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

Development and validation of a prognostic multivariable model to predict insufficient clinical response to methotrexate in rheumatoid arthritis

Objective The objective was to predict insufficient response to 3 months methotrexate (MTX) in DMARD naïve rheumatoid arthritis patients. Methods A Multivariable logistic regression model of rheumatoid arthritis patients starting MTX was developed in a derivation cohort with 285 patients starting MTX in a clinical multicentre, stratified single-blinded trial, performed in seven secondary care clinics and a tertiary care clinic. The model was validated in a validation cohort with 102 patients starting MTX at a tertiary care clinic. Outcome was insufficient response (disease activity score (DAS)28 >3.2) after 3 months of MTX treatment. Clinical characteristics, lifestyle variables, genetic and metabolic biomarkers were determined at baseline in both cohorts. These variables were dichotomized and used to construct a multivariable prediction model with backward logistic regression analysis. Results The prediction model for insufficient response in the derivation cohort, included: DAS28>5.1, Health Assessment Questionnaire>0.6, current smoking, BMI>25 kg/m2, ABCB1 rs1045642 genotype, ABCC3 rs4793665 genotype, and erythrocyte-folate<750 nmol/L. In the derivation cohort, AUC of ROC curve was 0.80 (95%CI: 0.73–0.86), and 0.80 (95%CI: 0.69–0.91) in the validation cohort. Betas of the prediction model were transformed into total risk score (range 0–8). At cutoff of 4, probability for insufficient response was 44%. Sensitivity was 71%, specificity 72%, with positive and negative predictive value of 72% and 71%. Conclusions A prognostics prediction model for insufficient response to MTX in 2 prospective RA cohorts by combining genetic, metabolic, clinical and lifestyle variables was developed and validated. This model satisfactorily identified RA patients with high risk of insufficient response to MTX

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

Methotrexate polyglutamates in erythrocytes are associated with lower disease activity in juvenile idiopathic arthritis patients

Objective: To determine association of erythrocyte methotrexate polyglutamates (MTX-PG) with disease activity and adverse effects in a prospective juvenile idiopathic arthritis (JIA) cohort. Methods: One hundred and thirteen JIA patients were followed from MTX start until 12 months. Erythrocyte MTX-PGs with 1-5 glutamate residues were measured at 3 months with tandem mass spectrometry. The outcomes were Juvenile Arthritis Disease Activity Score (JADAS)-27 and adverse effects. To determine associations of MTX-PGs with JADAS-27 at 3 months and during 1 year of MTX treatment, linear regression and linear mixed-model analyses were used. To determine associations of MTX-PGs with adverse effects during 1 year of MTX treatment, logistic regression was used. Analyses were corrected for JADAS-27 at baseline and co-medication. Results: Median JADAS-27 decreased from 12.7 (IQR: 7.8-18.2) at baseline to 2.9 (IQR: 0.1-6.5) at 12 months. Higher concentrations of MTX-PG3 (β: -0.006, p=0.005), MTX-PG4 (β: -0.015, p=0.004), MTX-PG5 (β: -0.051, p=0.011) and MTX-PG3-5 (β: -0.004, p=0.003) were associated with lower disease activity at 3 months. Higher concentrations of MTX-PG3 (β: -0.005, p=0.028), MTX-PG4 (β: -0.014, p=0.014), MTX-PG5 (β: -0.049, p=0.023) and MTX-PG3-5 (β: -0.004, p=0.018) were associated with lower disease activity over 1 year. None of the MTX-PGs was associated with adverse effects. Conclusions: In the first prospective study in JIA, long-chain MTX-PGs were associated with lower JADAS-27 at 3 months and during 1 year of MTX treatment. Erythrocyte MTX-PG could be a plausible candidate for therapeutic drug monitoring of MTX in JIA