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Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking

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 predicti...

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Autores principales: Scott, Ian C., Seegobin, Seth D., Steer, Sophia, Tan, Rachael, Forabosco, Paola, Hinks, Anne, Eyre, Stephen, Morgan, Ann W., Wilson, Anthony G., Hocking, Lynne J., Wordsworth, Paul, Barton, Anne, Worthington, Jane, Cope, Andrew P., Lewis, Cathryn M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3778023/
https://www.ncbi.nlm.nih.gov/pubmed/24068971
http://dx.doi.org/10.1371/journal.pgen.1003808
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author Scott, Ian C.
Seegobin, Seth D.
Steer, Sophia
Tan, Rachael
Forabosco, Paola
Hinks, Anne
Eyre, Stephen
Morgan, Ann W.
Wilson, Anthony G.
Hocking, Lynne J.
Wordsworth, Paul
Barton, Anne
Worthington, Jane
Cope, Andrew P.
Lewis, Cathryn M.
author_facet Scott, Ian C.
Seegobin, Seth D.
Steer, Sophia
Tan, Rachael
Forabosco, Paola
Hinks, Anne
Eyre, Stephen
Morgan, Ann W.
Wilson, Anthony G.
Hocking, Lynne J.
Wordsworth, Paul
Barton, Anne
Worthington, Jane
Cope, Andrew P.
Lewis, Cathryn M.
author_sort Scott, Ian C.
collection PubMed
description 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.
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spelling pubmed-37780232013-09-25 Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking Scott, Ian C. Seegobin, Seth D. Steer, Sophia Tan, Rachael Forabosco, Paola Hinks, Anne Eyre, Stephen Morgan, Ann W. Wilson, Anthony G. Hocking, Lynne J. Wordsworth, Paul Barton, Anne Worthington, Jane Cope, Andrew P. Lewis, Cathryn M. PLoS Genet Research Article 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. Public Library of Science 2013-09-19 /pmc/articles/PMC3778023/ /pubmed/24068971 http://dx.doi.org/10.1371/journal.pgen.1003808 Text en © 2013 Scott et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Scott, Ian C.
Seegobin, Seth D.
Steer, Sophia
Tan, Rachael
Forabosco, Paola
Hinks, Anne
Eyre, Stephen
Morgan, Ann W.
Wilson, Anthony G.
Hocking, Lynne J.
Wordsworth, Paul
Barton, Anne
Worthington, Jane
Cope, Andrew P.
Lewis, Cathryn M.
Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking
title Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking
title_full Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking
title_fullStr Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking
title_full_unstemmed Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking
title_short Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking
title_sort predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3778023/
https://www.ncbi.nlm.nih.gov/pubmed/24068971
http://dx.doi.org/10.1371/journal.pgen.1003808
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