Evidence map›Paper›PMID 38580710›Full record

ArticleScientific reports2024

Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy.

Xuan Wang, Molei Liu, Isabelle-Emmanuella Nogues, Tony Chen, Xin Xiong, Clara-Lea Bonzel, Harrison Zhang, Chuan Hong, Yin Xia, Kumar Dahal and 9 more

Abstract read
PubMed Full text at PMC Publisher
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

19 authors.

Xuan Wang *Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA.
Molei Liu *Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.
Isabelle-Emmanuella Nogues *Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Tony ChenDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Xin XiongDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Clara-Lea BonzelDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Harrison ZhangDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Chuan HongDepartment of Biostatistics, Duke University, Durham, NC, USA.
Yin XiaDepartment of Statistics and Data Science, Fudan University, Shanghai, China.
Kumar DahalDepartment of Biostatistics, Duke University, Durham, NC, USA.
Lauren CostaMassachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA.
Jing CuiDepartment of Biostatistics, Duke University, Durham, NC, USA.
VA Million Veteran Program
J Michael GazianoMassachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA.
Seoyoung C KimDivision of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA.
Yuk-Lam HoMassachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA.
Kelly ChoMassachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA.
Tianxi Cai *Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. tcai@hsph.harvard.edu.
Katherine P Liao *Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. kliao@bwh.harvard.edu.

Funding

VERITY: Value and Evidence in Rheumatology using bioInformaTics, and advanced analYticsP30AR072577 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI Daniel Hal Solomon · 2017 to 2026
$9.8M
NIAMS NIH HHS P30 AR072577
6 · The paper itself

Abstract

The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly screen for potential treatment effects, e.g., IL6R variant as a proxy for IL6R antagonists. This approach offers an opportunity to address the limited power in clinical trials to study differential treatment effects across patient subgroups. However, limited methods exist to efficiently test for differences across subgroups in the thousands of multiple comparisons generated as part of a PheWAS. In this study, we developed an approach that maximizes the power to test for heterogeneous genotype-phenotype associations and applied this approach to an IL6R PheWAS among individuals of African (AFR) and European (EUR) ancestries. We identified 29 traits with differences in IL6R variant-phenotype associations, including a lower risk of type 2 diabetes in AFR (OR 0.96) vs EUR (OR 1.0, p-value for heterogeneity = 8.5 × 10

Indexed as

Diabetes Mellitus, Type 2Genetic Association StudiesHumansPhenotypePolymorphism, Single NucleotideReceptors, Interleukin-6IL6R protein, humanReceptors, Interleukin-6

Identifiers

PMID38580710
PMCPMC10997791

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.