WrightMedical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
Peter JoshiCentre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Joshua C DennyDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Harry CampbellCentre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Evropi TheodoratouCentre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
University of Edinburgh · GBVanderbilt University Medical Center · USGlasgow Centre for Population Health · GBMedical Research Council · GBMRC Institute of Genetics and Molecular Medicine · GBNational Health Service Scotland · GBSichuan University · CNUniversity of Ioannina · GR

Funding

Exploring Statin Pleiotropic Effects within a Very Large EHR Cohort - Diversity SupplementR01HL133786 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI WEI, WEI-QI · 2017 to 2020
$1.7M
Cancer Research UK 22804Medical Research Council G0800604Medical Research Council MC_QA137853Medical Research Council MR/K015346/1NHLBI NIH HHS R01 HL133786
6 · The paper itself

Abstract

objectivesWe aimed to investigate the role of serum uric acid (SUA) level in a broad spectrum of disease outcomes using data for 120 091 individuals from UK Biobank.

methodsWe performed a phenome-wide association study (PheWAS) to identify disease outcomes associated with SUA genetic risk loci. We then implemented conventional Mendelianrandomisation (MR) analysis to investigate the causal relevance between SUA level and disease outcomes identified from PheWAS. We next applied MR Egger analysis to detect and account for potential pleiotropy, which conventional MR analysis might mistake for causality, and used the HEIDI (heterogeneity in dependent instruments) test to remove cross-phenotype associations that were likely due to genetic linkage.

resultsOur PheWAS identified 25 disease groups/outcomes associated with SUA genetic risk loci after multiple testing correction (P<8.57e-05). Our conventional MR analysis implicated a causal role of SUA level in three disease groups: inflammatory polyarthropathies (OR=1.22, 95% CI 1.11 to 1.34), hypertensive disease (OR=1.08, 95% CI 1.03 to 1.14) and disorders of metabolism (OR=1.07, 95% CI 1.01 to 1.14); and four disease outcomes: gout (OR=4.88, 95% CI 3.91 to 6.09), essential hypertension (OR=1.08, 95% CI 1.03 to 1.14), myocardial infarction (OR=1.16, 95% CI 1.03 to 1.30) and coeliac disease (OR=1.41, 95% CI 1.05 to 1.89). After balancing pleiotropic effects in MR Egger analysis, only gout and its encompassing disease group of inflammatory polyarthropathies were considered to be causally associated with SUA level. Our analysis highlighted a locus (

conclusionsElevated SUA level is convincing to cause gout and inflammatory polyarthropathies, and might act as a marker for the wider range of diseases with which it associates. Our findings support further investigation on the clinical relevance of SUA level with cardiovascular, metabolic, autoimmune and respiratory diseases.

Indexed as

Genome-Wide Association StudyMultimorbidityAdultArthritisAutoimmune DiseasesBiological Specimen BanksCeliac DiseaseFemaleGenetic Predisposition to DiseaseGoutHumansHypertensionMaleMendelian Randomization AnalysisMiddle AgedMyocardial InfarctionUric Acidepidemiologygene polymorphismgout

Identifiers

PMID29437585
PMCPMC6029646
OpenAlexW2785956627

What Socratic holds

Textmetadata
LicenceCC BY
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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.