A Toolbox for Personalized Medicine of Methotrexate Therapy in Arthritis

__Abstract__ Rheumatoid arthritis (RA) is a chronic autoimmune disease. It is characterized by swelling and pain of the joints, uncontrolled proliferation of synovial tissue and multisystem co-morbidities like cardiovascular disease and thyroid disease. RA mainly affects the joints of the extremities like hands, feet, knees, wrist and elbows. Joint damage can occur early in the disease course when the disease is not treated effectively. More than 21% of United states adults (46.4 million persons) were found to have self-reported physician diagnosed arthritis. The specific diagnose of RA has a prevalence of 1%. The prevalence of RA among women is approximately double that in men. There is still no cure for RA, despite the fact that treatment strategy has changed considerably over the years. Early initiation of therapy is effective in prevention of joint damage and results in milder medication regimens while maintaining disease remission. Early in the disease, inflammation is less self-perpetuating and easier to suppress, therefore it is important to start treatment as early as possible in order to optimize outcome, minimize medical costs, improve quality o

Higher baseline global leukocyte DNA methylation is associated with MTX non-response in early RA patients

BACKGROUND: Low-dose methotrexate (MTX) is the first-line therapy in early rheumatoid arthritis (eRA). Up to 40% of eRA patients do not benefit from MTX therapy. MTX has been shown to inhibit one-carbon metabolism, which is involved in the donation of methyl groups. In this study, we investigate baseline global DNA methylation and changes in DNA methylation during treatment in relation to clinical non-response after 3 months of MTX treatment. METHODS: Two hundred ninety-four blood samples were collected from the Treatment in the Rotterdam Early Arthritis Cohort (tREACH, ISRCTN26791028), a multicenter, stratified single-blind clinical trial of eRA patients. Global DNA (hydroxy)methylation was quantified using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) and validated with a global DNA LINE-1 methylation technique. MTX response was determined as ΔDAS28. Additionally, patients were stratified into two response groups according to the European League Against Rheumatism (EULAR) response criteria. Associations between global DNA methylation and response were examined using univariate regression models adjusted for baseline DAS28, baseline erythrocyte folate levels, and body mass index (BMI). RESULTS: Higher baseline global DNA methylation was associated with less decrease of DAS28 (β = 0.15, p = 0.013) and with MTX non-response (OR = 0.010, 95% CI = 0.001-0.188). This result was validated in LINE-1 elements (β = 0.22, p = 0.026). Changes in global DNA (hydroxy)methylation were not associated with MTX response over 3 months. CONCLUSIONS: These results show that higher baseline global DNA methylation in treatment naïve eRA patients is associated with decreased clinical response after 3 months of treatment of eRA patients and can be further evaluated as a predictor for MTX therapy non-response. TRIAL REGISTRATION: ISRCTN, ISRCTN26791028 , registered 23 August 2007-retrospectively registered

Complex machine-learning algorithms and multivariable logistic regression on par in the prediction of insufficient clinical response to methotrexate in rheumatoid arthritis

The goals of this study were to examine whether machine-learning algorithms outper-form multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to in-vestigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the “treatment in the Rotterdam Early Arthritis CoHort” (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Fi-nally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68–0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67–0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61–0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression’s sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly, the prediction model was specific to MTX (combination) therapy response