Predicting the severity of joint damage in rheumatoid arthritis; the contribution of genetic factors

Steenbergen, H.W. van; Tsonaka, Roula; Huizinga, Tom W J; Cessie, Saskia le; Mil, Annette H M van der Helm-van

doi:10.1136/annrheumdis-2013-204277

Cited by 11 publications

(20 citation statements)

References 26 publications

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“…To evaluate the usefulness of anti-CarP antibodies, in addition to ACPA and RF status, in identifying patients with no, moderate or severe progression, we assessed the net percentage of correctly reclassified patients 22. The progression in SHS during 5 years (ΔSHS 0–5 years ) was categorised in three severity groups: ΔSHS 0–5 years ≤5, 6–25 and >25 units, indicating no/little, moderate and severe radiographic progression.…”

Section: Methodsmentioning

confidence: 99%

The association between anti-carbamylated protein (anti-CarP) antibodies and radiographic progression in early rheumatoid arthritis: a study exploring replication and the added value to ACPA and rheumatoid factor

et al. 2016

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Section: Methodsmentioning

confidence: 99%

The association between anti-carbamylated protein (anti-CarP) antibodies and radiographic progression in early rheumatoid arthritis: a study exploring replication and the added value to ACPA and rheumatoid factor

et al. 2016

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“…This suggests that genetic variants are potential prognostic markers. Current knowledge of the genetic architecture of radiograph progression is limited, with up to half of its heritability unexplained 2 .…”

mentioning

confidence: 99%

Do Genetic Susceptibility Variants Associate with Disease Severity in Early Active Rheumatoid Arthritis?

Scott

Rijsdijk²,

Walker³

et al. 2015

J Rheumatol

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“…However, there is currently a lack of genetic information for prediction of radiographic damage. According to a report by van Steenbergen et al, prediction accuracy of severe radiographic progression reached only 62% using a model consisting of 17 known genetic loci from several replication studies and meta-analysis and clinical factors [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Biological function integrated prediction of severe radiographic progression in rheumatoid arthritis: a nested case control study

Joo

Kim

et al. 2017

Arthritis Res Ther

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BackgroundRadiographic progression is reported to be highly heritable in rheumatoid arthritis (RA). However, previous study using genetic loci showed an insufficient accuracy of prediction for radiographic progression. The aim of this study is to identify a biologically relevant prediction model of radiographic progression in patients with RA using a genome-wide association study (GWAS) combined with bioinformatics analysis.MethodsWe obtained genome-wide single nucleotide polymorphism (SNP) data for 374 Korean patients with RA using Illumina HumanOmni2.5Exome-8 arrays. Radiographic progression was measured using the yearly Sharp/van der Heijde modified score rate, and categorized in no or severe progression. Significant SNPs for severe radiographic progression from GWAS were mapped on the functional genes and reprioritized by post-GWAS analysis. For robust prediction of radiographic progression, tenfold cross-validation using a support vector machine (SVM) classifier was conducted. Accuracy was used for selection of optimal SNPs set in the Hanyang Bae RA cohort. The performance of our final model was compared with that of other models based on GWAS results and SPOT (one of the post-GWAS analyses) using receiver operating characteristic (ROC) curves. The reliability of our model was confirmed using GWAS data of Caucasian patients with RA.ResultsA total of 36,091 significant SNPs with a p value <0.05 from GWAS were reprioritized using post-GWAS analysis and approximately 2700 were identified as SNPs related to RA biological features. The best average accuracy of ten groups was 0.6015 with 85 SNPs, and this increased to 0.7481 when combined with clinical information. In comparisons of the performance of the model, the 0.7872 area under the curve (AUC) in our model was superior to that obtained with GWAS (AUC 0.6586, p value 8.97 × 10-5) or SPOT (AUC 0.7449, p value 0.0423). Our model strategy also showed superior prediction accuracy in Caucasian patients with RA compared with GWAS (p value 0.0049) and SPOT (p value 0.0151).ConclusionsUsing various biological functions of SNPs and repeated machine learning, our model could predict severe radiographic progression relevantly and robustly in patients with RA compared with models using only GWAS results or other post-GWAS tools.Electronic supplementary materialThe online version of this article (doi:10.1186/s13075-017-1414-x) contains supplementary material, which is available to authorized users.

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Predicting the severity of joint damage in rheumatoid arthritis; the contribution of genetic factors

Abstract: Genetic risk factors together explained 12-18% of the variance in radiologic progression. Adding genetic factors improved the predictive accuracy, but 38% of the patients were still incorrectly classified, limiting the value for use in clinical practice.

Cited by 11 publications

References 26 publications

The association between anti-carbamylated protein (anti-CarP) antibodies and radiographic progression in early rheumatoid arthritis: a study exploring replication and the added value to ACPA and rheumatoid factor

The association between anti-carbamylated protein (anti-CarP) antibodies and radiographic progression in early rheumatoid arthritis: a study exploring replication and the added value to ACPA and rheumatoid factor

Do Genetic Susceptibility Variants Associate with Disease Severity in Early Active Rheumatoid Arthritis?

Biological function integrated prediction of severe radiographic progression in rheumatoid arthritis: a nested case control study

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