Evidence map›Paper›PMID 37676828›Full record

ArticleRheumatology (Oxford, England)2024

Lymphocyte subset phenotyping for the prediction of progression to inflammatory arthritis in anti-citrullinated-peptide antibody-positive at-risk individuals.

Innocent Anioke, Laurence Duquenne, Rekha Parmar, Kulveer Mankia, Farag Shuweihdi, Paul Emery, Frederique Ponchel

Open access · hybridAbstract read
In one paragraph

Article in Rheumatology (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

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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

7 authors at 2 institutions in 2 countries.

Innocent AniokeLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK.ORCID 0000-0001-7600-4823
Laurence DuquenneLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK.ORCID 0000-0001-7631-0986
Rekha ParmarLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK.ORCID 0000-0002-8002-6440
Kulveer MankiaLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK.ORCID 0000-0002-7945-6582
Farag ShuweihdiLeeds Institute of Health Sciences, University of Leeds, School of Medicine, Leeds, UK.ORCID 0000-0003-1199-2992
Paul EmeryLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK.ORCID 0000-0002-7429-8482
Frederique PonchelLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK.ORCID 0000-0002-3969-7701
University of Leeds · GBUniversity of Nigeria · NG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesInflammatory arthritis (IA) is considered the last stage of a disease continuum, where features of systemic autoimmunity can appear years before clinical synovitis. Time to progression to IA varies considerably between at-risk individuals, therefore the identification of biomarkers predictive of progression is of major importance. We previously reported on the value of three CD4+T cell subsets as biomarkers of progression. Here, we aim to establish the value of 18 lymphocyte subsets (LS) for predicting progression to IA.

methodsParticipants were recruited based on a new musculoskeletal complaint and being positive for anti-citrullinated-peptide antibody. Progression (over 10 years) was defined as the development of clinical synovitis. LS analysis was performed for lymphocyte lineages, naive/memory subsets, inflammation-related cells (IRC) and regulatory cells (Treg/B-reg). Modelling used logistic/Cox regressions.

resultsOf 210 patients included, 93 (44%) progressed to IA, 41/93 (44%) within 12 months (rapid progressors). A total of 5/18 LS were associated with progression [Treg/CD4-naïve/IRC (adjusted P < 0.0001), CD8 (P = 0.021), B-reg (P = 0.015)] and three trends (NK-cells/memory-B-cells/plasmablasts). Unsupervised hierarchical clustering using these eight subsets segregated three clusters of patients, one cluster being enriched [63/109(58%)] and one poor [10/45(22%)] in progressors. Combining all clinical and LS variables, forward logistic regression predicted progression with accuracy = 85.7% and AUC = 0.911, selecting smoking/rheumatoid-factor/HLA-shared-epitope/tender-joint-count-78 and Treg/CD4-naive/CD8/NK-cells/B-reg/plasmablasts. To predict rapid progression, a Cox regression was performed resulting in a model combining smoking/rheumatoid factor and IRC/CD4-naive/Treg/NK-cells/CD8+T cells (AUC = 0.794).

conclusionOverall, progression was predicted by specific LS, suggesting potential triggers for events leading to the development of IA, while rapid progression was associated with a different set of subsets.

Indexed as

Anti-Citrullinated Protein AntibodiesDisease ProgressionAdultAgedArthritis, RheumatoidBiomarkersFemaleHumansImmunophenotypingLymphocyte SubsetsMaleMiddle AgedPredictive Value of TestsSynovitisAnti-Citrullinated Protein AntibodiesBiomarkersbiomarkersimmunological techniqueslaboratory diagnosislymphocytesRArheumatic diseasesT cells

Identifiers

PMID37676828
PMCPMC11147546
OpenAlexW4386512810

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Registered trials

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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.