Evidence map›Paper›PMID 32759501›Full record

ArticleJCI insight2020

Patient ancestry significantly contributes to molecular heterogeneity of systemic lupus erythematosus.

Michelle D Catalina, Prathyusha Bachali, Anthony E Yeo, Nicholas S Geraci, Michelle A Petri, Amrie C Grammer, Peter E Lipsky

Open access · goldAbstract read
In one paragraph

Article in JCI insight, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
7.7field-weighted citation impact, top 2% 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

41 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.

  1. Pooled it
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  14. Annals of the rheumatic diseases · 2024
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  20. The autoimmune tautology revisited.Journal of translational autoimmunity · 2023
    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

7 authors at 4 institutions in 1 country.

Michelle D CatalinaAMPEL BioSolutions LLC & RILITE Research Institute, Charlottesville, Virginia, USA.
Prathyusha BachaliAMPEL BioSolutions LLC & RILITE Research Institute, Charlottesville, Virginia, USA.
Anthony E YeoYeo Analytics LLC, Jersey City, New Jersey.
Nicholas S GeraciAMPEL BioSolutions LLC & RILITE Research Institute, Charlottesville, Virginia, USA.
Michelle A PetriDivision of Rheumatology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Amrie C GrammerAMPEL BioSolutions LLC & RILITE Research Institute, Charlottesville, Virginia, USA.
Peter E LipskyAMPEL BioSolutions LLC & RILITE Research Institute, Charlottesville, Virginia, USA.
Ampel BioSolutions (United States) · USEP Analytics (United States) · USJohns Hopkins University · USOno Pharmaceutical (United States) · US

Funding

Hopkins Lupus CohortR01AR069572 · NIAMS · JOHNS HOPKINS UNIVERSITY · PI PETRI, MICHELLE A · 2016 to 2020
$3.4M
NIAMS NIH HHS R01 AR069572
6 · The paper itself

Abstract

Gene expression signatures can stratify patients with heterogeneous diseases, such as systemic lupus erythematosus (SLE), yet understanding the contributions of ancestral background to this heterogeneity is not well understood. We hypothesized that ancestry would significantly influence gene expression signatures and measured 34 gene modules in 1566 SLE patients of African ancestry (AA), European ancestry (EA), or Native American ancestry (NAA). Healthy subject ancestry-specific gene expression provided the transcriptomic background upon which the SLE patient signatures were built. Although standard therapy affected every gene signature and significantly increased myeloid cell signatures, logistic regression analysis determined that ancestral background significantly changed 23 of 34 gene signatures. Additionally, the strongest association to gene expression changes was found with autoantibodies, and this also had etiology in ancestry: the AA predisposition to have both RNP and dsDNA autoantibodies compared with EA predisposition to have only anti-dsDNA. A machine learning approach was used to determine a gene signature characteristic to distinguish AA SLE and was most influenced by genes characteristic of the perturbed B cell axis in AA SLE patients.

Indexed as

Genetic Predisposition to DiseasePolymorphism, Single NucleotideStandard of CareAdultAutoantibodiesBiomarkersBlack or African AmericanCase-Control StudiesFemaleFollow-Up StudiesGenotypeHumansLupus Erythematosus, SystemicMaleMiddle AgedPrognosisAutoantibodiesBiomarkersAutoimmunityRheumatology

Identifiers

PMID32759501
PMCPMC7455079
OpenAlexW3047319477

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.