Evidence map›Paper›PMID 24068971›Full record

ArticlePLoS genetics2013

Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking.

Ian C Scott, Seth D Seegobin, Sophia Steer, Rachael Tan, Paola Forabosco, Anne Hinks, Stephen Eyre, Ann W Morgan, Anthony G Wilson, Lynne J Hocking and 5 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
6.8field-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

35 citing papers in PubMed, 67 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Genetic and environmental risk factors for rheumatoid arthritis.Best practice & research. Clinical rheumatology · 2017
    Review
  20. Article
4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

15 authors at 8 institutions in 2 countries.

Ian C ScottAcademic Department of Rheumatology, Centre for Molecular and Cellular Biology of Inflammation, King's College London, London, United Kingdom ; Department of Medical and Molecular Genetics, King's College London, London, United Kingdom.
Seth D Seegobin
Sophia Steer
Rachael Tan
Paola Forabosco
Anne Hinks
Stephen Eyre
Ann W Morgan
Anthony G Wilson
Lynne J Hocking
Paul Wordsworth
Anne Barton
Jane Worthington
Andrew P Cope
Cathryn M Lewis
King's College London · GBUniversity of Manchester · GBIstituto di Genetica Molecolare · ITKing's College Hospital · GBNational Institute for Health Research · GBNuffield Orthopaedic Centre · GBUniversity of Aberdeen · GBUniversity of Sheffield · GB

Funding

Arthritis Research UK 17552Arthritis Research UK 19739Arthritis Research UK 20385Medical Research Council G1001799Versus Arthritis 19739Wellcome TrustWellcome Trust 076113Wellcome Trust 085475Wellcome Trust 090355Wellcome Trust 095219MA
6 · The paper itself

Abstract

The improved characterisation of risk factors for rheumatoid arthritis (RA) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA). Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs) and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA)-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls); UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls). HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC) value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG). Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001); ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively.

Indexed as

Models, GeneticAdultAgedAge of OnsetAllelesArthritis, RheumatoidAutoantibodiesEpitopesFemaleGenetic Predisposition to DiseaseHLA-DRB1 ChainsHumansKaplan-Meier EstimateMaleMiddle AgedPolymorphism, Single NucleotideAutoantibodiesEpitopesHLA-DRB1 Chains

Identifiers

PMID24068971
PMCPMC3778023
OpenAlexW2025276299

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.