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

ABSTRACT: Radiographic 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.We 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.A 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).Using 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.

SUBMITTER: Joo YB

PROVIDER: S-EPMC5655942 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTo identify fine specificity anti-citrullinated protein antibodies (ACPA) associated with incident rheumatoid arthritis-associated interstitial lung disease (RA-ILD).MethodsThis nested case-control study within the Brigham RA Sequential Study matched incident RA-ILD cases to RA-noILD controls on time of blood collection, age, sex, RA duration, and rheumatoid factor status. A multiplex assay measured ACPA and anti-native protein antibodies from stored serum prior to RA-ILD onset. Logistic regression models calculated odds ratios (OR) with 95% confidence intervals (CI) for RA-ILD, adjusting for prospectively-collected covariates. We estimated optimism-corrected area under the curves (AUC) using internal validation. Model coefficients generated a risk score for RA-ILD.FindingsWe analyzed 84 incident RA-ILD cases (mean age 67 years, 77% female, 90% White) and 233 RA-noILD controls (mean age 66 years, 80% female, 94% White). We identified six fine specificity antibodies that were associated with RA-ILD. The antibody isotypes and targeted proteins were: IgA2 to citrullinated histone 4 (OR 0.08 per log-transformed unit, 95% CI 0.03-0.22), IgA2 to citrullinated histone 2A (OR 4.03, 95% CI 2.03-8.00), IgG to cyclic citrullinated filaggrin (OR 3.47, 95% CI 1.71-7.01), IgA2 to native cyclic histone 2A (OR 5.52, 95% CI 2.38-12.78), IgA2 to native histone 2A (OR 4.60, 95% CI 2.18-9.74), and IgG to native cyclic filaggrin (OR 2.53, 95% CI 1.47-4.34). These six antibodies predicted RA-ILD risk better than all clinical factors combined (optimism-corrected AUC=0·84 versus 0·73). We developed a risk score for RA-ILD combining these antibodies with the clinical factors (smoking, disease activity, glucocorticoid use, obesity). At 50% predicted RA-ILD probability, the risk scores both without (score=2·6) and with (score=5·9) biomarkers achieved specificity ≥93% for RA-ILD.InterpretationSpecific ACPA and anti-native protein antibodies improve RA-ILD prediction. These findings implicate synovial protein antibodies in the pathogenesis of RA-ILD and suggest clinical utility in predicting RA-ILD once validated in external studies.FundingNational Institutes of Health.

Project description:IntroductionIt is increasingly becoming accepted that inflammation may play an important role in the pathogenesis of Alzheimer's disease (AD), as several immune-related genes have been associated with AD. Among these is tumor necrosis factor (TNF)-α, a proinflammatory cytokine known to play an important role in autoimmune disorders, including rheumatoid arthritis (RA). Although AD and RA appear to involve similar pathological mechanisms through the production of TNF-α, the relationship between AD and RA remains unknown.ObjectiveTo determine the relative risk of AD among RA patients and non-RA patients, and whether anti-TNF therapy for RA was associated with a lower risk of AD in RA patients.MethodsWe performed a nested case-control study of more than 8.5 million commercially insured adults (aged ≥18 years) in all 50 US states, Puerto Rico, and US Virgin Islands in the Verisk Health claims database. We derived a sub-cohort of subjects with a diagnosis of RA (controls), or RA and AD (cases), matching cases and controls based on age, sex, exposure assessment period, and methotrexate treatment. We also assessed relative risk of AD following exposure to standard RA therapies, including anti-TNF agents (infliximab, adalimumab, etanercept), methotrexate, prednisone, sulfasalazine, and rituximab. Odds ratios were adjusted for comorbidities, including coronary artery disease, diabetes mellitus, and peripheral vascular disease.ResultsAD was more prevalent (p < 0.0001) among RA patients (0.79 %) than among those without RA (0.11 %). Chronic conditions such as coronary artery disease (odds ratio [OR] 1.48; 95 % confidence interval [CI] 1.04-2.05; p = 0.03), diabetes (OR 1.86; 95 % CI 1.32-2.62; p = 0.0004), and peripheral vascular disease (OR 1.61; 95 % CI 1.06-2.43; p = 0.02) significantly increased the relative risk of AD among RA patients. Exposure to anti-TNF agents as a class, but not other immunosuppressive drugs studied, was associated with lowered risk of AD among RA patients (unadjusted OR 0.44; 95 % CI 0.22-0.87; p = 0.02; adjusted OR 0.45; 95 % CI 0.23-0.90; p = 0.02). Sub-group analysis demonstrated that of the three anti-TNF agents studied, only etanercept (unadjusted OR, 0.33; 95 % CI 0.08-0.94; p = 0.03; adjusted OR 0.30; 95 % CI 0.08-0.89; p = 0.02) was associated with a decreased risk of AD in RA patients.ConclusionThere is an increased risk of AD in the studied RA population. The relative risk of AD among RA subjects was lowered in those exposed to etanercept. Anti-TNF therapy with etanercept shows promise as a potential treatment for AD.

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

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